Beauty or Brains? The Paradoxical Impact of Anchors Attractiveness on Purchase Intentions

Beauty or Brains? 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The Paradoxical Impact of Anchors Attractiveness on Purchase Intentions Zhenwei Yan, Asad Ur Rehman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7237601/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pervasive belief that higher physical attractiveness universally enhances livestreaming sales has dominated industry practices; however, emerging evidence suggests that excessive attractiveness may backfire by triggering consumer skepticism. This study investigates the dual-edged role of streamer attractiveness by examining the beauty suspicion effect where surpassing an attractiveness threshold undermines perceived professionalism, thereby eroding consumer trust. Grounded in social perception theory and the credibility-persuasion framework, we propose a model delineating how physical attractiveness influences professional credibility, affective response, and beauty doubt, which collectively shape customer trust and purchase intention, moderated by product type and consumer individual differences. A structured online survey was conducted with 335 livestreaming viewers, and the data were analyzed using a hybrid structural equation modeling (SEM) approach. Results reveal that while attractiveness initially boosts credibility and affective response, it simultaneously heightens beauty doubt. Crucially, professional credibility and affective response foster trust, whereas beauty doubt diminishes it, with trust further driving purchase intention. In sum, the findings challenge the "more attractiveness is better" axiom, offering nuanced insights for streamers and platforms to strategically balance aesthetics and expertise. Theoretical contributions extend to attractiveness thresholds in digital persuasion, while practical implications guide talent selection and product-streamer matching. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Beauty suspicion effect Professional credibility Consumer trust Live-streaming commerce Purchase intention Figures Figure 1 Figure 2 Figure 3 1. Introduction The meteoric rise of livestreaming commerce has redefined digital retail, transforming passive online shopping into an interactive, real-time, and socially enriched experience (Kang et al., 2021 ). By bridging the gap between physical and virtual retail, livestreaming platforms empower sellers to engage consumers through dynamic demonstrations, instant feedback, and communal participation features that conventional e-commerce lacks (Forrester, 2021 ). This shift has propelled livestreaming into a multibillion-dollar industry, exemplified by record-breaking sales events like China’s Singles’ Day, where livestreaming accounted for $ 6 billion in transactions (Hallanan, 2020 ). Amid this boom, streamers the human faces of livestreaming commerce have emerged as pivotal influencers, with brands often prioritizing physical attractiveness as a key driver of consumer engagement and sales (Wu et al., 2022 ). Yet, this prevailing "beauty premium" assumption overlooks a critical paradox: excessive attractiveness may inadvertently erode trust, triggering skepticism about a streamer’s expertise and authenticity. This research offers an interesting research context worth further investigation. Extant research on livestreaming commerce has predominantly focused on technological affordances (e.g., interactivity, media richness) and platform design (Ang et al., 2018 ; Zuo & Xiao, 2021 ), while largely treating streamer attractiveness as a linear, universally beneficial trait. However, emerging evidence suggests that attractiveness operates as a double-edged sword: while it initially captures attention and enhances likability, surpassing an optimal threshold may provoke beauty doubt, a cognitive bias wherein consumers question whether an overly attractive streamer’s appeal substitutes for substantive product knowledge (Luo et al., 2023 ). Despite the growing body of research on livestreaming commerce, critical gaps remain in understanding the paradoxical role of streamer attractiveness in shaping consumer trust and purchase decisions.Prior studies predominantly assume a monotonic relationship between attractiveness and persuasion, suggesting that higher attractiveness invariably enhances credibility and sales (Wu et al., 2022 ). However, this overlooks the possibility of an attractiveness threshold, beyond which beauty may trigger skepticism rather than trust. While traditional advertising literature acknowledges the "beauty-is-beastly" effect (e.g., overly attractive spokespeople being distrusted for certain roles; Lynn, 2009), this phenomenon remains underexplored in livestreaming commerce, where real-time interaction and perceived authenticity are paramount. Grounded in social perception theory (Fiske et al., 2007 ) and the credibility-persuasion framework (Pornpitakpan, 2004 ; Yang et al., 2025 ), this study posits that physical attractiveness simultaneously amplifies professional credibility, affective response, and beauty doubt, with downstream effects on customer trust and purchase intention. Importantly, these relationships are moderated by product type where utilitarian vs. hedonic goods may alter the weight of attractiveness and consumer individual differences, such as susceptibility to interpersonal influence or need for cognition. With the intensification of homogeneous competition on traditional e-commerce platforms, live streaming e-commerce has become a key channel for businesses to break through traffic bottlenecks, thanks to its unique advantages of "real-time display, real-time interaction, and emotional awakening". This research challenges the monolithic "more attractiveness is better" heuristic in digital persuasion, introducing the beauty suspicion effect as a boundary condition to the attractiveness-trust link. Next, it advances livestreaming literature by delineating how attractiveness thresholds interact with perceived expertise and emotional appeal to shape consumer decisions a mechanism underexplored in prior work (Xue et al., 2020 ). Moreover, this research offers actionable insights for platforms and brands: matching streamer aesthetics to product categories (e.g., high-attractiveness streamers for beauty products vs. expert-oriented streamers for tech gadgets) and segmenting audiences based on cognitive traits. By employing a hybrid SEM approach to analyze survey data from 335 livestream viewers, this study not only refines theoretical models of digital influencer effectiveness but also equips practitioners to strategically balance aesthetics and credibility in talent selection. As livestreaming commerce evolves from a novelty to a necessity, understanding the nuanced role of streamer attractiveness is critical. This research underscores that in the battle for consumer trust, brains and beauty are not mutually exclusive, but their equilibrium dictates commercial success. 2. Theoretical background 2.1. Purchase intention Purchase intention represents the conscious planning of consumer participation in transaction behavior, shaped by rational evaluation and emotional responses to marketing stimuli (Morwitz&Schmittlein, 1992 ). As a recognized indicator of actual purchasing behavior, it is a key indicator for evaluating marketing effectiveness and predicting business success (Wu et al., 2022 ). When consumers feel high credibility among communicators, consistent with the effectiveness of the advertised product, and experience positive emotional engagement, their purchase intention will be enhanced (Ladhari et al., 2020 ). Although not all intentions can be translated into transactions, they provide important diagnostic value for understanding consumer psychology and improving promotional strategies (Baker et al., 2016). In live streaming commerce, purchase intention is particularly susceptible to two counteracting forces: heuristic attraction based on source attractiveness and systematic evaluation based on professional knowledge trust (Chaiken, 1980 ). Although traditional views suggest that physical attractiveness generally enhances persuasiveness (Patzer, 2006 ), recent research has revealed a "beauty premium penalty" where excessive attractiveness triggers suspicion and offsets its initial advantage (Li et al., 2023 ). On the one hand, attractive anchors may enhance their purchase intention through emotional arousal, utilizing the halo effect to cultivate positive brand associations (Nisbett&Wilson, 1977 ). This mechanism is particularly effective for hedonic products, where sensory satisfaction dominates functional evaluation (Voss et al., 2003 ). On the contrary, exceeding the attractiveness threshold may activate persuasive knowledge (Friestad&Wright, 1994 ), leading consumers to attribute the influence of streaming media to manipulative intentions rather than genuine expertise (Xie et al., 2024). This kind of "beauty doubt" can erode trust and indirectly suppress purchase intention, which is even more serious for practical products that require ability-based guarantees (Pornpitakpan, 2004 ). In short, the purchase intention in the live streaming environment reflects the tense relationship between aesthetic appeal and credibility erosion and is moderated by product and consumer unexpected events. This framework challenges the linear "beauty advantage" assumption and advocates for a balance between attractiveness and perceived expertise. 2.2. Social Cognitive Theory The dual impact of streaming media attractiveness on consumer trust and purchase intention can be further elucidated from the perspective of social cognitive theory (SCT) (Bandura, 1986 ). SCT posits that human behavior is formed by the dynamic interaction of individual cognitive factors, environmental influences, and behavioral outcomes, forming a triple reciprocal causal model. In the context of live streaming commerce, consumers' perception of the attractiveness of streaming media interacts with their cognitive evaluations (such as professionalism) and emotional responses (such as admiration or doubt), ultimately shaping trust and purchasing decisions. The core of SCT comes from credibility, where individuals evaluate the credibility and professional knowledge of communicators before accepting persuasive information (Pornpitakpan, 2004 ). Although physical attractiveness is often a heuristic clue to credibility, exceeding the attractiveness threshold may lead to cognitive dissonance, and consumers may question whether the attractiveness of streaming media can replace substantive expertise. This is consistent with SCT's emphasis on self-regulation, where consumers actively review external stimuli rather than passively accepting them. The resulting 'beauty skepticism effect' illustrates how environmental cues such as super attractiveness can disrupt the formation of cognitive trust. In addition, SCT emphasizes the role of emotional arousal in decision-making. Attraction induced emotional responses may enhance persuasiveness through emotional involvement, but excessive arousal can impair cognitive processes (Lerner et al., 2015 ), reflecting SCT's assertion that emotional states regulate attention and judgment. For example, high admiration may mask critical evaluation of product applicability, especially for practical goods. On the contrary, suspicion triggered by beauty doubt may activate defensive cognitive processing, and consumers may hold a skeptical attitude towards the information on streaming media (Tormala&Petty, 2004). In summary, SCT provides a powerful framework for understanding the contradictory effects of streamer attraction by integrating cognitive, emotional, and environmental mechanisms. It proposes the idea that digital persuasion is not just a function of source attraction, but a complex negotiation of perceived credibility, emotional resonance, and situational fit. 3. Hypotheses 3.1. Conceptual model This study proposes a research model (illustrated in Fig. 1 ) that examines the paradoxical influence of streamer attractiveness on purchase intentions, delineating the dual psychological mechanisms of professional credibility and beauty doubt, with product type and consumer individual differences as key moderators. Crucially, product type moderates these relationships. For hedonic products (e.g., fashion, cosmetics), attractiveness and affective responses dominate, as aesthetic alignment enhances persuasion. Conversely, for utilitarian products (e.g., electronics, appliances), professional credibility becomes paramount, and beauty doubt erodes trust when expertise appears secondary to looks. Meanwhile, consumer individual differences (e.g., need for cognition, skepticism propensity) further shape outcomes: analytical consumers prioritize credibility, while impulse-driven consumers respond more strongly to affective cues. The model advances a competing mediation perspective, where the net effect of attractiveness on purchase intention depends on the equilibrium between credibility-driven trust and beauty-induced skepticism. By integrating affective and cognitive pathways, the framework explains why highly attractive streamers may underperform in certain contexts, offering a nuanced lens for talent selection and product-streamer matching. 3.2 Physical attractiveness Physical attractiveness can be defined by the degree to which a streamer's appearance is perceived as aesthetically pleasing by viewers (Dion et al., 1972 ). In live-streaming commerce, physical attractiveness serves as a salient heuristic cue that shapes consumers' initial judgments (Langlois et al., 2000 ). According to social perception theory, individuals tend to attribute positive traits, such as competence and trustworthiness, to attractive individuals a phenomenon known as the "halo effect" (Nisbett & Wilson, 1977 ). However, excessive attractiveness may also trigger skepticism, as consumers question whether the streamer's appeal is leveraged to compensate for a lack of expertise (Praxmarer, 2011 ). This duality aligns with the credibility-persuasion framework, wherein attractiveness enhances perceived credibility yet simultaneously induces "beauty doubt," a cognitive dissonance arising from the incongruence between aesthetic appeal and perceived professionalism (Ohanian, 1990 ). Furthermore, attractiveness elicits affective responses by stimulating hedonic pleasure and parasocial engagement (Homer, 2008 ), yet its overemphasis may undermine rational decision-making. Thus, physical attractiveness operates as a double-edged sword, fostering both positive and negative consumer reactions. Based on this theoretical grounding, the following hypotheses are proposed. H1: Physical attractiveness positively correlates with professional credibility. H2: Physical attractiveness positively correlates with beauty doubt effect. H3: Physical attractiveness positively correlates with affective response. 3.3 Professional credibility Professional credibility is the extent to which a streamer is perceived as knowledgeable, competent, and trustworthy in their domain (Ohanian, 1990 ). Rooted in source credibility theory, professional credibility serves as a critical heuristic for consumers when evaluating persuasive messages, as expertise enhances message acceptance and reduces skepticism (Pornpitakpan, 2004 ). In live-streaming commerce, where product demonstrations and real-time interactions are central, a streamer’s perceived professionalism not only validates product claims but also mitigates uncertainty inherent in online transactions (Xie et al., 2015 ). Prior research in influencer marketing underscores that credibility fosters consumer trust by signaling reliability and reducing perceived risk (Sokolova & Kefi, 2020 ). Furthermore, professional credibility directly fuels purchase intention by enhancing the perceived utility of the recommended products (Ladhari et al., 2020 ). However, this relationship may be contingent on contextual factors, such as product type, where expertise signals are more salient for high-involvement goods (Petty & Cacioppo, 1986 ). Thus, the hypotheses formulated as follows. H4: Professional credibility positively correlates with customer trust. H5: Professional credibility positively correlates with purchase intention. 3.4 Beauty doubt effect Beauty doubt effect is consumers' skepticism toward highly attractive streamers, where excessive physical attractiveness triggers perceptions of compromised professionalism or manipulative intent (Praxmarer, 2011 ). Rooted in the attribution theory of persuasion (Eagly et al., 1978 ), this effect arises when attractiveness surpasses a threshold, leading audiences to attribute the streamer’s influence to superficial traits rather than expertise, thereby activating heuristic processing biases (Chaiken, 1980 ). Such doubt undermines trust by violating expectancy-confirmation mechanisms (Olson et al., 1996 ), as consumers perceive a mismatch between aesthetic appeal and substantive value. Prior research on the "beauty penalty" (Lee et al., 2015 ) in professional contexts further supports that extreme attractiveness can evoke stereotypes of incompetence or untrustworthiness, particularly in expertise-dependent settings. Thus, the beauty doubt effect operates as a countervailing force to the traditional attractiveness halo, attenuating trust when attractiveness is perceived as disproportionate to credibility. Hence, the following hypothesis is constructed. H6: Beauty doubt effect negatively correlates with customer trust. 3.5 Affective response Affective response (AR) refers to the emotional arousal and hedonic pleasure elicited by a streamer's physical attractiveness during live-streaming interactions (Batra & Holbrook, 1990 ). Rooted in the stimulus-organism-response (S-O-R) framework, AR captures the immediate, visceral reactions that transcend cognitive appraisal, including feelings of admiration, excitement, or aesthetic enjoyment (Donovan & Rossiter, 1982 ). Such emotional states can enhance parasocial bonding by fostering a sense of intimacy and likability (Horton & Wohl, 1956 ), thereby amplifying the viewer's receptivity to persuasive messages (Luo et al., 2025 ). However, AR operates as a double-edged mechanism: while positive affect facilitates trust through emotional contagion (Hatfield et al., 1994 ), its overreliance may divert attention from product-related information, potentially weakening rational decision-making (Pham et al., 2001 ). Prior research confirms that AR mediates the impact of aesthetic stimuli on behavioral outcomes, yet its efficacy depends on the congruence between emotional appeal and task relevance (Adaval, 2001 ). Thus, this study posits that AR serves as a critical pathway through which attractiveness influences trust and purchase intent, albeit contingent on contextual and individual factors. H7: Affective response positively correlates with customer trust. H8: Affective response positively correlates with purchase intention. 3.6 Customer trust Customer trust (CT) reflects a psychological state wherein consumers willingly accept vulnerability based on positive expectations of a streamer's reliability and intentions (Mayer et al., 1995 ). In live-streaming commerce, CT serves as a critical bridge between streamer attributes and transactional outcomes, as it mitigates perceived risks and fosters dependency on the streamer's recommendations (Gefen et al., 2003 ). Rooted in social exchange theory, CT emerges from the interplay of cognitive evaluations (e.g., professionalism) and affective responses (e.g., parasocial attachment), while being attenuated by heuristic-based suspicions (e.g., beauty doubt) (Doney & Cannon, 1997 ). Empirical evidence consistently demonstrates that CT enhances purchase intention (PI) by reducing decision uncertainty and facilitating heuristic processing (Pavlou et al., 2007 ). Notably, PI manifests only when CT surpasses a threshold that justifies the relinquishment of consumer autonomy to the streamer's influence (McKnight et al., 2002 ). Thus, the following hypothesis is advanced. H9: Customer trust positively correlates with purchase intention. 3.7 Product type Product type refers to the categorical distinction of goods based on their functional, experiential, or symbolic attributes (Dhar & Wertenbroch, 2000 ). Prior research suggests that product characteristics fundamentally alter consumer evaluation processes, particularly in influencer marketing contexts (Jin & Phua, 2014 ). For utilitarian products (e.g., electronics, tools), consumers prioritize functional competence and expertise, whereas for hedonic products (e.g., cosmetics, fashion), sensory and emotional appeal often dominate decision-making (Voss et al., 2003 ). This dichotomy implies that the interplay between streamer attractiveness and consumer trust is contingent on whether the product aligns with pragmatic or affective consumption goals. Specifically, professional credibility may exert stronger trust-building effects for utilitarian products due to their performance-dependent nature, while beauty doubt a skepticism toward highly attractive streamers’ expertise could be more detrimental for such products. Conversely, affective responses driven by attractiveness may enhance trust more prominently for hedonic products, where emotional engagement outweighs functional scrutiny. Thus, this study posits that product type systematically moderates the pathways through which attractiveness influences trust. H10a: Product type moderates the relationship between professional credibility and customer trust. H10b: Product type moderates the relationship between beauty doubt effect and customer trust. H10c: Product type moderates the relationship between affective response and customer trust. 3.8 Consumer individual differences Consumer individual differences (CID) encompass stable psychological traits that systematically influence how individuals process information and respond to marketing stimuli (Zhang & Shrum, 2009). Rooted in the Elaboration Likelihood Model (Petty & Cacioppo, 1986 ), CID shape whether consumers rely on heuristic cues (e.g., attractiveness) or systematic processing (e.g., expertise evaluation) when forming trust and purchase decisions. Cognitive needs (NFC), the tendency to engage in laborious cognitive processing and susceptibility to normative influences (SNI), the tendency to conform to social expectations, have been identified as key moderating factors in digital persuasion environments (Ladhari et al., 2020 ). High-NFC individuals are more likely to scrutinize professional credibility, whereas high-SNI consumers may prioritize affective responses driven by attractiveness. Furthermore, trait skepticism amplifies beauty doubt effects by triggering attributional scrutiny (i.e., questioning whether attractiveness signals competence or manipulation) (Pornpitakpan, 2004 ). Thus, CID create boundary conditions for how attractiveness-derived perceptions translate into behavioral outcomes. Hence, the following hypotheses are proposed. H11a: Consumer individual differences moderate the relationship between affective response and purchase intention, such that the effect is stronger for consumers with high susceptibility to normative influence (SNI) and weaker for those with high need for cognition (NFC). H11b: Consumer individual differences moderate the relationship between professional credibility and purchase intention, such that the effect is stronger for consumers with high need for cognition (NFC) and weaker for those with high susceptibility to normative influence (SNI). 4. Methods 4.1 Questionnaire Introduction and Source This study aimed to examine how highly attractive livestreamers influence consumer trust and purchase intention in e-commerce live streaming contexts. We developed our questionnaire based on previous studies (see Table 1 ). The survey included three dimensions: perceived streamer attractiveness and beauty skepticism, affective and trust mediators, and consumer characteristics and outcome variables. This study collected data by distributing the survey from February 1st to May 1st, 2025. The invitation link to the questionnaire was distributed across multiple social media and livestreaming commerce platforms, whereby only individuals with purchase experience in livestreaming commerce were targeted. A total of 335 valid questionnaires were returned. All participants completed the survey voluntarily and signed informed consent forms. The study received approval from the Institutional Review Board. We followed ethical guidelines throughout the research process. This study ensured data integrity and confirmed that the questionnaire demonstrated high reliability and validity. Table 1 Sources of Measurement Items for the Questionnaire Variables Items Sources Streamer Attractiveness and Beauty Doubt Physical Attractiveness 5 AlFarraj et al. ( 2021 ); Dion ( 2022 ). Beauty Doubt Effect 5 Kara-Yakoubian et al.(2022); Nguyen‐Viet & Nguyen (2024). Affective Response and Customer Trust Customer Trust 5 Alam et al. ( 2021 ); Aldboush & Ferdous ( 2023 ). Professional Credibility 5 Little & Green ( 2022 ); Simoneau & Cook ( 2024 ). Affective Response 7 Kimiagari & Malafe ( 2021 ); Zanger et al. ( 2022 ); Lavuri & Akram ( 2024 ). Consumer Characteristics and Outcome Variables Consumer Individual Differences 6 Buvár & Orosz (2023); Kallergi & Landeweerd ( 2025 ). Purchase Intention 6 Ivanova & Moreira ( 2023 ); Chen ( 2024 ). Product Type 4 Zhang & Wang ( 2023 ); Krabbe & Grodal ( 2023 ). 4.2 Profile of respondents This study obtained 335 valid responses. The sample was balanced and representative across gender, age, education level, disposable income, purchase frequency, and occupation. Figure 2 shows that female respondents slightly outnumbered males. The 25–40 age group was the largest. Most respondents held at least a college diploma. Undergraduates comprised the largest subgroup. Monthly disposable income mostly fell between RMB 6,001 and 20,000. Most respondents made 5–8 purchases per month. Most worked in corporations or government/institutional roles. Freelancers, the self-employed, and students were also represented. These traits suggest high online engagement and strong purchase potential. Next, this study conducted Pearson correlations (see Table 2 ). All eight variables: physical attractiveness, customer trust, affective response, professional credibility, consumer individual differences, purchase intention, beauty doubt effect, and product type showed significant positive correlations at p < .01. Physical Attractiveness correlated most strongly with purchase intention (r = .494), affective response (r = .445), and Customer Trust (r = .361). This suggests that higher streamer attractiveness enhances emotional experience and trust, which in turn boosts purchase intention. The strong correlation between customer trust and product type (r = .638) indicates that product attributes play a key role in trust formation. Consumer individual differences correlated significantly with Purchase Intention (r = .532). This highlights the influence of individual differences on purchase decisions. Although beauty doubt effect reflects skepticism, it still correlated positively with customer trust (r = .387) and purchase intention (r = .383). This shows that beauty doubt did not weaken the effect of attractiveness on trust or purchase intention. Table 2 Correlation results PA CT AR PC CID PI BDE PT Physical Attractiveness 1 Customer Trust .361 ** 1 Affective Response .445 ** .389 ** 1 Professional Credibility .374 ** .378 ** .276 ** 1 Consumer Individual Differences .369 ** .377 ** .358 ** .334 ** 1 Purchase Intention .494 ** .424 ** .464 ** .418 ** .532 ** 1 Beauty Doubt Effect .378 ** .387 ** .353 ** .293 ** .345 ** .383 ** 1 Product Type .385 ** .638 ** .278 ** .292 ** .270 ** .361 ** .248 ** 1 **. Correlations are significant at the 0.01 level (two-tailed). 4.3 Exploratory factor analysis 4.3.1 Reliability and validity analysis First, this study assessed the scale’s internal consistency and data suitability. Table 3 presents Cronbach’s alpha values. All dimensions surpassed the industry benchmark of α ≥ .70. Emotional response (α = .910), purchase intention (α = .902), and customer trust (α = .898) exceeded .90. Other dimensions ranged from .855 to .898. The overall scale achieved α = .930. This result indicates excellent internal consistency. Table 3 Reliability and validity Variables Items Alpha KMO Physical Attractiveness 5 .869 .878 Customer Trust 5 .898 .889 Affective Response 7 .910 .933 Professional Credibility 5 .860 .856 Consumer Individual Differences 6 .887 .909 Purchase Intention 6 .902 .918 Beauty Doubt Effect 5 .855 .865 Product Type 4 .839 .815 Overall 43 .930 .946 Next, this study evaluated the assumptions for factor analysis. The overall Kaiser–Meyer–Olkin measure was .946. Individual KMO values ranged from .815 for product type to .933 for emotional response. All values exceeded the recommended threshold of .60. Bartlett’s test of sphericity was significant, χ²(903) = 5892.43, p < .001. This result confirms adequate shared variance among items. Finally, this study conducted principal component analysis using varimax rotation. Extract factors for each dimension and validate the scale structure. This program tests the consistency between empirical factors and theoretical structures. 4.3.2 Factor number analysis This study followed the Kaiser criterion (eigenvalues > 1.0) in exploratory factor analysis. Table 4 presents the initial eigenvalues for 43 items: 13.201, 3.184, 2.912, 2.367, 2.281, 2.002, 1.752, and 1.041. Each exceeded the threshold of 1. The initial cumulative variance explained was 66.84%. This exceeds the 60% threshold commonly used in social science research. It indicates that the eight components adequately capture shared variance among items. Table 4 Total variance explained by factors Item Initial Eigenvalues Rotated Loadings Sum of Squares Eigenvalue Variance (%) Cumulative (%) Eigenvalue Variance (%) Cumulative (%) 1 13.201 30.700 30.700 4.780 11.116 11.116 2 3.184 7.405 38.105 4.060 9.442 20.558 3 2.912 6.772 44.878 3.867 8.992 29.550 4 2.367 5.506 50.384 3.454 8.032 37.582 5 2.281 5.305 55.688 3.339 7.764 45.346 6 2.002 4.656 60.344 3.282 7.633 52.979 7 1.752 4.075 64.418 3.240 7.535 60.514 8 1.041 2.422 66.840 2.720 6.326 66.840 This study then applied varimax rotation. After rotation, variance contributions were more evenly distributed. Factor 1 explained 11.12%, Factor 2 9.44%, Factor 3 8.99%, Factor 4 8.03%, Factor 5 7.76%, Factor 6 7.63%, Factor 7 7.54%, and Factor 8 6.33%. The cumulative variance explained after rotation remained 66.84%. This result preserved overall explanatory power and yielded more focused factor loadings. It facilitates subsequent factor naming and alignment with theoretical constructs. 4.4 Confirmatory Factor Analysis 4.4.1 Model Fit Indices Analysis This study conducted a confirmatory factor analysis (CFA) to assess the structural validity of the measurement model. We evaluated model fit using multiple goodness-of-fit indices (see Table 5 ). The chi-square statistic was χ²(832) = 970.65. The normed chi-square (χ²/df) was 1.17, below the threshold of 3.0. This indicates an acceptable discrepancy-to-df ratio. The normed chi-square is more robust than the traditional χ² test, which is sensitive to large samples. Table 5 Model fit indices for confirmatory factor analysis Model Fit CMIN DF CMIN/DF NFI RFI IFI TLI CFI GFI RMSEA Fit Results 970.650 832 1.167 .888 .878 .982 .981 .982 .887 .022 Judgment Std. - - 0.9 > 0.9 > 0.9 > 0.9 > 0.9 > 0.9 < 0.08 Incremental fit indices were also strong. The normed fit index (NFI) was .888 and the relative fit index (RFI) was .878, both slightly below .90. The incremental fit index (IFI) was .982, the Tucker–Lewis index (TLI) was .981, and the comparative fit index (CFI) was .982. These values exceed the .90 criterion, indicating the model replicates the theoretical structure well. The goodness-of-fit index (GFI) was .887, close to the recommended .90 benchmark. This reflects a high level of overall fit. Finally, the root mean square error of approximation (RMSEA) was .022, with the 90% confidence interval lower bound below .05. This value is well below the .08 threshold, further supporting excellent model fit. Although NFI and RFI are slightly below the ideal threshold, other key indices exceed their critical values. Considering the large sample size and model complexity, the eight-factor model demonstrates strong consistency with the data. 4.4.2 Convergent and Fornell–Larcker Discriminant Validity Analysis After completing the confirmatory factor analysis, we assessed convergent validity. This study used composite reliability (CR) and average variance extracted (AVE) for this purpose (see Table 6 ). Composite reliability values ranged from .86 to .93, exceeding the recommended threshold of .70. Average variance extracted values ranged from .54 to .64, surpassing the .50 minimum. All standardized factor loadings exceeded .70. These findings support strong convergent validity for each construct. Table 6 Presents results for convergent and Fornell–Larcker discriminant validity Factor PA PC BDE AR CT PI PT CID CR AVE Physical Attractiveness 0.76 0.87 0.57 Professional Credibility 0.43 0.74 0.86 0.55 Beauty Doubt Effect 0.44 0.34 0.74 0.86 0.54 Affective Response 0.5 0.31 0.4 0.77 0.91 0.59 Customer Trust 0.41 0.43 0.44 0.43 0.8 0.9 0.64 Purchase Intention 0.56 0.47 0.44 0.51 0.47 0.78 0.9 0.61 Product Type 0.46 0.34 0.29 0.32 0.74 0.42 0.75 0.84 0.57 Consumer Individual Differences 0.42 0.38 0.39 0.39 0.42 0.59 0.32 0.75 0.89 0.57 Moreover, this study assessed discriminant validity using the Fornell–Larcker criterion (see Table 6 ). We placed the square roots of AVE on the matrix diagonal. They ranged from .74 to .80. Each diagonal value exceeded the highest off-diagonal correlation (maximum r = .56). For example, the AVE square root for physical attractiveness was .76. This exceeded its correlations with Purchase Intention (r = .56) and affective response (r = .50). The AVE square root for professional credibility was .74, also above its highest correlation with another construct (r = .47). These outcomes indicate that each construct is more strongly related to its own indicators than to other constructs. This effectively rules out potential confounds among latent variables. 4.5 Structural Equation Model Path Analysis Given the strong reliability and validity of the measurement model, we then built a structural equation model. Table 7 shows significant positive paths from Physical Attractiveness to three mediators. Physical attractiveness predicted professional credibility (β = .417, C.R. = 6.97, p < .001), Beauty Doubt Effect (β = .474, C.R. = 7.11, p < .001), and Affective Response (β = .545, C.R. = 8.10, p < .001). These results suggest higher attractiveness enhances perceptions of professional credibility. It also increases emotional resonance and triggers moderate doubt. Then, each mediator significantly predicted Customer Trust. Professional credibility predicted customer Trust (β = .353, C.R. = 4.61, p < .001). Beauty doubt effect predicted customer trust (β = .309, C.R. = 4.52, p < .001). Affective response predicted customer trust (β = .290, C.R. = 4.43, p < .001). Table 7 Convergent validity Composite reliability and discriminant validity Hyp Path Estimate S.E. C.R. P Conclusion H1 Professional Credibility <--- Physical Attractiveness .417 .060 6.970 *** Support H2 Beauty Doubt Effect <--- Physical Attractiveness .474 .067 7.106 *** Support H3 Affective Response <--- Physical Attractiveness .545 .067 8.095 *** Support H4 Customer Trust <--- Professional Credibility .353 .077 4.605 *** Support H6 Customer Trust <--- Beauty Doubt Effect .309 .068 4.515 *** Support H7 Customer Trust <--- Affective Response .290 .065 4.430 *** Support H5 Purchase Intention <--- Professional Credibility .356 .072 4.965 *** Support H8 Purchase Intention <--- Affective Response .364 .062 5.876 *** Support H9 Purchase Intention <--- Customer Trust .189 .056 3.376 *** Support * p < 0.05; ** p < 0.01༛ *** p < 0.001. In predicting Purchase Intention, Professional Credibility had a direct positive effect (β = .356, C.R. = 4.97, p < .001). Beauty doubt effect also positively influenced Purchase Intention (β = .364, C.R. = 5.88, p < .001). Customer trust predicted purchase intention with a smaller effect (β = .189, C.R. = 3.38, p < .001). This underscores trust as a key driver of purchase behavior. Overall, our findings support the “Attractiveness → Mediation → Trust → Purchase” framework. They also highlight the positive role of the beauty doubt effect in fostering trust and purchase intention. 4.6 Moderating Effect Analysis 4.6.1 Moderating Effect of Product Type on the Professional Credibility → Customer Trust Path This study added a PC × PT interaction term to the structural model to test whether PT moderates the path from PC to CT (see Table 8 ). The interaction was significant (β = .232, SE = .056, t = 4.14, p < .001), indicating that higher PT enhances the impact of PC on CT. Table 8 Moderation Results for Product Type on the Professional Credibility → Customer Trust Path Experimental result Model Path coefficient (Estimate) standard error (S.E.) critical ratio (C.R.) P value Constant 3.1209 .8171 3.8195 .0002 Professional Credibility − .5782 .2111 -2.7388 .0065 Product Type − .2129 .2242 − .9496 .3430 Professional Credibility × Product Type .2324 .0562 4.1355 .0000 Conditional effects of regulatory effects Product Type (Effect) standard error (S.E.) critical ratio (t) P value lower limit (LLCI) upper limit (ULCI) 2.5000 .0028 .0830 .0334 .9733 − .1605 .1661 4.2500 .4094 .0633 6.4721 .0000 .2850 .5339 4.5000 .4675 .0719 6.5035 .0000 .3261 .6089 Simple-slope analysis showed no significant effect at a low Product Type level (2.50), β = .003, t = .08, p = .973. At a moderate-high level of Product Type (4.25), the path was significant, β = .409, t = 6.47, p < .001. At a high level (4.50), it was also significant, β = .468, t = 6.50, p < .001. The effect grew stronger as Product Type increased. These results suggest that consumers perceive the trust benefit from streamer professionalism only for high-involvement or high-value products. For low-involvement, routine fast-moving consumer goods, the enhancing effect is negligible. 4.6.2 Moderating Effect of Product Type on the Beauty Doubt Effect → Customer Trust Path We added a BDE × PT interaction term to test whether Product Type moderates the effect of BDE on CT (see Table 9 ). The model constant was β = 2.7009, p < .001. BDE had a significant negative main effect (β = −.4922, C.R. = −2.5663, p = .0107). PT’s main effect was not significant (β = −.0953, p = .6217). The interaction term was significant (β = .2101, C.R. = 4.2151, p < .001). This indicates that product type significantly moderates the beauty doubt effect → customer trust path. Table 9 Moderation Results for Product Type on the Beauty Doubt Effect → Customer Trust Path Experimental result Model Path coefficient (Estimate) standard error (S.E.) critical ratio (C.R.) P value Constant 2.7009 .7216 3.7430 .0002 Beauty Doubt Effect − .4922 .1918 -2.5663 .0107 Product Type − .0953 .1930 − .4940 .6217 Beauty Doubt Effect × Product Type .2101 .0499 4.2151 .0000 Conditional effects of regulatory effects Product Type (Effect) standard error (S.E.) critical ratio (t) P value lower limit (LLCI) upper limit (ULCI) 2.5000 .0331 .0778 .4259 .6705 − .1198 .1861 4.2500 .4008 .0549 7.3041 .0000 .2929 .5088 4.5000 .4534 .0618 7.3346 .0000 .3318 .5749 Simple-slope analysis showed no significant effect at a low Product Type level (2.50), β = .0331, t = .4259, p = .6705. At a moderate‐high level of Product Type (4.25), the effect was significant, β = .4008, t = 7.3041, p < .001. At a high level (4.50), it was β = .4534, t = 7.3346, p < .001. The effect grew stronger as Product Type increased. These findings suggest that Beauty Doubt Effect translates into trust benefits only for high‐involvement or high‐value products. For low‐involvement, fast‐moving consumer goods, the effect is negligible. 4.6.3 Moderating Effect of Product Type on the Affective Response → Customer Trust Path We added an AR × PT interaction term to test whether PT moderates the path from AR to CT (see Table 10 ). The main effect of AR on CT was not significant (β = .18, SE = .18, C.R. = 1.01, p = .315). PT had a significant positive effect on CT (β = .62, SE = .17, C.R. = 3.55, p < .001). The AR × PT interaction term was not significant (β = .019, SE = .046, C.R. = .41, p = .683). Table 10 Moderation Results for Product Type on the Affective Response → Customer Trust Path Experimental result Model Path coefficient (Estimate) standard error (S.E.) critical ratio (C.R.) P value Constant .2603 .6502 .4003 .6892 Affective Response .1802 .1791 1.0060 .3151 Product Type .6196 .1747 3.5459 .0004 Affective Response × Product Type .0189 .0464 .4082 .6834 Simple-slope analysis showed the conditional effect of Affective Response on Customer Trust was β = .23 at low Product Type (2.50). It was β = .26 at moderate level (4.25) and β = .27 at high level (4.50). None reached statistical significance. These findings indicate that Product Type does not moderate the relationship between Affective Response and Customer Trust. The effect remains non‐significant at all Product Type levels. 4.6.4 Moderating Effect of Consumer Individual Differences on the Affective Response → Purchase Intention Path We added an AR × CID interaction term to the structural model to test whether CID moderates the path from AR to PI (see Table 11 ). The main effect of AR on PI was significant and negative, β = −.3711, SE = .1602, C.R. = −2.3163, p = .0212. CID had a non-significant main effect, β = −.2176, p = .1661. The AR × CID interaction was significant, β = .1923, SE = .0429, C.R. = 4.4853, p < .001. This indicates that CID significantly moderates the AR → PI path. Table 11 Moderation Results for Consumer Individual Differences Experimental result Model Path coefficient (Estimate) standard error (S.E.) critical ratio (C.R.) P value Constant 3.0934 .5553 5.5711 .0000 Affective Response − .3711 .1602 -2.3163 .0212 Consumer Individual Differences − .2176 .1568 -1.3878 .1661 Affective Response × Consumer Individual Differences .1923 .0429 4.4853 .0000 Conditional effects of regulatory effects Consumer Individual Differences (Effect) standard error (S.E.) critical ratio (t) P value lower limit (LLCI) upper limit (ULCI) 2.3333 .0776 .0705 1.1006 .2719 − .0611 .2163 4.1667 .4302 .0525 8.2021 .0000 .3270 .5334 4.5000 .4943 .0606 8.1540 .0000 .3751 .6136 Simple-slope analysis showed no significant effect at low CID (2.33), β = .0776, t = 1.1006, p = .2719. At moderate‐high CID (4.17), the effect became significant and stronger, β = .4302, t = 8.2021, p < .001. At high CID (4.50), it further increased to β = .4943, t = 8.1540, p < .001. These findings suggest that emotional resonance elicited by the streamer translates into purchase intention only when consumers have high levels of individual traits, such as innovativeness, brand loyalty, or technology acceptance. For consumers with low individual differences, emotional arousal alone is insufficient to drive purchase behavior. 4.6.5 Moderating Effect of Consumer Individual Differences on the Professional Credibility → Purchase Intention Path We added a PC × CID interaction term to test whether CID moderates the path from PC to PI (see Table 12 ). PC had a significant negative main effect, β = –.684, SE = .186, C.R. = − 3.688, p = .0003. CID also had a significant negative main effect, β = –.562, SE = .190, C.R. = − 2.960, p = .0033. The PC × CID interaction was significant, β = .280, SE = .050, C.R. = 5.644, p < .001. This indicates that CID changes both the direction and magnitude of the PC→PI relationship. Table 12 Moderation Results for Consumer Individual Differences on the Professional Credibility → Purchase Intention Path Experimental result Model Path coefficient (Estimate) standard error (S.E.) critical ratio (C.R.) P value Constant 4.2476 .6813 6.2343 .0000 Professional Credibility − .6842 .1855 -3.6884 .0003 Consumer Individual Differences − .5619 .1898 -2.9603 .0033 Professional Credibility × Consumer Individual Differences .2799 .0496 5.6445 .0000 Conditional effects of regulatory effects Consumer Individual Differences (Effect) standard error (S.E.) critical ratio (t) P value lower limit (LLCI) upper limit (ULCI) 2.3333 − .0310 .0817 − .3797 .7044 − .1916 .1296 4.1667 .4822 .0605 7.9699 .0000 .3632 .6012 4.5000 .5755 .0699 8.2299 .0000 .4380 .7131 Simple-slope analysis showed no significant effect at low CID (2.33), β = –.031, t = –.380, p = .704. At moderate‐high CID (4.17), the effect was significant, β = .482, t = 7.970, p < .001. At high CID (4.50), it increased further, β = .576, t = 8.230, p < .001. The effect strengthened as CID rose. These findings suggest that PC translates into PI only for consumers with high levels of innovativeness, brand loyalty, or technology acceptance. For consumers low in these traits, professionalism alone does not generate purchase motivation and may feel forced. 5. Discussion Figure 3 shows that high streamer attractiveness increases professional credibility via the halo effect (Buvár et al., 2023 ). It also generates cautious skepticism and strengthens emotional resonance. Together, these rational and emotional processes build trust in both the streamer and the platform (Chen et al., 2024 ). This trust, driven by professional credibility and moderate skepticism, influences purchase intention. This pattern aligns with the attitude belief intention sequence in the theory of planned behavior (Kim et al., 2023 ). Professional credibility represents rational endorsement. Cautious skepticism reinforces confidence in authenticity. Emotional resonance closes psychological distance and fosters identification. When combined, these three mechanisms drive behavioral intention effectively, regardless of product type or individual differences. These findings support both the halo effect and the theory of planned behavior. They also show that, in e-commerce live streaming, the beauty doubt effect can work with professional credibility and emotional appeal to boost trust. This offers a fresh perspective for research at the intersection of interpersonal attraction and consumer psychology. Combined analysis of all path effects indicates that the influence is both stronger in magnitude and more robust. This pattern aligns with the persuasion model’s assertion that core attractiveness elements drive deep audience processing (Chen et al., 2024 ). It also echoes source credibility findings that credibility, expertise, and attractiveness vary in weighting across mediating stages (Von & Guess, 2023). It further supports the theory of planned behavior’s hypothesis that attitude components, despite varying strengths, each contribute continuously to behavioral intention (Wang et al., 2025 ). Based on these conclusions, live-stream e‐commerce should prioritize enhancing streamers’ visual presentation and personal charisma to maximize emotional and trust pathway amplification. At the same time, building a professional image and using cautious skepticism in messaging are crucial. They reinforce trust and boost purchase intention nearly as effectively as attractiveness. Although trust’s direct conversion effect is relatively weak, its role as a bridge between mediators and purchase behavior is critical. Therefore, platforms should continually optimize trust‐building mechanisms like comment feedback and trial experiences. This fosters synergy across visual, cognitive, and emotional stages and offers a quantifiable, traceable framework for refined live‐stream e‐commerce operations. From a macro perspective, live-stream platforms should begin with consumer segmentation and personalized recommendations. This ensures that users varying in innovativeness, loyalty, and technology acceptance receive content tailored to their needs. Next, platforms should enhance streamers’ professional image. Use scenario demonstrations, high-value knowledge sharing, and case presentations. This boosts professional credibility and conversion efficiency among low-difference audiences. Then, integrate real-time comment gamification with emotional incentive tasks and virtual prop interactions. This preserves emotional resonance while avoiding the trust gap caused by single-channel emotional appeals. Next, develop differentiated presentation styles and narration scripts aligned with product attributes. This deepens the link between product and streamer persona. It also amplifies the positive effect of cautious skepticism in high-involvement contexts. In addition, build a multi-dimensional trust ecosystem. Include user-generated comments, executive or expert endorsements, and core customer testimonials. This fosters ongoing psychological safety. Finally, leverage AI and big data to monitor audience emotions and behavior in real time. Use these insights to optimize streamer performance and marketing cadence. This enables flexible adaptation across scenarios and audiences and maximizes trust-to-purchase conversion. 6. Implications 6.1. Theoretical implications This research enriches the current knowledge of livestreaming commerce literature in several ways. First, while prior research has predominantly treated streamer attractiveness as a unidimensional driver of consumer engagement (Luo et al., 2023 ), this study challenges the "more attractiveness is better" assumption by introducing the beauty suspicion effect, a paradoxical mechanism through which excessive attractiveness undermines perceived professionalism and triggers consumer skepticism. This extends social perception theory by identifying an attractiveness threshold beyond which physical appeal erodes rather than enhances persuasion efficacy, offering a more nuanced understanding of digital source credibility. Second, the study bridges a critical gap in livestreaming literature by delineating the dual pathways through which attractiveness operates while it simultaneously enhances affective response and professional credibility, it also heightens beauty doubt. The framework of this study suggests that the net impact of attractiveness on trust and ultimately purchase intention depends on the trade-off between its effects of enhancing credibility and arousing suspicion. The non-significant moderating role of product type in the affective response-trust link further suggests that emotional arousal driven by attractiveness may be less context-dependent than cognitive evaluations, a novel insight for the credibility-persuasion literature. Third, the study extends the affective-cognitive response model by integrating beauty doubt as a distinct cognitive barrier. Unlike traditional skepticism constructs (e.g., perceived risk), beauty doubt emerges specifically from aesthetic overload, revealing how visual cues can trigger counterproductive inferences about competence. This advances our understanding of trust formation in digital environments, where surface-level attributes often dominate initial evaluations. Third, the study extends the affective-cognitive response model by integrating beauty doubt as a distinct cognitive barrier. Unlike traditional skepticism constructs (e.g., perceived risk), beauty doubt emerges specifically from aesthetic overload, revealing how visual cues can trigger counterproductive inferences about competence. This advances our understanding of trust formation in digital environments, where surface-level attributes often dominate initial evaluations. In summary, this study redefines attractiveness as a double-edged sword in digital persuasion, providing a framework to reconcile its opposing effects while opening up new avenues for exploring threshold dynamics in virtual influencer marketing. 6.2. Practical implications The findings of this study yield actionable insights for live-streaming platforms, merchants, and streamers to optimize the strategic deployment of attractiveness while mitigating its paradoxical drawbacks. First, while physical attractiveness enhances initial perceptions of credibility and affective response, streamers should avoid over-relying on aesthetic appeal at the expense of demonstrated expertise. Platforms and brands must recognize that surpassing an attractiveness threshold can trigger consumer skepticism, undermining trust. Thus, training programs should emphasize the cultivation of professional knowledge (e.g., product specifications, industry trends) alongside presentational skills to reinforce perceived competence and counteract beauty doubt. Second, the moderating role of product type suggests that attractiveness should be calibrated to product category. For high-involvement products (e.g., electronics, luxury goods), streamers with balanced attractiveness and expertise are preferable, as consumers prioritize functional credibility over aesthetic appeal. Conversely, for low-involvement hedonic products (e.g., cosmetics, fashion), highly attractive streamers may leverage affective responses more effectively. Merchants should adopt a product-streamer matching strategy, pairing utilitarian goods with "expert" anchors and experiential goods with "charismatic" anchors to align with consumer expectations. Third, the persistence of beauty doubt implies that streamers must proactively signal trustworthiness through transparent behaviors, such as disclosing product limitations, demonstrating hands-on usage, or inviting third-party testimonials. Live Q&A sessions and real-time comparisons can further attenuate skepticism by showcasing objectivity. Platforms could also introduce "expertise badges" or performance metrics (e.g., accuracy rates in past recommendations) to supplement visual cues with verifiable credibility markers. Fourth, individual differences (e.g., consumer need for cognition) moderate the impact of attractiveness on purchase decisions. Streamers should segment audiences and tailor content accordingly: analytical viewers may respond better to data-driven presentations, while impulse-driven viewers might engage more with emotionally charged narratives. Adaptive scripting tools or audience analytics could help dynamically adjust the balance between logical persuasion and affective appeals during broadcasts. In sum, the study advocates a "strategic authenticity" approach, where attractiveness is harmonized with substantiated expertise to maximize trust and conversion. By acknowledging the dual-edged nature of beauty, stakeholders can transcend the simplistic "more is better" heuristic and cultivate a more sustainable, trust-driven live-streaming ecosystem. 7. Limitations and future research Several limitations of this study warrant consideration, offering avenues for future research. First, the sample was drawn exclusively from a single cultural context, potentially constraining the generalizability of the findings across diverse markets where beauty standards and trust mechanisms may differ (e.g., Western vs. Eastern cultures). Future studies could adopt a cross-cultural comparative approach to examine how attractiveness thresholds and consumer skepticism vary by societal norms (Kang et al., 2021 ). Second, the reliance on cross-sectional data limits causal inferences regarding the proposed relationships. Longitudinal or experimental designs, such as manipulating streamer attractiveness levels in controlled settings could strengthen causal claims and clarify the temporal dynamics of beauty doubt formation. Third, while this study identifies product type and consumer individual differences as moderators, other contextual factors (e.g., shopping motivation, time pressure, or streamer-viewer interaction intensity) may further nuance the attractiveness-trust relationship. For instance, does urgency in limited-time promotions mitigate beauty doubt? How does viewer participation (e.g., real-time comments) alter perceptions of professionalism? Future research could integrate these variables to refine the model’s boundary conditions. These extensions would not only address the current limitations but also advance the understanding of attractiveness thresholds in evolving digital persuasion landscapes. 8. Conclusion As livestreaming commerce continues to reshape consumer purchasing behavior, the prevailing assumption that higher streamer attractiveness invariably enhances sales requires critical re-examination. This study advances a dual-pathway model that delineates how physical attractiveness simultaneously enhances professional credibility and affective response while triggering beauty doubt a paradoxical mechanism that ultimately shapes consumer trust and purchase intention. Grounded in social perception theory, our findings challenge the "more attractiveness is better" heuristic by revealing an inverted-U relationship, wherein excessive attractiveness undermines perceived expertise and fosters skepticism. The empirical results confirm that while attractiveness initially bolsters credibility and affective engagement, it also heightens beauty doubt, with downstream effects on trust and purchase decisions. Crucially, professional credibility and affective response strengthen trust, whereas beauty doubt erodes it, and trust robustly drives purchase intention. The moderating role of product type and consumer individual differences further refines these relationships, though the non-significant moderation of product type on affective response–trust linkage suggests affective reactions may be more universally influential than context-dependent. Ultimately, this research redefines attractiveness as a strategic resource not an unconditional asset in livestreaming commerce, where the interplay of beauty and brains dictates success. Declarations Ethical approval This study has been granted ethical approval by Henan University of Animal Husbandry and Economy (Approval No.: 20250322). The research was conducted in strict accordance with the Declaration of Helsinki and relevant Chinese ethical regulations, ensuring that the life, health, privacy, and dignity of all research subjects were fully protected. Funding: No Funding Author Contribution Zhenwei Yan: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing.Asad Ur Rehman: Conceptualization, Validation, Investigation, Resources, Writing – Review & Editing, Supervision.All authors reviewed and approved the final manuscript. Data Availability The data used in this study have been anonymized and securely stored. The research team will provide data for academic research upon reasonable request and can be applied for via email to the corresponding author, Zhenwei Yan ( [email protected] ). References Adaval, R. Sometimes it just feels right: The differential weighting of affect-consistent and affect-inconsistent product information. J. Consum. Res. 28 (1), 1–17 (2001). Alam, M. M. D., Karim, R. A. & Habiba, W. The relationship between CRM and customer loyalty: The moderating role of customer trust. Int. J. Bank. Mark. 39 (7), 1248–1272 (2021). Aldboush, H. 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The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types. J. Retailing Consumer Serv. 74 , 103432 (2023). Zuo, W. & Xiao, L. How live streaming features affect consumers' purchase intention in the context of cross-border e-commerce? A research based on SOR theory. Front. Psychol. 12 , 767876 (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":76836,"visible":true,"origin":"","legend":"\u003cp\u003eResearch model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7237601/v1/113ada3dbfabbddbc06bae4d.png"},{"id":98059112,"identity":"40aa64ce-6e7b-41e2-b5f1-20c431382699","added_by":"auto","created_at":"2025-12-12 10:30:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144858,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Basic Information\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7237601/v1/efd24f876a03eac7e519bc9d.png"},{"id":98059110,"identity":"6fc4cff5-90ac-495c-ad0f-80e0abc099cf","added_by":"auto","created_at":"2025-12-12 10:30:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194205,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized Path Coefficients\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7237601/v1/1e404f9dbe1392637fe65e7b.png"},{"id":101766284,"identity":"314a939e-53ff-4da0-ab59-cfeed223c9bd","added_by":"auto","created_at":"2026-02-03 12:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2028655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237601/v1/48657a57-f0d7-41e2-92f4-4610b7e3a75d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beauty or Brains? The Paradoxical Impact of Anchors Attractiveness on Purchase Intentions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe meteoric rise of livestreaming commerce has redefined digital retail, transforming passive online shopping into an interactive, real-time, and socially enriched experience (Kang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By bridging the gap between physical and virtual retail, livestreaming platforms empower sellers to engage consumers through dynamic demonstrations, instant feedback, and communal participation features that conventional e-commerce lacks (Forrester, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This shift has propelled livestreaming into a multibillion-dollar industry, exemplified by record-breaking sales events like China\u0026rsquo;s Singles\u0026rsquo; Day, where livestreaming accounted for \u003cspan\u003e$\u003c/span\u003e6\u0026nbsp;billion in transactions (Hallanan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Amid this boom, streamers the human faces of livestreaming commerce have emerged as pivotal influencers, with brands often prioritizing physical attractiveness as a key driver of consumer engagement and sales (Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, this prevailing \"beauty premium\" assumption overlooks a critical paradox: excessive attractiveness may inadvertently erode trust, triggering skepticism about a streamer\u0026rsquo;s expertise and authenticity. This research offers an interesting research context worth further investigation.\u003c/p\u003e\u003cp\u003eExtant research on livestreaming commerce has predominantly focused on technological affordances (e.g., interactivity, media richness) and platform design (Ang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zuo \u0026amp; Xiao, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while largely treating streamer attractiveness as a linear, universally beneficial trait. However, emerging evidence suggests that attractiveness operates as a double-edged sword: while it initially captures attention and enhances likability, surpassing an optimal threshold may provoke beauty doubt, a cognitive bias wherein consumers question whether an overly attractive streamer\u0026rsquo;s appeal substitutes for substantive product knowledge (Luo et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the growing body of research on livestreaming commerce, critical gaps remain in understanding the paradoxical role of streamer attractiveness in shaping consumer trust and purchase decisions.Prior studies predominantly assume a monotonic relationship between attractiveness and persuasion, suggesting that higher attractiveness invariably enhances credibility and sales (Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, this overlooks the possibility of an attractiveness threshold, beyond which beauty may trigger skepticism rather than trust. While traditional advertising literature acknowledges the \"beauty-is-beastly\" effect (e.g., overly attractive spokespeople being distrusted for certain roles; Lynn, 2009), this phenomenon remains underexplored in livestreaming commerce, where real-time interaction and perceived authenticity are paramount. Grounded in social perception theory (Fiske et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and the credibility-persuasion framework (Pornpitakpan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this study posits that physical attractiveness simultaneously amplifies professional credibility, affective response, and beauty doubt, with downstream effects on customer trust and purchase intention. Importantly, these relationships are moderated by product type where utilitarian vs. hedonic goods may alter the weight of attractiveness and consumer individual differences, such as susceptibility to interpersonal influence or need for cognition.\u003c/p\u003e\u003cp\u003eWith the intensification of homogeneous competition on traditional e-commerce platforms, live streaming e-commerce has become a key channel for businesses to break through traffic bottlenecks, thanks to its unique advantages of \"real-time display, real-time interaction, and emotional awakening\". This research challenges the monolithic \"more attractiveness is better\" heuristic in digital persuasion, introducing the beauty suspicion effect as a boundary condition to the attractiveness-trust link. Next, it advances livestreaming literature by delineating how attractiveness thresholds interact with perceived expertise and emotional appeal to shape consumer decisions a mechanism underexplored in prior work (Xue et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, this research offers actionable insights for platforms and brands: matching streamer aesthetics to product categories (e.g., high-attractiveness streamers for beauty products vs. expert-oriented streamers for tech gadgets) and segmenting audiences based on cognitive traits. By employing a hybrid SEM approach to analyze survey data from 335 livestream viewers, this study not only refines theoretical models of digital influencer effectiveness but also equips practitioners to strategically balance aesthetics and credibility in talent selection. As livestreaming commerce evolves from a novelty to a necessity, understanding the nuanced role of streamer attractiveness is critical. This research underscores that in the battle for consumer trust, brains and beauty are not mutually exclusive, but their equilibrium dictates commercial success.\u003c/p\u003e"},{"header":"2. Theoretical background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Purchase intention\u003c/h2\u003e\u003cp\u003ePurchase intention represents the conscious planning of consumer participation in transaction behavior, shaped by rational evaluation and emotional responses to marketing stimuli (Morwitz\u0026amp;Schmittlein, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). As a recognized indicator of actual purchasing behavior, it is a key indicator for evaluating marketing effectiveness and predicting business success (Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When consumers feel high credibility among communicators, consistent with the effectiveness of the advertised product, and experience positive emotional engagement, their purchase intention will be enhanced (Ladhari et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although not all intentions can be translated into transactions, they provide important diagnostic value for understanding consumer psychology and improving promotional strategies (Baker et al., 2016). In live streaming commerce, purchase intention is particularly susceptible to two counteracting forces: heuristic attraction based on source attractiveness and systematic evaluation based on professional knowledge trust (Chaiken, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Although traditional views suggest that physical attractiveness generally enhances persuasiveness (Patzer, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), recent research has revealed a \"beauty premium penalty\" where excessive attractiveness triggers suspicion and offsets its initial advantage (Li et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the one hand, attractive anchors may enhance their purchase intention through emotional arousal, utilizing the halo effect to cultivate positive brand associations (Nisbett\u0026amp;Wilson, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). This mechanism is particularly effective for hedonic products, where sensory satisfaction dominates functional evaluation (Voss et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). On the contrary, exceeding the attractiveness threshold may activate persuasive knowledge (Friestad\u0026amp;Wright, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), leading consumers to attribute the influence of streaming media to manipulative intentions rather than genuine expertise (Xie et al., 2024). This kind of \"beauty doubt\" can erode trust and indirectly suppress purchase intention, which is even more serious for practical products that require ability-based guarantees (Pornpitakpan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In short, the purchase intention in the live streaming environment reflects the tense relationship between aesthetic appeal and credibility erosion and is moderated by product and consumer unexpected events. This framework challenges the linear \"beauty advantage\" assumption and advocates for a balance between attractiveness and perceived expertise.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Social Cognitive Theory\u003c/h2\u003e\u003cp\u003eThe dual impact of streaming media attractiveness on consumer trust and purchase intention can be further elucidated from the perspective of social cognitive theory (SCT) (Bandura, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). SCT posits that human behavior is formed by the dynamic interaction of individual cognitive factors, environmental influences, and behavioral outcomes, forming a triple reciprocal causal model. In the context of live streaming commerce, consumers' perception of the attractiveness of streaming media interacts with their cognitive evaluations (such as professionalism) and emotional responses (such as admiration or doubt), ultimately shaping trust and purchasing decisions. The core of SCT comes from credibility, where individuals evaluate the credibility and professional knowledge of communicators before accepting persuasive information (Pornpitakpan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Although physical attractiveness is often a heuristic clue to credibility, exceeding the attractiveness threshold may lead to cognitive dissonance, and consumers may question whether the attractiveness of streaming media can replace substantive expertise. This is consistent with SCT's emphasis on self-regulation, where consumers actively review external stimuli rather than passively accepting them. The resulting 'beauty skepticism effect' illustrates how environmental cues such as super attractiveness can disrupt the formation of cognitive trust. In addition, SCT emphasizes the role of emotional arousal in decision-making. Attraction induced emotional responses may enhance persuasiveness through emotional involvement, but excessive arousal can impair cognitive processes (Lerner et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reflecting SCT's assertion that emotional states regulate attention and judgment. For example, high admiration may mask critical evaluation of product applicability, especially for practical goods. On the contrary, suspicion triggered by beauty doubt may activate defensive cognitive processing, and consumers may hold a skeptical attitude towards the information on streaming media (Tormala\u0026amp;Petty, 2004). In summary, SCT provides a powerful framework for understanding the contradictory effects of streamer attraction by integrating cognitive, emotional, and environmental mechanisms. It proposes the idea that digital persuasion is not just a function of source attraction, but a complex negotiation of perceived credibility, emotional resonance, and situational fit.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Hypotheses","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Conceptual model\u003c/h2\u003e\u003cp\u003eThis study proposes a research model (illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that examines the paradoxical influence of streamer attractiveness on purchase intentions, delineating the dual psychological mechanisms of professional credibility and beauty doubt, with product type and consumer individual differences as key moderators. Crucially, product type moderates these relationships. For hedonic products (e.g., fashion, cosmetics), attractiveness and affective responses dominate, as aesthetic alignment enhances persuasion. Conversely, for utilitarian products (e.g., electronics, appliances), professional credibility becomes paramount, and beauty doubt erodes trust when expertise appears secondary to looks. Meanwhile, consumer individual differences (e.g., need for cognition, skepticism propensity) further shape outcomes: analytical consumers prioritize credibility, while impulse-driven consumers respond more strongly to affective cues. The model advances a competing mediation perspective, where the net effect of attractiveness on purchase intention depends on the equilibrium between credibility-driven trust and beauty-induced skepticism. By integrating affective and cognitive pathways, the framework explains why highly attractive streamers may underperform in certain contexts, offering a nuanced lens for talent selection and product-streamer matching.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Physical attractiveness\u003c/h2\u003e\u003cp\u003ePhysical attractiveness can be defined by the degree to which a streamer's appearance is perceived as aesthetically pleasing by viewers (Dion et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). In live-streaming commerce, physical attractiveness serves as a salient heuristic cue that shapes consumers' initial judgments (Langlois et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to social perception theory, individuals tend to attribute positive traits, such as competence and trustworthiness, to attractive individuals a phenomenon known as the \"halo effect\" (Nisbett \u0026amp; Wilson, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). However, excessive attractiveness may also trigger skepticism, as consumers question whether the streamer's appeal is leveraged to compensate for a lack of expertise (Praxmarer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This duality aligns with the credibility-persuasion framework, wherein attractiveness enhances perceived credibility yet simultaneously induces \"beauty doubt,\" a cognitive dissonance arising from the incongruence between aesthetic appeal and perceived professionalism (Ohanian, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Furthermore, attractiveness elicits affective responses by stimulating hedonic pleasure and parasocial engagement (Homer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), yet its overemphasis may undermine rational decision-making. Thus, physical attractiveness operates as a double-edged sword, fostering both positive and negative consumer reactions. Based on this theoretical grounding, the following hypotheses are proposed.\u003c/p\u003e\u003cp\u003eH1: Physical attractiveness positively correlates with professional credibility.\u003c/p\u003e\u003cp\u003eH2: Physical attractiveness positively correlates with beauty doubt effect.\u003c/p\u003e\u003cp\u003eH3: Physical attractiveness positively correlates with affective response.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Professional credibility\u003c/h2\u003e\u003cp\u003eProfessional credibility is the extent to which a streamer is perceived as knowledgeable, competent, and trustworthy in their domain (Ohanian, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Rooted in source credibility theory, professional credibility serves as a critical heuristic for consumers when evaluating persuasive messages, as expertise enhances message acceptance and reduces skepticism (Pornpitakpan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In live-streaming commerce, where product demonstrations and real-time interactions are central, a streamer\u0026rsquo;s perceived professionalism not only validates product claims but also mitigates uncertainty inherent in online transactions (Xie et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Prior research in influencer marketing underscores that credibility fosters consumer trust by signaling reliability and reducing perceived risk (Sokolova \u0026amp; Kefi, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, professional credibility directly fuels purchase intention by enhancing the perceived utility of the recommended products (Ladhari et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, this relationship may be contingent on contextual factors, such as product type, where expertise signals are more salient for high-involvement goods (Petty \u0026amp; Cacioppo, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Thus, the hypotheses formulated as follows.\u003c/p\u003e\u003cp\u003eH4: Professional credibility positively correlates with customer trust.\u003c/p\u003e\u003cp\u003eH5: Professional credibility positively correlates with purchase intention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Beauty doubt effect\u003c/h2\u003e\u003cp\u003eBeauty doubt effect is consumers' skepticism toward highly attractive streamers, where excessive physical attractiveness triggers perceptions of compromised professionalism or manipulative intent (Praxmarer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Rooted in the attribution theory of persuasion (Eagly et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), this effect arises when attractiveness surpasses a threshold, leading audiences to attribute the streamer\u0026rsquo;s influence to superficial traits rather than expertise, thereby activating heuristic processing biases (Chaiken, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Such doubt undermines trust by violating expectancy-confirmation mechanisms (Olson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), as consumers perceive a mismatch between aesthetic appeal and substantive value. Prior research on the \"beauty penalty\" (Lee et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in professional contexts further supports that extreme attractiveness can evoke stereotypes of incompetence or untrustworthiness, particularly in expertise-dependent settings. Thus, the beauty doubt effect operates as a countervailing force to the traditional attractiveness halo, attenuating trust when attractiveness is perceived as disproportionate to credibility. Hence, the following hypothesis is constructed.\u003c/p\u003e\u003cp\u003eH6: Beauty doubt effect negatively correlates with customer trust.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Affective response\u003c/h2\u003e\u003cp\u003eAffective response (AR) refers to the emotional arousal and hedonic pleasure elicited by a streamer's physical attractiveness during live-streaming interactions (Batra \u0026amp; Holbrook, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Rooted in the stimulus-organism-response (S-O-R) framework, AR captures the immediate, visceral reactions that transcend cognitive appraisal, including feelings of admiration, excitement, or aesthetic enjoyment (Donovan \u0026amp; Rossiter, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Such emotional states can enhance parasocial bonding by fostering a sense of intimacy and likability (Horton \u0026amp; Wohl, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), thereby amplifying the viewer's receptivity to persuasive messages (Luo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, AR operates as a double-edged mechanism: while positive affect facilitates trust through emotional contagion (Hatfield et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), its overreliance may divert attention from product-related information, potentially weakening rational decision-making (Pham et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Prior research confirms that AR mediates the impact of aesthetic stimuli on behavioral outcomes, yet its efficacy depends on the congruence between emotional appeal and task relevance (Adaval, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Thus, this study posits that AR serves as a critical pathway through which attractiveness influences trust and purchase intent, albeit contingent on contextual and individual factors.\u003c/p\u003e\u003cp\u003eH7: Affective response positively correlates with customer trust.\u003c/p\u003e\u003cp\u003eH8: Affective response positively correlates with purchase intention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Customer trust\u003c/h2\u003e\u003cp\u003eCustomer trust (CT) reflects a psychological state wherein consumers willingly accept vulnerability based on positive expectations of a streamer's reliability and intentions (Mayer et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In live-streaming commerce, CT serves as a critical bridge between streamer attributes and transactional outcomes, as it mitigates perceived risks and fosters dependency on the streamer's recommendations (Gefen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Rooted in social exchange theory, CT emerges from the interplay of cognitive evaluations (e.g., professionalism) and affective responses (e.g., parasocial attachment), while being attenuated by heuristic-based suspicions (e.g., beauty doubt) (Doney \u0026amp; Cannon, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Empirical evidence consistently demonstrates that CT enhances purchase intention (PI) by reducing decision uncertainty and facilitating heuristic processing (Pavlou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Notably, PI manifests only when CT surpasses a threshold that justifies the relinquishment of consumer autonomy to the streamer's influence (McKnight et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Thus, the following hypothesis is advanced.\u003c/p\u003e\u003cp\u003eH9: Customer trust positively correlates with purchase intention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Product type\u003c/h2\u003e\u003cp\u003eProduct type refers to the categorical distinction of goods based on their functional, experiential, or symbolic attributes (Dhar \u0026amp; Wertenbroch, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Prior research suggests that product characteristics fundamentally alter consumer evaluation processes, particularly in influencer marketing contexts (Jin \u0026amp; Phua, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For utilitarian products (e.g., electronics, tools), consumers prioritize functional competence and expertise, whereas for hedonic products (e.g., cosmetics, fashion), sensory and emotional appeal often dominate decision-making (Voss et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This dichotomy implies that the interplay between streamer attractiveness and consumer trust is contingent on whether the product aligns with pragmatic or affective consumption goals. Specifically, professional credibility may exert stronger trust-building effects for utilitarian products due to their performance-dependent nature, while beauty doubt a skepticism toward highly attractive streamers\u0026rsquo; expertise could be more detrimental for such products. Conversely, affective responses driven by attractiveness may enhance trust more prominently for hedonic products, where emotional engagement outweighs functional scrutiny. Thus, this study posits that product type systematically moderates the pathways through which attractiveness influences trust.\u003c/p\u003e\u003cp\u003eH10a: Product type moderates the relationship between professional credibility and customer trust.\u003c/p\u003e\u003cp\u003eH10b: Product type moderates the relationship between beauty doubt effect and customer trust.\u003c/p\u003e\u003cp\u003eH10c: Product type moderates the relationship between affective response and customer trust.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Consumer individual differences\u003c/h2\u003e\u003cp\u003eConsumer individual differences (CID) encompass stable psychological traits that systematically influence how individuals process information and respond to marketing stimuli (Zhang \u0026amp; Shrum, 2009). Rooted in the Elaboration Likelihood Model (Petty \u0026amp; Cacioppo, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), CID shape whether consumers rely on heuristic cues (e.g., attractiveness) or systematic processing (e.g., expertise evaluation) when forming trust and purchase decisions. Cognitive needs (NFC), the tendency to engage in laborious cognitive processing and susceptibility to normative influences (SNI), the tendency to conform to social expectations, have been identified as key moderating factors in digital persuasion environments (Ladhari et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). High-NFC individuals are more likely to scrutinize professional credibility, whereas high-SNI consumers may prioritize affective responses driven by attractiveness. Furthermore, trait skepticism amplifies beauty doubt effects by triggering attributional scrutiny (i.e., questioning whether attractiveness signals competence or manipulation) (Pornpitakpan, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, CID create boundary conditions for how attractiveness-derived perceptions translate into behavioral outcomes. Hence, the following hypotheses are proposed.\u003c/p\u003e\u003cp\u003eH11a: Consumer individual differences moderate the relationship between affective response and purchase intention, such that the effect is stronger for consumers with high susceptibility to normative influence (SNI) and weaker for those with high need for cognition (NFC).\u003c/p\u003e\u003cp\u003eH11b: Consumer individual differences moderate the relationship between professional credibility and purchase intention, such that the effect is stronger for consumers with high need for cognition (NFC) and weaker for those with high susceptibility to normative influence (SNI).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Questionnaire Introduction and Source\u003c/h2\u003e\u003cp\u003eThis study aimed to examine how highly attractive livestreamers influence consumer trust and purchase intention in e-commerce live streaming contexts. We developed our questionnaire based on previous studies (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The survey included three dimensions: perceived streamer attractiveness and beauty skepticism, affective and trust mediators, and consumer characteristics and outcome variables. This study collected data by distributing the survey from February 1st to May 1st, 2025. The invitation link to the questionnaire was distributed across multiple social media and livestreaming commerce platforms, whereby only individuals with purchase experience in livestreaming commerce were targeted. A total of 335 valid questionnaires were returned. All participants completed the survey voluntarily and signed informed consent forms. The study received approval from the Institutional Review Board. We followed ethical guidelines throughout the research process. This study ensured data integrity and confirmed that the questionnaire demonstrated high reliability and validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSources of Measurement Items for the Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSources\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStreamer Attractiveness and Beauty Doubt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlFarraj et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Dion (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKara-Yakoubian et al.(2022); Nguyen‐Viet \u0026amp; Nguyen (2024).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAffective Response and Customer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlam et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Aldboush \u0026amp; Ferdous (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLittle \u0026amp; Green (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Simoneau \u0026amp; Cook (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKimiagari \u0026amp; Malafe (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Zanger et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Lavuri \u0026amp; Akram (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eConsumer Characteristics and Outcome Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBuv\u0026aacute;r \u0026amp; Orosz (2023); Kallergi \u0026amp; Landeweerd (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIvanova \u0026amp; Moreira (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Chen (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZhang \u0026amp; Wang (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Krabbe \u0026amp; Grodal (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Profile of respondents\u003c/h2\u003e\u003cp\u003eThis study obtained 335 valid responses. The sample was balanced and representative across gender, age, education level, disposable income, purchase frequency, and occupation. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that female respondents slightly outnumbered males. The 25\u0026ndash;40 age group was the largest. Most respondents held at least a college diploma. Undergraduates comprised the largest subgroup. Monthly disposable income mostly fell between RMB 6,001 and 20,000. Most respondents made 5\u0026ndash;8 purchases per month. Most worked in corporations or government/institutional roles. Freelancers, the self-employed, and students were also represented. These traits suggest high online engagement and strong purchase potential.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, this study conducted Pearson correlations (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All eight variables: physical attractiveness, customer trust, affective response, professional credibility, consumer individual differences, purchase intention, beauty doubt effect, and product type showed significant positive correlations at p\u0026thinsp;\u0026lt;\u0026thinsp;.01. Physical Attractiveness correlated most strongly with purchase intention (r\u0026thinsp;=\u0026thinsp;.494), affective response (r\u0026thinsp;=\u0026thinsp;.445), and Customer Trust (r\u0026thinsp;=\u0026thinsp;.361). This suggests that higher streamer attractiveness enhances emotional experience and trust, which in turn boosts purchase intention. The strong correlation between customer trust and product type (r\u0026thinsp;=\u0026thinsp;.638) indicates that product attributes play a key role in trust formation. Consumer individual differences correlated significantly with Purchase Intention (r\u0026thinsp;=\u0026thinsp;.532). This highlights the influence of individual differences on purchase decisions. Although beauty doubt effect reflects skepticism, it still correlated positively with customer trust (r\u0026thinsp;=\u0026thinsp;.387) and purchase intention (r\u0026thinsp;=\u0026thinsp;.383). This shows that beauty doubt did not weaken the effect of attractiveness on trust or purchase intention.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBDE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.361\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.445\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.389\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.374\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.378\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.276\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.369\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.377\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.334\u003csup\u003e**\u003c/sup\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.494\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.424\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.464\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.418\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.532\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.378\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.387\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.353\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.293\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.345\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.383\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.385\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.638\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.278\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.292\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.270\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.361\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.248\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e**. Correlations are significant at the 0.01 level (two-tailed).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Exploratory factor analysis\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Reliability and validity analysis\u003c/h2\u003e\u003cp\u003eFirst, this study assessed the scale\u0026rsquo;s internal consistency and data suitability. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents Cronbach\u0026rsquo;s alpha values. All dimensions surpassed the industry benchmark of α\u0026thinsp;\u0026ge;\u0026thinsp;.70. Emotional response (α\u0026thinsp;=\u0026thinsp;.910), purchase intention (α\u0026thinsp;=\u0026thinsp;.902), and customer trust (α\u0026thinsp;=\u0026thinsp;.898) exceeded .90. Other dimensions ranged from .855 to .898. The overall scale achieved α\u0026thinsp;=\u0026thinsp;.930. This result indicates excellent internal consistency.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability and validity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\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\u003eAlpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKMO\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.869\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=\"c1\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.887\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=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.918\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.855\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\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNext, this study evaluated the assumptions for factor analysis. The overall Kaiser\u0026ndash;Meyer\u0026ndash;Olkin measure was .946. Individual KMO values ranged from .815 for product type to .933 for emotional response. All values exceeded the recommended threshold of .60. Bartlett\u0026rsquo;s test of sphericity was significant, χ\u0026sup2;(903)\u0026thinsp;=\u0026thinsp;5892.43, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This result confirms adequate shared variance among items. Finally, this study conducted principal component analysis using varimax rotation. Extract factors for each dimension and validate the scale structure. This program tests the consistency between empirical factors and theoretical structures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Factor number analysis\u003c/h2\u003e\u003cp\u003eThis study followed the Kaiser criterion (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1.0) in exploratory factor analysis. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the initial eigenvalues for 43 items: 13.201, 3.184, 2.912, 2.367, 2.281, 2.002, 1.752, and 1.041. Each exceeded the threshold of 1. The initial cumulative variance explained was 66.84%. This exceeds the 60% threshold commonly used in social science research. It indicates that the eight components adequately capture shared variance among items.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal variance explained by factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInitial Eigenvalues\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eRotated Loadings Sum of Squares\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariance (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVariance (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCumulative (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study then applied varimax rotation. After rotation, variance contributions were more evenly distributed. Factor 1 explained 11.12%, Factor 2 9.44%, Factor 3 8.99%, Factor 4 8.03%, Factor 5 7.76%, Factor 6 7.63%, Factor 7 7.54%, and Factor 8 6.33%. The cumulative variance explained after rotation remained 66.84%. This result preserved overall explanatory power and yielded more focused factor loadings. It facilitates subsequent factor naming and alignment with theoretical constructs.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Confirmatory Factor Analysis\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1 Model Fit Indices Analysis\u003c/h2\u003e\u003cp\u003eThis study conducted a confirmatory factor analysis (CFA) to assess the structural validity of the measurement model. We evaluated model fit using multiple goodness-of-fit indices (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The chi-square statistic was χ\u0026sup2;(832)\u0026thinsp;=\u0026thinsp;970.65. The normed chi-square (χ\u0026sup2;/df) was 1.17, below the threshold of 3.0. This indicates an acceptable discrepancy-to-df ratio. The normed chi-square is more robust than the traditional χ\u0026sup2; test, which is sensitive to large samples.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel fit indices for confirmatory factor analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Fit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMIN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCMIN/DF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFit Results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e970.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudgment Std.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIncremental fit indices were also strong. The normed fit index (NFI) was .888 and the relative fit index (RFI) was .878, both slightly below .90. The incremental fit index (IFI) was .982, the Tucker\u0026ndash;Lewis index (TLI) was .981, and the comparative fit index (CFI) was .982. These values exceed the .90 criterion, indicating the model replicates the theoretical structure well. The goodness-of-fit index (GFI) was .887, close to the recommended .90 benchmark. This reflects a high level of overall fit. Finally, the root mean square error of approximation (RMSEA) was .022, with the 90% confidence interval lower bound below .05. This value is well below the .08 threshold, further supporting excellent model fit. Although NFI and RFI are slightly below the ideal threshold, other key indices exceed their critical values. Considering the large sample size and model complexity, the eight-factor model demonstrates strong consistency with the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2 Convergent and Fornell\u0026ndash;Larcker Discriminant Validity Analysis\u003c/h2\u003e\u003cp\u003eAfter completing the confirmatory factor analysis, we assessed convergent validity. This study used composite reliability (CR) and average variance extracted (AVE) for this purpose (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Composite reliability values ranged from .86 to .93, exceeding the recommended threshold of .70. Average variance extracted values ranged from .54 to .64, surpassing the .50 minimum. All standardized factor loadings exceeded .70. These findings support strong convergent validity for each construct.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePresents results for convergent and Fornell\u0026ndash;Larcker discriminant validity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBDE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\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\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\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\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\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\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\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\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78\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\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMoreover, this study assessed discriminant validity using the Fornell\u0026ndash;Larcker criterion (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). We placed the square roots of AVE on the matrix diagonal. They ranged from .74 to .80. Each diagonal value exceeded the highest off-diagonal correlation (maximum r\u0026thinsp;=\u0026thinsp;.56). For example, the AVE square root for physical attractiveness was .76. This exceeded its correlations with Purchase Intention (r\u0026thinsp;=\u0026thinsp;.56) and affective response (r\u0026thinsp;=\u0026thinsp;.50). The AVE square root for professional credibility was .74, also above its highest correlation with another construct (r\u0026thinsp;=\u0026thinsp;.47). These outcomes indicate that each construct is more strongly related to its own indicators than to other constructs. This effectively rules out potential confounds among latent variables.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Structural Equation Model Path Analysis\u003c/h2\u003e\u003cp\u003eGiven the strong reliability and validity of the measurement model, we then built a structural equation model. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows significant positive paths from Physical Attractiveness to three mediators. Physical attractiveness predicted professional credibility (β\u0026thinsp;=\u0026thinsp;.417, C.R. = 6.97, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Beauty Doubt Effect (β\u0026thinsp;=\u0026thinsp;.474, C.R. = 7.11, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Affective Response (β\u0026thinsp;=\u0026thinsp;.545, C.R. = 8.10, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results suggest higher attractiveness enhances perceptions of professional credibility. It also increases emotional resonance and triggers moderate doubt. Then, each mediator significantly predicted Customer Trust. Professional credibility predicted customer Trust (β\u0026thinsp;=\u0026thinsp;.353, C.R. = 4.61, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Beauty doubt effect predicted customer trust (β\u0026thinsp;=\u0026thinsp;.309, C.R. = 4.52, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Affective response predicted customer trust (β\u0026thinsp;=\u0026thinsp;.290, C.R. = 4.43, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConvergent validity Composite reliability and discriminant validity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical Attractiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\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\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCustomer Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01༛ *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn predicting Purchase Intention, Professional Credibility had a direct positive effect (β\u0026thinsp;=\u0026thinsp;.356, C.R. = 4.97, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Beauty doubt effect also positively influenced Purchase Intention (β\u0026thinsp;=\u0026thinsp;.364, C.R. = 5.88, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Customer trust predicted purchase intention with a smaller effect (β\u0026thinsp;=\u0026thinsp;.189, C.R. = 3.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This underscores trust as a key driver of purchase behavior. Overall, our findings support the \u0026ldquo;Attractiveness \u0026rarr; Mediation \u0026rarr; Trust \u0026rarr; Purchase\u0026rdquo; framework. They also highlight the positive role of the beauty doubt effect in fostering trust and purchase intention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Moderating Effect Analysis\u003c/h2\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.6.1 Moderating Effect of Product Type on the Professional Credibility \u0026rarr; Customer Trust Path\u003c/h2\u003e\u003cp\u003eThis study added a PC \u0026times; PT interaction term to the structural model to test whether PT moderates the path from PC to CT (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The interaction was significant (β\u0026thinsp;=\u0026thinsp;.232, SE\u0026thinsp;=\u0026thinsp;.056, t\u0026thinsp;=\u0026thinsp;4.14, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that higher PT enhances the impact of PC on CT.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Results for Product Type on the Professional Credibility \u0026rarr; Customer Trust Path\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eExperimental result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ecoefficient (Estimate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ecritical ratio (C.R.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.1209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.8171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.8195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.5782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.2111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-2.7388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.2129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.2242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.9496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.3430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProfessional Credibility \u0026times; Product Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.2324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.0562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e4.1355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eConditional effects of regulatory effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Effect)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ecritical ratio (t)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003elower limit (LLCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eupper limit (ULCI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e.0334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.9733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.1605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.1661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6.4721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.2850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.5339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6.5035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.3261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.6089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimple-slope analysis showed no significant effect at a low Product Type level (2.50), β\u0026thinsp;=\u0026thinsp;.003, t\u0026thinsp;=\u0026thinsp;.08, p\u0026thinsp;=\u0026thinsp;.973. At a moderate-high level of Product Type (4.25), the path was significant, β\u0026thinsp;=\u0026thinsp;.409, t\u0026thinsp;=\u0026thinsp;6.47, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. At a high level (4.50), it was also significant, β\u0026thinsp;=\u0026thinsp;.468, t\u0026thinsp;=\u0026thinsp;6.50, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. The effect grew stronger as Product Type increased. These results suggest that consumers perceive the trust benefit from streamer professionalism only for high-involvement or high-value products. For low-involvement, routine fast-moving consumer goods, the enhancing effect is negligible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e4.6.2 Moderating Effect of Product Type on the Beauty Doubt Effect \u0026rarr; Customer Trust Path\u003c/h2\u003e\u003cp\u003eWe added a BDE \u0026times; PT interaction term to test whether Product Type moderates the effect of BDE on CT (see Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The model constant was β\u0026thinsp;=\u0026thinsp;2.7009, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. BDE had a significant negative main effect (β = \u0026minus;.4922, C.R. = \u0026minus;2.5663, p\u0026thinsp;=\u0026thinsp;.0107). PT\u0026rsquo;s main effect was not significant (β = \u0026minus;.0953, p\u0026thinsp;=\u0026thinsp;.6217). The interaction term was significant (β\u0026thinsp;=\u0026thinsp;.2101, C.R. = 4.2151, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This indicates that product type significantly moderates the beauty doubt effect \u0026rarr; customer trust path.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Results for Product Type on the Beauty Doubt Effect \u0026rarr; Customer Trust Path\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eExperimental result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ecoefficient (Estimate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ecritical ratio (C.R.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2.7009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.7216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.7430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eBeauty Doubt Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.4922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-2.5663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.0953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.4940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.6217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eBeauty Doubt Effect \u0026times; Product Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.2101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.0499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e4.2151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eConditional effects of regulatory effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Effect)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ecritical ratio (t)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003elower limit (LLCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eupper limit (ULCI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.0331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e.4259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.6705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.1198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.1861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7.3041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.2929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.5088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7.3346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.3318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.5749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimple-slope analysis showed no significant effect at a low Product Type level (2.50), β\u0026thinsp;=\u0026thinsp;.0331, t\u0026thinsp;=\u0026thinsp;.4259, p\u0026thinsp;=\u0026thinsp;.6705. At a moderate‐high level of Product Type (4.25), the effect was significant, β\u0026thinsp;=\u0026thinsp;.4008, t\u0026thinsp;=\u0026thinsp;7.3041, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. At a high level (4.50), it was β\u0026thinsp;=\u0026thinsp;.4534, t\u0026thinsp;=\u0026thinsp;7.3346, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. The effect grew stronger as Product Type increased. These findings suggest that Beauty Doubt Effect translates into trust benefits only for high‐involvement or high‐value products. For low‐involvement, fast‐moving consumer goods, the effect is negligible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e4.6.3 Moderating Effect of Product Type on the Affective Response \u0026rarr; Customer Trust Path\u003c/h2\u003e\u003cp\u003eWe added an AR \u0026times; PT interaction term to test whether PT moderates the path from AR to CT (see Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The main effect of AR on CT was not significant (β\u0026thinsp;=\u0026thinsp;.18, SE\u0026thinsp;=\u0026thinsp;.18, C.R. = 1.01, p\u0026thinsp;=\u0026thinsp;.315). PT had a significant positive effect on CT (β\u0026thinsp;=\u0026thinsp;.62, SE\u0026thinsp;=\u0026thinsp;.17, C.R. = 3.55, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The AR \u0026times; PT interaction term was not significant (β\u0026thinsp;=\u0026thinsp;.019, SE\u0026thinsp;=\u0026thinsp;.046, C.R. = .41, p\u0026thinsp;=\u0026thinsp;.683).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Results for Product Type on the Affective Response \u0026rarr; Customer Trust Path\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eExperimental result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoefficient (Estimate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecritical ratio (C.R.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.2603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.6502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.4003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.6892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.1802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.1791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.3151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduct Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.6196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.1747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.0004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective Response \u0026times; Product Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.0189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.4082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.6834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimple-slope analysis showed the conditional effect of Affective Response on Customer Trust was β\u0026thinsp;=\u0026thinsp;.23 at low Product Type (2.50). It was β\u0026thinsp;=\u0026thinsp;.26 at moderate level (4.25) and β\u0026thinsp;=\u0026thinsp;.27 at high level (4.50). None reached statistical significance. These findings indicate that Product Type does not moderate the relationship between Affective Response and Customer Trust. The effect remains non‐significant at all Product Type levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e4.6.4 Moderating Effect of Consumer Individual Differences on the Affective Response \u0026rarr; Purchase Intention Path\u003c/h2\u003e\u003cp\u003eWe added an AR \u0026times; CID interaction term to the structural model to test whether CID moderates the path from AR to PI (see Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The main effect of AR on PI was significant and negative, β = \u0026minus;.3711, SE\u0026thinsp;=\u0026thinsp;.1602, C.R. = \u0026minus;2.3163, p\u0026thinsp;=\u0026thinsp;.0212. CID had a non-significant main effect, β = \u0026minus;.2176, p\u0026thinsp;=\u0026thinsp;.1661. The AR \u0026times; CID interaction was significant, β\u0026thinsp;=\u0026thinsp;.1923, SE\u0026thinsp;=\u0026thinsp;.0429, C.R. = 4.4853, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This indicates that CID significantly moderates the AR \u0026rarr; PI path.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Results for Consumer Individual Differences\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eExperimental result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ecoefficient (Estimate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ecritical ratio (C.R.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.0934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.5553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e5.5711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAffective Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.3711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-2.3163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.2176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-1.3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.1661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAffective Response \u0026times; Consumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.1923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.0429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e4.4853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eConditional effects of regulatory effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Effect)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ecritical ratio (t)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003elower limit (LLCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eupper limit (ULCI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.3333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.0776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.1006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.2719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.0611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.2163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.1667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.3270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.5334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.1540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.3751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.6136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimple-slope analysis showed no significant effect at low CID (2.33), β\u0026thinsp;=\u0026thinsp;.0776, t\u0026thinsp;=\u0026thinsp;1.1006, p\u0026thinsp;=\u0026thinsp;.2719. At moderate‐high CID (4.17), the effect became significant and stronger, β\u0026thinsp;=\u0026thinsp;.4302, t\u0026thinsp;=\u0026thinsp;8.2021, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. At high CID (4.50), it further increased to β\u0026thinsp;=\u0026thinsp;.4943, t\u0026thinsp;=\u0026thinsp;8.1540, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. These findings suggest that emotional resonance elicited by the streamer translates into purchase intention only when consumers have high levels of individual traits, such as innovativeness, brand loyalty, or technology acceptance. For consumers with low individual differences, emotional arousal alone is insufficient to drive purchase behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e4.6.5 Moderating Effect of Consumer Individual Differences on the Professional Credibility \u0026rarr; Purchase Intention Path\u003c/h2\u003e\u003cp\u003eWe added a PC \u0026times; CID interaction term to test whether CID moderates the path from PC to PI (see Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). PC had a significant negative main effect, β = \u0026ndash;.684, SE\u0026thinsp;=\u0026thinsp;.186, C.R. = \u0026minus;\u0026thinsp;3.688, p\u0026thinsp;=\u0026thinsp;.0003. CID also had a significant negative main effect, β = \u0026ndash;.562, SE\u0026thinsp;=\u0026thinsp;.190, C.R. = \u0026minus;\u0026thinsp;2.960, p\u0026thinsp;=\u0026thinsp;.0033. The PC \u0026times; CID interaction was significant, β\u0026thinsp;=\u0026thinsp;.280, SE\u0026thinsp;=\u0026thinsp;.050, C.R. = 5.644, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This indicates that CID changes both the direction and magnitude of the PC\u0026rarr;PI relationship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Results for Consumer Individual Differences on the Professional Credibility \u0026rarr; Purchase Intention Path\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eExperimental result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ecoefficient (Estimate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ecritical ratio (C.R.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4.2476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.6813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e6.2343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProfessional Credibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.6842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-3.6884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.5619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.1898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e-2.9603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eProfessional Credibility \u0026times; Consumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e.2799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e.0496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e5.6445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eConditional effects of regulatory effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Individual Differences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Effect)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error (S.E.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ecritical ratio (t)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003elower limit (LLCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eupper limit (ULCI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.3333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.0310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.3797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.7044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.1916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.1296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.1667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.4822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7.9699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.3632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.6012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.5755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.0699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.2299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e.4380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.7131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimple-slope analysis showed no significant effect at low CID (2.33), β = \u0026ndash;.031, t = \u0026ndash;.380, p\u0026thinsp;=\u0026thinsp;.704. At moderate‐high CID (4.17), the effect was significant, β\u0026thinsp;=\u0026thinsp;.482, t\u0026thinsp;=\u0026thinsp;7.970, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. At high CID (4.50), it increased further, β\u0026thinsp;=\u0026thinsp;.576, t\u0026thinsp;=\u0026thinsp;8.230, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. The effect strengthened as CID rose. These findings suggest that PC translates into PI only for consumers with high levels of innovativeness, brand loyalty, or technology acceptance. For consumers low in these traits, professionalism alone does not generate purchase motivation and may feel forced.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that high streamer attractiveness increases professional credibility via the halo effect (Buv\u0026aacute;r et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It also generates cautious skepticism and strengthens emotional resonance. Together, these rational and emotional processes build trust in both the streamer and the platform (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This trust, driven by professional credibility and moderate skepticism, influences purchase intention. This pattern aligns with the attitude belief intention sequence in the theory of planned behavior (Kim et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Professional credibility represents rational endorsement. Cautious skepticism reinforces confidence in authenticity. Emotional resonance closes psychological distance and fosters identification. When combined, these three mechanisms drive behavioral intention effectively, regardless of product type or individual differences. These findings support both the halo effect and the theory of planned behavior. They also show that, in e-commerce live streaming, the beauty doubt effect can work with professional credibility and emotional appeal to boost trust. This offers a fresh perspective for research at the intersection of interpersonal attraction and consumer psychology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCombined analysis of all path effects indicates that the influence is both stronger in magnitude and more robust. This pattern aligns with the persuasion model\u0026rsquo;s assertion that core attractiveness elements drive deep audience processing (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It also echoes source credibility findings that credibility, expertise, and attractiveness vary in weighting across mediating stages (Von \u0026amp; Guess, 2023). It further supports the theory of planned behavior\u0026rsquo;s hypothesis that attitude components, despite varying strengths, each contribute continuously to behavioral intention (Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Based on these conclusions, live-stream e‐commerce should prioritize enhancing streamers\u0026rsquo; visual presentation and personal charisma to maximize emotional and trust pathway amplification. At the same time, building a professional image and using cautious skepticism in messaging are crucial. They reinforce trust and boost purchase intention nearly as effectively as attractiveness. Although trust\u0026rsquo;s direct conversion effect is relatively weak, its role as a bridge between mediators and purchase behavior is critical. Therefore, platforms should continually optimize trust‐building mechanisms like comment feedback and trial experiences. This fosters synergy across visual, cognitive, and emotional stages and offers a quantifiable, traceable framework for refined live‐stream e‐commerce operations.\u003c/p\u003e\u003cp\u003eFrom a macro perspective, live-stream platforms should begin with consumer segmentation and personalized recommendations. This ensures that users varying in innovativeness, loyalty, and technology acceptance receive content tailored to their needs. Next, platforms should enhance streamers\u0026rsquo; professional image. Use scenario demonstrations, high-value knowledge sharing, and case presentations. This boosts professional credibility and conversion efficiency among low-difference audiences. Then, integrate real-time comment gamification with emotional incentive tasks and virtual prop interactions. This preserves emotional resonance while avoiding the trust gap caused by single-channel emotional appeals. Next, develop differentiated presentation styles and narration scripts aligned with product attributes. This deepens the link between product and streamer persona. It also amplifies the positive effect of cautious skepticism in high-involvement contexts. In addition, build a multi-dimensional trust ecosystem. Include user-generated comments, executive or expert endorsements, and core customer testimonials. This fosters ongoing psychological safety. Finally, leverage AI and big data to monitor audience emotions and behavior in real time. Use these insights to optimize streamer performance and marketing cadence. This enables flexible adaptation across scenarios and audiences and maximizes trust-to-purchase conversion.\u003c/p\u003e"},{"header":"6. Implications","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Theoretical implications\u003c/h2\u003e\u003cp\u003eThis research enriches the current knowledge of livestreaming commerce literature in several ways. First, while prior research has predominantly treated streamer attractiveness as a unidimensional driver of consumer engagement (Luo et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study challenges the \"more attractiveness is better\" assumption by introducing the beauty suspicion effect, a paradoxical mechanism through which excessive attractiveness undermines perceived professionalism and triggers consumer skepticism. This extends social perception theory by identifying an attractiveness threshold beyond which physical appeal erodes rather than enhances persuasion efficacy, offering a more nuanced understanding of digital source credibility. Second, the study bridges a critical gap in livestreaming literature by delineating the dual pathways through which attractiveness operates while it simultaneously enhances affective response and professional credibility, it also heightens beauty doubt. The framework of this study suggests that the net impact of attractiveness on trust and ultimately purchase intention depends on the trade-off between its effects of enhancing credibility and arousing suspicion. The non-significant moderating role of product type in the affective response-trust link further suggests that emotional arousal driven by attractiveness may be less context-dependent than cognitive evaluations, a novel insight for the credibility-persuasion literature. Third, the study extends the affective-cognitive response model by integrating beauty doubt as a distinct cognitive barrier. Unlike traditional skepticism constructs (e.g., perceived risk), beauty doubt emerges specifically from aesthetic overload, revealing how visual cues can trigger counterproductive inferences about competence. This advances our understanding of trust formation in digital environments, where surface-level attributes often dominate initial evaluations. Third, the study extends the affective-cognitive response model by integrating beauty doubt as a distinct cognitive barrier. Unlike traditional skepticism constructs (e.g., perceived risk), beauty doubt emerges specifically from aesthetic overload, revealing how visual cues can trigger counterproductive inferences about competence. This advances our understanding of trust formation in digital environments, where surface-level attributes often dominate initial evaluations. In summary, this study redefines attractiveness as a double-edged sword in digital persuasion, providing a framework to reconcile its opposing effects while opening up new avenues for exploring threshold dynamics in virtual influencer marketing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Practical implications\u003c/h2\u003e\u003cp\u003eThe findings of this study yield actionable insights for live-streaming platforms, merchants, and streamers to optimize the strategic deployment of attractiveness while mitigating its paradoxical drawbacks. First, while physical attractiveness enhances initial perceptions of credibility and affective response, streamers should avoid over-relying on aesthetic appeal at the expense of demonstrated expertise. Platforms and brands must recognize that surpassing an attractiveness threshold can trigger consumer skepticism, undermining trust. Thus, training programs should emphasize the cultivation of professional knowledge (e.g., product specifications, industry trends) alongside presentational skills to reinforce perceived competence and counteract beauty doubt. Second, the moderating role of product type suggests that attractiveness should be calibrated to product category. For high-involvement products (e.g., electronics, luxury goods), streamers with balanced attractiveness and expertise are preferable, as consumers prioritize functional credibility over aesthetic appeal. Conversely, for low-involvement hedonic products (e.g., cosmetics, fashion), highly attractive streamers may leverage affective responses more effectively. Merchants should adopt a product-streamer matching strategy, pairing utilitarian goods with \"expert\" anchors and experiential goods with \"charismatic\" anchors to align with consumer expectations. Third, the persistence of beauty doubt implies that streamers must proactively signal trustworthiness through transparent behaviors, such as disclosing product limitations, demonstrating hands-on usage, or inviting third-party testimonials. Live Q\u0026amp;A sessions and real-time comparisons can further attenuate skepticism by showcasing objectivity. Platforms could also introduce \"expertise badges\" or performance metrics (e.g., accuracy rates in past recommendations) to supplement visual cues with verifiable credibility markers. Fourth, individual differences (e.g., consumer need for cognition) moderate the impact of attractiveness on purchase decisions. Streamers should segment audiences and tailor content accordingly: analytical viewers may respond better to data-driven presentations, while impulse-driven viewers might engage more with emotionally charged narratives. Adaptive scripting tools or audience analytics could help dynamically adjust the balance between logical persuasion and affective appeals during broadcasts. In sum, the study advocates a \"strategic authenticity\" approach, where attractiveness is harmonized with substantiated expertise to maximize trust and conversion. By acknowledging the dual-edged nature of beauty, stakeholders can transcend the simplistic \"more is better\" heuristic and cultivate a more sustainable, trust-driven live-streaming ecosystem.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Limitations and future research","content":"\u003cp\u003eSeveral limitations of this study warrant consideration, offering avenues for future research. First, the sample was drawn exclusively from a single cultural context, potentially constraining the generalizability of the findings across diverse markets where beauty standards and trust mechanisms may differ (e.g., Western vs. Eastern cultures). Future studies could adopt a cross-cultural comparative approach to examine how attractiveness thresholds and consumer skepticism vary by societal norms (Kang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Second, the reliance on cross-sectional data limits causal inferences regarding the proposed relationships. Longitudinal or experimental designs, such as manipulating streamer attractiveness levels in controlled settings could strengthen causal claims and clarify the temporal dynamics of beauty doubt formation. Third, while this study identifies product type and consumer individual differences as moderators, other contextual factors (e.g., shopping motivation, time pressure, or streamer-viewer interaction intensity) may further nuance the attractiveness-trust relationship. For instance, does urgency in limited-time promotions mitigate beauty doubt? How does viewer participation (e.g., real-time comments) alter perceptions of professionalism? Future research could integrate these variables to refine the model\u0026rsquo;s boundary conditions. These extensions would not only address the current limitations but also advance the understanding of attractiveness thresholds in evolving digital persuasion landscapes.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eAs livestreaming commerce continues to reshape consumer purchasing behavior, the prevailing assumption that higher streamer attractiveness invariably enhances sales requires critical re-examination. This study advances a dual-pathway model that delineates how physical attractiveness simultaneously enhances professional credibility and affective response while triggering beauty doubt a paradoxical mechanism that ultimately shapes consumer trust and purchase intention. Grounded in social perception theory, our findings challenge the \"more attractiveness is better\" heuristic by revealing an inverted-U relationship, wherein excessive attractiveness undermines perceived expertise and fosters skepticism. The empirical results confirm that while attractiveness initially bolsters credibility and affective engagement, it also heightens beauty doubt, with downstream effects on trust and purchase decisions. Crucially, professional credibility and affective response strengthen trust, whereas beauty doubt erodes it, and trust robustly drives purchase intention. The moderating role of product type and consumer individual differences further refines these relationships, though the non-significant moderation of product type on affective response\u0026ndash;trust linkage suggests affective reactions may be more universally influential than context-dependent. Ultimately, this research redefines attractiveness as a strategic resource not an unconditional asset in livestreaming commerce, where the interplay of beauty and brains dictates success.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003eThis study has been granted ethical approval by Henan University of Animal Husbandry and Economy (Approval No.: 20250322). The research was conducted in strict accordance with the Declaration of Helsinki and relevant Chinese ethical regulations, ensuring that the life, health, privacy, and dignity of all research subjects were fully protected.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo Funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhenwei Yan: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing.Asad Ur Rehman: Conceptualization, Validation, Investigation, Resources, Writing \u0026ndash; Review \u0026amp; Editing, Supervision.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study have been anonymized and securely stored. The research team will provide data for academic research upon reasonable request and can be applied for via email to the corresponding author, Zhenwei Yan ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdaval, R. Sometimes it just feels right: The differential weighting of affect-consistent and affect-inconsistent product information. \u003cem\u003eJ. Consum. Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), 1\u0026ndash;17 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlam, M. M. D., Karim, R. A. \u0026amp; Habiba, W. The relationship between CRM and customer loyalty: The moderating role of customer trust. \u003cem\u003eInt. J. Bank. Mark.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e (7), 1248\u0026ndash;1272 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAldboush, H. H. \u0026amp; Ferdous, M. 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Psychol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 767876 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beauty suspicion effect, Professional credibility, Consumer trust, Live-streaming commerce, Purchase intention","lastPublishedDoi":"10.21203/rs.3.rs-7237601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7237601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pervasive belief that higher physical attractiveness universally enhances livestreaming sales has dominated industry practices; however, emerging evidence suggests that excessive attractiveness may backfire by triggering consumer skepticism. This study investigates the dual-edged role of streamer attractiveness by examining the beauty suspicion effect where surpassing an attractiveness threshold undermines perceived professionalism, thereby eroding consumer trust. Grounded in social perception theory and the credibility-persuasion framework, we propose a model delineating how physical attractiveness influences professional credibility, affective response, and beauty doubt, which collectively shape customer trust and purchase intention, moderated by product type and consumer individual differences. A structured online survey was conducted with 335 livestreaming viewers, and the data were analyzed using a hybrid structural equation modeling (SEM) approach. Results reveal that while attractiveness initially boosts credibility and affective response, it simultaneously heightens beauty doubt. Crucially, professional credibility and affective response foster trust, whereas beauty doubt diminishes it, with trust further driving purchase intention. In sum, the findings challenge the \"more attractiveness is better\" axiom, offering nuanced insights for streamers and platforms to strategically balance aesthetics and expertise. Theoretical contributions extend to attractiveness thresholds in digital persuasion, while practical implications guide talent selection and product-streamer matching.\u003c/p\u003e","manuscriptTitle":"Beauty or Brains? The Paradoxical Impact of Anchors Attractiveness on Purchase Intentions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 10:30:06","doi":"10.21203/rs.3.rs-7237601/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13910036-69db-49b6-92c4-9b189234eff4","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59493297,"name":"Business and commerce/Business and management"},{"id":59493298,"name":"Social science/Business and management"},{"id":59493299,"name":"Business and commerce/Information systems and information technology"},{"id":59493300,"name":"Biological sciences/Psychology"},{"id":59493301,"name":"Social science/Psychology"},{"id":59493302,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-02-03T12:11:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 10:30:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7237601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7237601","identity":"rs-7237601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}