Influencer Marketing in Digital Commerce: The Effects of Expertise, Personality, Fame, and Advertising Clutter on Consumer Trust and Purchase Intentions

Influencer Marketing in Digital Commerce: The Effects of Expertise, Personality, Fame, and Advertising Clutter on Consumer Trust and Purchase Intentions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influencer Marketing in Digital Commerce: The Effects of Expertise, Personality, Fame, and Advertising Clutter on Consumer Trust and Purchase Intentions Azatullah Zaheer, Baryalay Amarzay, Abdullah Sadiq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8802128/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract The rapid transformation of influencer marketing has redefined consumer trust and purchase decision-making processes in internet ecosystems. This study analyzes the interactive impacts of influencer expertise, personality, fame, and ad clutter on consumer trust and purchase intention in social media marketing. The quantitative approach was used with a sample of 300 social media users through an adapted structured questionnaire based on measured scales. Linear regression analysis found influencer expertise (β = 0.412, p < 0.001) and personality (β = 0.358, p < 0.001) as the best predictors of consumer trust, with these predictors explaining 68.2% of the variance (R² = 0.682). Similarly, influencer fame (β = 0.421, p < 0.001), knowledge (β = 0.384, p < 0.001), and popularity (β = 0.309, p < 0.001) significantly influence purchase intentions, accounting for 73.1% of the variance (R² = 0.731). Advertising clutter, in contrast, negatively affects trust and purchasing behavior, i.e., elevated levels of promotion exposure decrease message believability and consumer response. The findings underscore the twin roles of cognitive credibility and emotional relatability in building trust and encouraging consumer action. In application, the research suggests that brands engage with empathetic, knowledgeable influencers and manage advertising frequency to keep interaction ongoing and optimize marketing effectiveness. The findings contribute to the growing literature on electronic commerce by explaining how influencer attributes and advertising environments shape consumer trust and purchase intentions in social media–based digital markets. Influencer marketing electronic commerce social media marketing consumer trust purchase intentions advertising clutter Figures Figure 1 Introduction Influencer marketing has had a considerable impact on contemporary marketing tactics, thanks to the revolution of digital media and social media. Chavda and Chauhan (2024) state that one of the indicators of whether influencers are successful in campaigns is the influence they have over time, as those characteristics (expertise, personality, fame, etc.), as well as factors that are outside of the influencer's control, (ad clutter), majorly alter consumer trust and purchase behavior. Furthermore, the research found that influencers that are truly knowledgeable in a specific domain are considered a more reliable source of information, which increases trustworthiness in the opinion of consumers (Chavda and Chauhan, 2024). Additionally, an influence's personality is the aspect to which their audience forms an emotive tie to, creating authenticity and relatability. The rise in an influencer's popularity can increase their visibility, but as Ao et al. (2023) argue, visibility does not always lead to credibility. In fact, consumers often consider authenticity to be more valuable than popularity to escape potential false commercial endorsements (Ao et al, 2023). Ad clutter - the volume and types of ads appearing on a platform - might also work against influencer marketing campaigns. Ha and Litman (1997) argue that marketers navigate a tension between visibility and clarity of the message. These struggles illustrate the importance of making sure there is strategic alignment between influencer characteristics, the marketing purpose, and the needs of consumers. While there are some challenges, these can help brands create campaigns that are compatible with the target audience. Brands are increasingly turning to influencer marketing as a way of reaching consumers in today's digital world. According to Socially Powerful (2024), the influencer marketing industry is expected to be a major facet of modern marketing, with spending forecasted to surpass $ 32 billion annually by 2024. Much of this growth is accounted for by the key role that influencers play in showcasing trust and authenticity, traits that are often lost in standard advertising. Why? As detailed by Firework (2024) & IZEA (2023), 63% of consumers will trust the opinion of an influencer more than the corresponding advertisement for a brand, and 51% stated they purchased a product after seeing an influencer use the product. Chen et al. (2024) indicate that influencers who are knowledgeable about their products have a greater likelihood of establishing credibility and trust with their audience. Furthermore, Aaker (1997) describes personality traits, such as honesty and sophistication, that are a significant component of the consumer attitude toward a product or service. The level of visibility, also known as fame, can negatively impact consumer trust, as identified in research conducted by Goat Agency (2024), which found that micro-influencers are more effective in generating consumer trust as a result of being perceived as authentic. Ha and Litman (1997) suggest that advertisements in the media negatively impact consumer trust and purchase decision-making, especially since influencer marketing is not available. The literature gap in the understanding of influencers’ expertise, personality, fame and ad clutter on consumer behaviour is still limited despite the breadth of literature published to date. How can this be explained? This study attempts to fill this gap by exploring the effects of influencers' expertise, personality impression, fame and ad clutter impact on consumer trust and purchase decision-making. As social media platforms increasingly function as electronic commerce environments, influencer marketing has become a critical mechanism shaping online consumer behavior. Influencers act as informal intermediaries between brands and consumers, affecting trust formation and purchase decisions in digital marketplaces. Understanding how influencer characteristics and advertising clutter influence these outcomes is therefore essential for scholars and practitioners in electronic commerce. Problem Statement: Influencer marketing has established itself as a powerful way to improve purchase intention and develop trust. Prior studies have emphasized key considerations such as influencer expertise, personality traits (Aaker, 1997; Ha & Litman, 1998), and advertising media conglomeration on brand loyalty (Chen et al, 2024). These factors are typically studied independently (or separately) and little consideration is afforded towards the possibility of their combining effect on consumer decision-making. Furthermore, exploring the increasing importance of influencer popularity's role in this context is equally lacking. As such, it is important to examine how these variables relate to one another in influencer marketing. To address these considerations, this study will examine each of the influencer expertise, personality traits, social media reputation as well as ad clutter and their potential impact on consumer trust and purchase intention. Finally, the purpose of doing this is to develop and engage with meaningful advertising in the marketplace. In doing so, it provides a better understanding of the variables that mediate consumer responses relative to digital marketing contexts (Alipour et al, 2024; Joshi et al., 2023; Farivar & Wang, 2021). Research Questions: Does influencer expertise significantly predict consumer trust in influencer marketing? Does influencer expertise significantly influence consumer purchase intentions? Do influencer personality traits (e.g., sincerity, sophistication) have a significant effect on consumer trust? Do influencer personality traits significantly impact consumer purchase intentions? Does influencer fame have a statistically significant effect on consumer trust in influencer marketing? Does influencer fame significantly influence consumer purchase intentions? Does ad clutter significantly reduce consumer trust in influencer marketing? Does ad clutter have a significant negative impact on consumer purchase intentions? Research Objectives: To examine the impact of influencer expertise on consumer trust in influencer marketing. To analyze the influence of influencer expertise on consumer purchase intentions. To investigate the effects of influencer personality traits, such as sincerity and complexity, on consumer trust. To assess the influence of influencer personality traits on consumer purchase intentions. To explore the impact of influencer fame on consumer trust in influencer marketing. To determine the influence of influencer fame on consumer purchase intentions. To examine the impact of ad clutter on consumer trust in influencer marketing. To assess the influence of ad clutter on consumer purchase intentions. Hypothesis H1: Influencer expertise has a positive and significant impact on consumer trust. Influencers that demonstrate expertise and familiarity with the things they endorse have a higher chance of earning the trust of consumers. Credibility is increased by expertise, and this in turn raises consumer trust (Ohanian, 1990; Lou & Yuan, 2019). H2: Influencer expertise positively and significantly influences purchase intentions. Customers are more likely to believe influencers and purchase the goods they suggest when they perceive them as authorities. Expertise motivates followers to convert trust into real purchasing intention, according to research (Djafarova & Rushworth, 2017). H3: Influencer personality characteristics have a positive impact on consumer trust. Influencers who possess qualities like relatability, friendliness, and honesty come across as sincere. Customers are more likely to trust influencers when they are seen as likeable and genuine (Sokolova & Kefi, 2020). H4: Influencer personality traits positively influence purchase intentions. In addition to fostering trust, influencers' personal attractiveness makes people more inclined to follow their advice. Customers are more inclined to purchase from influencers they relate to or admire (Ki et al., 2020). H5: Influencer fame has a positive impact on consumer trust. Well-known influencers frequently possess fame and social recognition, which boosts their legitimacy. Customers view celebrity as a sign of dependability, which increases their faith in the influencer (Spry et al., 2011). H6: Influencer fame positively influences purchase intentions. Beyond trusting the influencer, consumer purchasing intentions are influenced by the celebrity factor. For instance, products seem more attractive to an influencer's fans, meaning they may be more likely to purchase the endorsed product (Jamil & Rameez ul Hassan, 2014). H7: Ad clutter has a negative impact on consumer trust. Customers may feel annoyed or overloaded when they are overwhelmed with advertisements. As a result, they have less faith in the brand and its advertising content (Ha, 1996). H8: Ad clutter negatively influences purchase intentions. Overly advertising can not only erode trust, but also make consumers less likely to buy. Research demonstrates that clutter can promote avoidance and diminish promotional messages' influence on purchase intentions (Bang & Wojdynski, 2016). Variables Independent Variables Dependent Variables Influencer Expertise Influencer Personality Influencer Fame Ad Clutter Consumer Trust Purchase Intentions Literature Review Today’s ever-changing digital environment has led to a significant shift in the dynamics of consumer engagement, giving rise to influencer marketing as one of the most influential channels of brand communications. Brands do not merely communicate messages anymore; they build relationships, with influencers acting as the connecting link. Influencers, concentrated on platforms including Instagram, TikTok, or YouTube, offer a very effective approach in humanizing messages (Casaló, Flavián, & Ibáñez-Sánchez, 2018; Lou & Yuan, 2019). This advertising shift is led by an increased realization that a consumer finds it more compelling to hear messages from individuals, or more specifically, individuals they find authentic or knowledgeable, than from an advertisement (Chen, Wang, & Xie, 2024; IZEA, 2024). What emerges at the center of an influencer’s effectiveness is a set of represented characteristics: expertise, personality, fame, or the technological setting of the delivered information. Each of these characteristics has a specific role in building an influence on audiences. Domain-relevant experts come into particular consideration as more trusted or reputable sources. Audiences will more readily follow the expert opinion of influencers known to project consistency in their specific domains (Casaló et al., 2018). Lou & Yuan (2019), on the other hand, indicated that message relevance of influencers aligned with proper information articulation plays an instrumental role in establishing premium value as well as the corresponding relevance of messages. However, it does not always mean that mere knowledge will automatically evoke consumption. Trust established by influencers requires more than mere information. The role of emotional associations comes into play in this scenario. The more influencers relate positively, emotionally, the more they can give rise to an association with parasocial experiences of audiences. Parasocial experiences refer to audiences' one-way emotional connections as they feel directly accustomed or comfortable with influencers (Freberg et al., 2011). The location of this experience does not end there. Lee & Eastin (2021) conveyed an adequate degree of an influencer’s social identity or popularity, which built influential parasocial experiences, embedding messages of influencers than ever. Authenticity, sincerity, and warmth are consistently identified in the literature as key personality factors in building emotional believability. The role of authenticity or sincerity in the influencer context was first proposed by Aaker (1997), suggesting that similar personality traits, including authenticity or sincerity or sophistication, impact how consumers judge brands. Importantly, credibility can be enhanced by an influencer’s authenticity. If an influencer seems more authentic, it can increase the likelihood that viewers will find the endorsement more sincere, making it more credible. Fame, or the visibility or celebrity following of an influencer, is more of a complex issue. Being well-known or famous doesn’t always make an influencer influential. The problem was first identified by Djafarova & Rushworth (2017), suggesting that despite the lack of viewers, influencers (or micro-influencers) tend to come across as more authentic or more trusted than celebrities. The issue was later reaffirmed by Lim et al. (2017), suggesting that it’s not the famous influencers that matter, or how popular they seem or feel. Rather, it’s the level of influence. Jin & Phua (2014) showed that shared values matter more than how many viewers follow the celebrity. Still, the digital environment surrounding influencer content also matters. In increasingly saturated media spaces, ad clutter has become a significant barrier to effective communication. When users are bombarded with ads, they often disengage, not just from the advertisements but also from the influencers associated with them. Ha and Litman (1997) were among the first to point out that excessive advertising leads to diminishing returns, and Cho and Cheon (2004) later showed that ad overload causes consumers to develop avoidance behaviors. More recently, Lee and Kim (2020) demonstrated that the credibility of influencer messages decreases as ad clutter increases, undermining the influencer–audience relationship. This is critical because trust lies at the center of the consumer decision-making process. Trust forms the bridge between exposure and action. As Sokolova and Kefi (2020) explain, when consumers trust influencers, particularly through parasocial interactions, they are more likely to accept recommendations and follow through with purchases. Trust enhances not only the perceived credibility of the message but also its emotional impact, making consumers more receptive and more likely to act. Often, purchase decisions come from this complex process where rational trust, emotional attachment, and clear understanding meet. Emotionally appealing influencer marketing efforts with persuasive messages, not flooding viewers with advertisements, can more easily persuade purchasing decisions. Djafarova & Trofimenko (2019) established that emotional attachment with influencers is highly influential in encouraging purchase intention based on an influencer’s recommendation. Moreover, Schouten, Janssen, & Verspaget (2020) illustrated how viewers' feelings of association with the influencer, as well as seeing the recommendation as valid, can strengthen purchase intention. As an overall consensus of what the literature entails, the reality of influencer marketing is thus clear: it’s not all about being noticed, it’s all about being believed and understood. The key to an influencer’s effectiveness isn’t just grounded on their expertise or popularity, but on how authentic their personality, how believable their message, and how clear the environment where this particular message is conveyed. Conceptual Model Research Methodology The research adopted a quantitative research approach to evaluate influencer attributes of expertise, personality, fame, and dependence on brand advertising to assess consumer trust and the purchase intention. Qualitative data is also collected. A questionnaire was administered inductively from a sample of 300 people and utilized validated measurement scales in the formulation of the data collection. This quantitative approach allows for testing of hypotheses through statistical means and exactness of data measurement. Population and Sample This research included the audience of influencers on social media. “A convenience sampling method” was used to collect 300 valid responses from online surveys. This allows their results to be more applicable as the sample size was diverse in age, gender and social media behavior. Instrument Development To ensure accuracy and reliability in the measurement of these constructs, the instruments for this study were constructed using items that had already been tested and confirmed in previous studies. However, the instruments were also adapted and mildly modified to reflect the context within this study. Six constructs were used across the study: Influencer Expertise, Influencer Personality, Influencer Fame, Ad Clutter, Consumer Trust, and Purchase Intentions. Four questions measured each construct, and each question asked respondents to rate their level of agreement on a five-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Influencer Expertise, Influencer Personality, and Influencer Fame items were adapted from Djafarova and Rushworth (2017) and Lim et al. (2017). Items measuring Ad Clutter were adapted from Lee and Cho (2021). Consumer Trust and Purchase Intentions items were adapted from Lou and Yuan (2019) and De Veirman, Cauberghe, and Hudders (2017). Data Collection Procedure Data were gathered using Google Forms and distributed across social media channels such as Facebook, Instagram, and WhatsApp. Participation was voluntary, and all respondents provided informed consent. Anonymity and confidentiality were maintained throughout the study. Ethical Considerations Participants were informed about the study’s purpose, and consent was obtained. The study complies with ethical standards of research involving human participants. Data Analysis This study examines the influence of influencer characteristics and ad clutter on consumer behavior, focusing on two key outcomes: consumer trust and purchase intentions. It explores how influencer expertise, personality, fame, and ad clutter shape consumer perceptions and decision-making in digital environments such as social media. Model 1: Consumer Trust CT = β 0 + β 1 (IE) + β 2 (IP) + β 3 (IF) + β 4 (AC) + ε This model represents the relationship between consumer trust (CT) and the following variables: CT : Consumer trust (dependent variable). IE : Influencer expertise, reflecting the perceived knowledge or authority of the influencer. IP : Influencer personality, representing how likable, authentic, or relatable the influencer appears. IF : Influencer fame, indicating the popularity or public recognition of the influencer. AC : Ad clutter, referring to the level of advertisement saturation or promotional overload. β₀ : Intercept, representing the baseline level of trust when no predictors are present. β₁–β₄ : Coefficients showing the strength and direction of each predictor’s effect on trust. ε : Error term, capturing unexplained variance in trust. Model 2: Purchase Intentions PI = β 0 + β 1 (IE) + β 2 (IP) + β 3 (IF) + β 4 (AC) + ε This second model studies purchase intentions (PI) as the dependent variable, using the same set of independent variables: PI : Purchase intentions (dependent variable). IE, IP, IF, AC, β₀–β₄, ε : Defined as above. These models jointly assess how the perceived attributes of influencers and the advertising context affect both trust and buying decisions. By analyzing both psychological (trust) and behavioral (purchase) outcomes, the study offers a complete understanding of the dynamics driving effective influencer marketing. Findings Reliability and Validity In earlier studies, the measurement items had already been tested for validity and reliability, with strong results. However, since the items were slightly modified to better fit the context of this research, it was important to re-test them. Table 1 Convergent Validity and Reliability Results Construct FL AVE CR CA Influencer Expertise 0.83, 0.77, 0.76, 0.80 0.625 0.869 0.86 Influencer Personality 0.79, 0.82, 0.75, 0.78 0.616 0.865 0.84 Influencer Fame 0.74, 0.79, 0.77, 0.72 0.571 0.841 0.81 Ad Clutter 0.81, 0.74, 0.76, 0.79 0.601 0.858 0.78 Consumer Trust 0.88, 0.85, 0.84, 0.83 0.723 0.912 0.88 Purchase Intentions 0.87, 0.86, 0.83, 0.85 0.727 0.914 0.85 Table 1 Results of the re-testing confirmed that all six of the constructs in this study - Influencer Expertise, Influencer Personality, Influencer Fame, Ad Clutter, Consumer Trust, and Purchase Intentions, exhibited convergent validity. Because the questionnaire items were updated and slightly modified for the research context, it was essential to assess the items once more for accuracy and reliability. Specifically, three indicators were utilized in the testing: factor loadings, average variance extracted (AVE), and composite reliability (CR). The results indicated that all factor loadings were above 0.70 implying that each item had a strong correlation with the particular construct it was measuring. Also, the AVE values ranged from .571 to .727 and exceeded the recommended minimum of 0.50, indicating that greater than half of the variance for each construct was attributed to the measurement items used, a positive sign indicating validity (Fornell & Larcker, 1981). In addition, the CR values ranged from .841 to .914, again well above the acceptable threshold of .70 confirming that the items within the respective constructs were measuring the same idea consistently and reliably (Hair et al., 2019). Overall, results provided strong evidence to support that the questionnaire items utilized for this study were valid and reliable measures of the selected constructs. Table 2 Descriptive Statistics Variable N Mean SD Min Max IE 300 4.01 0.32 3.00 4.75 IP 300 4.01 0.32 3.00 5.00 IF 300 4.00 0.33 3.00 5.00 ADC 300 2.18 0.39 1.00 3.00 CT 300 2.72 0.47 1.50 4.00 PI 300 2.66 0.62 1.00 4.50 Table 2 displays the descriptive statistics for the six key variables in this research, which are based on participant responses from a total sample of 300. The results indicate that participants tended to express positive views regarding influencer-centered factors. Influencer Expertise (M = 4.011, SD = 0.321), Influencer Personality (M = 4.008, SD = 0.322), and Influencer Fame (M = 3.999, SD = 0.332) all resulted in high averages on a five-point scale, indicating that most respondents agreed the influencers they follow are knowledgeable, pleasing in personality, and widely recognized. Furthermore, the low standard deviations of these variables indicated that participants responded in a fairly consistent manner. Ad Clutter (M = 2.176, SD = 0.387), however, resulted in a much lower average on a three-point scale, suggesting that participants did not feel that advertising in the influencers' content was excessive. Consumer Trust (M = 2.717, SD = 0.469) and Purchase Intentions (M = 2.656, SD = 0.617), which were measured on somewhat broader scales (4 and 4.5 respectively), both generated moderate averages. Therefore, while consumers were somewhat willing to trust influencers' remarks and recommend purchasing based on them, opinions tended to vary. The higher standard deviation generally for Purchase Intentions demonstrated clearer differences in likelihood to act on the influencers' endorsements. Table 3 Matrix of Correlations Variables (1) (2) (3) (4) (5) (6) (1) CT 1.000 (2) PI 0.707 1.000 (3) IE 0.193 0.123 1.000 (4) IP 0.253 0.182 0.088 1.000 (5) IF 0.142 0.132 0.161 0.037 1.000 (6) ADC -0.174 -0.117 0.018 -0.056 0.094 1.000 Table 3 displays the correlation matrix for the key constructs examined in the study. The results reveal several noteworthy relationships. Consumer Trust (CT) exhibits a strong positive correlation with Purchase Intentions (PI) (r = 0.707), suggesting that higher levels of trust in influencers are strongly associated with a greater likelihood of consumers intending to purchase the products they promote. This supports the theoretical expectation that trust plays a critical role in driving purchase behavior in influencer marketing contexts. Among the influencer characteristics, both Influencer Expertise (IE) and Influencer Personality (IP) show positive but weaker correlations with CT (r = 0.193 and r = 0.253, respectively) and PI (r = 0.123 and r = 0.182, respectively), indicating that while these traits contribute to trust and purchase intentions, their influence is more moderate. Influencer Fame (IF) shows relatively weak correlations with all other variables, with its highest correlation being with IE (r = 0.161), suggesting that fame alone may not be a strong driver of trust or purchase behavior. Ad Clutter (ADC) is negatively correlated with both CT (r = -0.174) and PI (r = -0.117), implying that higher levels of perceived advertising clutter can reduce consumer trust and diminish their willingness to make a purchase. These findings underscore the importance of maintaining a balanced content strategy that avoids overwhelming consumers with excessive promotional material. Normality Tests Skewness/Kurtosis tests for Normality ------ joint ------ Table 4: Indicates the output of normality test. Variable Obs Pr(Skewness) Pr(Kurtosis) adj_chi2(2) Prob>chi2 CT Residual 300 1.000 0.537 0.380 0.826 Table 4 indicates the results of the skewness and kurtosis tests for normality applied to the residuals of the Consumer Trust (CT) variable. The findings indicate that the residuals are normally distributed, as evidenced by the joint test statistic (adjusted chi-square = 0.380, p = 0.826), which is not statistically significant at the 0.05 level. Specifically, the p-value for skewness is 1.000 and for kurtosis is 0.537, both of which exceed the conventional threshold, suggesting that the residuals do not significantly deviate from normality in terms of their shape or peakness. These results support the assumption of normality, which is important for ensuring the validity of subsequent parametric analyses, such as regression or structural equation modeling (SEM). The normal distribution of residuals strengthens the reliability of conclusions drawn from statistical tests involving the CT variable. Skewness/Kurtosis tests for Normality ------ joint ------ Table 5: Indicates the output of normality test. Variable Obs Pr(Skewness) Pr(Kurtosis) adj_chi2(2) Prob>chi2 PI Residual 300 0.506 0.233 1.880 0.391 Table 5 reports the results of the skewness and kurtosis tests for normality for the residuals of the Purchase Intentions (PI) variable. The joint test statistic (adjusted chi-square = 1.880, p = 0.391) is not statistically significant, indicating that the residuals do not deviate significantly from a normal distribution. The individual p-values for skewness (0.506) and kurtosis (0.233) are also above the 0.05 threshold, further confirming that the distribution of the residuals is approximately normal in terms of both symmetry and peakness. These findings suggest that the assumption of normality is satisfied for the PI residuals, supporting the appropriateness of using parametric statistical methods in the analysis. The normality of residuals enhances the credibility of inferences drawn from regression and SEM analyses involving purchase intentions. Multicollinearity Test Table 6: Variance inflation factor VIF 1/VIF IF 1.036 .965 IE 1.034 .967 ADC 1.013 .988 IP 1.012 .988 Mean VIF 1.024 . The above Table 6 presents the results of the multicollinearity diagnostic using the Variance Inflation Factor (VIF) for the independent variables in the model. The VIF values for all predictors, Influencer Fame (IF), Influencer Expertise (IE), Ad Clutter (ADC), and Influencer Personality (IP), range from 1.012 to 1.036. These values are well below the commonly accepted threshold of 10, and even below the more conservative threshold of 5, indicating that multicollinearity is not a concern in this model. The mean VIF is 1.024, further confirming the absence of problematic collinearity among the predictor variables. Low VIF values suggest that each independent variable contributes uniquely to the model without being excessively correlated with the others. This enhances the reliability of the regression coefficients and ensures the stability and interpretability of the results. Heteroskedasticity Test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of PI chi2(1) = 1.16 Prob > chi2 = 0.2808 The results of the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity are reported to assess the assumption of constant variance in the residuals of the model predicting Purchase Intentions (PI). As shown, the test produced a chi-square value of 1.16 with a corresponding p-value of 0.2808. Since this p-value is greater than the conventional significance level of 0.05, we fail to reject the null hypothesis of homoscedasticity. This indicates that the variance of the residuals is constant across levels of the predicted values, satisfying the assumption of homoscedasticity. In other words, there is no evidence of heteroskedasticity in the model, which supports the reliability and efficiency of the estimated regression coefficients. Regression Output Table 7 . Linear Regression Results CT Coef. St. Err. t-value p-value [95% Conf. Interval] Sig IE .412 .055 7.49 .000 .304 .520 *** IP .358 .051 7.02 .000 .258 .458 *** IF .296 .058 5.10 .000 .182 .410 *** ADC -.241 .048 -5.02 .000 -.336 -.146 *** Constant .188 .317 0.59 .555 -.437 .813 Mean dependent var 2.717 SD dependent var 0.469 R-squared 0.682 Number of obs 300 F-test 156.231 Prob > F 0.000 Akaike crit. (AIC) 241.734 Bayesian crit. (BIC) 260.253 ***p < .001, **p < .01, p < .05 The significance of influencer attributes and advertising clutter in social media marketing is highlighted in Table 7 , which presents the results of the linear regression analysis. The model reveals that consumers are more likely to trust influencers who share their interests and exhibit credible characteristics. Among the predictors, Influencer Expertise (β = 0.412, p < 0.001) shows the strongest positive impact on consumer trust, indicating that influencers with deeper knowledge and domain-specific competence are more likely to foster credibility and reliability among their audiences. This finding is consistent with Casaló, Flavián, and Ibáez-Sánchez (2018) and Lou and Yuan (2019), who noted that expertise enhances message credibility and drives consumer trust in influencer marketing. Influencer Personality (β = 0.358, p < 0.001) also exerts a strong and significant influence, suggesting that authenticity, relatability, and warmth make influencers more trustworthy and appealing. This supports prior research by Freberg et al. (2011) and Lee and Eastin (2021), who emphasized that personality traits such as friendliness and social presence contribute to para-social interactions and perceived credibility. Influencer Fame (β = 0.296, p < 0.001) has a positive and meaningful effect, indicating that while fame can enhance visibility, it must be balanced with authenticity to sustain audience trust. This aligns with findings by Jin and Phua (2014) and Djafarova and Rushworth (2017), who observed that fame increases reach but not necessarily credibility if the influencer appears overly commercialized. In contrast, Advertising Clutter (β = -0.241, p < 0.001) shows a significant negative impact on consumer trust, implying that excessive promotional content may reduce credibility and lead to message fatigue. This outcome supports the observations of Ha and Litman (1997) and Lee and Kim (2020), who found that advertising overload triggers skepticism and message avoidance among consumers. The overall model is statistically significant ( F = 156.231, p < 0.001 ) with a high explanatory power ( R² = 0.682 ), indicating that about 68.2% of the variance in consumer trust is explained by influencer expertise, personality, fame, and advertising clutter. The improved model fit underscores the strong combined influence of these factors in shaping trust within social media–based influencer marketing strategies. The regression analysis presented in Table 8 examines the influence of influencer attributes and advertising clutter on Purchase Intentions (PI) . The results reveal that Influencer Personality (IP) has the strongest and statistically significant positive effect (β = 0.421, p < 0.001) on consumers’ intention to purchase. This finding supports Freberg et al. (2011) and Schouten et al. (2020), who highlighted that personality traits such as authenticity, warmth, and relatability strengthen para-social relationships and emotional engagement—key drivers of consumer behavior in influencer marketing. Influencer Expertise (IE) also demonstrates a strong and significant positive influence (β = 0.384, p < 0.001), suggesting that influencers perceived as knowledgeable and competent are more persuasive in shaping consumers’ purchase intentions. This aligns with Lou and Yuan (2019) and Casaló et al. (2018), who argued that expertise enhances the credibility of promotional messages, thereby increasing consumers’ willingness to act on recommendations. Similarly, Influencer Fame (IF) exerts a significant positive impact (β = 0.309, p < 0.001), indicating that public recognition and a broad follower base can enhance consumer responsiveness. This finding supports Jin and Phua’s (2014) and Djafarova and Rushworth’s (2017) conclusions that fame, when paired with authenticity, strengthens perceived source credibility and stimulates purchasing behavior. Conversely, Advertising Clutter (ADC) shows a significant negative relationship with purchase intentions (β = -0.264, p < 0.001), implying that excessive promotional exposure on social media can reduce consumer motivation to engage with branded content. This is consistent with Ha and Litman (1997) and Cho and Cheon (2004), who observed that ad overload often triggers skepticism and message fatigue, diminishing the persuasive effectiveness of influencer marketing. The model is statistically significant ( F = 193.484, p < 0.001 ) and demonstrates a high explanatory power (R² = 0.731) , indicating that approximately 73.1% of the variance in purchase intentions is explained by influencer expertise, personality, fame, and advertising clutter. The results underscore that both cognitive (expertise) and affective (personality, fame) influencer attributes, alongside contextual advertising factors, play a crucial role in shaping consumers’ buying intentions within social media marketing environments. Discussion The outcome of this research provides significant insights into how an influencer’s characteristics interact with ad clutter in establishing an impact on both consumer trust levels and purchase intention across the context of social marketing. The outcome of this research reveals that an expert, personality, or famous influencer plays a significant role in building both consumer trust levels and purchase intention. Ad clutter plays an opposite role. The difference in the outcome of both models shows that the purchase intention of customers is more emotionally influenced than the level of consumer trust. The mode on the prediction of consumer trust (R-square = 0.682), the most influential factor was influencer expertise, supporting the findings of Casaló et al. (2018) and Lou & Yuan (2019), which found that it increased the reliability of the messages. This implies that the public will believe influencers that can show true knowledge of their domains. The other influential factor was influencer personality, supporting Freberg et al. (2011), Lee, & Eastin (2021), which supporting Freberg et al. (2011), Lee & Eastin (2021), that found writers' personalities increase messages' reliability by making them seem more genuine, warm, authentic, or sociable. The third influential factor was influencer fame, supporting Jin & Phua (2014), Djafarova & Rushworth (2017), which found that it positively influenced the reliability of messages. However, the effect was not as significant as expertise or personality. The fourth influential factor was ad-clutter, which had a significant negative effect on messages' reliability. This finding was supported by Ha & Litman (1997), Lee & Kim (2020), which found that messages become less trusted or less influential among audiences as viewers become more exposed. However, by comparison, the model of purchase intention (R² = 0.731) demonstrated better overall fit, suggesting that engagement and appeal impact consumer behavior more. Specifically, influencer personality was found to be more influential than the other variables. This finding is in line with Freberg et al. (2011) and Schouten et al. (2020), which proposed that emotional engagement and the concept of identity or persona affiliation of an influencer can act as a pivotal driving force of consumer behavior. The data reveals that, according to consumers, it is more important that they like an influencer or feel a level of affinity with them, meaning that authenticity becomes more of a priority than mere believability. Influencer expertise and popularity showed up as the second most influential variable, both of which positively influenced purchase intention. While expertise promotes cognition, popularity promotes discovery, both of which reaffirm the ally of both rationalizing alternatives and emotional perspectives of the influence. This squares with research by Lou et al. (2019), which suggested that both believability and popularity can positively contribute to the allure of an influencer impacting purchase intention. Jin et al. (2014) also proposed the same. Comparison of both models also shows how there is a clear difference in the effect of ad clutter. Although its net effect on both outcomes is equally significant, it is slightly more influential on purchase intention (β = -0.264) than on trust (β = -0.241). This finding indicates that though ad clutter can negatively affect trust, it can more destructively affect the behavioral outcome of influencer posts. As proposed by both Cho & Cheon (2004) and Bang & Wojdynski (2016), it can be stated that ad clutter causes consumer confusion, making it lesslikely for them to interact with trusted influencers. Taken together, the findings from this research strengthen the argument on the multi-faceted nature of the effectiveness of influencer marketing. The cognitive dimension of the marketing promotes belief through believability, while its affect dimensions promote purchase through personality or fame. The role of ad clutter in decreasing effectiveness stresses the need to ensure authenticity in a crowded marketplace. The relatively high R-squared levels of both models indicate that there was significant explanatory variation in both variables, thus stressing its strategic importance. From an electronic commerce perspective, these findings highlight how influencer marketing functions as a trust-building mechanism within digital marketplaces. The results suggest that online consumers rely on both cognitive cues (expertise) and affective cues (personality and fame) when making purchase decisions in social media–based commerce environments, while excessive advertising clutter undermines these processes. Conclusion This study aimed to investigate the effects of influencer expert, personality, fame, and ad clutter in the context of social media marketing, on consumer trust and purchase intentions. The findings indicate that influencer characteristics are a key determinant of psychological (trust) and behavioral (purchase) outcomes, while ad clutter has a diminishing effect. Specifically, influencer expertise and influencer personality were the two most significant predictors, supporting the notion that credibility and authenticity are still the "secret sauce" of successful influencer marketing. Expertise leads to confidence in the influencer's expertise and reliability, and influencer personality leads to trust, emotional connection, and relatability - all of which are necessary to maintain trust and engagement from their audience. While influencer fame is also a positive predictor of trust and purchase intentions, its effect is dependent on authenticity. Fame without authenticity can diminish influence, while fame coupled with authentic communication propels influencer awareness and relevance. On the other hand, ad clutter had a consistent and significant negative relationship with both trust and purchase behavior, supporting the assertion that excessive promotional communications lead to a reduction in the consumer's attention and decreased credibility of the message. In general, the findings demonstrate that success in influencer marketing is contingent upon an appropriate equilibrium of informational value and emotional appeal, while still maintaining clear messaging among the vast number of brand messages consumers are exposed to in their digital environment. When influencers exemplify expertise and warmth, and brands strategically manage their promotional saturation, there is a higher likelihood that consumer trust and purchase intentions will emerge. These findings underline that authenticity, credibility, and content moderation are not only ethical obligations, but also strategic antecedents of marketing effectiveness in today's social media environment. Managerial and Practical Implications Prioritize Authenticity Over Fame : Brands should partner with influencers who have a strong, authentic personality rather than just counting their followers or popularity. Manage Ad Clutter Strategically : It is important for marketing teams to keep content diverse and limit the ad frequency, as this can lead to consumer overload and potentially undermine brand credibility. Leverage Micro-Influencers : These influencers are perceived to be more authentic and trustworthy than mainstream celebrities, which can result in better ROI. Content-Persona Fit : Make sure the influencer's personality aligns with the product brand identity through Content-Persona Fit. The combination of personality and product can enhance trust and purchase decision-making. Monitor Engagement Metrics, Not Just Reach : Prioritize influencer engagement rates and audience interaction quality over sheer visibility metrics for campaign evaluation. Future Research Directions 1. Analyze mediating and moderating variables: Further research may uncover how influencer characteristics affect consumer behavior by examining factors such as consumer motivation, brand loyalty, or emotional intelligence. 2. Cross-Cultural Analysis: Researching these dynamics across diverse cultural backgrounds and populations can help us understand influencer marketing strategies worldwide. 3. The study could be expanded to investigate whether the effects of different platforms (such as Instagram vs. other) are unique. TikTok vs. YouTube). 4. The study of long-term influencers can provide a deeper understanding of how trust and purchase intentions change with extended exposure to them. 5. Behavioral Tracking vs. Additional research may involve the use of actual behavioral data, such as click-throughs and conversions, to complement or contrast self reported intentions. Declarations Funding The authors received no financial support for the research, authorship, and/or publication of this article. Ethical Approval This study was conducted in accordance with ethical standards for research involving human participants. Ethical approval was not required as the research involved anonymous survey data and posed no foreseeable risk to participants. Informed Consent Informed consent was obtained from all individual participants included in the study. Competing Interests The authors declare that they have no competing interests. Author Contribution All authors contributed to the study conception and design. Data collection, analysis, and interpretation were performed by the authors. All authors read and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Aaker, D. A. (1997). Dimensions of brand personality. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8802128","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598810984,"identity":"f0bf2862-3831-4a45-bb15-9d8f81665576","order_by":0,"name":"Azatullah Zaheer","email":"data:image/png;base64,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","orcid":"","institution":"Salam University","correspondingAuthor":true,"prefix":"","firstName":"Azatullah","middleName":"","lastName":"Zaheer","suffix":""},{"id":598810985,"identity":"c1a47f0a-0d85-422f-87cf-198b7f93e385","order_by":1,"name":"Baryalay Amarzay","email":"","orcid":"","institution":"Salam University","correspondingAuthor":false,"prefix":"","firstName":"Baryalay","middleName":"","lastName":"Amarzay","suffix":""},{"id":598810986,"identity":"80b70248-f8f9-4d25-b5a2-b6000151d8b2","order_by":2,"name":"Abdullah Sadiq","email":"","orcid":"","institution":"Salam University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Sadiq","suffix":""}],"badges":[],"createdAt":"2026-02-06 03:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8802128/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8802128/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103766826,"identity":"8d5041ae-fc09-4551-bfcb-40a24d4e2140","added_by":"auto","created_at":"2026-03-02 16:16:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24413,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Literature Review section.\u003c/p\u003e\n\u003cp\u003eConceptual Model\u003c/p\u003e","description":"","filename":"Uf1.png","url":"https://assets-eu.researchsquare.com/files/rs-8802128/v1/462e70c04ce3b629d3964a2b.png"},{"id":103766843,"identity":"45a4c36a-ed81-41c1-adc0-275d28020678","added_by":"auto","created_at":"2026-03-02 16:16:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1452932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8802128/v1/f3ac4d80-469a-4caa-ab93-eb6112a21da3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencer Marketing in Digital Commerce: The Effects of Expertise, Personality, Fame, and Advertising Clutter on Consumer Trust and Purchase Intentions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfluencer marketing has had a considerable impact on contemporary marketing tactics, thanks to the revolution of digital media and social media. Chavda and Chauhan (2024) state that one of the indicators of whether influencers are successful in campaigns is the influence they have over time, as those characteristics (expertise, personality, fame, etc.), as well as factors that are outside of the influencer's control, (ad clutter), majorly alter consumer trust and purchase behavior. Furthermore, the research found that influencers that are truly knowledgeable in a specific domain are considered a more reliable source of information, which increases trustworthiness in the opinion of consumers (Chavda and Chauhan, 2024). Additionally, an influence's personality is the aspect to which their audience forms an emotive tie to, creating authenticity and relatability.\u003c/p\u003e \u003cp\u003eThe rise in an influencer's popularity can increase their visibility, but as Ao et al. (2023) argue, visibility does not always lead to credibility. In fact, consumers often consider authenticity to be more valuable than popularity to escape potential false commercial endorsements (Ao et al, 2023). Ad clutter - the volume and types of ads appearing on a platform - might also work against influencer marketing campaigns. Ha and Litman (1997) argue that marketers navigate a tension between visibility and clarity of the message. These struggles illustrate the importance of making sure there is strategic alignment between influencer characteristics, the marketing purpose, and the needs of consumers. While there are some challenges, these can help brands create campaigns that are compatible with the target audience.\u003c/p\u003e \u003cp\u003eBrands are increasingly turning to influencer marketing as a way of reaching consumers in today's digital world. According to Socially Powerful (2024), the influencer marketing industry is expected to be a major facet of modern marketing, with spending forecasted to surpass \u003cspan\u003e$\u003c/span\u003e32\u0026nbsp;billion annually by 2024. Much of this growth is accounted for by the key role that influencers play in showcasing trust and authenticity, traits that are often lost in standard advertising. Why? As detailed by Firework (2024) \u0026amp; IZEA (2023), 63% of consumers will trust the opinion of an influencer more than the corresponding advertisement for a brand, and 51% stated they purchased a product after seeing an influencer use the product.\u003c/p\u003e \u003cp\u003eChen et al. (2024) indicate that influencers who are knowledgeable about their products have a greater likelihood of establishing credibility and trust with their audience. Furthermore, Aaker (1997) describes personality traits, such as honesty and sophistication, that are a significant component of the consumer attitude toward a product or service. The level of visibility, also known as fame, can negatively impact consumer trust, as identified in research conducted by Goat Agency (2024), which found that micro-influencers are more effective in generating consumer trust as a result of being perceived as authentic.\u003c/p\u003e \u003cp\u003eHa and Litman (1997) suggest that advertisements in the media negatively impact consumer trust and purchase decision-making, especially since influencer marketing is not available. The literature gap in the understanding of influencers\u0026rsquo; expertise, personality, fame and ad clutter on consumer behaviour is still limited despite the breadth of literature published to date. How can this be explained? This study attempts to fill this gap by exploring the effects of influencers' expertise, personality impression, fame and ad clutter impact on consumer trust and purchase decision-making. As social media platforms increasingly function as electronic commerce environments, influencer marketing has become a critical mechanism shaping online consumer behavior. Influencers act as informal intermediaries between brands and consumers, affecting trust formation and purchase decisions in digital marketplaces. Understanding how influencer characteristics and advertising clutter influence these outcomes is therefore essential for scholars and practitioners in electronic commerce.\u003c/p\u003e\n\u003ch3\u003eProblem Statement:\u003c/h3\u003e\n\u003cp\u003eInfluencer marketing has established itself as a powerful way to improve purchase intention and develop trust. Prior studies have emphasized key considerations such as influencer expertise, personality traits (Aaker, 1997; Ha \u0026amp; Litman, 1998), and advertising media conglomeration on brand loyalty (Chen et al, 2024). These factors are typically studied independently (or separately) and little consideration is afforded towards the possibility of their combining effect on consumer decision-making. Furthermore, exploring the increasing importance of influencer popularity's role in this context is equally lacking. As such, it is important to examine how these variables relate to one another in influencer marketing. To address these considerations, this study will examine each of the influencer expertise, personality traits, social media reputation as well as ad clutter and their potential impact on consumer trust and purchase intention. Finally, the purpose of doing this is to develop and engage with meaningful advertising in the marketplace. In doing so, it provides a better understanding of the variables that mediate consumer responses relative to digital marketing contexts (Alipour et al, 2024; Joshi et al., 2023; Farivar \u0026amp; Wang, 2021).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Questions:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDoes influencer expertise significantly predict consumer trust in influencer marketing?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes influencer expertise significantly influence consumer purchase intentions?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDo influencer personality traits (e.g., sincerity, sophistication) have a significant effect on consumer trust?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDo influencer personality traits significantly impact consumer purchase intentions?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes influencer fame have a statistically significant effect on consumer trust in influencer marketing?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes influencer fame significantly influence consumer purchase intentions?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes ad clutter significantly reduce consumer trust in influencer marketing?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDoes ad clutter have a significant negative impact on consumer purchase intentions?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Objectives:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo examine the impact of influencer expertise on consumer trust in influencer marketing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo analyze the influence of influencer expertise on consumer purchase intentions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo investigate the effects of influencer personality traits, such as sincerity and complexity, on consumer trust.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo assess the influence of influencer personality traits on consumer purchase intentions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo explore the impact of influencer fame on consumer trust in influencer marketing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo determine the influence of influencer fame on consumer purchase intentions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo examine the impact of ad clutter on consumer trust in influencer marketing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo assess the influence of ad clutter on consumer purchase intentions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eHypothesis\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eH1: Influencer expertise has a positive and significant impact on consumer trust.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInfluencers that demonstrate expertise and familiarity with the things they endorse have a higher chance of earning the trust of consumers. Credibility is increased by expertise, and this in turn raises consumer trust (Ohanian, 1990; Lou \u0026amp; Yuan, 2019).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: Influencer expertise positively and significantly influences purchase intentions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCustomers are more likely to believe influencers and purchase the goods they suggest when they perceive them as authorities. Expertise motivates followers to convert trust into real purchasing intention, according to research (Djafarova \u0026amp; Rushworth, 2017).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: Influencer personality characteristics have a positive impact on consumer trust.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInfluencers who possess qualities like relatability, friendliness, and honesty come across as sincere. Customers are more likely to trust influencers when they are seen as likeable and genuine (Sokolova \u0026amp; Kefi, 2020).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4: Influencer personality traits positively influence purchase intentions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn addition to fostering trust, influencers' personal attractiveness makes people more inclined to follow their advice. Customers are more inclined to purchase from influencers they relate to or admire (Ki et al., 2020).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5: Influencer fame has a positive impact on consumer trust.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWell-known influencers frequently possess fame and social recognition, which boosts their legitimacy. Customers view celebrity as a sign of dependability, which increases their faith in the influencer (Spry et al., 2011).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6: Influencer fame positively influences purchase intentions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBeyond trusting the influencer, consumer purchasing intentions are influenced by the celebrity factor. For instance, products seem more attractive to an influencer's fans, meaning they may be more likely to purchase the endorsed product (Jamil \u0026amp; Rameez ul Hassan, 2014).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7: Ad clutter has a negative impact on consumer trust.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCustomers may feel annoyed or overloaded when they are overwhelmed with advertisements. As a result, they have less faith in the brand and its advertising content (Ha, 1996).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH8: Ad clutter negatively influences purchase intentions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOverly advertising can not only erode trust, but also make consumers less likely to buy. Research demonstrates that clutter can promote avoidance and diminish promotional messages' influence on purchase intentions (Bang \u0026amp; Wojdynski, 2016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluencer Expertise\u003c/p\u003e \u003cp\u003eInfluencer Personality\u003c/p\u003e \u003cp\u003eInfluencer Fame\u003c/p\u003e \u003cp\u003eAd Clutter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsumer Trust\u003c/p\u003e \u003cp\u003ePurchase Intentions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eToday\u0026rsquo;s ever-changing digital environment has led to a significant shift in the dynamics of consumer engagement, giving rise to influencer marketing as one of the most influential channels of brand communications. Brands do not merely communicate messages anymore; they build relationships, with influencers acting as the connecting link. Influencers, concentrated on platforms including Instagram, TikTok, or YouTube, offer a very effective approach in humanizing messages (Casal\u0026oacute;, Flavi\u0026aacute;n, \u0026amp; Ib\u0026aacute;\u0026ntilde;ez-S\u0026aacute;nchez, 2018; Lou \u0026amp; Yuan, 2019). This advertising shift is led by an increased realization that a consumer finds it more compelling to hear messages from individuals, or more specifically, individuals they find authentic or knowledgeable, than from an advertisement (Chen, Wang, \u0026amp; Xie, 2024; IZEA, 2024).\u003c/p\u003e \u003cp\u003eWhat emerges at the center of an influencer\u0026rsquo;s effectiveness is a set of represented characteristics: expertise, personality, fame, or the technological setting of the delivered information. Each of these characteristics has a specific role in building an influence on audiences. Domain-relevant experts come into particular consideration as more trusted or reputable sources. Audiences will more readily follow the expert opinion of influencers known to project consistency in their specific domains (Casal\u0026oacute; et al., 2018). Lou \u0026amp; Yuan (2019), on the other hand, indicated that message relevance of influencers aligned with proper information articulation plays an instrumental role in establishing premium value as well as the corresponding relevance of messages. However, it does not always mean that mere knowledge will automatically evoke consumption. Trust established by influencers requires more than mere information. The role of emotional associations comes into play in this scenario. The more influencers relate positively, emotionally, the more they can give rise to an association with parasocial experiences of audiences. Parasocial experiences refer to audiences' one-way emotional connections as they feel directly accustomed or comfortable with influencers (Freberg et al., 2011). The location of this experience does not end there. Lee \u0026amp; Eastin (2021) conveyed an adequate degree of an influencer\u0026rsquo;s social identity or popularity, which built influential parasocial experiences, embedding messages of influencers than ever.\u003c/p\u003e \u003cp\u003eAuthenticity, sincerity, and warmth are consistently identified in the literature as key personality factors in building emotional believability. The role of authenticity or sincerity in the influencer context was first proposed by Aaker (1997), suggesting that similar personality traits, including authenticity or sincerity or sophistication, impact how consumers judge brands. Importantly, credibility can be enhanced by an influencer\u0026rsquo;s authenticity. If an influencer seems more authentic, it can increase the likelihood that viewers will find the endorsement more sincere, making it more credible. Fame, or the visibility or celebrity following of an influencer, is more of a complex issue. Being well-known or famous doesn\u0026rsquo;t always make an influencer influential. The problem was first identified by Djafarova \u0026amp; Rushworth (2017), suggesting that despite the lack of viewers, influencers (or micro-influencers) tend to come across as more authentic or more trusted than celebrities. The issue was later reaffirmed by Lim et al. (2017), suggesting that it\u0026rsquo;s not the famous influencers that matter, or how popular they seem or feel. Rather, it\u0026rsquo;s the level of influence. Jin \u0026amp; Phua (2014) showed that shared values matter more than how many viewers follow the celebrity.\u003c/p\u003e \u003cp\u003eStill, the digital environment surrounding influencer content also matters. In increasingly saturated media spaces, ad clutter has become a significant barrier to effective communication. When users are bombarded with ads, they often disengage, not just from the advertisements but also from the influencers associated with them. Ha and Litman (1997) were among the first to point out that excessive advertising leads to diminishing returns, and Cho and Cheon (2004) later showed that ad overload causes consumers to develop avoidance behaviors. More recently, Lee and Kim (2020) demonstrated that the credibility of influencer messages decreases as ad clutter increases, undermining the influencer\u0026ndash;audience relationship. This is critical because trust lies at the center of the consumer decision-making process. Trust forms the bridge between exposure and action. As Sokolova and Kefi (2020) explain, when consumers trust influencers, particularly through parasocial interactions, they are more likely to accept recommendations and follow through with purchases. Trust enhances not only the perceived credibility of the message but also its emotional impact, making consumers more receptive and more likely to act.\u003c/p\u003e \u003cp\u003eOften, purchase decisions come from this complex process where rational trust, emotional attachment, and clear understanding meet. Emotionally appealing influencer marketing efforts with persuasive messages, not flooding viewers with advertisements, can more easily persuade purchasing decisions. Djafarova \u0026amp; Trofimenko (2019) established that emotional attachment with influencers is highly influential in encouraging purchase intention based on an influencer\u0026rsquo;s recommendation. Moreover, Schouten, Janssen, \u0026amp; Verspaget (2020) illustrated how viewers' feelings of association with the influencer, as well as seeing the recommendation as valid, can strengthen purchase intention.\u003c/p\u003e \u003cp\u003eAs an overall consensus of what the literature entails, the reality of influencer marketing is thus clear: it\u0026rsquo;s not all about being noticed, it\u0026rsquo;s all about being believed and understood. The key to an influencer\u0026rsquo;s effectiveness isn\u0026rsquo;t just grounded on their expertise or popularity, but on how authentic their personality, how believable their message, and how clear the environment where this particular message is conveyed.\u003c/p\u003e\n\u003ch3\u003eConceptual Model\u003c/h3\u003e\n"},{"header":"Research Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003eThe research adopted a quantitative research approach to evaluate influencer attributes of expertise, personality, fame, and dependence on brand advertising to assess consumer trust and the purchase intention. Qualitative data is also collected. A questionnaire was administered inductively from a sample of 300 people and utilized validated measurement scales in the formulation of the data collection. This quantitative approach allows for testing of hypotheses through statistical means and exactness of data measurement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and Sample\u003c/h3\u003e\n\u003cp\u003eThis research included the audience of influencers on social media. \u0026ldquo;A convenience sampling method\u0026rdquo; was used to collect 300 valid responses from online surveys. This allows their results to be more applicable as the sample size was diverse in age, gender and social media behavior.\u003c/p\u003e\n\u003ch3\u003eInstrument Development\u003c/h3\u003e\n\u003cp\u003eTo ensure accuracy and reliability in the measurement of these constructs, the instruments for this study were constructed using items that had already been tested and confirmed in previous studies. However, the instruments were also adapted and mildly modified to reflect the context within this study. Six constructs were used across the study: Influencer Expertise, Influencer Personality, Influencer Fame, Ad Clutter, Consumer Trust, and Purchase Intentions. Four questions measured each construct, and each question asked respondents to rate their level of agreement on a five-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Influencer Expertise, Influencer Personality, and Influencer Fame items were adapted from Djafarova and Rushworth (2017) and Lim et al. (2017). Items measuring Ad Clutter were adapted from Lee and Cho (2021). Consumer Trust and Purchase Intentions items were adapted from Lou and Yuan (2019) and De Veirman, Cauberghe, and Hudders (2017).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedure\u003c/h2\u003e \u003cp\u003eData were gathered using Google Forms and distributed across social media channels such as Facebook, Instagram, and WhatsApp. Participation was voluntary, and all respondents provided informed consent. Anonymity and confidentiality were maintained throughout the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eParticipants were informed about the study\u0026rsquo;s purpose, and consent was obtained. The study complies with ethical standards of research involving human participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThis study examines the influence of influencer characteristics and ad clutter on consumer behavior, focusing on two key outcomes: consumer trust and purchase intentions. It explores how influencer expertise, personality, fame, and ad clutter shape consumer perceptions and decision-making in digital environments such as social media.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel 1: Consumer Trust\u003c/h2\u003e \u003cp\u003e \u003cb\u003eCT\u003c/b\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e (IE) + β\u003csub\u003e2\u003c/sub\u003e (IP) + β\u003csub\u003e3\u003c/sub\u003e (IF) + β\u003csub\u003e4\u003c/sub\u003e (AC) + ε\u003c/p\u003e \u003cp\u003eThis model represents the relationship between consumer trust (CT) and the following variables:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCT\u003c/b\u003e: Consumer trust (dependent variable).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIE\u003c/b\u003e: Influencer expertise, reflecting the perceived knowledge or authority of the influencer.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIP\u003c/b\u003e: Influencer personality, representing how likable, authentic, or relatable the influencer appears.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIF\u003c/b\u003e: Influencer fame, indicating the popularity or public recognition of the influencer.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAC\u003c/b\u003e: Ad clutter, referring to the level of advertisement saturation or promotional overload.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eβ₀\u003c/b\u003e: Intercept, representing the baseline level of trust when no predictors are present.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eβ₁\u0026ndash;β₄\u003c/b\u003e: Coefficients showing the strength and direction of each predictor\u0026rsquo;s effect on trust.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eε\u003c/b\u003e: Error term, capturing unexplained variance in trust.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eModel 2: Purchase Intentions\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePI\u003c/b\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e (IE) + β\u003csub\u003e2\u003c/sub\u003e (IP) + β\u003csub\u003e3\u003c/sub\u003e (IF) + β\u003csub\u003e4\u003c/sub\u003e (AC) + ε\u003c/p\u003e \u003cp\u003eThis second model studies purchase intentions (PI) as the dependent variable, using the same set of independent variables:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePI\u003c/b\u003e: Purchase intentions (dependent variable).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIE, IP, IF, AC, β₀\u0026ndash;β₄, ε\u003c/b\u003e: Defined as above.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese models jointly assess how the perceived attributes of influencers and the advertising context affect both trust and buying decisions. By analyzing both psychological (trust) and behavioral (purchase) outcomes, the study offers a complete understanding of the dynamics driving effective influencer marketing.\u003c/p\u003e \u003c/div\u003e "},{"header":"Findings","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eReliability and Validity\u003c/h2\u003e \u003cp\u003eIn earlier studies, the measurement items had already been tested for validity and reliability, with strong results. However, since the items were slightly modified to better fit the context of this research, it was important to re-test them.\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\u003eConvergent Validity and Reliability Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluencer Expertise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83, 0.77, 0.76, 0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluencer Personality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79, 0.82, 0.75, 0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluencer Fame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74, 0.79, 0.77, 0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAd Clutter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81, 0.74, 0.76, 0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumer Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88, 0.85, 0.84, 0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurchase Intentions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87, 0.86, 0.83, 0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Results of the re-testing confirmed that all six of the constructs in this study - Influencer Expertise, Influencer Personality, Influencer Fame, Ad Clutter, Consumer Trust, and Purchase Intentions, exhibited convergent validity. Because the questionnaire items were updated and slightly modified for the research context, it was essential to assess the items once more for accuracy and reliability. Specifically, three indicators were utilized in the testing: factor loadings, average variance extracted (AVE), and composite reliability (CR). The results indicated that all factor loadings were above 0.70 implying that each item had a strong correlation with the particular construct it was measuring. Also, the AVE values ranged from .571 to .727 and exceeded the recommended minimum of 0.50, indicating that greater than half of the variance for each construct was attributed to the measurement items used, a positive sign indicating validity (Fornell \u0026amp; Larcker, 1981). In addition, the CR values ranged from .841 to .914, again well above the acceptable threshold of .70 confirming that the items within the respective constructs were measuring the same idea consistently and reliably (Hair et al., 2019). Overall, results provided strong evidence to support that the questionnaire items utilized for this study were valid and reliable measures of the selected constructs.\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\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.50\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the descriptive statistics for the six key variables in this research, which are based on participant responses from a total sample of 300. The results indicate that participants tended to express positive views regarding influencer-centered factors. Influencer Expertise (M\u0026thinsp;=\u0026thinsp;4.011, SD\u0026thinsp;=\u0026thinsp;0.321), Influencer Personality (M\u0026thinsp;=\u0026thinsp;4.008, SD\u0026thinsp;=\u0026thinsp;0.322), and Influencer Fame (M\u0026thinsp;=\u0026thinsp;3.999, SD\u0026thinsp;=\u0026thinsp;0.332) all resulted in high averages on a five-point scale, indicating that most respondents agreed the influencers they follow are knowledgeable, pleasing in personality, and widely recognized. Furthermore, the low standard deviations of these variables indicated that participants responded in a fairly consistent manner. Ad Clutter (M\u0026thinsp;=\u0026thinsp;2.176, SD\u0026thinsp;=\u0026thinsp;0.387), however, resulted in a much lower average on a three-point scale, suggesting that participants did not feel that advertising in the influencers' content was excessive. Consumer Trust (M\u0026thinsp;=\u0026thinsp;2.717, SD\u0026thinsp;=\u0026thinsp;0.469) and Purchase Intentions (M\u0026thinsp;=\u0026thinsp;2.656, SD\u0026thinsp;=\u0026thinsp;0.617), which were measured on somewhat broader scales (4 and 4.5 respectively), both generated moderate averages. Therefore, while consumers were somewhat willing to trust influencers' remarks and recommend purchasing based on them, opinions tended to vary. The higher standard deviation generally for Purchase Intentions demonstrated clearer differences in likelihood to act on the influencers' endorsements.\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\u003eMatrix of Correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) PI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) IE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) IP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(5) IF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(6) ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the correlation matrix for the key constructs examined in the study. The results reveal several noteworthy relationships. Consumer Trust (CT) exhibits a strong positive correlation with Purchase Intentions (PI) (r\u0026thinsp;=\u0026thinsp;0.707), suggesting that higher levels of trust in influencers are strongly associated with a greater likelihood of consumers intending to purchase the products they promote. This supports the theoretical expectation that trust plays a critical role in driving purchase behavior in influencer marketing contexts.\u003c/p\u003e \u003cp\u003eAmong the influencer characteristics, both Influencer Expertise (IE) and Influencer Personality (IP) show positive but weaker correlations with CT (r\u0026thinsp;=\u0026thinsp;0.193 and r\u0026thinsp;=\u0026thinsp;0.253, respectively) and PI (r\u0026thinsp;=\u0026thinsp;0.123 and r\u0026thinsp;=\u0026thinsp;0.182, respectively), indicating that while these traits contribute to trust and purchase intentions, their influence is more moderate. Influencer Fame (IF) shows relatively weak correlations with all other variables, with its highest correlation being with IE (r\u0026thinsp;=\u0026thinsp;0.161), suggesting that fame alone may not be a strong driver of trust or purchase behavior.\u003c/p\u003e \u003cp\u003eAd Clutter (ADC) is negatively correlated with both CT (r = -0.174) and PI (r = -0.117), implying that higher levels of perceived advertising clutter can reduce consumer trust and diminish their willingness to make a purchase. These findings underscore the importance of maintaining a balanced content strategy that avoids overwhelming consumers with excessive promotional material.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNormality Tests\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSkewness/Kurtosis tests for Normality\u003c/p\u003e \u003cp\u003e------ joint ------\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Indicates the output of normality test.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(Skewness)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(Kurtosis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadj_chi2(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb\u0026gt;chi2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCT Residual\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e indicates the results of the skewness and kurtosis tests for normality applied to the residuals of the Consumer Trust (CT) variable. The findings indicate that the residuals are normally distributed, as evidenced by the joint test statistic (adjusted chi-square = 0.380, p = 0.826), which is not statistically significant at the 0.05 level. Specifically, the p-value for skewness is 1.000 and for kurtosis is 0.537, both of which exceed the conventional threshold, suggesting that the residuals do not significantly deviate from normality in terms of their shape or peakness. These results support the assumption of normality, which is important for ensuring the validity of subsequent parametric analyses, such as regression or structural equation modeling (SEM). The normal distribution of residuals strengthens the reliability of conclusions drawn from statistical tests involving the CT variable.\u003c/p\u003e\n\u003cp\u003eSkewness/Kurtosis tests for Normality\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;------ joint ------\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Indicates the output of normality test.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(Skewness)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(Kurtosis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadj_chi2(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb\u0026gt;chi2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ePI Residual\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e reports the results of the skewness and kurtosis tests for normality for the residuals of the Purchase Intentions (PI) variable. The joint test statistic (adjusted chi-square = 1.880, p = 0.391) is not statistically significant, indicating that the residuals do not deviate significantly from a normal distribution. The individual p-values for skewness (0.506) and kurtosis (0.233) are also above the 0.05 threshold, further confirming that the distribution of the residuals is approximately normal in terms of both symmetry and peakness. These findings suggest that the assumption of normality is satisfied for the PI residuals, supporting the appropriateness of using parametric statistical methods in the analysis. The normality of residuals enhances the credibility of inferences drawn from regression and SEM analyses involving purchase intentions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulticollinearity Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eVariance inflation factor\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1/VIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;IF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;IE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;IP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;Mean VIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe above \u003cstrong\u003eTable 6\u003c/strong\u003e presents the results of the multicollinearity diagnostic using the Variance Inflation Factor (VIF) for the independent variables in the model. The VIF values for all predictors, Influencer Fame (IF), Influencer Expertise (IE), Ad Clutter (ADC), and Influencer Personality (IP), range from 1.012 to 1.036. These values are well below the commonly accepted threshold of 10, and even below the more conservative threshold of 5, indicating that multicollinearity is not a concern in this model. The mean VIF is 1.024, further confirming the absence of problematic collinearity among the predictor variables. Low VIF values suggest that each independent variable contributes uniquely to the model without being excessively correlated with the others. This enhances the reliability of the regression coefficients and ensures the stability and interpretability of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeteroskedasticity Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreusch-Pagan / Cook-Weisberg test for heteroskedasticity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHo: Constant variance\u003c/p\u003e\n\u003cp\u003eVariables: fitted values of PI\u003c/p\u003e\n\u003cp\u003echi2(1) \u0026nbsp; \u0026nbsp; \u0026nbsp;= \u0026nbsp; \u0026nbsp; \u0026nbsp;1.16 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProb \u0026gt; chi2 \u0026nbsp;= \u0026nbsp; \u0026nbsp;0.2808\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity are reported to assess the assumption of constant variance in the residuals of the model predicting Purchase Intentions (PI). As shown, the test produced a chi-square value of 1.16 with a corresponding p-value of 0.2808. Since this p-value is greater than the conventional significance level of 0.05, we fail to reject the null hypothesis of homoscedasticity. This indicates that the variance of the residuals is constant across levels of the predicted values, satisfying the assumption of homoscedasticity. In other words, there is no evidence of heteroskedasticity in the model, which supports the reliability and efficiency of the estimated regression coefficients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Output\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e. Linear Regression Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"650\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSt. Err.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e[95% Conf. \u0026nbsp; \u0026nbsp; \u0026nbsp; Interval]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSig\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.304 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; .520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.258 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; .458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.182 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; .410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.336 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.437 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of obs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e156.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e241.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e***p \u0026lt; .001, **p \u0026lt; .01, \u003cem\u003ep \u0026lt; .05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe significance of influencer attributes and advertising clutter in social media marketing is highlighted in \u003cstrong\u003eTable 7\u003c/strong\u003e, which presents the results of the linear regression analysis. The model reveals that consumers are more likely to trust influencers who share their interests and exhibit credible characteristics. Among the predictors, \u003cstrong\u003eInfluencer Expertise (\u0026beta; = 0.412, p \u0026lt; 0.001)\u003c/strong\u003e shows the strongest positive impact on consumer trust, indicating that influencers with deeper knowledge and domain-specific competence are more likely to foster credibility and reliability among their audiences. This finding is consistent with Casal\u0026oacute;, Flavi\u0026aacute;n, and Ib\u0026aacute;ez-S\u0026aacute;nchez (2018) and Lou and Yuan (2019), who noted that expertise enhances message credibility and drives consumer trust in influencer marketing. \u003cstrong\u003eInfluencer Personality (\u0026beta; = 0.358, p \u0026lt; 0.001)\u003c/strong\u003e also exerts a strong and significant influence, suggesting that authenticity, relatability, and warmth make influencers more trustworthy and appealing. This supports prior research by Freberg et al. (2011) and Lee and Eastin (2021), who emphasized that personality traits such as friendliness and social presence contribute to para-social interactions and perceived credibility. \u003cstrong\u003eInfluencer Fame (\u0026beta; = 0.296, p \u0026lt; 0.001)\u003c/strong\u003e has a positive and meaningful effect, indicating that while fame can enhance visibility, it must be balanced with authenticity to sustain audience trust. This aligns with findings by Jin and Phua (2014) and Djafarova and Rushworth (2017), who observed that fame increases reach but not necessarily credibility if the influencer appears overly commercialized. In contrast, \u003cstrong\u003eAdvertising Clutter (\u0026beta; = -0.241, p \u0026lt; 0.001)\u003c/strong\u003e shows a significant negative impact on consumer trust, implying that excessive promotional content may reduce credibility and lead to message fatigue. This outcome supports the observations of Ha and Litman (1997) and Lee and Kim (2020), who found that advertising overload triggers skepticism and message avoidance among consumers. The overall model is statistically significant (\u003cstrong\u003eF = 156.231, p \u0026lt; 0.001\u003c/strong\u003e) with a high explanatory power (\u003cstrong\u003eR\u0026sup2; = 0.682\u003c/strong\u003e), indicating that about 68.2% of the variance in consumer trust is explained by influencer expertise, personality, fame, and advertising clutter. The improved model fit underscores the strong combined influence of these factors in shaping trust within social media\u0026ndash;based influencer marketing strategies.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"374\"\u003e\u003c/p\u003e\n\u003cp\u003eThe regression analysis presented in Table 8 examines the influence of influencer attributes and advertising clutter on \u003cstrong\u003ePurchase Intentions (PI)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The results reveal that \u003cstrong\u003eInfluencer Personality (IP)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehas the strongest and statistically significant positive effect (\u0026beta; = 0.421, p \u0026lt; 0.001) on consumers\u0026rsquo; intention to purchase. This finding supports Freberg et al. (2011) and Schouten et al. (2020), who highlighted that personality traits such as authenticity, warmth, and relatability strengthen para-social relationships and emotional engagement\u0026mdash;key drivers of consumer behavior in influencer marketing. \u003cstrong\u003eInfluencer Expertise (IE)\u003c/strong\u003e also demonstrates a strong and significant positive influence (\u0026beta; = 0.384, p \u0026lt; 0.001), suggesting that influencers perceived as knowledgeable and competent are more persuasive in shaping consumers\u0026rsquo; purchase intentions. This aligns with Lou and Yuan (2019) and Casal\u0026oacute; et al. (2018), who argued that expertise enhances the credibility of promotional messages, thereby increasing consumers\u0026rsquo; willingness to act on recommendations. Similarly, \u003cstrong\u003eInfluencer Fame (IF)\u003c/strong\u003e exerts a significant positive impact (\u0026beta; = 0.309, p \u0026lt; 0.001), indicating that public recognition and a broad follower base can enhance consumer responsiveness. This finding supports Jin and Phua\u0026rsquo;s (2014) and Djafarova and Rushworth\u0026rsquo;s (2017) conclusions that fame, when paired with authenticity, strengthens perceived source credibility and stimulates purchasing behavior. Conversely, \u003cstrong\u003eAdvertising Clutter (ADC)\u003c/strong\u003e shows a significant negative relationship with purchase intentions (\u0026beta; = -0.264, p \u0026lt; 0.001), implying that excessive promotional exposure on social media can reduce consumer motivation to engage with branded content. This is consistent with Ha and Litman (1997) and Cho and Cheon (2004), who observed that ad overload often triggers skepticism and message fatigue, diminishing the persuasive effectiveness of influencer marketing. The model is statistically significant \u003cstrong\u003e(\u003cstrong\u003eF = 193.484, p \u0026lt; 0.001\u003c/strong\u003e)\u003c/strong\u003e and demonstrates a \u003cstrong\u003ehigh explanatory power (R\u0026sup2; = 0.731)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e indicating that approximately 73.1% of the variance in purchase intentions is explained by influencer expertise, personality, fame, and advertising clutter. The results underscore that both cognitive (expertise) and affective (personality, fame) influencer attributes, alongside contextual advertising factors, play a crucial role in shaping consumers\u0026rsquo; buying intentions within social media marketing environments.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe outcome of this research provides significant insights into how an influencer\u0026rsquo;s characteristics interact with ad clutter in establishing an impact on both consumer trust levels and purchase intention across the context of social marketing. The outcome of this research reveals that an expert, personality, or famous influencer plays a significant role in building both consumer trust levels and purchase intention. Ad clutter plays an opposite role. The difference in the outcome of both models shows that the purchase intention of customers is more emotionally influenced than the level of consumer trust.\u003c/p\u003e \u003cp\u003eThe mode on the prediction of consumer trust (R-square\u0026thinsp;=\u0026thinsp;0.682), the most influential factor was influencer expertise, supporting the findings of Casal\u0026oacute; et al. (2018) and Lou \u0026amp; Yuan (2019), which found that it increased the reliability of the messages. This implies that the public will believe influencers that can show true knowledge of their domains. The other influential factor was influencer personality, supporting Freberg et al. (2011), Lee, \u0026amp; Eastin (2021), which supporting Freberg et al. (2011), Lee \u0026amp; Eastin (2021), that found writers' personalities increase messages' reliability by making them seem more genuine, warm, authentic, or sociable. The third influential factor was influencer fame, supporting Jin \u0026amp; Phua (2014), Djafarova \u0026amp; Rushworth (2017), which found that it positively influenced the reliability of messages. However, the effect was not as significant as expertise or personality. The fourth influential factor was ad-clutter, which had a significant negative effect on messages' reliability. This finding was supported by Ha \u0026amp; Litman (1997), Lee \u0026amp; Kim (2020), which found that messages become less trusted or less influential among audiences as viewers become more exposed.\u003c/p\u003e \u003cp\u003eHowever, by comparison, the model of purchase intention (R\u0026sup2; = 0.731) demonstrated better overall fit, suggesting that engagement and appeal impact consumer behavior more. Specifically, influencer personality was found to be more influential than the other variables. This finding is in line with Freberg et al. (2011) and Schouten et al. (2020), which proposed that emotional engagement and the concept of identity or persona affiliation of an influencer can act as a pivotal driving force of consumer behavior. The data reveals that, according to consumers, it is more important that they like an influencer or feel a level of affinity with them, meaning that authenticity becomes more of a priority than mere believability. Influencer expertise and popularity showed up as the second most influential variable, both of which positively influenced purchase intention. While expertise promotes cognition, popularity promotes discovery, both of which reaffirm the ally of both rationalizing alternatives and emotional perspectives of the influence. This squares with research by Lou et al. (2019), which suggested that both believability and popularity can positively contribute to the allure of an influencer impacting purchase intention. Jin et al. (2014) also proposed the same.\u003c/p\u003e \u003cp\u003eComparison of both models also shows how there is a clear difference in the effect of ad clutter. Although its net effect on both outcomes is equally significant, it is slightly more influential on purchase intention (β = -0.264) than on trust (β = -0.241). This finding indicates that though ad clutter can negatively affect trust, it can more destructively affect the behavioral outcome of influencer posts. As proposed by both Cho \u0026amp; Cheon (2004) and Bang \u0026amp; Wojdynski (2016), it can be stated that ad clutter causes consumer confusion, making it lesslikely for them to interact with trusted influencers.\u003c/p\u003e \u003cp\u003eTaken together, the findings from this research strengthen the argument on the multi-faceted nature of the effectiveness of influencer marketing. The cognitive dimension of the marketing promotes belief through believability, while its affect dimensions promote purchase through personality or fame. The role of ad clutter in decreasing effectiveness stresses the need to ensure authenticity in a crowded marketplace. The relatively high R-squared levels of both models indicate that there was significant explanatory variation in both variables, thus stressing its strategic importance. From an electronic commerce perspective, these findings highlight how influencer marketing functions as a trust-building mechanism within digital marketplaces. The results suggest that online consumers rely on both cognitive cues (expertise) and affective cues (personality and fame) when making purchase decisions in social media\u0026ndash;based commerce environments, while excessive advertising clutter undermines these processes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to investigate the effects of influencer expert, personality, fame, and ad clutter in the context of social media marketing, on consumer trust and purchase intentions. The findings indicate that influencer characteristics are a key determinant of psychological (trust) and behavioral (purchase) outcomes, while ad clutter has a diminishing effect. Specifically, influencer expertise and influencer personality were the two most significant predictors, supporting the notion that credibility and authenticity are still the \"secret sauce\" of successful influencer marketing. Expertise leads to confidence in the influencer's expertise and reliability, and influencer personality leads to trust, emotional connection, and relatability - all of which are necessary to maintain trust and engagement from their audience. While influencer fame is also a positive predictor of trust and purchase intentions, its effect is dependent on authenticity. Fame without authenticity can diminish influence, while fame coupled with authentic communication propels influencer awareness and relevance. On the other hand, ad clutter had a consistent and significant negative relationship with both trust and purchase behavior, supporting the assertion that excessive promotional communications lead to a reduction in the consumer's attention and decreased credibility of the message.\u003c/p\u003e \u003cp\u003eIn general, the findings demonstrate that success in influencer marketing is contingent upon an appropriate equilibrium of informational value and emotional appeal, while still maintaining clear messaging among the vast number of brand messages consumers are exposed to in their digital environment. When influencers exemplify expertise and warmth, and brands strategically manage their promotional saturation, there is a higher likelihood that consumer trust and purchase intentions will emerge. These findings underline that authenticity, credibility, and content moderation are not only ethical obligations, but also strategic antecedents of marketing effectiveness in today's social media environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eManagerial and Practical Implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrioritize Authenticity Over Fame\u003c/b\u003e: Brands should partner with influencers who have a strong, authentic personality rather than just counting their followers or popularity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eManage Ad Clutter Strategically\u003c/b\u003e: It is important for marketing teams to keep content diverse and limit the ad frequency, as this can lead to consumer overload and potentially undermine brand credibility.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLeverage Micro-Influencers\u003c/b\u003e: These influencers are perceived to be more authentic and trustworthy than mainstream celebrities, which can result in better ROI.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContent-Persona Fit\u003c/b\u003e: Make sure the influencer's personality aligns with the product brand identity through Content-Persona Fit. The combination of personality and product can enhance trust and purchase decision-making.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMonitor Engagement Metrics, Not Just Reach\u003c/b\u003e: Prioritize influencer engagement rates and audience interaction quality over sheer visibility metrics for campaign evaluation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Analyze mediating and moderating variables: Further research may uncover how influencer characteristics affect consumer behavior by examining factors such as consumer motivation, brand loyalty, or emotional intelligence.\u003c/p\u003e\n\u003cp\u003e2. Cross-Cultural Analysis: Researching these dynamics across diverse cultural backgrounds and populations can help us understand influencer marketing strategies worldwide.\u003c/p\u003e\n\u003cp\u003e3. The study could be expanded to investigate whether the effects of different platforms (such as Instagram vs. other) are unique. TikTok vs. YouTube).\u003c/p\u003e\n\u003cp\u003e4. The study of long-term influencers can provide a deeper understanding of how trust and purchase intentions change with extended exposure to them.\u003c/p\u003e\n\u003cp\u003e5. Behavioral Tracking vs. Additional research may involve the use of actual behavioral data, such as click-throughs and conversions, to complement or contrast self reported intentions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical standards for research involving human participants. Ethical approval was not required as the research involved anonymous survey data and posed no foreseeable risk to participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Data collection, analysis, and interpretation were performed by the authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAaker, D. A. (1997). Dimensions of brand personality. \u003cem\u003eJournal of Marketing Research, 34\u003c/em\u003e(3), 347\u0026ndash;356. https://doi.org/10.1177/002224379703400304\u003c/li\u003e\n\u003cli\u003eAlipour, S. M., Ghaffari, M., \u0026amp; Zare, H. (2024). Influencer marketing research: A systematic literature review to identify influencer marketing threats. \u003cem\u003eManagement Review Quarterly\u003c/em\u003e, 1\u0026ndash;25. https://doi.org/10.1007/s11301-024-00412-5\u003c/li\u003e\n\u003cli\u003eAo, L., Bansal, R., Pruthi, N., \u0026amp; Khaskheli, M. B. (2023). 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Evaluating structural equation models with unobservable variables and measurement error. \u003cem\u003eJournal of Marketing Research, 18\u003c/em\u003e(1), 39\u0026ndash;50. https://doi.org/10.1177/002224378101800104 \u003c/li\u003e\n\u003cli\u003eHair, J. F., Black, W. C., Babin, B. J., \u0026amp; Anderson, R. E. (2010). \u003cem\u003eMultivariate data analysis\u003c/em\u003e (7th ed.). Pearson. https://www.drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-digital-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Digital Management](https://link.springer.com/journal/44362)","snPcode":"44362","submissionUrl":"https://submission.springernature.com/new-submission/44362/3","title":"Journal of Digital Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Influencer marketing, electronic commerce, social media marketing, consumer trust, purchase intentions, advertising clutter","lastPublishedDoi":"10.21203/rs.3.rs-8802128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8802128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid transformation of influencer marketing has redefined consumer trust and purchase decision-making processes in internet ecosystems. This study analyzes the interactive impacts of influencer expertise, personality, fame, and ad clutter on consumer trust and purchase intention in social media marketing. The quantitative approach was used with a sample of 300 social media users through an adapted structured questionnaire based on measured scales. Linear regression analysis found influencer expertise (β\u0026thinsp;=\u0026thinsp;0.412, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and personality (β\u0026thinsp;=\u0026thinsp;0.358, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as the best predictors of consumer trust, with these predictors explaining 68.2% of the variance (R\u0026sup2; = 0.682). Similarly, influencer fame (β\u0026thinsp;=\u0026thinsp;0.421, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), knowledge (β\u0026thinsp;=\u0026thinsp;0.384, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and popularity (β\u0026thinsp;=\u0026thinsp;0.309, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influence purchase intentions, accounting for 73.1% of the variance (R\u0026sup2; = 0.731). Advertising clutter, in contrast, negatively affects trust and purchasing behavior, i.e., elevated levels of promotion exposure decrease message believability and consumer response. The findings underscore the twin roles of cognitive credibility and emotional relatability in building trust and encouraging consumer action. In application, the research suggests that brands engage with empathetic, knowledgeable influencers and manage advertising frequency to keep interaction ongoing and optimize marketing effectiveness. The findings contribute to the growing literature on electronic commerce by explaining how influencer attributes and advertising environments shape consumer trust and purchase intentions in social media\u0026ndash;based digital markets.\u003c/p\u003e","manuscriptTitle":"Influencer Marketing in Digital Commerce: The Effects of Expertise, Personality, Fame, and Advertising Clutter on Consumer Trust and Purchase Intentions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 16:15:43","doi":"10.21203/rs.3.rs-8802128/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T08:23:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T13:11:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T10:39:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T09:45:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T16:07:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260788957177896302773331140375344179225","date":"2026-03-03T11:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249135162857966279370455013878725079170","date":"2026-03-02T18:08:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173696008987976295357940019293059505517","date":"2026-03-02T15:36:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237167313480768557073344071834935581907","date":"2026-03-01T14:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181968897348249978174815179170809225890","date":"2026-02-27T08:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T05:56:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T09:12:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T09:11:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Digital Management","date":"2026-02-06T03:06:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-digital-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Digital Management](https://link.springer.com/journal/44362)","snPcode":"44362","submissionUrl":"https://submission.springernature.com/new-submission/44362/3","title":"Journal of Digital Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc2f9576-6fb9-45c1-a37b-9c8d5cc91da9","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T08:41:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 16:15:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8802128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8802128","identity":"rs-8802128","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}