The Persuasive Power of Social Media Influencers in Brand Credibility and Purchase Intention

The study addresses how social media influencers (SMIs) shape followers’ perceptions and behaviors amid rising social media usage and declining effectiveness of traditional advertising. It highlights that SMIs, perceived as knowledgeable and relatable, can influence consumer attitudes and behaviors through content and relationship-building. Research gaps include limited understanding of what determines parasocial relationships (PSRs) between influencers and followers, scarce consideration of consumers’ persuasion knowledge in influencer contexts, and insufficient examination of influencers’ personal and content-driven attributes distinct from traditional celebrities. The study aims to identify: (1) factors shaping users’ PSR with online celebrities; (2) how PSR affects users’ purchase intention and perceived brand credibility of endorsed brands; and (3) how persuasion knowledge moderates the effects of PSR on brand credibility and purchase intention.

Grounded in the Elaboration Likelihood Model (ELM), the study posits that SMIs persuade via central (message/content) and peripheral (source) cues. It integrates Ducoffe’s advertising value model to focus on content informativeness and entertainment, and considers authenticity and homophily as source characteristics. Hypotheses: H1—Informative value of influencer content positively influences PSR; H2—Entertainment value positively influences PSR; H3—Influencer authenticity positively influences PSR; H4—Influencer homophily positively influences PSR. PSR is conceptualized as a one-sided relationship common in media contexts and increasingly relevant to SMIs, influencing attitudes and behaviors. Outcomes: H5—PSR positively influences perceived brand credibility; H6—PSR positively influences purchase intention. Moderation: H7—Persuasion knowledge moderates (negatively) the PSR–brand credibility link; H8—Persuasion knowledge moderates the PSR–purchase intention link. Age and gender are included as controls.

Design: Cross-sectional online survey. Measurement development drew from established scales. Content value (informativeness, entertainment) measured using two sets of five 7-point semantic differential items (Voss et al., 2003; Lou & Yuan, 2019). Other constructs on 7-point Likert scales (1=Strongly disagree to 7=Strongly agree): authenticity (4 items; Ilicic & Webster, 2016), homophily (4; Onofrei et al., 2022), PSR (5; Kim et al., 2015), persuasion knowledge (5; Hwang & Zhang, 2018), brand credibility (5; Baek & King, 2011), purchase intention (4; Yoo & Donthu, 2014). Instruments were translated to Chinese with back-translation and pilot-tested with 50 respondents; refinements reduced ambiguity. Data collection: Self-administered online survey via Sojump, August–September 2022. Screening ensured respondents followed at least one SMI and prompted them to consider a favorite influencer while responding. Outreach targeted 800; 527 responses received. Exclusions: 263 not followers of any influencer, 48 failed attention check. Final valid sample: N=216. Demographics: 57.4% female; age 18–25 (57.8%), 26–30 (24.1%), >30 (18.1%); 80.6% diploma/bachelor; monthly income ≤3000 CNY (40.7%). Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.3.9. Assessed reliability (loadings >0.70, Cronbach’s alpha and composite reliability >0.70) and convergent validity (AVE >0.50). Discriminant validity via Fornell–Larcker and cross-loadings. Collinearity checked (VIF <10). Common method bias assessed with Harman’s single-factor test (<50% variance explained). Model fit indices reported (SRMR, NFI). Bootstrapping with 5000 resamples tested path significance; blindfolding used for predictive relevance (Q2). Age and gender used as controls.

- Measurement model: All loadings >0.70; Cronbach’s alpha and composite reliability >0.70; AVE >0.50. Discriminant validity satisfied. VIF <10. Harman’s single-factor test <50% variance, suggesting limited CMB. - Model fit: SRMR=0.053 (<0.08), NFI=0.803 (≥0.80). - Antecedents of PSR: H1 supported—Informative value → PSR (β=0.174, p=0.036); H2 not supported—Entertainment value → PSR (β=0.134, p=0.089); H3 supported—Authenticity → PSR (β=0.309, p=0.005); H4 supported—Homophily → PSR (β=0.230, p=0.014). PSR R2=0.492. - Consequences of PSR: H5 supported—PSR → Brand credibility (β=0.550, p<0.001), R2(BC)=0.573; H6 supported—PSR → Purchase intention (β=0.490, p<0.001), R2(PI)=0.405. - Moderation by persuasion knowledge: H7 supported—PSR × Persuasion knowledge → Brand credibility (β=−0.077, p=0.018) indicating a negative moderation; H8 not supported—PSR × Persuasion knowledge → Purchase intention (β=0.033, p=0.334). - Effect sizes (f2): Informative value → PSR=0.03 (small); Entertainment value → PSR=0.02 (small); Authenticity → PSR=0.09 (small); Homophily → PSR=0.05 (small); PSR → Brand credibility=0.52 (large); PSR → Purchase intention=0.30 (medium). - Predictive relevance: Q2 values >0 for PSR (0.336), brand credibility (0.470), and purchase intention (0.339), indicating predictive relevance. - Controls: Age and gender did not significantly influence PSR, brand credibility, or purchase intention.

Findings show that influencers’ authenticity is the strongest driver of PSR, followed by homophily and informative content, underscoring that genuine, similar, and useful communication fosters stronger follower bonds. Contrary to some prior work, entertainment value did not significantly shape PSR, suggesting that informational quality may be more impactful than entertainment in building relational ties with followers in this context. Strong PSRs substantially enhance perceived brand credibility and moderately increase purchase intentions, reinforcing PSR as a key mechanism through which influencer marketing operates. Persuasion knowledge weakens the positive effect of PSR on brand credibility but does not diminish the PSR–purchase intention link, implying that while knowledgeable followers may discount brand trust somewhat, their purchase propensity remains tied to their relational bond with the influencer. The results advance theory by integrating ELM, the advertising value model, and persuasion knowledge into a unified framework explaining how content and source cues build PSR and, in turn, drive brand evaluations and intentions.

This study integrates content value (informativeness and entertainment) and influencer characteristics (authenticity and homophily) to explain how PSR with influencers shapes brand credibility and purchase intention, within an ELM-based framework augmented by persuasion knowledge. Empirical results indicate authenticity, homophily, and informative content promote PSR; PSR strongly improves brand credibility and purchase intention; and persuasion knowledge dampens the PSR–brand credibility link but not PSR–purchase intention. The study contributes theoretically by demonstrating the central role of PSR and by clarifying the nuanced role of persuasion knowledge. Practically, it suggests brands prioritize influencers who exhibit authenticity, perceived similarity, and provide informative content to cultivate PSR and improve campaign outcomes. Future research should test generalizability across cultures and platforms, reevaluate entertainment value’s role in other contexts, examine additional drivers (e.g., interactivity, emotional attachment, follower involvement), explore other moderators (e.g., platform type, duration of following), and differentiate across influencer categories.

- Sample limited to Chinese respondents with a relatively small final N=216, constraining generalizability; cross-cultural validation with larger, diverse samples is needed. - Entertainment value did not significantly affect PSR in this study; further research should reassess this relationship across contexts and platforms. - The model focused on content value, authenticity, and homophily; other relevant factors (e.g., interactivity, emotional attachment, follower involvement) were not included. - Only persuasion knowledge was tested as a moderator; future studies should examine additional moderators (e.g., platform type, length of time following influencers). - Influencer heterogeneity was not examined; future work could analyze effects by influencer type or category.