The power of social media influencers: unveiling the impact on consumers’ impulse buying behaviour

The paper addresses how social media influencers (SMIs) shape consumers’ impulse buying tendencies in a Web 2.0 context where platforms like TikTok and Instagram have transformed content sharing, consumption, and marketing. With global spending on influencer marketing reaching $21.1 billion in 2023, influencers—especially in fashion—affect consumer purchase intentions and trends. The study focuses on how influencer credibility affects trust in influencer-branded posts and, in turn, the urge to buy impulsively (UBI). It further evaluates whether persuasion knowledge moderates, and product affection mediates, the trust–UBI relationship. Guided by signaling theory and the Stimulus–Organism–Response (SOR) model, the research conceptualizes influencer-, message-, and media-credibility signals as stimuli that build trust, shape affect (product affection) as the organism state, and trigger UBI as the behavioral response. Contributions include integrating signaling theory with SOR for influencer marketing, examining communication-factor credibility (beyond influencer traits) as drivers of trust, highlighting the roles of trust and product affection in purchase decisions, and assessing the moderating role of persuasion knowledge.

Influencer marketing and UBI: Influencer marketing engages SMIs to endorse products for enhanced firm performance. Prior research clusters into (1) desirable influencer traits, (2) negative impacts of SMIs, and (3) effects on consumer outcomes. A gap remains regarding influencer marketing’s impact on impulse buying, where the urge to buy impulsively (UBI) precedes impulse buying decisions. While impulse buying has been explored in e-commerce and other contexts, fewer studies examine its prevalence in influencer marketing. Signaling theory: In contexts of information asymmetry, marketers send signals to reduce perceived risk. In influencer marketing, signals can be product- or recommendation-based and relate to influencer, message, and media credibility. Such signals are posited to foster trust in influencer-branded posts, shaping purchase decisions and UBI. SOR model: External stimuli affect internal states (cognition/emotion), leading to responses. Here, influencer posts and message/media credibility (S) influence trust and product affection (O), prompting UBI (R). Research model and hypotheses: Source credibility (similarity, expertise, trustworthiness) enhances trust in influencer-branded posts (H1). Message credibility (information quality/value and vicarious expressions) positively affects trust (H2). Media credibility (interactivity, transparency) positively affects trust (H3). Trust positively influences UBI (H4). The model further posits moderation by persuasion knowledge and mediation by product affection between trust and UBI.

Design and measures: A structured questionnaire using established scales (5-point Likert) measured constructs: influencer credibility (10 items; Munnaika et al., Ohanian), message credibility (8; Lit, Voss), media credibility (7; Munnaika), trust in branded posts (7; Wu and Lin), UBI (3; Parboteeah et al.), persuasion knowledge (4; Vashisht & Royne, Wu), and product affection (4; Moritz & Watson). Demographics were also collected. Sample and data collection: Data were collected from Pakistani social media users/followers of influencers (n = 481). Demographics reported include age 20–29 (58.1%), 30–39 (38.6%), 40+ (3.3%); gender male (54.3%), female (45.7%); education high school (2.7%), college (10.0%), bachelor’s (35.8%), master’s (35.4%), PhD (16.2%). Analysis approach: PLS-SEM (SmartPLS 4) with bootstrapping estimated path coefficients, standard errors, and p-values. Measurement model assessment included factor loadings, VIF, AVE, composite reliability (CR), and Cronbach’s alpha. Discriminant validity was supported via Fornell–Larcker (square roots of AVE exceeded inter-construct correlations) and HTMT (< 0.90). Structural model assessment reported variance explained (R²), path estimates, mediation (Hair et al., 2017 procedures), and moderation analyses. Model fit indices indicated good fit (e.g., X²/df ≈ 1.816; GFI ≈ 0.955; CFI ≈ 0.961; SRMR ≈ 0.060; RMSEA ≈ 0.066). Predictive relevance was established with Q² > 0 for endogenous constructs.

- Credibility drivers of trust: Influencer credibility → Trust (β = 0.181, t = 3.272, p < 0.001); Message credibility → Trust (β = 0.187, t = 2.919, p = 0.001); Media credibility → Trust (β = 0.193, t = 3.092, p < 0.001). These explain about 40% of the variance in trust. - Trust and UBI: Trust → UBI (β = 0.408, t = 11.277, p < 0.001), with the model explaining ≈ 33% of the variance in UBI. - Mediation: Trust → Product affection (β = 0.649, t = 16.148, p < 0.001); Product affection → UBI (β = 0.461, t = 7.345, p < 0.001). Indirect effect Trust → Product affection → UBI: β = 0.301 (SE = 0.042), t = 7.711, p < 0.001. Total effect Trust → UBI: 0.611; direct effect Trust → UBI: 0.313 (p < 0.001). - Moderation: Persuasion knowledge negatively moderates the relationship between influencer credibility and UBI—individuals with lower persuasion knowledge exhibit stronger UBI in response to influencer credibility. - Model fit: X²/df = 1.816; GFI = 0.955; CFI = 0.961; SRMR = 0.060; RMSEA = 0.066, indicating acceptable-to-good fit. - Effect sizes (ƒ² examples): Trust (0.219, medium) on UBI; Message credibility (0.396, large) on Trust; other small-to-medium effects reported. - Predictive relevance (Q²): UBI = 0.444; Product affection ≈ 0.295, supporting predictive capability.

Findings support that signals of influencer credibility (similarity, expertise, trustworthiness), message credibility (informational value and vicarious expressions), and media credibility (interactivity, transparency) are salient in building trust in influencer-branded posts. Trust, in turn, heightens the urge to buy impulsively. The results extend signaling theory by demonstrating that grouped credibility signals across source, message, and media jointly foster trust, and enrich the SOR framework by identifying trust and product affection as organism states leading to UBI. The observed role of expertise partly diverges from some prior work (e.g., reported differences with Lou and Yuan), possibly due to differing behavioral outcomes (impulse vs planned intention). The moderating effect of persuasion knowledge indicates that higher persuasion knowledge attenuates the path to UBI, suggesting that consumer literacy and recognition of persuasive intent can dampen impulsive responses. Mediation via product affection underscores the importance of affective responses to endorsed products as a mechanism linking trust to UBI. Overall, credibility across communication components is as critical as influencer traits alone in generating favorable attitudinal and behavioral outcomes.

By integrating signaling theory with the SOR framework, the study shows that the credibility of influencers, messages, and media cultivates trust in influencer-branded posts, which stimulates product affection and increases consumers’ urge to buy impulsively. Persuasion knowledge weakens the effectiveness of credibility cues on impulsive urges, while product affection mediates the trust–UBI link. The research contributes theoretically by modeling multi-component credibility signals and empirically validating their roles in trust formation and UBI. Practically, it advises marketers to prioritize credible influencers, informative and expressive content, and transparent, interactive media channels to foster trust and favorable affect, thereby enhancing impulse purchase tendencies. Future work should consider platform differences, cultural contexts, consumer advertising literacy, motivations, and additional communication components, and employ designs that can establish causality.

- Platform scope: Focused on Facebook; results may vary on other platforms (e.g., Instagram, TikTok). - Context and generalizability: Data from followers in Pakistan; cultural specificity limits generalization. - Design: Non-probability sampling and self-reported measures introduce potential biases (response and common method). - Causality: Cross-sectional design limits causal inference; experimental or mixed-methods designs are recommended. - Outcome measurement: The study centers on UBI rather than observed impulse buying behavior; future work should include behavioral measures. - Additional factors: Future research could examine cultural factors, influencer demographics, consumer motivations and psychological traits, and the roles of virtual/AI influencers and emerging channels. - Ethical and transparency considerations: Need to further assess how persuasion knowledge and disclosure practices influence outcomes.