Trust as the Engine of Influence: A Source-Credibility and Parasocial-Interaction Study of Social Media Influencers, Consumer Trust, and Purchase Behaviour
DOI:
https://doi.org/10.62843/jssr.v6i4.755Keywords:
Social Media Influencers, Consumer Trust, Purchase Behaviour, Source Credibility, Parasocial Interaction, Authenticity, EngagementAbstract
Social media influencers (SMIs) have now evolved as a pivotal player in digital marketing but the influence on consumer trust and buying behavior is only partially understood. This study focuses on the relationship between perceived engagement and authenticity of influencers and their trust and through trust, purchase behaviour, based on the theories of Source Credibility Theory and Parasocial Interaction Theory. The data collected were analysed in SPSS with reliability analysis, Pearson correlation, and three simple linear regressions, as well as an approximate indirect-effect test, using 81 active social media users aged between 18 and 45. Purchase behaviour was significantly predicted by trust, and an approximate Sobel test showed that part of the association between engagement and purchase behaviour was explained by the association between engagement and trust, suggesting that engagement affected purchase behaviour directly and indirectly via trust, in a dual-pathway model.Trust significantly predicted purchase behaviour and engagement and authenticity strongly predicted both trust and purchase behaviour, as an approximate Sobel test indicated that engagement was a direct influence on purchase behaviour, and an indirect influence via trust. Interestingly, the mean score for all three constructs was below the scale mid-line, indicating that even active users are still wary of the content from influencers. The study provides a consumer-focused perspective on digital persuasion, and managerial recommendations for establishing trust in the context of an integrated, collaborative approach that is authentic and transparent, while recognizing the constraints of a small convenience sample and correlational design.
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