From Posts to Profits: A Systematic Review on How Social Media Influencers Shape Brand Communication and Success
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Abstract
Background: Social media influencers (SMIs) have proved themselves as media stars in the field of brand communication, but little work has yet been done focusing on credibility, authenticity, engagement, or technological aspects of SMIs. However, no systematic review has until now analysed both psychological and emotional SMI characteristics together and investigated the impact of segmentation of the audience on the effectiveness of the brand communication.
Methods: This study is a systematic review of the literature done according to the PRISMA 2020 guidelines. The searches were conducted in Scopus, Web of Science, ScienceDirect and Google Scholar databases from January 2015 to December 2022. Moreover, both written (peer-reviewed) and published studies in English in full text that explored the impact of SMI on brand outcomes were included. The Mixed Methods Appraisal Tool (MMAT Version 2018) was used to assess methodological quality.
Result: A total of 59 studies met the inclusion criteria and were subsequently synthesised from a total of 72,536 initial records identified. The themes that appeared are credibility and trust as the most important mediator of the brand outcomes; authenticity as the intervening variable that ranged between creating various positive influences depending on the experience; excessiveness as an interfering variable of the impression of credibility when it comes to experiences linked to likeness and trust; virtual influencers as underperforming, compared to human influencers, that it positively moderates the experience when it comes to communication effectiveness. Many times, micro-influencers were able to create more engagement for lower reach.
Conclusions: The effectiveness of SMI is influenced by the interactions of credibility, authenticity, audience characteristics, and platform context. This review suggests a new conceptual framework and recommends future research, such as a longitudinal study, cross-cultural evaluation, and AI-powered influences on the environment.
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