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Influencer marketing effectiveness: A meta-analytic review

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posted on 2024-11-29, 10:52 authored by Meizhi Pan, Markus Blut, Arezou Ghiassaleh, Zach WY Lee

Influencer marketing significantly impacts consumer behavior and decision-making. However, identifying the drivers of influencer marketing effectiveness and conditions that enhance their impact remains challenging. This meta-analysis, which synthesizes 1,531 effect sizes from 251 papers, assesses influencer marketing effectiveness by examining its antecedents, mediators, and moderators. Building on the persuasion knowledge model to develop and test a framework, we identify post, follower, and influencer characteristics as key antecedents impacting both non-transactional (i.e., attitude, behavioral engagement, and purchase intention) and transactional (i.e., purchase behavior and sales) marketing outcomes. For non-transactional outcomes, follower characteristics (social identity) have the strongest effects on consumer attitudes and behavioral engagement, while post characteristics (informational value and hedonic value) exert stronger effects on purchase intention. For transactional outcomes, influencer characteristics (influencer communication) have the strongest effects on purchase behavior. These antecedents also affect marketing outcomes indirectly through persuasion knowledge and source credibility. Moderation results indicate that direct and indirect effects of antecedents depend on social media types (i.e., nature of connection and usage) and product types (i.e., information availability and status-signaling capability). These results consolidate and advance the literature and offer insights into enhancing the effectiveness of influencer marketing.

History

Author affiliation

College of Business Marketing & Strategy

Version

  • VoR (Version of Record)

Published in

Journal of the Academy of Marketing Science

Publisher

Springer Science and Business Media LLC

issn

0092-0703

eissn

1552-7824

Copyright date

2024

Available date

2024-11-29

Language

en

Deposited by

Professor Zach Lee

Deposit date

2024-10-23

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