What determines the effectiveness of social media influencer marketing? An fsQCA-based study of influencer characteristics and content features’ configurational effects

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number115170
Journal / PublicationJournal of Business Research
Volume189
Online published30 Dec 2024
Publication statusPublished - Feb 2025

Abstract

Social media platforms are increasingly important for promoting brands. Focusing on social media influencer marketing (SMIM), this study employs fuzzy set qualitative comparative analysis (fsQCA) to explore the conf igurations of influencer characteristics and content features sufficient for high SMIM effectiveness. The study identifies different configurations of influencer characteristics and content features that drive high likes, high favorites, and high comments on social media. It also reveals that single conditions are not necessary for high SMIM effectiveness. The existence of influencer popularity is the core condition for achieving high likes, high favorites, and high comments in many configurations. Furthermore, the study shows that diverse configurations of influencer characteristics and content characteristics lead to highly appealing SMIM, highlighting the importance of different paths to achieve the same goal. This research contributes to the understanding of social media marketing and influencer marketing, providing valuable insights for developing effective social media marketing strategies. © 2024 Elsevier Inc.

Research Area(s)

  • Social media, Fuzzy set qualitative comparative analysis, influencer marketing Influencer characteristics, Posted content, Configurational effects

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

What determines the effectiveness of social media influencer marketing? An fsQCA-based study of influencer characteristics and content features’ configurational effects. / Ma, Caiyuan; Wang, Billy Wei; Dai, Lingqi et al.
In: Journal of Business Research, Vol. 189, 115170, 02.2025.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review