Does parasocial interaction with weight loss vloggers affect compliance? The role of vlogger characteristics, consumer readiness, and health consciousness

By N. Sakib, Mohammadali Zolfagharian, A. Yazdanparast. 2020

184 citations recorded by the source corpus.

Abstract Drawing on the notion of parasocial interaction (PSI) and the social comparison theory, this experimental research examines how nutritionist video bloggers (vloggers) influence consumer compliance intentions toward healthy, weight-loss diets. Drawing on two samples (Mexican Americans and White Caucasians) and using structural equation modeling and mediation analysis, this research highlights vloggers’ credibility and physical attractiveness, but not homophily, as salient source characteristics that influence PSI, which in turn reinforces compliance intention. Moreover, consumer readiness, consisting of role clarity, ability, and motivation, serves as a partial mediator of the PSI–compliance intention relationship. Lastly, consumer health consciousness emerged as a significant moderator of the PSI–compliance intention relationship among White Caucasians, but not among Mexican Americans. The findings and their implications are discussed.

Subfields: Literature and Literary Theory, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2737c32944b4520656a2ec5a4afa2d0b196a765a

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Citations in the corpus

Parasocial interactions with real and virtual influencers: The role of perceived similarity and human-likeness (2022, background): “This holds especially true in the context of video streaming—a domain in which the conveyed personality, humor, and credibility of an influencer is evidently much more important than their physical appearance (e.g. Sakib et al., 2020; Zhao et al., 2021).”
Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! (2020, methodology): “…to analyzing mediation or conditional process models with constructs, some researchers augment their SEM studies with separate regression analyses using Hayes’s (2018) PROCESS macro for SPSS or SAS (e.g., Finoti et al., 2017; Giovanis, 2016; Giovanis et al., 2015; Gong, 2018; Sakib et al., 2020).”