scholarly journals The Effects of “Live,” Authentic, and Emotional Instagram Images on Congressional Candidate Evaluations

2021 ◽  
Vol 7 (4) ◽  
pp. 205630512110629
Author(s):  
Diana Zulli ◽  
Terri L. Towner

This study examines how Instagram’s design and norms influence expectations for political imagery and, subsequently, the effects of these images on electability, vote likelihood, and candidate evaluations. Using the Elaboration Likelihood Model, we propose three norms of Instagram that likely function as heuristic cues and affect the reception of political visual communication on the platform: liveness, authenticity, and emotionality. We experimentally test these visual features on Congressional candidate images, finding some evidence that live, authentic, and emotional images positively influence vote likelihood but negatively impact electability. Results also indicate that live, authentic, and emotional images either have no or negative effects on female candidate evaluations or have no or positive effects on male candidate evaluations.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tao Zhou

PurposeThe purpose of this research is to draw on the elaboration likelihood model (ELM) to examine users' information adoption intention in online health communities (OHC).Design/methodology/approachThe authors collected 350 valid responses using a survey and conducted the moderated regression analysis to examine the research model.FindingsThe results indicated that users' information adoption intention is influenced by both central cues (argument quality) and peripheral cues (source credibility and emotional support). In addition, self-efficacy moderates the effect of both central cues and peripheral cues on information adoption intention.Originality/valuePrevious research has focused on the effect of individual motivations such as reciprocity and benefits on user behavior, and has seldom disclosed the influencing process of external factors on OHC users' behavioral decision. This research tries to fill the gap by adopting ELM to uncover the mechanism underlying OHC users' information adoption.


2017 ◽  
Vol 94 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Gu ◽  
Yunjie (Calvin) Xu ◽  
Heng Xu ◽  
Cheng Zhang ◽  
Hong Ling

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