Perception of the leaders of the United States, the United Kingdom, and Spain in television conferences about Covid-19

Author:

Rodero Emma1ORCID

Affiliation:

1. Universitat Pompeu Fabra

Abstract

The discourses of the political leaders about the Covid-19 pandemic have become central to many television networks. Many citizens follow these press conferences, as they can be affected by the government’s measures against the virus. Due to its importance, analyzing how these individuals perceive their political leaders in these television appearances is relevant to determining their influence. Therefore, this study aims to analyze the perception of the ex-President of the United States (Donald Trump), the Prime Minister of the United Kingdom (Boris Johnson), and the President of the Government of Spain (Pedro Sánchez) in television conferences devoted to battling against the Covid-19 pandemic. After watching two videos of each leader, a gender-balanced sample of 360 citizens of these three countries (the United States, the United Kingdom, and Spain) evaluated the politicians’ attractiveness, credibility, and persuasiveness as well as the influence of these factors on the public’s attitude toward them and voting intention using an online survey. The results showed that the Spanish President, Pedro Sánchez, was considered the most attractive, credible, and persuasive leader with the most favorable attitude toward him and the highest voting intention, followed by the Prime Minister, Boris Johnson, and the American politician, Donald Trump. Therefore, the most physically and vocally attractive leader with the highest credibility and persuasiveness achieved the most favorable attitude and the highest voting intention. The most influential variables of the structural model analyzed in this study were persuasiveness and credibility, which mainly affected the attitude toward the politician and, to a lesser extent, voting intention.

Publisher

Ediciones Profesionales de la Informacion SL

Subject

Library and Information Sciences,Information Systems,General Medicine

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