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How repeated exposure to persuasive messaging shapes message responses over time: a longitudinal experiment.

  • Published In: Human Communication Research, 2024, v. 50, n. 4. P. 518 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Skurka, Chris; Keating, David M 3 of 3

Abstract

Repeated exposure theories have articulated several processes that explain how message repetition shapes persuasion over time, yet these processes are often studied in isolation. Moreover, repeated exposure theories have largely failed to specify the temporal trajectories of negative message responses over time. Integrating theorizing on repeated exposure, psychological reactance, and message fatigue, we conducted a mixed-design experiment (N  =   1,416 total observations), varying the amount of daily exposure audiences had to campaign advertisements about distracted driving over nearly 2   weeks. Exposure to these messages (compared to control messages) led to greater message elaboration and reactance but not fatigue or attitudes. We found no evidence that these message reactions shifted linearly, logarithmically, or quadratically over time, with the exception that anger increased linearly with subsequent exposures. These findings suggest constraints on predictions made by repeated exposure frameworks, and post hoc analyses underscore the need to distinguish fatigue's two dimensions conceptually and operationally. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Human Communication Research. 2024/10, Vol. 50, Issue 4, p518
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0360-3989
  • DOI:10.1093/hcr/hqae008
  • Accession Number:180172323
  • Copyright Statement:Copyright of Human Communication Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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