A Multilevel Analysis of Antimarijuana Public Service Announcement Effectiveness

René Weber · Amber Westcott-Baker · Grace E. Anderson

28 citations0/0 files storedCommunication Monographs

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Media Influence and Health Media Studies and Communication Behavioral Health and Interventions

Abstract

In public-health campaign research, 3 prominent theories of persuasion and media effects—elaboration likelihood model (ELM), activation model of information exposure (AMIE), and limited capacity model of motivated mediated message processing (LC4MP)—have been used to predict message effectiveness. Although conceptually overlapping, these theories suggest contradictory predictions about individual-level and message-level factors on persuasion outcomes. In this study, we contrast and test competing predictions of antidrug message effectiveness from 3 recent publications that draw on ELM, AMIE, and LC4MP. We use televised antimarijuana messages, young-adult samples, and a multilevel modeling approach. Significant interactions between individual- and message-level factors were found predicting message effectiveness as theory dictates; these results replicate some, but not all of the findings from the aforementioned publications.

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References

1986The Elaboration Likelihood Model of Persuasion 1994Behavioral Expressions and Biosocial Bases of Sensation Seeking 2006A 10-Year Retrospective of Research in Health Mass Media Campaigns: Where Do We Go From Here?2001Public Communication Campaigns 2003Message Properties, Mediating States, and Manipulation Checks: Claims, Evidence, and Data Analysis in Experimental Persuasive Message Effects Research 2021Advances in Experimental Psychology 2013Public Communication Campaigns 2007The Relationship Between the Perceived and Actual Effectiveness of Persuasive Messages: A Meta-Analysis With Implications for Formative Campaign Research

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2014Conceptual, Empirical, and Practical Issues in Developing Valid Measures of Public Communication Campaign Exposure 2017The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

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