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    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
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    • 2007 Dissertations (MU)
    • 2007 MU dissertations - Freely available online
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    Examining the effects of blame vs. attack anti-tobacco messages using the limited capacity model of motivated mediated message processing

    Moore, Jensen J., 1975-
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    Date
    2007
    Format
    Thesis
    Metadata
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    Abstract
    Previous research using cognitive and persuasive measures posits that traditional blame anti-tobacco advertisements which conceptualize smoking problems and consequences as caused by the individual are superior to the new attack ads which challenge the institutions behind tobacco products. The current study utilizes Lang's Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) to examine attack vs. blame and high vs. low Message Sensation Value (MSV) anti-tobacco ads as well as individual Motivation Activation which influences what parts of incoming information are encoded and stored. A total of 226 participants took part in a 2 (Message Type: Blame/Attack) X 2 (Message Sensation Value: low/high) X 2 (Positivity Offset: low/high) X 2 (Negativity Bias: low/high) repeated measures experiment. Findings suggest that high MSV Attack ads - like those used by the national Truth campaign - are more effective than Blame ads in terms of encoding (STRTs, Response Latency, Recognition Memory), Persuasiveness (Aad, Evaluation of the Argument, Behavioral Intent), and Emotional Response (Arousal, Positive & Negative Valence). In addition, aversive activation (i.e., NB) was more influential than appetitive activation (i.e., PO) in terms of anti-tobacco message effectiveness.
    URI
    https://doi.org/10.32469/10355/4875
    https://hdl.handle.net/10355/4875
    Degree
    Ph. D.
    Thesis Department
    Journalism (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • 2007 MU dissertations - Freely available online
    • Journalism electronic theses and dissertations (MU)

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