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Persuasive commentary: using the elaboration likelihood model to predict attitudinal change online

2013
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    Citations
  • 1,767
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    Mentions
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    Social Media
Metric Options:   Counts1 Year3 Year

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Thesis / Dissertation Description

The current study examined the persuasiveness (based on the Elaboration Likelihood Model) of user comments on the evaluation of an Internet news article. Participants reviewed a news article concerning the implementation of a new comprehensive exam for all senior-level undergraduates, which was manipulated such that the news article information was either self-relevant (evoking central route processing) or self-irrelevant (evoking peripheral route processing). In addition, comments that followed the news article were also manipulated by both strength and quantity. Attitudes toward the topic of comprehensive exams for seniors were assessed via an attitudinal scale and thought listing task after viewing both the news article and subsequent commentary. Because individuals who process information centrally are more likely to parse information for logical development, it was predicted that individuals who centrally process information would be more likely to be influenced by the strength of comments than comment quantity. Alternatively, because individuals who process information peripherally pay attention to peripheral cues in lieu of logical development, it was predicted that these individuals would be more likely to be influenced by comment quantity than comment strength. Results suggested a possible conformity effect in both attitude and thought responses, such that comment presence alone evoked more positive attitudes and positive thoughts toward the proposed exam when compared to a no-comment control group. Additionally, contrary to the ELM, results suggested self-relevance was only a marginally significant factor when comparing attitudinal and thought response differences based on comment strength and a non-significant factor based on comment quantity. Finally, implications of utilizing consensus information (i.e., all pro-issue user comments) as well as caveats regarding the application of the ELM to online contexts are discussed.

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