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An empirical comparison of the Extended Parallel Process Model with the Terror Management Health Model.

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Fear appeals are processed differently based on the threat. The Terror Management Health Model explains responses to death threats better than the Extended Parallel Process Model, especially when ego involvement is considered.

Keywords:
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Area of Science:

  • Psychology
  • Health Communication
  • Behavioral Science

Background:

  • Fear-appeal messages are widely used in health communication.
  • Existing models like the Extended Parallel Process Model (EPPM) explain message processing based on threat perception.
  • The Terror Management Health Model (TMHM) proposes that the nature of the threat, particularly death, influences responses.

Purpose of the Study:

  • To compare the predictive accuracy of EPPM and TMHM in explaining responses to fear appeals.
  • To investigate the role of threat nature (death vs. non-death) and ego involvement in message processing.
  • To account for maladaptive responses not explained by existing fear-appeal theories.

Main Methods:

  • An experiment was conducted to test the Extended Parallel Process Model and the Terror Management Health Model.
  • Participants were exposed to fear appeals with varying threat levels and types (arrest, injury, death).
  • Persuasive outcomes and ego involvement were measured to assess message processing and behavioral intentions.

Main Results:

  • EPPM accurately predicted outcomes for fear appeals involving threats of arrest or serious injury.
  • TMHM, considering ego involvement, was more accurate in predicting outcomes for fear appeals involving the threat of death.
  • The study identified limitations in the EPPM's 'level-of-fear' construct when dealing with death-related threats.

Conclusions:

  • The nature of the threat significantly impacts fear-appeal processing, especially when death is involved.
  • Incorporating ego involvement and consciousness of death enhances the explanation of responses to fear appeals.
  • These findings offer a more comprehensive framework for understanding and predicting responses to health-related fear appeals, including maladaptive ones.