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How Do Individuals' Risk Perception and Cognitive Factors Influence Their Intention to Misuse AI in Video Production?

Author

Listed:
  • Oh, Ryeong
  • Kim, Seongcheol

Abstract

Misusing AI in video production, such as creating deepfakes, leads to significant social losses and crimes. While individual users are exposed to losses and crimes, little research examines the factors influencing their intention to misuse AI in video production. To fill the gap, this study aims to identify the key factors influencing individuals' intention to misuse AI and their resistance to AI. An online survey was conducted in June 2023, and data was collected from 400 respondents in Korea. The analysis of mediation has substantiated the impact of perceived risk on the misuse of AI voices as well as the favorability towards AI humans to resistance. In addition, this study successfully established the mediating effects of attitudes and subjective norms on the relationships between risk perception, favorability, misuse, and resistance. This study revealed that to reduce intentions of misuse, it is imperative to enhance the perception of risk and cultivate negative attitudes toward the misuse of AI. Notably, this study also identified variations in these effects across different modalities. Furthermore, it emphasizes the importance of improving humans' perceptions of risk to artificial intelligence to address the potential for misuse.

Suggested Citation

  • Oh, Ryeong & Kim, Seongcheol, 2024. "How Do Individuals' Risk Perception and Cognitive Factors Influence Their Intention to Misuse AI in Video Production?," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302478, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itsb24:302478
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    References listed on IDEAS

    as
    1. Marine Cambefort & Elyette Roux, 2019. "A typology of the perceived risks in the context of consumer brand resistance," Post-Print hal-03513383, HAL.
    2. Anol Bhattacherjee & Neset Hikmet, 2007. "Physicians' resistance toward healthcare information technology: a theoretical model and empirical test," European Journal of Information Systems, Taylor & Francis Journals, vol. 16(6), pages 725-737, December.
    3. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    4. Marine Cambefort & Elyette Roux, 2019. "A typology of the perceived risks in the context of consumer brand resistance," Post-Print hal-02492718, HAL.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    AI misuse; disinformation; AI video production; deepfakes;
    All these keywords.

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