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Adapting to generative AI: Examining the users' coping strategies of generative AI image systems

Author

Listed:
  • Lee, Crystal T.
  • Shen, Yung-Cheng
  • Wang, Chiang-Hui
  • Hung, Hsiu-Yu

Abstract

The rapid growth of generative artificial intelligence (GenAI) systems has transformed how users interact with GenAI content. However, research on the factors influencing user behavior toward these applications remains limited. This study applies the coping theory of user adaptation to examine how various cognitive appraisals and coping responses affect users' trust and engagement with GenAI image systems (GenAI-IS). This study consists of two studies. The first qualitative study involves in-depth, semi-structured interviews with 20 respondents from diverse backgrounds to identify the benefits and risks associated with GenAI-IS. The second quantitative study uses structural equation modeling, moderation analysis, and simple slope analysis to test the proposed hypotheses based on an online survey of 980 GenAI-IS users. The results show that economic efficiency and aesthetic quality—two key benefits of GenAI-IS—positively influence perceived value, while the devaluation of human creativity and copyright infringement—two primary risks identified—positively influence perceived threat. Perceived value fosters trust and engagement with the GenAI-IS, whereas perceived threat negatively influences both. Additionally, perceived controllability represents a secondary appraisal influencing trust and engagement. Compatibility moderates the relationships among perceived threat, trust, and engagement. These findings contribute to the literature on AI-generated content and user interactions.

Suggested Citation

  • Lee, Crystal T. & Shen, Yung-Cheng & Wang, Chiang-Hui & Hung, Hsiu-Yu, 2025. "Adapting to generative AI: Examining the users' coping strategies of generative AI image systems," Technological Forecasting and Social Change, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:tefoso:v:218:y:2025:i:c:s0040162525002240
    DOI: 10.1016/j.techfore.2025.124193
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