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<p>This study examines how users engage with generative AI systems in the presence of perceived technological risks, focusing on the interplay between risk perception, adaptive behavior, and continued use. Drawing on a risk coping perspective, the study conceptualizes generative AI use as an ongoing process in which users evaluate potential informational and privacy-related threats and adjust their practices accordingly. the analysis explores how perceived risks shape user attitudes, coping strategies, and patterns of sustained engagement. Rather than treating risk as a simple deterrent, the findings show that users often respond through adaptive behaviors such as information verification and privacy management, enabling continued use despite recognized limitations. By distinguishing between different forms of perceived risk and user responses, the study advances understanding of how individuals navigate uncertainty in AI-mediated environments. More broadly, the findings illuminate shifting expectations around responsibility, trust, and epistemic labor in everyday interactions with generative AI technologies, with implications for human–AI interaction, digital risk governance, and the sustainability of AI-supported knowledge practices.</p>

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Bridging risk and retention: risk coping with privacy and hallucination in AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Don Shin,
  • Azmat Rasul,
  • Khuram Shahzad

摘要

This study examines how users engage with generative AI systems in the presence of perceived technological risks, focusing on the interplay between risk perception, adaptive behavior, and continued use. Drawing on a risk coping perspective, the study conceptualizes generative AI use as an ongoing process in which users evaluate potential informational and privacy-related threats and adjust their practices accordingly. the analysis explores how perceived risks shape user attitudes, coping strategies, and patterns of sustained engagement. Rather than treating risk as a simple deterrent, the findings show that users often respond through adaptive behaviors such as information verification and privacy management, enabling continued use despite recognized limitations. By distinguishing between different forms of perceived risk and user responses, the study advances understanding of how individuals navigate uncertainty in AI-mediated environments. More broadly, the findings illuminate shifting expectations around responsibility, trust, and epistemic labor in everyday interactions with generative AI technologies, with implications for human–AI interaction, digital risk governance, and the sustainability of AI-supported knowledge practices.