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Extending the planned risk information seeking model (PRISM) in the context of artificial intelligence: from the perspective of individual differences

Author

Listed:
  • Pengya Ai
  • Benjamin Li
  • Bo Hu
  • Heng Zhang

Abstract

As artificial intelligence is increasingly being integrated into daily life, understanding the factors that drive individuals to seek AI-related information becomes more important. This study employs the Planned Risk Information Seeking Model as a theoretical framework to explore AI information-seeking behaviours from the perspective of individual differences. Apart from the effects of individual differences, the findings were generally in line with the theoretical model. However, negative affect negatively predicted information insufficiency and was not significantly related to information seeking intention. Furthermore, examination of the effects of individual differences revealed that I-type epistemic curiosity positively predicted both information insufficiency and information seeking intention, yet D-type epistemic curiosity was not significantly related to information insufficiency and information seeking intention. Moreover, information innovativeness was found to be negatively related to information insufficiency but positively related to information seeking intention. Theoretical and practical implications are discussed.

Suggested Citation

  • Pengya Ai & Benjamin Li & Bo Hu & Heng Zhang, 2026. "Extending the planned risk information seeking model (PRISM) in the context of artificial intelligence: from the perspective of individual differences," Behaviour and Information Technology, Taylor & Francis Journals, vol. 45(2), pages 299-313, January.
    • Handle: RePEc:taf:tbitxx:v:45:y:2026:i:2:p:299-313
    DOI: 10.1080/0144929X.2025.2517211
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