Journalists' Technological Trust and Willingness to Use Generative AI: A Perspective Based on Risk Perception Theory

This study explores the impact of perceived risk and trust on the willingness of journalists to use generative AI (GenAl) technology. As AI evolves, its applications in journalism, such as automated news writing and personalized content recommendations, have grown significantly. However, the adoption of GenAl by journalists is influenced by various factors, with perceived risk and trust being paramount. Perceived risk encompasses the uncertainties and potential negative outcomes associated with using the technology, while trust pertains to the confidence in the technology's reliability, security, and effectiveness. This research aims to fill the gap in empirical studies on these factors by constructing a theoretical model based on risk perception and trust theories. The study employs a detailed survey, validated through confirmatory factor analysis (CFA), to measure journalists' perceived risks, trust, and their willingness to use GenAl. Findings from this research are expected to offer valuable insights for the journalism industry, providing strategies to mitigate perceived risks and enhance trust, thereby promoting broader adoption of GenAl. Additionally, the results will contribute to the theoretical understanding of technology adoption in journalism and other professional domains.