How media exposure interacts with information sensitivity to shape the privacy paradox: a study on personal information disclosure intention in AI-powered digital twin cities

Advances in Artificial Intelligence (AI) have catalyzed the development of large-scale data-driven public projects, including digital twin cities. Despite the potential for improving public services, some projects also raise privacy concerns due to the involvement of personal data. News media has long been recognized as an important source of influence on public acceptance of new technologies, but the effects of positive versus negative media exposure on the public’s privacy perceptions remain unclear. This study examines how positive and negative media exposure interact with information sensitivity to shape public perception of AI-powered digital twin cities regarding privacy. Structural equation modelling of data from an online survey (N = 1000) in Singapore demonstrated that perceived public benefits outweighed privacy concerns in shaping personal data disclosure intention to AI-powered digital twin cities. Positive media exposure was associated with lower privacy concerns, while negative media exposure was linked to higher privacy concerns at a similar magnitude. While positive media exposure was associated with higher perceived public benefits, negative media exposure did not significantly influence perceived public benefits. Critically, both positive and negative media exposure moderated the impact of information sensitivity. Specifically, information sensitivity indirectly shaped disclosure intention through lower perceived benefits and higher privacy concerns, but positive media exposure mitigated these effects. Notably, negative media exposure also weakened the link between information sensitivity and privacy concerns, suggesting that media—whether positive or negative—can temper the impact of information sensitivity on privacy concerns.