Understanding AI anxiety based on terror management theory: A meta analytical construction

With the rapid development of artificial intelligence (AI) technology and its increasing integration into daily life, individuals have become progressively anxious about the potential loss of control over AI, which has sparked growing academic interest in artificial intelligence anxiety (AIAX). While numerous studies have explored the antecedents and consequences of AIAX, conflicting findings and a lack of consensus on key factors have impeded a clear understanding of this phenomenon. To address this gap, we conducted a meta-analysis of AIAX. Specifically, we proposed a theoretical framework based on Terror Management Theory (TMT) to examine the antecedents and outcomes of AIAX, and tested it using meta-analytic techniques (k = 59, n = 91708). Our results indicate that, among the antecedent variables, perceived existential threat is the strongest predictor, followed by perceived job replacement threat, negative attitudes toward technology, and perceived identity threat—each of which positively impacts AIAX, while individuals’ sense of competence negatively influences AIAX. Additionally, factors such as personal negative emotional states, vulnerability, AI anthropomorphism, positive AI behavioral patterns, and negative media portrayals of AI were found to have small but significant associations with AIAX. Regarding outcomes, AIAX triggers defensive responses that diminish individuals’ performance expectancy, sociability evaluations, adoption tendencies, and learning tendencies toward AI. Our study not only identifies key antecedents and outcomes of AIAX, but also integrates TMT into AI research, offering a theoretical framework that can guide future investigations.