Context matters: Analyzing the factors influencing artificial intelligence acceptance across five different applications

This study examines factors influencing the acceptance of AI across five application scenarios: personalized product recommendations, learning platforms, job screening, traffic control, and medical diagnosis aids. The research focuses on trust, acceptability, ethicality, societal benefit, transparency, skill augmentation, perceived risk, and need for regulation. The findings suggest that perceived risk does not always reduce AI's acceptability or trust towards AI. Despite being highly risky, traffic control AI had the highest levels of trust and acceptance, indicating that the perceived benefit of AI usage can outweigh risk concerns. Ethicality and trust consistently emerged as significant predictors of AI acceptance across all the proposed scenarios. Furthermore, the need for regulatory oversight was more pronounced in riskier scenarios. The study reveals significant differences across scenarios, emphasizing that AI acceptance is highly context dependent. The findings suggest that AI regulation and deployment strategies should be tailored to specific contexts to enhance public acceptance. • Trust and ethicality are key predictors of AI acceptance across different scenarios. • AI acceptance varies significantly across application contexts, from product recommendations to medical aid. • Traffic control AI had the highest trust and acceptance despite its high-risk perception. • Higher perceived societal benefits increase AI acceptance in riskier applications like healthcare. • The need for regulation is strongest in scenarios involving greater perceived risks, such as medical diagnosis.