Using generative AI as decision-support tools: unraveling users’ trust and AI appreciation

This study examines how organisational users accept recommendations when collaborating with Generative Artificial Intelligence (GenAI) to inform decisions, balancing perceived benefits and privacy concerns. Combining the theory of consumption values and privacy calculus theory, this work develops a research model capturing the key factors driving users’ trust in GenAI and AI appreciation. Structural equation modelling analysis (N = 211) reveals that functional, social, emotional, and epistemic values positively impact the perceived benefits of disclosing information for advice. Information sensitivity increases perceived privacy risks, while information control reduces this perception. Perceived benefits positively influence users’ trust, while perceived risks negatively affect it. Trust in GenAI is a significant predictor of AI appreciation. This study contributes to human-AI collaboration research by illuminating a mechanism leading to users’ trust and AI appreciation while addressing privacy concerns. The findings offer actionable insights for managers and organisations seeking to adopt GenAI for their decision support system.