BUILDING TRUST IN HUMAN-AI COLLABORATION IN THE AGE OF DIGITALIZATION: A COMPREHENSIVE STUDY

Trust is a cornerstone of effective human-AI collaboration, particularly in an era of rapid digitalization where AI systems are increasingly integrated into decision-making processes across various sectors. This study investigates the critical factors influencing trust namely Transparency, Interpretability, and Satisfaction, and their sector-specific dynamics in healthcare, finance, and customer service. Utilizing a cross-sectional survey of 500 participants and stratified sampling, the research highlights pivotal role of transparency and interpretability in fostering trust, particularly in high-stakes sectors such as healthcare and finance. Transparency (β = 0.512, p < 0.001) and interpretability (β = 0.602, p < 0.001) significantly enhance trust, with stronger effects observed in healthcare (????2 = 0.494) and finance (????2 = 0.511) compared to customer service (????2= 0.374). Satisfaction had been emerged as a crucial mediating variable that amplifies the relationship between transparency and trust. The indirect effect of transparency on trust through satisfaction (β = 0.223, p < 0.001) underscores the importance of user-centric design in building trust. Furthermore, satisfaction demonstrates a stronger influence on trust in customer service (????=0.653), emphasizing its importance in customer-facing applications. This study provides theoretical contributions by extending trust frameworks to sector-specific contexts and offers actionable insights for AI system developers and policymakers. The findings advocate for tailored trust-building strategies, prioritizing transparency and interpretability in healthcare and finance, while emphasizing user satisfaction in customer service. The research will advances the understanding of trust dynamics in human-AI collaboration, addressing the ethical, operational, and design challenges of AI systems in a digitalized world.