Managing Employees Well-Being through AI- Enabled Chatbots Examining the Roles of User Trust and Perceived Support

Abstract

The rapid integration of artificial intelligence (AI) in organizational settings has transformed employee support systems, with AI-enabled chatbots emerging as accessible tools for enhancing workplace well-being. This study investigates the impact of AI-enabled chatbot usage on employee well-being, focusing on the mediating role of user trust and the moderating influence of perceived organizational support (POS). A quantitative, cross-sectional research design was employed, and data were collected from 450 employees across multiple industries who regularly interact with AI chatbots. Reliable and validated measures were used to assess chatbot usage, trust, POS, and well-being. The results indicated that AI-enabled chatbot usage significantly predicts employee well-being, with user trust partially mediating this relationship. Additionally, perceived organizational support moderated the effect, such that employees perceiving higher organizational support derived greater psychological benefits from chatbot interactions. Descriptive statistics revealed generally positive perceptions of chatbots, while correlation and regression analyses confirmed strong associations between key variables. Mediation and moderation analyses, conducted using PROCESS macro, demonstrated that the combined effects of trust and organizational support are critical for maximizing the benefits of AI interventions. These findings highlight the importance of trust-building, managerial endorsement, and clear communication in deploying AI-enabled chatbots effectively. The study provides theoretical insights into human-AI interaction and practical guidance for organizations aiming to enhance employee well-being through digital tools. Limitations include cross-sectional design, self-reported data, and convenience sampling, suggesting avenues for future research through longitudinal studies and exploration of AI design features.

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