Revolutionising White‐Collar Help‐Seeking in Organisations: The Impact and Dynamics of Artificial Intelligence‐Driven Chatbots

ABSTRACT This study explores how white‐collar workers perceive AI‐driven chatbots, and how affordances and constraints shape their help‐seeking behaviours. Help‐seeking is essential for learning and collaboration but is often limited by social barriers. Drawing on Affordance Theory, this research examines how technological and social affordances influence chatbot use. Based on 24 in‐depth interviews, conducted between May and December 2023, at the inception of ChatGPT's public diffusion, the study reveals that chatbots offer judgement‐free support for repetitive and informational tasks, fostering autonomy and bypassing interpersonal discomfort. However, concerns about trust, limited capabilities, surveillance and job security constrain adoption. Individual mindset and age, together with organisational culture (e.g., innovation climate), further influence use. Chatbots redefine help‐seeking as a human‐machine dynamic, complementing rather than replacing human support. The study highlights the importance of designing chatbot systems that align with user needs and workplace cultures. In doing so, it contributes to debates on algorithmic management and worker autonomy and offers practical implications for integrating AI tools to support knowledge sharing and collaboration in work environments.