Trust and Dependence on Robotic Decision Support

This article investigates people's trust and dependence on robotic decision support systems (DSSs), which provide cognitive assistance through suggestions. Robotic DSSs may not always offer optimal suggestions, requiring people to rely carefully to maximize performance. We analyze user reliance on suboptimal robots for solving instantaneous and sequential decision-making tasks with a math and card game, respectively. In instantaneous tasks, we find that the users' perceived anthropomorphism $(p < . 001$) and the robot's behavior after a decision support failure ($p < . 001$) significantly impact user trust. In a sequential task where the effectiveness of the human–robot team is not revealed until after several decisions, we find that introducing a user-initiated decision proposal before the robot reveals its recommendation can mitigate overreliance ($p < . 05$) and users' task expertise is critical in determining appropriate dependence on the robot's suggestions ($p < . 01$). Combined, these studies are synergistic and the first to jointly examine the influence of various factors on user trust and dependence, offering guidance for designing robotic DSSs to maximize human–robot task performance.