Abstract

Effective task allocation between humans and automation has attracted much attention since the introduction of automation technologies that assist humans in task performance. Yet, the rise of intelligent systems (IntelSys) capable of making task-allocation judgment raises an urgent question: who (human or IntelSys) should have the authority to make the decision to allocate tasks between humans and automation? Drawing on the perspective of team-based decision-making, this study proposes four hypotheses that compare the impacts of three decision-making approaches (DMAs) on human-automation team performance. To test the hypotheses, we conducted a large-scale experiment with 662 participants playing on a gaming platform. The results suggest that the effectiveness of DMAs is contingent on task uncertainty and human expertise. Our findings provide critical insights on whether humans or IntelSys should assume the role of decision-maker in human-automation teams under different scenarios.

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