Believing vs. Achieving -- The Disconnect between Efficacy Beliefs and Collaborative Outcomes

2026-03-11

AI Summary

This study investigates how individuals’ self-efficacy beliefs influence their reliance decisions and team performance in human–AI collaboration. Through a controlled experiment (N = 240) integrating delegated tasks, validated efficacy belief scales, and contextual manipulations, the research demonstrates that self-efficacy significantly drives delegation behavior yet exerts limited impact on actual team performance. It further uncovers, for the first time, that such beliefs act as cognitive anchors, systematically inducing an “AI optimism” bias. The findings reveal that providing explicit AI performance information effectively mitigates this bias, whereas data- or AI-related contextual cues asymmetrically amplify the influence of efficacy beliefs on decision-making. These results challenge prevailing human–AI collaboration design paradigms centered primarily on transparency, suggesting a need to account for cognitive biases rooted in users’ self-perceptions.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

As artificial intelligence (AI) becomes increasingly integrated into workflows, humans must decide when to rely on AI advice. These decisions depend on general efficacy beliefs, i.e., humans' confidence in their own abilities and their perceptions of AI competence. While prior work has examined factors influencing AI reliance, the role of efficacy beliefs in shaping collaboration remains underexplored. Through a controlled experiment (N=240) where participants made repeated delegation decisions, we investigate how efficacy beliefs translate into instance-wise efficacy judgments under varying contextual information. Our explorative findings reveal efficacy beliefs as persistent cognitive anchors, leading to systematic "AI optimism". Contextual information operates asymmetrically: while AI performance information selectively eliminates the AI optimism bias, data or AI information amplify how efficacy discrepancies influence delegation decisions. Although efficacy discrepancies influence delegation behavior, they show weaker effects on human-AI team performance. As these findings challenge transparency-focused approaches, we propose design guidelines for effective collaborative settings.

Problem

• efficacy beliefs
• human-AI collaboration
• AI reliance
• collaborative outcomes
• AI optimism

Innovation

• efficacy beliefs
• AI reliance
• cognitive anchoring
• AI optimism
• human-AI collaboration