Drivers of Acceptance in Human-Robot Collaboration: Interpretative Insights from Healthcare Manufacturing

Collaborative robots (cobots) are transforming healthcare manufacturing, offering better working conditions and greater productivity. Yet, most studies remain confined to lab settings, providing limited insight into how these robots perform in real-world environments. This gap calls for research that explores their practical application, challenges, and impact within actual organisational contexts. This thesis fills this critical gap by exploring how factors influencing the acceptance of collaborative robots shape system performance in healthcare manufacturing. It does so through three key objectives: (1) identifying critical factors influencing acceptance in collaborative robot applications, (2) developing a conceptual framework, grounded in empirical findings, that associates acceptance-related factors with system performance, and (3) offering actionable recommendations to create conditions that support favourable user responses to collaborative robots.

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