Control Modality and Accuracy on the Trust and Acceptance of Construction Robots

This study investigates how control modalities and recognition accuracy influence construction workers’ trust and acceptance of collaborative robots. Sixty participants evaluated voice and gesture control under varying levels of recognition accuracy while performing tiling together with collaborative robots. Experimental results indicated that recognition accuracy significantly affected perceived enjoyment (PE, p = 0.010), ease of use (PEOU, p = 0.030), and intention to use (ITU, p = 0.022), but not trust, usefulness (PU), or attitude (ATT). Furthermore, the interaction between control modality and accuracy shaped most acceptance factors (PE, p = 0.049; PEOU, p = 0.006; PU, p = 0.006; ATT, p = 0.003, and ITU, p < 0.001) except trust. In general, high recognition accuracy enhanced user experience and adoption intentions. Voice interfaces were favored when recognition accuracy was high, whereas gesture interfaces were more acceptable under low-accuracy conditions. These findings highlight the importance of designing high-accuracy, task-appropriate interfaces to support technology acceptance in construction. The preference for voice interfaces under accurate conditions aligns with the noisy, fast-paced nature of construction sites, where efficiency is paramount. By contrast, gesture interfaces offer resilience when recognition errors occur. The study provides practical guidance for robot developers, interface designers, and construction managers, emphasizing that carefully matching interaction modalities and accuracy levels to on-site demands can improve acceptance and long-term adoption in this traditionally conservative sector.