Assessing Trust in Construction AI-Powered Collaborative Robots using Structural Equation Modeling
Newsha Emaminejad, Lisa Kath, and Reza Akhavian

TL;DR
This study investigates the psychological and technical factors influencing trust in AI-powered construction cobots among AEC professionals, highlighting safety, reliability, transparency, and worker well-being as key elements.
Contribution
It applies Structural Equation Modeling to identify critical factors affecting trust and adoption of AI cobots in construction, offering insights for effective implementation and workforce integration.
Findings
Safety and reliability significantly influence cobot adoption.
Transparency improves trust and enhances security and communication.
Fear of job replacement impacts workers' mental health.
Abstract
This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals' trust in collaborative robots (cobots) powered by artificial intelligence (AI). The study employed a nationwide survey of 600 AEC industry practitioners to gather in-depth responses and valuable insights into the future opportunities for promoting the adoption, cultivation, and training of a skilled workforce to leverage this technology effectively. A Structural Equation Modeling (SEM) analysis revealed that safety and reliability are significant factors for the adoption of AI-powered cobots in construction. Fear of being replaced resulting from the use of cobots can have a substantial effect on the mental health of the affected workers. A lower error rate in jobs involving cobots, safety measurements, and security of data collected…
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Taxonomy
TopicsBIM and Construction Integration · Occupational Health and Safety Research
