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    Assessing Trust in Construction AI-Powered Collaborative Robots Using Structural Equation Modeling

    Source: Journal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 003::page 04024011-1
    Author:
    Newsha Emaminejad
    ,
    Lisa Kath
    ,
    Reza Akhavian
    DOI: 10.1061/JCCEE5.CPENG-5660
    Publisher: ASCE
    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). This study seeks to address the critical knowledge gaps surrounding the establishment and reinforcement of trust among AEC professionals in their collaboration with AI-powered cobots. In the context of the construction industry, where the complexities of tasks often necessitate human–robot teamwork, understanding the technical and psychological factors influencing trust is paramount. Such trust dynamics play a pivotal role in determining the effectiveness of human–robot collaboration on construction sites. This research employed a nationwide survey of 600 AEC industry practitioners to shed light on these influential factors, providing valuable insights to calibrate trust levels and facilitate the seamless integration of AI-powered cobots into the AEC industry. Additionally, it aimed to gather insights into opportunities for promoting the adoption, cultivation, and training of a skilled workforce to effectively leverage this technology. 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 by cobots from jobsites significantly impact reliability, and the transparency of cobots’ inner workings can benefit accuracy, robustness, security, privacy, and communication and result in higher levels of automation, all of which demonstrated as contributors to trust. The study’s findings provide critical insights into the perceptions and experiences of AEC professionals toward adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company’s goals workers’ welfare.
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      Assessing Trust in Construction AI-Powered Collaborative Robots Using Structural Equation Modeling

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4297339
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    contributor authorNewsha Emaminejad
    contributor authorLisa Kath
    contributor authorReza Akhavian
    date accessioned2024-04-27T22:43:24Z
    date available2024-04-27T22:43:24Z
    date issued2024/05/01
    identifier other10.1061-JCCEE5.CPENG-5660.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297339
    description abstractThis 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). This study seeks to address the critical knowledge gaps surrounding the establishment and reinforcement of trust among AEC professionals in their collaboration with AI-powered cobots. In the context of the construction industry, where the complexities of tasks often necessitate human–robot teamwork, understanding the technical and psychological factors influencing trust is paramount. Such trust dynamics play a pivotal role in determining the effectiveness of human–robot collaboration on construction sites. This research employed a nationwide survey of 600 AEC industry practitioners to shed light on these influential factors, providing valuable insights to calibrate trust levels and facilitate the seamless integration of AI-powered cobots into the AEC industry. Additionally, it aimed to gather insights into opportunities for promoting the adoption, cultivation, and training of a skilled workforce to effectively leverage this technology. 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 by cobots from jobsites significantly impact reliability, and the transparency of cobots’ inner workings can benefit accuracy, robustness, security, privacy, and communication and result in higher levels of automation, all of which demonstrated as contributors to trust. The study’s findings provide critical insights into the perceptions and experiences of AEC professionals toward adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company’s goals workers’ welfare.
    publisherASCE
    titleAssessing Trust in Construction AI-Powered Collaborative Robots Using Structural Equation Modeling
    typeJournal Article
    journal volume38
    journal issue3
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-5660
    journal fristpage04024011-1
    journal lastpage04024011-15
    page15
    treeJournal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 003
    contenttypeFulltext
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