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Systematic Literature Review of Competencies for Human–Robot Collaboration in Construction: A Task-Technology Fit Perspective.

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Titel: Systematic Literature Review of Competencies for Human–Robot Collaboration in Construction: A Task-Technology Fit Perspective.
Autoren: Olukanni, Ebenezer, Akanmu, Abiola, Jebelli, Houtan
Quelle: Journal of Computing in Civil Engineering; Jan2026, Vol. 40 Issue 1, p1-26, 26p
Schlagwörter: HUMAN-robot interaction, CONSTRUCTION industry, ROBOTICS, PROFESSIONAL competence, TECHNOLOGY Acceptance Model, TALENT development, ACADEMIC programs
Abstract: The integration of robotic technologies into the construction industry is revolutionizing traditional construction methods and competencies, resulting in a growing demand for new knowledge, skills, and abilities that enhance collaboration between human workers and construction robots. Competency refers to an integrated theoretical understanding (knowledge), practical proficiencies (skills), and innate characteristics (abilities) for successful job performance. Defining the competencies for human–robot collaboration could inform training programs to prepare the current and future workforce and accelerate the adoption of robotic technologies to address the industry's challenges of productivity, safety, and lack of skilled workers. This paper identifies essential competencies for effective human–robot collaboration in construction to facilitate safe and efficient interaction between workers and robots during task execution. A systematic literature review and bibliometric analysis were conducted to identify robot task applications and innovative technologies driving this shift in construction. Using the Task-Technology Fit theory, the identified task applications and technologies form the basis for identifying essential competencies for human–robot collaboration. The identified competencies include 20 knowledge areas, 10 skills, and 12 abilities. This paper contributes to the body of knowledge by defining the foundational and fundamental competencies for human–robot collaboration in construction. It also contributes to the Task-Technology Fit model within the construction industry by establishing competency as an alignment (fit) between human–robot collaboration task applications and robotic technologies, which serves as a metric for enhancing human task performance. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The integration of robotic technologies into the construction industry is revolutionizing traditional construction methods and competencies, resulting in a growing demand for new knowledge, skills, and abilities that enhance collaboration between human workers and construction robots. Competency refers to an integrated theoretical understanding (knowledge), practical proficiencies (skills), and innate characteristics (abilities) for successful job performance. Defining the competencies for human–robot collaboration could inform training programs to prepare the current and future workforce and accelerate the adoption of robotic technologies to address the industry's challenges of productivity, safety, and lack of skilled workers. This paper identifies essential competencies for effective human–robot collaboration in construction to facilitate safe and efficient interaction between workers and robots during task execution. A systematic literature review and bibliometric analysis were conducted to identify robot task applications and innovative technologies driving this shift in construction. Using the Task-Technology Fit theory, the identified task applications and technologies form the basis for identifying essential competencies for human–robot collaboration. The identified competencies include 20 knowledge areas, 10 skills, and 12 abilities. This paper contributes to the body of knowledge by defining the foundational and fundamental competencies for human–robot collaboration in construction. It also contributes to the Task-Technology Fit model within the construction industry by establishing competency as an alignment (fit) between human–robot collaboration task applications and robotic technologies, which serves as a metric for enhancing human task performance. [ABSTRACT FROM AUTHOR]
ISSN:08873801
DOI:10.1061/JCCEE5.CPENG-6674