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  • Research Article
  • Cite Count Icon 1
  • 10.1007/s42524-025-5042-x
An overview of crude oil price forecasting based on big data technology
  • Nov 22, 2025
  • Frontiers of Engineering Management
  • Ting Yao + 2 more

  • Research Article
  • 10.1007/s42524-025-4210-3
Measuring the impact of human—AI collaboration on knowledge diffusion in new product development projects
  • Oct 21, 2025
  • Frontiers of Engineering Management
  • Ying Han + 5 more

  • Research Article
  • 10.1007/s42524-025-5019-9
Harnessing emerging technologies to address data gaps in natural disaster risk management: A conceptual framework and applications
  • Jul 17, 2025
  • Frontiers of Engineering Management
  • Yining Huang + 5 more

  • Research Article
  • 10.1007/s42524-025-5004-3
Large language models: Technology, intelligence, and thought
  • Jun 26, 2025
  • Frontiers of Engineering Management
  • Zhidong Cao + 2 more

  • Research Article
  • 10.1007/s42524-025-4212-1
Charging model of scientific and technological achievements transformation platform considering platform system attractiveness
  • Jun 25, 2025
  • Frontiers of Engineering Management
  • Qiang Hu + 5 more

  • Open Access Icon
  • Research Article
  • 10.1007/s42524-025-4224-x
Industry perception of competencies for human—robot collaboration in the construction industry: A Delphi study
  • Jun 5, 2025
  • Frontiers of Engineering Management
  • Ebenezer Olukanni + 2 more

Abstract Abstract Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human—robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human—robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human—robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human—robot interface, construction robot applications, human—robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human—robot interface, and communication; and abilities such as safety awareness, continuous learning, problemsolving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human—robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices.

  • Research Article
  • 10.1007/s42524-025-4243-7
Assessing supply chain risks for chip industry with LDA and multi-layer Bayesian network method
  • Jun 5, 2025
  • Frontiers of Engineering Management
  • Fuqiang Wang + 3 more

  • Research Article
  • 10.1007/s42524-025-5054-6
Toward smart charging, synergistic infrastructure and storable grid for coordinated power and transportation systems
  • May 24, 2025
  • Frontiers of Engineering Management
  • Chenlei Liao + 2 more

  • Research Article
  • 10.1007/s42524-025-5503-2
Technical innovations of “Meng Xiang” ocean drilling vessel
  • May 24, 2025
  • Frontiers of Engineering Management
  • Haibin Zhang + 3 more

  • Research Article
  • 10.1007/s42524-025-4148-5
Physics-informed online deep learning for advanced control of shield tail clearance in tunnel construction
  • Apr 28, 2025
  • Frontiers of Engineering Management
  • Lulu Wang + 4 more