A Comprehensive Evaluation of Factors Influencing Acceptance of Robotic Assistants in Field Construction Work
Publication: Journal of Management in Engineering
Volume 39, Issue 3
Abstract
The adoption of human–robot collaboration (HRC) in various forms is widely expected to help improve productivity, reduce human physical workload, and alleviate the issues created by a skilled labor shortage in the construction industry. One potential deterrent to adoption of such collaborative work methods often cited by industry stakeholders is resistance by workers stemming from their fear of losing their jobs or having to learn new approaches and methods of performing construction work. Although significant prior studies have used path analyses to statistically evaluate hypotheses about technology adoption models by estimating the relationships in the models, no research has specifically investigated the intention to work in HRC through such an approach. This study addresses this knowledge gap and empirically investigates the factors affecting construction personnel’s behavioral intention to accept HRC. HRC in this study is envisioned as robotic assistants performing the physically demanding repetitive work while construction personnel focus on cognitive tasks necessary to plan and supervise the work. An HRC adoption model based on the technology acceptance model (TAM) and innovation diffusion theory (IDT) is presented. This study expands a TAM-IDT model with new constructs such as job satisfaction and openness to training to explore an improved model that increases the understanding of construction personnel’s intention to adopt HRC. Data collected from 156 construction personnel were analyzed to identify the key components based on exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The proposed model, consisting of eight external and four internal variables, was examined using structural equation modeling (SEM). Results indicated that variables such as perceived usefulness (PU) and perceived ease of use (PEU) had a positive and significant impact on behavioral intention (BI) to work in the described HRC system. Both theoretical and management implications emerging from the study are offered as insights to assist the construction industry in managing the introduction and adoption of HRC for on-site construction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work presented in this paper was supported financially by two United States National Science Foundation (NSF) Awards 2025805 and 2128623. The support of the NSF is gratefully acknowledged. The authors would also like to acknowledge the support of the construction industry advisory team, consisting of construction industry and union worker representatives collaborating on the two NSF awarded projects, who helped distribute the survey to their members. Any opinions, findings, and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF or the collaborating partners.
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Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 39 • Issue 3 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 12, 2022
Accepted: Jan 4, 2023
Published online: Feb 22, 2023
Published in print: May 1, 2023
Discussion open until: Jul 22, 2023
ASCE Technical Topics:
- Automation and robotics
- Business management
- Construction engineering
- Construction industry
- Construction management
- Construction methods
- Data analysis
- Employment
- Engineering fundamentals
- Human and behavioral factors
- Labor
- Methodology (by type)
- Models (by type)
- Personnel management
- Practice and Profession
- Research methods (by type)
- Structural models
- Systems engineering
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- Xiaoyun Liang, Usman Rasheed, Jiannan Cai, Bastian Wibranek, Ibukun Awolusi, Impacts of Collaborative Robots on Construction Work Performance and Worker Perception: Experimental Analysis of Human–Robot Collaborative Wood Assembly, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14390, 150, 8, (2024).
- Hongrui Yu, Vineet R. Kamat, Carol C. Menassa, Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots, Journal of Computing in Civil Engineering, 10.1061/JCCEE5.CPENG-5731, 38, 4, (2024).
- Yara Nassar, Gilles Albeaino, Idris Jeelani, Masoud Gheisari, Raja R. A. Issa, Human-Robot Collaboration Levels in Construction: Focusing on Individuals’ Cognitive Workload, Construction Research Congress 2024, 10.1061/9780784485262.065, (639-648), (2024).