How to Facilitate Safety Performance of Human–Robot Collaboration in High-Rise Construction Scenarios: An Empirical Model

Despite the growing use of collaborative robots in high-rise construction, ensuring safe human–robot collaboration (HRC) in hazardous environments remains a critical challenge. Addressing the gap that previous studies optimized human, robot, or task factors in isolation without a systemic coordination perspective, this study develops and empirically validates a Human–Task–Robot Alignment (HTRA) framework to explain how alignment mechanisms enhance safety performance in the construction of HRC. Data from 84 high-rise HRC projects were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that HTRA serves as a pivotal mechanism for safety improvement, where Human–Robot Alignment (HRA) and Task–Human Alignment (THA) exert stronger effects on bilateral safety perception than Task–Robot Alignment (TRA), underscoring the centrality of human factors at the current stage of HRC development. Moreover, this study identifies a sequential cognitive–behavioral path from safety perception to safety intention and safety behavior performance, explaining how alignment enhances safety performance. Standardized HRC guidelines further strengthen HRA, facilitating safer and more efficient collaboration. This study extends Task–Technology Fit theory to a triadic human–task–robot context and advances the concept of bilateral safety, providing theoretical and managerial guidance for developing next-generation safe collaboration systems in construction.

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Publication Details

Journal
Systems
Published
2025-11-07
DOI
https://doi.org/10.3390/systems13111000
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
0.00

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article

How to Facilitate Safety Performance of Human–Robot Collaboration in High-Rise Construction Scenarios: An Empirical Model

Ningshuang Zeng, Guangchong Chen, Pinpin Lin, Qiming Li
Systems
Occupational Health and Safety Research
article

How to Facilitate Safety Performance of Human–Robot Collaboration in High-Rise Construction Scenarios: An Empirical Model

Ningshuang Zeng, Guangchong Chen, Pinpin Lin, Qiming Li
article en

Abstract

Despite the growing use of collaborative robots in high-rise construction, ensuring safe human–robot collaboration (HRC) in hazardous environments remains a critical challenge. Addressing the gap that previous studies optimized human, robot, or task factors in isolation without a systemic coordination perspective, this study develops and empirically validates a Human–Task–Robot Alignment (HTRA) framework to explain how alignment mechanisms enhance safety performance in the construction of HRC. Data from 84 high-rise HRC projects were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that HTRA serves as a pivotal mechanism for safety improvement, where Human–Robot Alignment (HRA) and Task–Human Alignment (THA) exert stronger effects on bilateral safety perception than Task–Robot Alignment (TRA), underscoring the centrality of human factors at the current stage of HRC development. Moreover, this study identifies a sequential cognitive–behavioral path from safety perception to safety intention and safety behavior performance, explaining how alignment enhances safety performance. Standardized HRC guidelines further strengthen HRA, facilitating safer and more efficient collaboration. This study extends Task–Technology Fit theory to a triadic human–task–robot context and advances the concept of bilateral safety, providing theoretical and managerial guidance for developing next-generation safe collaboration systems in construction.

SystemsVol. 13(11)
Shanghai University (CN), Southeast University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 54%
Occupational Health and Safety Research
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How to Facilitate Safety Performance of Human–Robot Collaboration in High-Rise Construction Scenarios: An Empirical Model — Ningshuang Zeng, Guangchong Chen, et al. · Systems (2025) | TGRS Research Map | TGRS