A growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.

The urban renewal (UR) process involves various stakeholders and related activities, and the various risks arising from this endeavor can affect these stakeholders. Additionally, the impact of adverse factors such as policy discontinuity and inequitable distribution of benefits among stakeholders can easily result in collective tensions or conflicts, as well as the gradual emergence of potential social frictions and confrontations. These social risks (SRs) not only impede the smooth execution of urban renewal projects but also pose challenges to social harmony and stability. Hence, to mitigate and control the SRs in the UR process (URSRs) and ensure the successful implementation of effective and sustainable UR projects, it is of paramount importance to gain a comprehensive understanding of the occurrence and evolution mechanisms of these SRs. Although existing studies have touched upon the influence of stakeholder conflicts on URSRs, there remains a lack of systematic examination of the evolution mechanisms of these risks from the perspective of stakeholder theory. The resulting fragmented and specialized comprehension of URSRs has hindered the effectiveness of risk governance strategies. This study adopts stakeholder theory to analyze the potential sources of risk throughout the entire UR process. By considering the conflicts of interests among stakeholders, a systematic analysis of the evolution mechanisms of URSRs is explored and targeted governance recommendations for URSRs are proposed.

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects.

With the rapid advancement of digital technologies, smart construction has emerged as a transformative approach within the construction industry. Central to the success of human-machine collaboration is human-machine trust, which plays a critical role in safety, performance, and the adoption of intelligent systems. This study develops and empirically tests a comprehensive structural equation model to explore the formation mechanism of human-machine trust in smart construction. Drawing on the three-domain framework, five primary constructs—role cognition; controllability; technology attachment; equipment reliability; and autonomy—are identified across individual and system dimensions. The model also incorporates trust propensity and task complexity as contextual moderators. A questionnaire survey of 288 construction professionals in China was conducted, and partial least squares structural equation modelling (PLS-SEM) was employed to analyze the data. The results confirm that all five constructs significantly and positively influence human-machine trust, with role cognition and autonomy having the strongest effects. Furthermore, trust propensity positively moderates the impact of individual traits, while task complexity negatively moderates the effect of equipment characteristics on trust formation. These findings provide valuable theoretical insights and practical guidance for the design of trustworthy intelligent systems, which can foster safer and more effective human-machine collaboration in smart construction.

Human–Robot Collaboration (HRC) in construction projects promises enhanced productivity, safety, and quality, yet realizing these benefits requires understanding the multifaceted human and robotic factors that influence team performance. This study develops and validates a multidimensional framework that links key human abilities (operational skill, decision-making ability, and learning ability) and robot capacities (functionality and operability) to HRC team performance, with task complexity considered as contextual influence. A field survey of construction practitioners (n = 548) was analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct effects and human–robot synergies. Results reveal that all five main effects are positive and significant, indicating that both human abilities and robot capacities have significant contribution. Moreover, every hypothesized two-way interaction is supported, evidencing strong interaction effects. Three-way moderation analyses further reveal that task complexity significantly strengthened the interactions of human abilities with robot functionality, whereas its interactions with robot operability were not significant. The study contributes an integrated and theory-driven model of HRC team performance that accounts for human abilities and robot capacities under varying task complexity, and validated constructs that can be used to diagnose and predict performance. The findings offer actionable guidance for project managers by recommending that they prioritize user-friendly robot operability to translate worker expertise into performance across a wide range of tasks, invest in training to strengthen operators’ skills and decision-making, and, for complex tasks, pair highly skilled workers with high-functionality robots to maximize performance gains.

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC.