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Insufficient intra-organizational diffusion of prefabricated construction (PC) technology hinders its implementation benefits. More research on intra-organizational diffusion of PC technology is required to improve organizational performance in PC implementation. This study aims to explore the antecedents and consequence of intra-organizational diffusion of PC technology.

This study established an antecedent and consequence model of intra-organizational diffusion of PC technology from the perspective of contingency theory. The model was empirically tested by 91 valid questionnaires using partial least squares-structural equation modeling (PLS-SEM).

The results indicate that organizational support (OSU), organizational climate (OC) and mechanistic organizational structure (OST) positively influence intra-organizational diffusion of PC technology, which further positively influences organizational performance. The relationship between OC and intra-organizational diffusion of PC technology is mediated by OSU. Technological turbulence negatively moderates the relationship between OSU and intra-organizational diffusion.

This is the first paper to study the antecedents and consequence of intra-organizational diffusion of PC technology and an expansion of contingency theory in the research field of PC technology. This study helps to theoretically stimulate future research on intra-organizational diffusion and practically promote intra-organizational diffusion of PC technology along with improving organizational performance in its implementation.

The need for infrastructure is growing as urbanization picks up speed, and the infrastructure REITs financing model has been crucial in reviving the vast infrastructure stock, alleviating the pressure on government funds and diversifying investment entities. This study aims to propose a framework to better assess the risks of infrastructure REITs, which can serve for the researchers and the policy makers to propose risk mitigation strategies and policy recommendations more purposively to facilitate successful implementation and long-term development of infrastructure REITs.

The infrastructure REITs risk evaluation index system is established through literature review and factor analysis, and the optimal comprehensive weight of the index is calculated using the combination weight. Then, a risk evaluation cloud model of infrastructure REITs is constructed, and experts quantify the qualitative language of infrastructure REITs risks. This paper verifies the feasibility and effectiveness of the model by taking a basic REITs project in China as an example. This paper takes infrastructure REITs project in China as an example, to verify the feasibility and effectiveness of the cloud evaluation method.

The research outcome shows that infrastructure REITs risks manifest in the risk of policy and legal, underlying asset, market, operational and credit. The main influencing factors in terms of their weights are tax policy risk, operation and management risk, liquidity risk, termination risk and default risk. The financing project is at a higher risk, and the probability of risk is 64.2%.

This research contributes to the existing body of knowledge by supplementing a set of scientific and practical risk evaluation methods to assess the potential risks of infrastructure REITs project, which contributes the infrastructure financing risk management system. Identify key risk factors for infrastructure REITs with underlying assets, which contributes to infrastructure REITs project management. This research can help relevant stakeholders to control risks throughout the infrastructure investment and financing life cycle, provide them with reference for investment and financing decision-making and promote more sustainable and healthy development of infrastructure REITs in developing countries.

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.

This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).

GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.

Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.