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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.

Near misses are important references for the construction industry to move toward zero injuries, and are of great significance in reducing accidents and improving safety education. To fully improve the construction industry's understanding and standardize the management process of near-miss events, this paper describes a systematic review of the research front and intellectual basis of near-miss events based on scientometric technique and CiteSpace.

The authors reviewed and summarized the research wave and definitions of near-miss events in construction. The science mapping approach is used to conduct quantitative analyses of 120 relevant articles published between 2009 and 2019. Three research themes are identified via author analysis, keyword analysis and co-citation analysis: the construction of near-miss management systems, near-miss events research and characteristic research.

It is found that improving the data collection method to maximize the quality of near-miss reports, optimizing and verifying the event analysis model considering the characteristics of near-miss events in construction, establishing a more comprehensive framework for the analysis of near-miss events and building a highly inclusive technology integration platform are the four main development directions for the future.

According to Heinrich's law, incidents are mainly blamed on near-miss events such as workers' unsafe behaviors. Due to the complexity and variability of the construction site, near-miss events in construction may have different features. This article helps promote the understanding of near misses in academia, standardizing the management process of near-miss events, which is conducive to mining the potential value of such events in practice. Some insights into the research front and the intellectual base of near-miss research in construction are proposed.

The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.

An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.

Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.

This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.

This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.

The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.

The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

Effective construction safety training has been considered to play a significant role in reducing the incidence of accidents. However, the current safety training methods pay less attention to the relationship between workers' personalized characteristics and their learning needs, which results in workers' low learning participation and poor training effect. The purpose of this paper is to improve the participation and effect of safety training for construction workers with a persona-based approach.

This paper presents a persona-based approach to safety tag generation and training material recommendation. By extracting the demographic characteristics and behavior patterns tags of construction workers, a neural network algorithm is introduced to calculate the learning needs tags of workers, and the collaborative filtering recommendation method is integrated to enrich the innovation of recommendation results. Offline experiments and online experiments are designed to verify the rationality of the proposed method.

The results show that the learning needs of workers are closely related to their background. The proposed method can effectively improve workers' interest in materials and the training effect compared with conventional safety training methods. The research provides a theoretical and practical reference for promoting active safety management and achieving worker-centered safety management.

First, a persona-based approach is introduced to establish a novel framework for solving the problem of personalized construction safety management. Second, an artificial intelligence algorithm is used to automatically extract the learning needs tag values and design a hybrid recommendation method for construction workers' personalized safety training. The collaborative filtering method is integrated to enrich the innovation of recommendation results.

The outbreak of the pandemic makes it more difficult to manage the safety or health of construction workers in infrastructure construction. Risk events in construction workers' material handling tasks are highly relevant to workers' work-related musculoskeletal disorders. However, there are still many problems to be resolved in recognizing risk events accurately. The purpose of this research is to propose an automatic and non-invasive recognition method for construction workers in material handling tasks during the pandemic based on smartphone and machine learning.

This research proposes a method to recognize and classify four different risk events by collecting specific acceleration and angular velocity patterns through built-in sensors of smartphones. The events were simulated with anterior handling and shoulder handling methods in the laboratory. After data segmentation and feature extraction, five different machine learning methods are used to recognize risk events and the classification performances are compared.

The classification result of the shoulder handling method was slightly better than the anterior handling method. By comparing the accuracy of five different classifiers, cross-validation results showed that the classification accuracy of the random forest algorithm was the highest (76.71% in anterior handling method and 80.13% in shoulder handling method) when the window size was 0.64 s.

Less attention has been paid to the risk events in workers' material handling tasks in previous studies, and most events are recorded by manual observation methods. This study provided a simple and objective way to judge the risk events in manual material handling tasks of construction workers based on smartphones, which can be used as a non-invasive way for managers to improve health and labor productivity during the pandemic.

After the occurrence of natural disasters, the rapid and accurate delivery of geo-afforestation is the key to emergency rescue and is the fundamental solution to the “last mile” problem. It provides technical support for the territorial rapid and accurate delivery. The paper aims to discuss this issue.

Through literature and theoretical research, combined with the research experience of scholars, the qualitative research method is adopted. The paper establishes a three-dimensional analysis framework, including disaster predictability, resource availability and transport accessibility, and proposes the integrated delivery triangular technical framework, including integrated monitoring analysis, the territorial virtual public storage and transportation.

Based on the analysis of the factors affecting the effectiveness of emergency delivery, the paper establishes a three-dimensional analysis framework, including disaster predictability, resource availability and transport accessibility, and proposes the integrated delivery triangular technical framework, including integrated monitoring analysis, the territorial virtual public storage and transportation. The triangular technical characteristics and functions of emergency delivery are studied, providing theoretical basis and technical support for effective rescue and emergency planning.

The territorial emergency delivery refers to the process of rapid and accurate delivery without relying on external territorial supplies. The paper establishes a three-dimensional analysis framework, including disaster predictability, resource availability and transport accessibility, and proposes the integrated delivery triangular technical framework, including integrated monitoring analysis, the territorial virtual public storage and transportation.

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

Analysis of customer corporate data from 5,339 returns at the retail level provides insights from the customer return experience. Expanding our theoretical understanding, a deductive research approach then examines how those factors impact customer repurchase intentions both online and at brick-and-mortar stores. Stage two of the study employs a scenario-based role-playing experiment with consumer respondents to test hypotheses derived from signaling theory and justice theory.

Results find that returns policy and loyalty program capabilities are essential in creating a positive customer in-store experience. Moreover, a return experience enhanced by frontline employee service can retain existing shoppers and drive additional store traffic, further stimulating retailer sales.

These findings refine our understanding of returns management in evolving omnichannel retailing and offer practical insights for retailers to manage customer relationships through in-store returns.

The objective of this study is to explore the impact of a government-supported initiative for operational security, specifically the establishment of the national security emergency industry demonstration base, on the profitability of local publicly traded companies. Additionally, the study investigates the significance of firms' blockchain strategies and technologies within this framework.

Using the differences-in-differences (DID) approach, this study evaluates the impact of China's national security emergency industry demonstration bases (2015–2022) on the profitability of local firms. Data from the China Research Data Service (CNRDS) platform and investor Q&As informed our analysis of firms' blockchain strategy and technology, underpinned by detailed data collection and a robust DID model.

Emergency industry demonstration bases have notably boosted enterprise profitability in both return on assets (ROA) and return on equity (ROE). Companies adopting blockchain strategies and operational technology see a clear rise in profitability over non-blockchain peers. Additionally, the technical operation of blockchain presents a more pronounced advantage than at the strategic level.

We introduced a new perspective, emphasizing the enhancement of corporate operational safety and financial performance through the pathway of emergency industry policies, driven by the collaboration between government and businesses. Furthermore, we delved into the potential application value of blockchain strategies and technologies in enhancing operational security and the emergency industry.

Using the logistics service supply chain (LSSC) as a research object, this study focuses on the relationship between integration quality (IQ), value co-creation (VCC), and LSSC resilience. Moreover, it discusses the moderating role of digital technology (DT).

Based on data about China, this study used the structural equation model to test the research hypothesis. To verify the validity of each construct, this study used various established scales in the literature to conduct exploratory and confirmatory analysis.

The results show that IQ is an essential antecedent variable that promotes VCC and LSSC resilience. Moreover, this study confirms that DT has a positive moderating effect on the relationship between IQ, VCC, and resilience.

This study constructs a research framework to examine LSSC resilience and expands the theoretical research on the VCC theory in the supply chain literature. Moreover, this paper studies supply chain integration from a new perspective, that is, IQ, which is more in line with the reality of LSSC.