Search Results (1,162)

The productivity of smallholder coffee cultivation is declining due to ageing trees, making the rejuvenation of coffee trees with superior seeds essential. This rejuvenation process requires the support and participation of various stakeholders, including the government, banks, investors, universities, community leaders, experts, extension workers, and other parties. The nature of an incomplete contract in building partnership with farmers requires confidence building to avoid higher costs in enforcing a new behavior. However, this study shows that the accumulation of social capital also leads to higher expenses in maintaining these relationships. This study aimed to develop a social capital model to enhance partnerships between coffee farmers and relevant stakeholders. The analysis used a system dynamics model for coffee production and farmer income. The data collection involved the gathering of data and information from 17 actors in the coffee industry in Lampung, particularly in Kopista community. The study reveals that the social capital model must be constructed from four components: (1) trust, (2) ongoing cooperative activities, (3) social capital connections, and (4) memories of successful cooperative actions. Active involvement and instruction from specialists on the concept of social capital and partnership models can enhance cooperation by maintaining social connections. The policy implication of this study is that the development of a social capital model and partnership must be constructed by mentoring for economic benefits and must be continuously supported. Full article

In hot and humid work environments, construction workers can experience heat stress and heat-related illnesses (HRIs). While several studies have investigated engineering and administrative control methods to prevent certain heat stress risk factors, a comprehensive understanding of all existing risk factors and their corresponding control strategies is still lacking. It is crucial to identify gaps in current control strategies and develop a safety management framework for effective heat stress control by implementing existing measures. In addition, the effectiveness of the most common control strategies must be rigorously evaluated to ensure their efficacy and to guide future research aimed at enhancing these strategies or developing more effective ones. This study employed a mixed literature review methodology to address this knowledge gap. A structured literature review investigated and synthesized heat stress risk factors and control methods to find the gaps in control options to address underestimated risk factors. Furthermore, a comprehensive systematic literature review, including trend analysis, scientometric analysis, and meta-analysis, determined research foci and evaluated the effectiveness of the heat stress control methods. The scientometric analysis identified 11 clusters, encompassing key research themes such as environmental risk factors (e.g., high-temperature environments, climate change), administrative controls (e.g., work–rest schedules, climate change risk assessment), and personal interventions (e.g., cooling vests and sleep-related strategies). These findings highlight that the most commonly studied control methods are cooling vests, work–rest schedules, and cooling interventions. According to these results and the availability of quantitative results, the meta-analysis evaluated nine datasets of reductions in core body temperature by using types of cooling vests and anti-heat-stress uniforms and established the significant effectiveness of this control strategy in mitigating heat stress with a medium effect size. Moreover, five potential research studies have been identified to address gaps in control strategies for certain underestimated risk factors, including leveraging sensor technologies, conducting control training, dynamic work–rest schedules, using cutting-edge PPE, and governmental initiatives. Insights gained from this study enhance decision making for resource allocation, selection of control options, and intervention prioritization within a heat-stress-control framework based on the safety management system. The findings also highlight the effectiveness of cooling vests and areas that need to be developed, and evaluate potential heat-stress-control methods in construction. Full article

Industry 5.0 (I5.0) builds upon Industry 4.0 by emphasizing the role of workers in production processes and prioritizing socio-economic-environmental sustainability. It has been shown that I5.0 can enhance sustainability within supply chains (SCs). However, companies in emerging economies, especially small and medium-sized manufacturing enterprises (SMEs), which are crucial to developing economies, face challenges in implementing these concepts. These SMEs are in the early stages of adopting I5.0 to foster sustainability in their SCs and require urgent identification of key I5.0 enablers. Unfortunately, the current literature lacks research on this topic specifically within the context of SMEs in emerging economies. To bridge this gap, this study identifies the enablers of I5.0 that promote sustainability diffusion in SCs, using China’s SME manufacturing sector as a case study. The integrated framework for applying multiple criteria decision-making (MCDM) techniques in this study aims to assist decision-makers in evaluating different options and making optimal choices in a systematic and structured manner when faced with complex situations. The study employs the fuzzy Delphi method (FDM) to identify 15 key I5.0 enablers and categorize them into three clusters. Grey-DEMATEL is subsequently utilized to determine the causal relationships, rank the importance of the enablers, and construct an interrelationship diagram. This study found that ‘availability and functionality of resources’; ‘top management support, active participation, and effective governance’; ‘support from government, regulators, and financial resources’; and ‘introduction of safer and more efficient robotic systems for human–robot interaction and collaboration’ serve as the primary means of resolving issues. Overall, this study helps managers, practitioners, and policymakers interested in I5.0 applications to promote sustainability in the supply chain. Full article

This study investigates the impact of workplace physical hazards on job satisfaction in the construction industry, focusing on the mediating role of mental threats and the moderating effects of perceived job quality and security. The study findings indicate that exposure to physical hazards significantly contributes to mental stress, leading to reduced job satisfaction. Importantly, a heightened awareness of physical risks amplifies the mental burden, further decreasing job satisfaction. Furthermore, the study highlights that perceived job quality and job security can buffer the negative effects of mental threats on job satisfaction, suggesting that enhancing these factors may alleviate some of the adverse impacts of physical hazards. This research provides important insights into the complex relationships between physical work conditions, psychological stress, and employee satisfaction. It emphasizes the need for construction companies to implement practices that not only reduce physical hazards but also improve perceived job quality and security to foster employee well-being. The study contributes to the literature on occupational health and safety, offering practical implications for managers and policymakers aiming to enhance job satisfaction and retention in physically demanding environments. Future research should explore the long-term effects of these relationships and how they may extend to other high-risk industries. Full article

This study addresses occupational safety in reinforced concrete construction, an area marked by high accident rates and significant worker injury risks. By focusing on activity–body part (A–BP) combinations, this research introduces a novel framework for quantifying injury risks across construction activities. Reinforced concrete construction tasks are categorized into ten specific activities within three major work types: rebar work, formwork, and concrete placement. These are further analyzed concerning six critical body parts frequently injured on-site: head/face, arm/shoulder, wrist/hand, torso, leg/pelvis, and foot/ankle. Using data from 2283 construction accident reports and expert surveys, the probability and severity of injuries for each A–BP element were calculated. Probability scores were derived from actual incident data, while severity scores were determined via expert evaluations, considering injury impact and the required recovery time. To ensure precision and comparability, scores were standardized across scales, enabling a final risk assessment for each A–BP. Results identified that wrist and hand injuries during rebar work activities, particularly cutting and shaping, exhibited the highest risk, underscoring the need for focused protective measures. This study contributes to construction safety management by providing detailed insights into injury risk based on activity–body part interactions, offering safety managers data-driven recommendations for tailored protective equipment, enhanced training, and preventive protocols. This research framework not only helps optimize safety interventions on conventional construction sites but also establishes a basis for future studies aimed at adapting these strategies to evolving construction methods. Full article

The linear heating and formation of steel plates is one of the most critical technologies in shipbuilding. Excellent technology not only provides good hydrodynamics for the hull but also affects the whole hull construction cycle and cost. In the heating and formation of a steel plate, the material, size, and thickness of the steel plate; heating temperature; heating position; and many other factors affect the formation of a steel plate. It is a very difficult process to know the influence relationship between various factors. In this study, a steel plate model is established by the Gaussian regression method, which can predict the steel plate deformation according to the selected steel plate material, size and thickness, heating temperature, and heating position. The accuracy of the model was evaluated, and the Gaussian process regression model has a better accuracy compared to other machine learning algorithm models. Finally the model visualization; designing the UI; selecting the steel plate material, size, and thickness; and inputting the heating temperature, the deformation magnitude, and stress magnitude of the steel plate can be obtained. The model can provide guidance to field workers for the heating and formation of hull steel plates and achieve efficient and fast formation of target steel plates. Full article

This study examines the relevance of interpersonal relationships in the work environment, focusing specifically on analyzing associations between positive relational management, which refers to the use of relational resources that enable adaptation to the workplace, and key organizational variables such as flourishing, individual-directed organizational citizenship behaviors (OCBis), and life satisfaction. Given the importance of this topic, a structural model is required for the possible relationship between positive relational management and other organizational variables relevant to occupational well-being. As a preliminary step, the Positive Relational Management Scale (PRMS) was analyzed and validated in a sample of 348 Spanish workers. The results revealed that the overall model has a good fit, with reliable and valid construct measures. Moreover, the three-dimensional structure of the model was confirmed, although gender invariance was not satisfied. In conclusion, the results confirm the simple mediation hypothesis, in which flourishing mediates the relationship between positive relational management and life satisfaction. In contrast, multiple mediations between the variables could not be confirmed. This study highlights the importance of interpersonal relationships for employee well-being in the workplace. Full article

Ensuring workplace safety in high-risk sectors is critical to achieving sustainable productivity and occupational health, particularly in industries prone to unsafe practices. Drawing on social exchange theory (SET), this study examines the impact of empowerment leadership (EL) on electricity workers’ unsafe behaviors (EWUBs) in Jordan, focusing on the mediating roles of safety motivation (SM) and work engagement (WE), as well as the moderating role of the error management climate (EMC). A quantitative approach was employed, collecting data from 409 electricity workers across various regions of Jordan. The data were analyzed using structural equation modeling (SEM) employing SmartPLS 4 to assess the relationships of these variables and AMOS 24.0 to compute the study measurement model’s internal consistency and construct validity. The results demonstrate that empowerment leadership significantly reduces electricity workers’ unsafe behaviors through increased safety motivation and work engagement. Furthermore, the error management climate moderates the relationship between empowerment leadership and work engagement (Estimate = 0.238, t = 7.783, <0.001) is stronger when the error management climate is high and weaker but also insignificant when the error management climate is low (Estimate = 0.045, t = 1.015, >0.05). The research highlights the crucial role of empowerment leadership in promoting safety motivation and work engagement, which (Estimate = 0.238, t = 7.783, <0.001) is stronger and essential for minimizing unsafe behavior in high-risk industries like electricity. The findings highlight the pivotal role of shaping employees’ unsafe behavior and offers practical implications for policymakers and institutions aiming to promote employees’ safety behavior. Future studies also emphasize fostering an error management climate to reinforce these effects and organizations should focus on leadership development and creating a supportive error management climate to maximize safety outcomes. Full article

Delays in the installation process of precast concrete (PC) components significantly impact the project execution. However, traditional scheduling and risk assessment methods fail to consider this process complexity and uncertainty adequately. With a systematic approach, this study analyzed complex delay mechanisms in the PC installation of three component types (columns, beams, and slabs) using 1881 observations across five work steps. Specifically, this study used k-means clustering to divide the observations into groups with certain characteristics. These groups were assessed quantitatively using the delay intensity metric. Based on the assessment, this study revealed six severe delay paths for different component types, which may combine to generate severe combinations of delay factors, considering factors such as the component size, wind conditions, worker availability, and installation location. This research contributed to PC construction management by presenting a systematic analysis of delay factors and by proposing specific severe delay paths during PC installation, offering project managers a basis for schedule optimization and risk management. Full article

The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency. Full article

The tasks conducted on a high-rise building are complex and dangerous, and the construction safety of the construction personnel needs to have a higher guarantee. In this study, the key physiological indicators of high-rise construction workers were monitored and collected in real time by selecting a smart wearable device integrated with multiple sensors. On this basis, the key physiological index parameters are analyzed and screened, which are taken as input parameters, and the construction risk prediction results are taken as output. The BP neural network model and support vector machine (SVM) are, respectively, used to establish the safety risk prediction model of high-rise construction workers based on key indicators, to quantitatively assess the construction risk of the construction workers in the process of high-rise construction. The results showed that heart rate and blood pressure had the greatest impact on the construction safety of the construction worker, followed by the duration of work, age, working period, and gender. Compared with the BP neural network, the risk prediction model established by SVM can obtain more accurate prediction results under the condition of a smaller training data set. The presented research can not only effectively reduce the health threats caused by the physical and psychological effects faced by construction personnel when working at altitude and ensure construction safety, but also further enrich the application scenarios of multi-sensor data-driven equipment and expand its application in the construction field. Full article

In recent years, the increasing demand for worker safety and workflow efficiency in the construction industry has drawn considerable attention to the application of automated ergonomic technologies. To gain a comprehensive understanding of the current research landscape in this field, this study conducts an in-depth visual analysis of the literature on automated ergonomic risk assessment published between 2001 and 2024 in the Web of Science database using CiteSpace and VOSviewer. The analysis systematically reviews key research themes, collaboration networks, keywords, and citation patterns. Building on this, an SWOT analysis is employed to evaluate the core technologies currently widely adopted in the construction sector. By focusing on the integrated application of wearable sensors, artificial intelligence (AI), big data analytics, virtual reality (VR), and computer vision, this research highlights the significant advantages of these technologies in enhancing worker safety and optimizing construction processes. It also delves into potential challenges related to the complexity of these technologies, high implementation costs, and concerns regarding data privacy and worker health. While these technologies hold immense potential to transform the construction industry, future efforts will need to address these challenges through technological optimization and policy support to ensure broader adoption. Full article

With the continuous development of the global construction industry and urbanization, the accident rate in the construction industry has also been increasing year by year, with construction workers’ risk-taking behavior being an important factor. Therefore, effectively reducing the occurrence of construction workers’ risk-taking behavior and improving safety in the construction industry are of great significance to both academia and industry management. Based on the relevant literature on construction workers’ risk-taking behaviors published between 1 January 2012 and 28 August 2024, this study uses CiteSpace software to visualize and analyze the countries, institutions, authors, cited works, and keywords of 272 selected articles. It aims to analyze the development and current status of construction workers’ risk-taking behavior from multiple perspectives, reveal the research hotspots, and predict future development trends. The results of this study show that, firstly, the emergence of risk-taking behavior among construction workers is closely related to a variety of factors, such as work pressure, environmental factors, safety atmosphere, organizational culture, etc. Therefore, future research needs to further explore how to consider these factors comprehensively to understand the causes of risk-taking behaviors more comprehensively. Second, the research methods of risk-taking behaviors of construction workers are becoming increasingly diversified, and the means of research have shifted from a single empirical analysis to a comprehensive analysis, incorporating advanced equipment. Third, the focus of the research object has been gradually shifted from the traditional behavioral patterns of adolescents to the occupational groups, especially construction workers, which strengthens the safety management field. Fourth, the management mode is also gradually standardized, and the scope of future research can be extended to all stages of the occurrence of the behavior, and the methodology is more focused on precision and effectiveness. This study not only helps scholars to have a comprehensive understanding of the current state of research and the future direction of development in this field. It also provides valuable references for managers to improve safety management strategies in practice. Full article

With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an [email protected] average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management. Full article

Amid an aging workforce and labor shortages, this study investigates the key factors influencing construction workers’ safety compliance behavior (SCB). SCB is categorized into three distinct types: non-compliance behavior, general behavior, and compliance behavior. The study compares and analyzes the differences in influencing factors between the new generation and older generation of construction workers. By integrating the SCB framework with a multi-layer perceptron (MLP) model, this research develops a safety compliance behavior–artificial neural network (SCB-ANN) model. An enhanced method for optimizing connection weight (CW) is applied to identify the key determinants of SCB. The findings reveal that the SCB-ANN model offers superior predictive accuracy compared to a standard MLP model. Additionally, the refined CW method significantly improves the neural network’s interpretability. The analysis shows that organizational factors have a stronger influence on the new generation of construction workers (NGCWs), while individual factors play a more crucial role for the older generation (OGCWs). As a result, the study proposes tailored safety management measures for different worker groups to mitigate non-compliance behaviors, providing a robust foundation for future research and the development of safety management strategies. Full article