Search Results (237)

This paper researches the mobility behavior of employees in the Special Economic Zone in Niepołomice in Poland. The survey compares transportation behavior of factory and office workers before, during, and after COVID-19 pandemic. Workers’ trips include local ones within the administrative area as well as regional ones covering larger distances. The paper indicates a lack of research on economic zones, which are significant trip generators in the area. Moreover, the article shows the impact of industrial areas on commute behavior and the emerging need to accommodate regional mobility needs. The article aggregates survey conclusions and proposes transport solutions to improve workers’ commutes. Finally, the work reports social participation in the Sustainable Urban Mobility Plan, which includes the Niepołomice Investment Zone. Full article

Employee turnover remains a major concern for businesses globally. In Western contexts, the concept of psychological contract breach (PCB) is often employed to understand this phenomenon. This paper takes Bangladesh’s readymade garment (RMG) sector as a case study to explore the factors that support employee retention in their jobs, despite reported poor working conditions and associated issues in garment factories. Data were gathered among 400 RMG workers and linear regression analysis was used to answer this question. We demonstrated that while PCB was positively related to turnover intention, its impact on the retention of Bangladeshi garment workers was relatively minimal. Qualitative data showed how cultural and social factors distinct from known Western retention causes shaped our findings. The evidence presented in this paper sheds new light on employee retention in a Bangladeshi context, where socio-cultural issues challenge the PCB theory, which was developed largely based on Western economies. Full article

This study investigates the use of a hybrid leadership style in three kibbutz factories—two in privatized communities and one in a cooperative community. The factory leaders integrate multiple leadership styles in managing their enterprises. This blended style reflects a hybrid approach to management that has democratic and autocratic elements as well as a transformational leadership style that is also community-oriented. The goals of the managers are to make the factory operations sustainable while remaining loyal to communal values. We conducted 75 interviews in the three kibbutzim with individuals from various ranks, ranging from senior leadership to production workers. In addition, to supplement the information, we analyzed organizational documents, including internal newsletters, reports, and booklets summarizing 50 years of activity, as well as news articles that provided up-to-date information on business transactions that contributed to the success of the kibbutz industries. The result identified a hybrid style that combines the communal, transformational, and democratic or autocratic styles. Many features of communal leadership were evident in the practices of kibbutz members rather than those of outsiders and by strategies focused on maintaining the industry for kibbutz members in the long run and an egalitarian communal style. The hybrid style contains democratic features such as transparent and open communication, and a transformational style was also found in key components of this leadership style, including innovation, professionalism, dynamism, adaptability to environmental changes, and human sensitivity. Full article

This article traces the trajectory of China’s dominant literary field as it shifted from proletarian to intellectual literature in the early reform era. It examines the conditions and cultural logic underlying the striking phenomenon whereby former industrial workers, once incorporated into the literary field, rapidly distanced themselves from the very genre historically rooted in their own industrial experiences, namely, worker literature. Focusing on writers emerging from factories and on Shanghai Literature—a journal once known for publishing worker literature. The article analyzes the reconfiguration of class and identity that accompanied China’s transition from its high socialist past. I argue that socialist worker literature never fully reconciled the structural antagonism between manual and mental labor. In the early reform era, factory-based writers appropriated literature as a mode of symbolic escape and ideological critique. Hence, literature itself became a site where the contradictions of socialist and capitalist modernity were negotiated and contested. Full article

This study evaluates the validity and factorial structure of the affective job satisfaction scale (BIASJ) among 234 workers from private institutions of social solidarity (IPSS) in Portugal. Emotional job satisfaction, a key marker of psychological well-being, is associated with positive outcomes for employees and organizations. The sample was mainly female, with an average age of 39.15 years (SD = 8.22). The BIASJ and Maslach burnout inventory (MBI) measured job satisfaction and burnout. The BIASJ demonstrated high internal consistency (Cronbach’s alpha = 0.87, McDonald’s omega = 0.88) and a unidimensional structure. Significant negative correlations with emotional exhaustion and depersonalization supported its criterion validity. The results confirm the BIASJ as a reliable instrument for assessing job satisfaction in IPSS settings in Portugal. Future research should incorporate more diverse, gender-balanced samples and utilize probability sampling to improve generalizability. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

Background/Objectives: Job stress negatively affects physical and psychological health and can lead to behavioral changes such as unhealthy eating. This study aimed to evaluate the relationship between job stress levels, adherence to the Mediterranean diet, and the phytochemical index (PI). Methods: The study included 200 healthy individuals aged 18–50 working at the Tuzla Gum Factory. Data were collected through demographic and dietary questionnaires, two-day 24-h food records, PI values, and anthropometric measurements. Job stress was assessed using the Job Stress Scale, and Mediterranean diet adherence was assessed with the Mediterranean Diet Adherence Questionnaire. Results: Waist and hip circumference, waist/hip ratio, and BMI were significantly higher in individuals with high levels of job stress (p < 0.01). Unskilled workers reported higher stress than professionals (p < 0.01). Significant differences were found in carbohydrate and fiber intake among males and in energy, protein, carbohydrate, and vitamin A intake among females with varying stress levels (p < 0.01). No significant difference in Mediterranean diet adherence was observed between medium and high stress groups. However, women had higher adherence and PI scores than men (p < 0.01). Diet adherence was better among managers than service-sales and technical staff (p < 0.01). PI scores were higher in medium stress than high stress individuals (p < 0.05) and in those with a higher BMI compared to a normal BMI (p < 0.01). Conclusions: Job stress influences both anthropometric parameters and dietary habits. Effective stress management may improve adherence to the Mediterranean diet and phytochemical intake. Workplace strategies supporting healthy eating behaviors are recommended. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

High-rise industrial buildings are particularly susceptible to vibration-induced comfort issues, which can negatively impact both the health and productivity of workers and office staff. Unlike most existing studies that focus on local structural components, this study proposes and validates a wave propagation analysis (WPA) method to predict peak accelerations of the floor caused by excitations located on different floors. The method is validated through on-site vibration tests conducted on a high-rise industrial building with shared factory and office space. A simplified regression-based propagation equation is further developed to facilitate practical design applications. The regression parameters are fitted using theoretical calculation results, enabling rapid prediction of peak acceleration responses on the same or different floors. To enhance vibration control, tuned mass dampers (TMDs) are installed on selected floors, and additional tests are conducted with the TMDs activated. An insertion loss-based correction is introduced into the WPA framework to account for the TMD’s frequency-dependent attenuation effects. The extended method supports both accurate prediction of vibration reduction and optimisation of TMD placement across multiple floors in high-rise industrial buildings. Full article

Previous discussions on customer injustice and emotional labor have primarily focused on employee–customer dyads, often neglecting the role of service co-workers in shaping emotional labor dynamics. To address this gap, the current study integrates intrapersonal and interpersonal factors to explore their joint effects on employees’ emotional labor strategies when encountering customer injustice. A full-factorial experimental design with 2 (self-construal: independent vs. interdependent) × 3 (service co-workers: alone vs. positive attitudes vs. negative attitudes toward customer injustice) is employed, using data from 179 frontline service employees at high-star hotels in Shanghai, with self-construal and service co-workers operationalized as manipulated conditions. Results reveal that self-construal significantly influences surface acting: interdependent individuals are more inclined to engage in surface acting than independent individuals. By contrast, self-construal has no direct effect on deep acting. While service co-workers do not moderate the relationship between self-construal and surface acting, they play a critical role in the relationship between self-construal and deep acting: for interdependent employees, service co-workers’ attitudes (rather than their mere presence) decisively impact deep acting, with positive attitudes promoting deeper emotional engagement and negative attitudes reducing it. This study advances a dual-path framework highlighting how intrapersonal dispositions (self-construal) and interpersonal impression cues (service co-workers’ attitudes) interact to shape emotional labor. By expanding the traditional employee–customer dyad to a triadic model, the study bridges impression management theory and workplace injustice research, offering theoretical insights into how intrapersonal traits and interpersonal dynamics jointly shape contextualized emotional labor. This thereby provides a theoretical foundation for nuanced management strategies in service organizations. Full article

To address the issues of low efficiency and high omission rates in monitoring workers’ compliance with safety dress codes in intelligent factories, this paper proposes the SFA-YOLO network, an enhanced real-time detection model based on a Selective Feature Attention mechanism. This model enables inspection robots to automatically and accurately identify whether the workers’ attire meets the safety standards. First, this paper constructs a comprehensive dataset of safety attire, including images captured under various scenarios, personnel numbers, and operational conditions. All images are manually annotated to enhance the model’s generalization capability. The dataset contains 3966 images, covering four classes: vest, no-vest, helmet, and no-helmet. Second, the proposed model integrates the SFA mechanism to improve the YOLO architecture. This mechanism combines multi-scale feature fusion with a gated feature extraction module to improve detection accuracy, strengthening the model’s ability to detect occluded targets, partial images, and small objects. Additionally, a lightweight network structure is adopted to meet the inference speed requirements of real-time monitoring. The experimental results demonstrate that the SFA-YOLO model achieves a detection precision of 89.3% and a frame rate of 149 FPS in the safety attire detection task, effectively balancing precision and real-time performance. Compared to YOLOv5n, the proposed model achieves a 5.2% improvement in precision, an 11.5% increase in recall, a 13.1% gain in mAP@0.5, and a 12.5% improvement in mAP@0.5:0.95. Furthermore, the generalization experiment confirms the model’s robustness in various task environments. Compared with conventional YOLO models, the proposed method performs more stably in safety attire detection, offering a reliable technical foundation for safety management in intelligent factories. Full article

The choice between domestic and foreign production is one of the most important decisions not only for international business management but also for economic diplomacy and industrial policy. The reality is not a binary choice, but some firms use both. Why do companies maintain labour-intensive production in developed countries in the globalised world? To understand business challenges and strategies, this study examines small and medium-sized enterprises (SMEs) in the garment factory agglomeration in the North (Kenpoku) area of Iwate Prefecture, Japan. The in-depth case study, with a special focus on the six competitiveness factors of Japanese apparel firms, recognises that the ‘Made in Japan’ branding strategy is one of the effective ways to attract Japanese customers. This marketing strategy may motivate some firms to consider international market development. However, most Japanese SME apparel manufacturers play the role of original equipment manufacturer (OEM) for specific domestic market-oriented apparel companies. To meet customers’ strict delivery requirements, our case SMEs are developing multi-skilled workers to cope with high-mix small-lot production and fast delivery simultaneously. This management innovation is essential for building long-term business relationships and trust with corporate apparel buyers and surviving competition from products made in China and other developing countries. Full article

In this study, we proposed a method for detecting chips in the mouth of glass bottles using machine learning. In recent years, Japanese cosmetic glass bottles have gained attention for their advancements in manufacturing technology and eco-friendliness through the use of recycled glass, leading to an increase in the volume of glass bottle exports overseas. Although cosmetic bottles are subject to strict quality inspections from the standpoint of safety, the complicated shape of the glass bottle mouths makes automated inspections difficult, and visual inspections have been the norm. Visual inspections conducted by workers have become problematic because it has become clear that the standard of judgment differs from worker to worker and that inspection accuracy deteriorates after long hours of work. To address these issues, the development of inspection systems for glass bottles using image processing and machine learning has been actively pursued. While conventional image processing methods can detect chips in glass bottles, the target glass bottles are those without screw threads, and the light from the light source is diffusely reflected by the screw threads in the glass bottles in this study, resulting in a loss of accuracy. Additionally, machine learning-based inspection methods are generally limited to the body and bottom of the bottle, excluding the mouth from analysis. To overcome these challenges, this study proposed a method to extract only the screw thread regions from the bottle image, using a dedicated machine learning model, and perform defect detection. To evaluate the effectiveness of the proposed approach, accuracy was assessed by training models using images of both the entire mouth and just the screw threads. Experimental results showed that the accuracy of the model trained using the image of the entire mouth was 98.0%, while the accuracy of the model trained using the image of the screw threads was 99.7%, indicating that the proposed method improves the accuracy by 1.7%. In a demonstration experiment using data obtained at a factory, the accuracy of the model trained using images of the entire mouth was 99.7%, whereas the accuracy of the model trained using images of screw threads was 100%, indicating that the proposed system can be used to detect chips in factories. Full article

Optimizing multi-factory remanufacturing systems with social welfare considerations presents critical challenges in task allocation and process coordination. This study addresses this gap by proposing a hybrid disassembly line balancing and multi-factory remanufacturing process optimization problem, considering workers with government benefits. A mixed-integer programming model is formulated to maximize profit, and its correctness is verified using the CPLEX solver. Furthermore, a discrete zebra optimization algorithm is proposed to solve the model, integrating a survival-of-the-fittest strategy to improve its optimization capabilities. The effectiveness and convergence of the algorithm are demonstrated through experiments on disassembly cases, with comparisons made to six peer algorithms and CPLEX. The experimental results highlight the importance of this research in improving resource utilization efficiency, reducing environmental impacts, and promoting sustainable development. Full article

Objective: This study aims to assess the impact of prolonged occupational exposure to chrysotile asbestos on the epithelial cells of the upper respiratory tract and the levels of surfactant protein D (SP-D) in female workers. Methods: Buccal epithelial cell samples were collected from 40 workers at JSC “Kostanay Minerals”, fixed using the May–Grünwald method, and stained with the Romanowsky–Giemsa technique. SP-D levels were measured using an enzyme-linked immunosorbent assay (ELISA). Results: Workers exposed to asbestos dust exhibited a significant increase in cytological abnormalities and higher SP-D levels compared to the control group. Conclusion: Prolonged exposure to chrysotile-containing dust leads to degenerative changes in upper respiratory tract epithelial cells, characterized by cytological and cytogenetic abnormalities, alongside elevated SP-D levels, highlighting the need for preventive health measures. Full article