Search Results (1,209)

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

Technological change appears with different levels of intensity across occupations, countries and regions. This study examines the labour market effects of digitalisation in the European Union, focusing on blue-collar occupations, specifically ISCO major group 7, Craft and related trades workers, and low-skilled occupations, specifically ISCO major group 9, Elementary occupations. The empirical basis of the study is the Cedefop Skills-OVATE database. The large-scale analysis covers the content of online job advertisements in 2020 and 2024. The results show that digitalisation does not affect the labour market in a uniform way. In blue-collar occupations, demand for digital skills shows a relatively stable pattern over the period examined. In contrast, in low-skilled occupations, a decline can be observed in the share of explicitly required digital skills. This suggests that digitalisation affects occupational groups in differentiated ways. The findings also reveal persistent patterns across EU Member States. Countries with higher levels of technological development consistently show higher digital intensity, while lower values are more characteristic of former socialist countries. The comparison of the two years indicates that these structures changed only to a limited extent, suggesting that the digital divide remains relatively stable in spatial terms. Full article

The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was conducted with 51 participants. Each participant completed hazard identification in four conditions: traditional–pre, GEN-AI–pre, traditional–post, and GEN-AI–post, with a short training session on hazard identification delivered between the pre- and post-stages. Effectiveness was measured using the F1 score, combining both precision and recall. For analysis, traditional and GEN-AI performance were compared at each stage using paired-sample t-tests, and the overall pattern was tested using a 2 × 2 repeated measures ANOVA. The results showed that GEN-AI support produced significantly higher performance than the traditional approaches at both stages (p < 0.05). The repeated measures ANOVA confirmed a strong overall method effect. However, the overall intervention effect was small, and the method × intervention interaction was negligible, with no statistically significant change over time (p > 0.05). Overall, the findings indicated that GEN-AI support improved hazard identification accuracy relative to traditional approaches in this dataset, with limited evidence of additional gains from the training intervention. This study contributes towards providing empirical evidence that GEN-AI improves hazard identification and strengthens proactive prevention, but final outputs need human validation. Full article

Background: In increasingly dynamic and uncertain organizational environments, employees’ proactive behavior—characterized by self-initiation, future orientation, and change orientation—is critical for organizational adaptability and long-term competitiveness. Prior research has primarily examined how externally provided job resources stimulate proactive behavior. More recent work has begun to consider employees’ personal resources, but it largely adopts a capability level-based view, conceptualizing them as self-evaluations of individuals’ ability to control and influence their environment. This focus overlooks capability malleability-based personal resources that shape more fundamental beliefs about the malleability of human capability. Objective: Drawing on the job demands–resources (JD–R) model, this study investigates how employees’ growth mindset—reflecting beliefs that human capability can be developed—promotes proactive behavior by alleviating workplace anxiety, an anticipatory emotional state rooted in concerns about future work-related threats. We further examine time pressure as a key boundary condition. Method: A three-wave, multisource survey design was employed, collecting data from 326 employee–supervisor dyads. Results: The results show that employees’ growth mindset is negatively associated with workplace anxiety, which in turn positively predicts proactive behavior. Moreover, time pressure strengthens both the anxiety-buffering effect of growth mindset and the indirect effect of growth mindset on proactive behavior via workplace anxiety. Conclusions: By incorporating capability malleability-based personal resources into the JD–R model, this study advances understanding of the antecedents of proactive behavior beyond capability level-based self-evaluations toward deeper beliefs about the malleability of human capability. Applications: This study offers practical implications for managers seeking to cultivate employee proactivity. Full article

The study applies a multiphase, multimethod research approach based on participatory methodology. It integrates the perspectives of professionals from the tourism and hospitality industry and academic experts with the aim of developing an integrated conceptual model of the influence of AI and IoT technologies on work processes, skill development, and job attractiveness in the industry. The research provides a comprehensive understanding of how digital technologies indirectly shape employment through changes in work organization and the development of transferable digital and socio-emotional skills. The paper aims to contribute to redefining the perception of work in tourism and hospitality by emphasizing the sector not only as a career choice, but also as a platform for acquiring skills transferable to other industries. The findings revealed that employees’ intentions to enter or remain in the industry are not directly influenced by AI and IoT technologies; rather, these effects are mediated through changes in work processes and, more importantly, through skill development. The study contributes theoretically by developing and empirically validating an integrated conceptual model that connects technology implementation, work transformation, skill development, and employment outcomes. From a practical perspective, the results highlight the importance of human-centered implementation strategies based on training, communication, and employee inclusion in order to maximize the benefits of digital technologies. Full article

The COVID-19 pandemic intensified concerns about burnout in healthcare leadership, yet evidence specific to nurse managers remains fragmented. This integrative review synthesized recent research, organized burnout-associated factors using the Job Demands–Resources (JD–R) model, and examined pre-pandemic and pandemic-era shifts in research focus. Following Whittemore and Knafl’s methodology, four databases (Ichushi-Web, PubMed, CINAHL, and MEDLINE) were searched for peer-reviewed quantitative, qualitative, and mixed-methods studies published from 1 April 2019 to 31 August 2025 that examined burnout levels, prevalence, or related factors among nurse managers. Extracted findings were mapped to Job demands, Job resources, and Personal resources and compared according to the data-collection period. Twenty-five studies were included, with substantial heterogeneity in burnout instruments and cutoff values. Core job demands related to managerial responsibility, workload, and resource management were identified throughout the literature, while pandemic-era studies additionally highlighted frequent protocol changes, heightened uncertainty, and fear of infection. Key resources included organizational support, positive team communication, peer support, and adequate workload and material resources, and resilience was more frequently reported in pandemic-era studies. Overall, the findings demonstrate how crisis-related shifts in demands and resources shape burnout risk among nurse managers and support the application of JD–R–informed, context-adaptive prevention strategies. They also underscore the need for standardized burnout assessment and more robust interventional and longitudinal research. Full article

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the income effect of digital infrastructure development by using the rollout of the Broadband China policy as a quasi-natural experiment. The analysis draws on panel data for 77 prefecture-level administrative units in the Yellow River Basin, one of China’s major agricultural regions, from 2009 to 2021. A staggered difference in differences model is used to estimate the policy effect. The results show that digital infrastructure development significantly increases rural residents’ income. Under the log income specification, the baseline coefficient indicates an average income increase of about 8.33%. The mechanism analysis shows that innovation capacity and nonfarm employment both serve as positive partial transmission channels, with innovation capacity explaining a larger share of the total effect. The heterogeneity results suggest that the income effect is stronger in regions with higher GDP and larger population size. These findings indicate that digital infrastructure can support rural income growth when it is linked with local innovation capacity, employment opportunities outside agriculture, and rural development policies suited to local conditions. Full article

Despite increasing academic interest in return migration, limited understanding remains of how individual resources, workplace experiences, and perceptions of the origin country interact to shape return migration intention among migrant workers in major industrial destinations. This study investigates return migration intention among Myanmar migrant workers in Samut Sakhon Province, Thailand, using a multilevel framework that links micro-level individual and household characteristics, meso-level workplace and social experiences, and macro-level assessments of conditions in Myanmar. A quantitative research design was employed, with data collected from 506 Myanmar migrant workers using proportional stratified random sampling. The data were analyzed using descriptive statistics, chi-square tests, t-tests, and binary logistic regression. The results indicate that the majority of respondents did not intend to return to Myanmar within the next 10–15 years. Workplace discrimination emerged as the strongest positive predictor of return migration intention, while higher income and annual remittance behavior also increased the likelihood of intending to return. Conversely, having family in Thailand, perceived opportunities for job change or promotion, satisfaction with wages and welfare, and perceived safety in Myanmar reduced the likelihood of return migration intention. The findings suggest that future mobility plans cannot be explained solely by economic calculation. They are also shaped by family arrangements, workplace treatment, and migrants’ assessments of the feasibility and desirability of return. The study advances return migration scholarship by demonstrating the pivotal role of workplace discrimination within a multilevel explanation of return migration intention. Full article

The dynamic development of the digital economy, accompanied by changes in the employment structure and the rise in the knowledge workforce, has led to growing interest in the concept of democratic leadership as a foundation for organisational sustainable development. Highly skilled knowledge workers seek to participate in decision-making processes, value autonomy, and expect their work to provide satisfaction and a sense of fulfilment. The transition towards sustainable development requires a new approach by leaders in managing knowledge workers. This paper presents the results of a study aimed at identifying the relationships between democratic leadership style, job satisfaction, and performance of knowledge workers. These goals are achieved through the analysis of a survey of 396 knowledge workers whose work involved the extensive use of IT in Polish companies. Structural equation modelling is used to analyse the survey data. The results indicate that the democratic leadership style has a positive effect on job satisfaction, which in turn positively influences employee job performance. The analysis further reveals that job satisfaction mediates the relationship between democratic leadership and employee performance. The findings are discussed in the context of the leadership challenges associated with sustainable organisational development in the digital economy. Full article

This study examines how pre-service English teachers in Turkey experience the dimensions of proactive personality, job crafting, flow, and work engagement while making sense of their professional lives. In the phenomenological design research, semi-structured interviews were conducted with 13 teacher candidates selected through purposive sampling. Qualitative data were transcribed and subsequently analyzed using MAXQDA 2022 qualitative data analysis software and high inter-coder reliability was found. The findings were grouped under four themes. Under the heading of proactive personality, solution-focus, patience and communication, seeking support, and continuous development came to the fore. In the flow experience, open feedback from students, activities that made learning enjoyable, changes in the perception of time, and full participation in the natural flow of the lesson stood out. Professional craftsmanship manifested itself through strategies such as establishing trust-based relationships with students, colleagues, and parents, adapting methods, self-care, and time management. Dedication to work was defined through passion for the profession and the vitality provided by working with different student profiles. The results showed that teacher candidates demonstrated resilience-oriented professional strategies by combining individual initiative, social support, and continuous development tendencies. Furthermore, student-centered feedback and community-based relationships are understood to strengthen flow experiences and nurture dedication to work. The study points to the importance of supporting proactive tendencies in teacher training and designing learning environments conducive to flow. Full article

Organizations today face growing pressure from both digital transformation and sustainability. This creates challenges in aligning human resource practices with the skills and engagement of employees. While these two areas are often studied separately, less attention has been given to how sustainability-oriented HR practices function in digitally transforming organizations. This study examines the relationships among Sustainable Human Resource Management (SHRM) practices, green training, green skills, digital readiness, and employee engagement across organizations in North Macedonia. Drawing on the Job Demands—Resources framework, the study focuses on how organizational practices and employee competencies are associated within contexts shaped by both technological and environmental change. Using survey data from 152 employees representing 152 organizations, the findings indicate that SHRM practices are positively associated with green training and green skills. More importantly, green training emerges as a key mechanism linking SHRM practices with employee competencies, suggesting that sustainability-oriented HR systems are most closely associated with employee capabilities when supported by structured development processes. Green skills are positively associated with employee engagement, and this relationship appears stronger in organizations characterized by higher levels of digital readiness. These findings highlight the importance of training and digital context in shaping employee outcomes. The study contributes to the literature by integrating sustainability, HRM, and digital transformation perspectives, and by emphasizing the role of developmental mechanisms in linking organizational practices with employee outcomes. Full article

The employment and labor market landscape has undergone significant transformations globally, including the three Central European countries examined in this study. Over the past decades, organizations in this region have transitioned from a state of full employment to labor shortages, raising the question: What factors have driven these changes? Our study aims to present a theoretical framework highlighting key macro-level factors, such as demographic trends, economic development, labor market dynamics, the impact of the COVID-19 pandemic, and the role of robotization and artificial intelligence. Based on two empirical studies conducted in 2019 and 2022 among Czech, Hungarian, and Slovak organizations, we analyzed the extent and causes of labor shortages, as well as the labor market effects of robotization. Using descriptive and non-parametric statistical methods, including frequency analysis and Mann–Whitney U tests, the study examined key trends and compared the two periods to identify significant shifts. The analytical approach of this study primarily aims to compare perceptions across occupational groups and between the two survey waves (2019 and 2022). Because most variables were measured on ordinal Likert-type scales and the datasets represent independent cross-sectional samples rather than a panel dataset, non-parametric methods were considered the most appropriate. More advanced causal modeling techniques, such as regression or factor analysis, were not applied because the objective of the research was exploratory and comparative rather than to establish causal relationships between variables. The findings reveal significant shifts in the perceived causes of labor shortages across occupational groups in the surveyed Central European organizations. In particular, increasing labor shortages were observed in specific job categories, alongside changes in the relative importance of the underlying drivers of labor shortages. While adopting robotization and artificial intelligence has been positively received, demographic decline and emigration remain critical challenges. The study provides practical insights for policymakers and corporate leaders regarding labor market challenges, workforce planning, and the potential role of robotization and artificial intelligence in addressing labor shortages. Although the research is based on a non-representative sample, it offers valuable insights into the Central European region’s employment and labor market trends. Future research could examine whether, in hard-to-fill positions, robotization and AI primarily provide indirect support by augmenting and reallocating human work, or whether they may serve as direct substitutes. Full article

Industry 4.0 is transforming the way companies manufacture, improve, and distribute products, moving toward fast, intelligent, and flexible manufacturing, which will bring about fundamental changes in enterprises’ production capabilities. The Flexible Job Shop Scheduling Problem (FJSP) allows a single job to be divided into multiple operations, each of which can be processed on multiple machines. Due to its high flexibility and complexity, traditional scheduling methods are difficult to meet the needs of dynamic production. Dispatching rules struggle to effectively perceive the global precedence relationships among jobs and the distribution of machine workloads; metaheuristic approaches suffer from slow iterative convergence; existing deep reinforcement learning methods often employ a single policy network to handle both operation sequencing and machine assignment in a coupled manner, which tends to cause training instability and slow convergence. This paper proposes a deep reinforcement learning model that integrates Multi-Proximal Policy Optimization (MPPO) and Dual Attention Network (DAN) to address the FJSP. The model uses the operation message attention block and machine message attention block of DAN to capture the dependency relationships between operations and the dynamic competitive relationships between machines, respectively, and extract deep features. At the same time, MPPO designs dual actor networks to handle operation sequencing and machine assignment decisions separately, and combines a centralized critic to optimize the policy. This balances exploration and exploitation and improves training stability. Experiments are conducted based on the SD1 and SD2 datasets. In FJSP instances of four scales, the model is compared with PPO-DAN, PPO-HGNN, traditional scheduling rules, and OR-Tools. The results show that the algorithm reduces makespan by up to 4.2% on SD1 and 10.1% on SD2. Moreover, it achieves better performance than traditional scheduling rules. Its comprehensive performance is superior to that of the comparison methods, verifying its effectiveness and practical application potential in solving the FJSP. Full article

The wake of artificial intelligence (AI) tools has witnessed a lot of changes at workplaces. Job crafting (JC) has also embraced the predictive quality of using AI tools such as ChatGPT. Drawing on Conservation of Resources theory, this study was conducted as an effort to understand the role that ChatGPT plays in job crafting by enhancing work engagement (WE) in the presence of work-related curiosity (WRC). Time-lagged data from 314 employees from the information technology (IT) sector was used to test the relationship by using partial least square structural equation modeling. The results showed that ChatGPT and job crafting are linked to each other in the presence of work engagement. The results further showed that WE mediated and work-related curiosity moderated the relationship between ChatGPT and job crafting. These results are instrumental in understanding the significance of AI adoption in business and can be used as a potential tool for crafting jobs toward other work-related outcomes. The research holds significance for mangers and policymakers of the IT sector in terms of establishing AI adoption to Predict positive behaviors in employees, and it also highlights future avenues. Full article

Social workers are an integrated part of the criminal justice system. In this field, criminal justice social workers (CJSWs) face challenges related to structure and professional autonomy. During the COVID-19 pandemic, correctional services (CS) implemented strict infection control measures through early releases, lockdowns in prisons with isolation, and the cessation of visits. This research explores how CJSWs experienced working during the COVID-19 pandemic, with a specific focus on perceived influence on their working conditions, changes in users’ needs, and adaptation to new demands related to infection control. This is a mixed-methods study of a small sample of social workers in Norway’s criminal justice sector (N = 75). Findings indicated that they experienced a negative impact of COVID-19 on their contact with colleagues and their target group, as well as on their ability to provide services to the latter. In addition, there is an indication of a heavier workload for CJSWs during COVID-19. Our results are analysed using the job demand–resource model (JD-R). The results suggest that infection control measures created additional demands and strain on CJSWs in caring for vulnerable and at-risk groups, while at the same time reducing contact with their own colleagues and supervisors, experiencing what we address as a ‘double negative’. Full article