The research provides insight into the factors that determine absenteeism in different types of organizations, in order to facilitate the adjustment of employees and organizations in the transition process from Industry 4.0 to Industry 5.0. The aim of the study is to predict the absenteeism of employees in the context of job characteristics and mental health. Additionally, the research investigated the effect of size, ownership, and sector of the companies on absenteeism, job characteristics, and mental health. The sample included responses from 502 employees of different sociodemographic characteristics that work in various types of organizations, performing white-collar and blue-collar jobs. A short mental health questionnaire—Mental Health Inventory, MHI-5—was used in order to measure mental health. The Job Characteristics Questionnaire was used to measure the employees’ perceptions of their job characteristics—job variety, autonomy, feedback, dealing with others, task identity, and friendship. The absenteeism is operationalized with the question: “During the past 12 months, how many days were you absent from work for any reason?”. The findings suggest that mental health and job characteristics significantly reduce absenteeism among different sectors. The result showed that the size, ownership, and sector of the organization significantly affect the absenteeism, job characteristics, and the mental health of the employees. The results support the premises of Industry 5.0 and offer a new human-centric approach to absenteeism through the promotion of mental health through long-term organizational strategies and a more inclusive approach to employees’ preferences in relation to job characteristics. The study offers a new, double-sided model of absenteeism, determining causal factors from the perspective of personal and organizational factors.

Objectives: The paper analyses the impact of socio-demographic characteristics of the employees in conditions of uncertainty in the organizations. Material and Methods: The study was conducted on a sample of 210 employees using the Mental Health Inventory – 38 (MHI-38), Satisfaction with...

Background: The information age has ushered in a novel economic model known as the information economy, challenging traditional bureaucratic mechanisms that were primarily seen as tools to mitigate uncertainty. Within this context, complex leadership emerges as a paradigm that thrives on systemic interactions and intricate dynamics. Here, the onus lies on establishing the framework and conditions that empower the adaptive essence of leadership to flourish. Purpose: The aim of this research is to establish a framework for optimal leadership management, uncovering previously unexplored factors shaping employee work behavior. By identifying and defining key components within management mechanisms that greatly enhance employee performance, the objective is to cultivate rooted and effective management practices. Study design/methodology/approach: The research involved 480 respondents from diverse socio-demographic backgrounds and companies. Complexity leadership was assessed using the Complexity Leadership Scale, measuring Generative and Administrative leadership. Employee cooperation was evaluated with the Scala Supportive and Non-Controlling Supervision. Statistical methods included multiple regression, Pearson correlation, t-tests, and MANOVA. Findings/conclusions: The evaluation shows that both Generative and Administrative leadership are moderately expressed. Generative leadership positively correlates with Supportive and Non-Controlling Supervision, achieving statistically significant results. In contrast, Administrative leadership has negative correlations with these supervisory styles. Limitations/future research: The limitations of the research are of a methodological nature and refer to the sample of employees, according to demographic characteristics and company characteristics. Also, questionnaires based on self-assessment most often imply a certain number of socially desirable answers. Future research should include a larger sample to improve external validity and ensure that the empirical evidence obtained is variable across a range of organizational settings.

The increasing adoption of servitization has led many manufacturing companies to operate simultaneously in manufacturing and service activities, creating dual-sector business models characterized by heightened organizational complexity. Although prior research acknowledges that both internal capabilities and contextual conditions shape organizational outcomes, fewer studies have examined these variables within the same empirical model in companies operating under both manufacturing and service logics. Drawing on the resource-based view and contingency theory, this study examines the effects of organizational culture, organizational commitment, knowledge management, environmental uncertainty, and employee retention on organizational performance in dual-sector companies, while also assessing whether these relationships vary by company size. Survey data were collected from 433 employees working in dual-sector companies and were analyzed using confirmatory factor analysis, covariance-based structural equation modeling, and supplementary hierarchical regression analysis. The findings indicate that environmental uncertainty and employee retention did not receive empirical support as independent direct predictors in the structural model. Organizational commitment, knowledge management, and two dimensions of organizational culture—consistency and adaptability—are significant positive predictors of perceived organizational performance. The moderation analysis does not provide strong evidence that company size changes these relationships, although the interaction suggests that environmental uncertainty may be more consequential in large firms. This study contributes to research on servitization by showing that, in dual-sector companies, performance is most strongly associated with internal capabilities that support coordination, shared meaning, and knowledge integration across manufacturing and service activities. For managers, the results highlight the importance of strengthening commitment, adaptive coordination, and cross-domain knowledge processes rather than relying on retention efforts alone.

This paper aims to investigate how aspects of health and job characteristics can predict sickness absenteeism in the manufacturing sector, a critical concern in the context of Industry 5.0 and advanced manufacturing. As Industry 5.0 emphasizes human-centric and sustainable approaches, understanding and managing employee well being is paramount. The research was conducted among 175 participants of different socio-demographic characteristics. The variables were operationalized with a test battery that included - the Mental Health Inventory (MHI - 5; Berwick et al., 1991), The General Heath Questionnaire (GHQ-12, Goldberg & Hillier, 1979), Job Characteristic Inventory (JCI, Sims et al. 1976), and the rate of the sickness absenteeism was measured by the self-reported question: During the past 12 months, how many days in total were you absent from work due to illness? The results have shown that different aspects of health and specific job characteristics determined sickness absenteeism in correlation and prediction. The results underscore the importance of aligning health management strategies with the principles of Industry 5.0, ultimately contributing to enhanced operational efficiency and sustainability in the manufacturing sector. This research not only addresses current challenges but also provides a framework for future studies aimed at improving workforce well being in advanced manufacturing settings providing preconditions for sustainable workplaces in the manufacturing sector.

The aim of this paper is to represent the innovative approaches to the natural disaster management, emphasizing the role of Artificial Intelligence (AI) and the Internet of Things (IoT) in transformation of natural disaster management based on predictive solutions. The paper highlights the improvements in real-time monitoring, early warning systems, and predictive accuracy for natural disasters. The integration of AI and the IoT has revolutionized natural disaster management and prediction. AI algorithms analyze datasets from IoT sensors deployed in vulnerable areas, enabling real-time monitoring and early warning systems. These technologies enhance predictive accuracy for disasters. Machine learning models process historical and real-time data to identify patterns and forecast events with unprecedented precision. This synergy improves emergency response, minimizes casualties, and mitigates economic losses. Possible challenges in implementation of innovative approaches to Natural Disaster Management based on AI and IoT include ensuring data security, managing the high volume of data, and integrating AI-IoT systems into existing infrastructure. The innovative approaches to Natural Disaster Management leads to optimized resource allocation, minimized economic losses, and support of efficient recovery efforts. By integrating these advanced technologies, businesses and economic systems can achieve a competitive advantage, ensuring stability and sustainability in the face of increasing global challenges.