This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.

This study aims to determine the extent to which talent identification is implemented in talent management. A Systematic Literature Review (SLR) was conducted to summarize the application of talent identification in the last six years. Researchers use Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to process scientific articles. The literature reveals that while topics related to talent management garner significant attention, research on talent identification within talent management remains relatively scarce despite a gradual increase each year. We compared documents indexed by Scopus Q1 and Q2. The results show that the United States accounted for a significant portion of research on talent identification, representing 16% of the total existing research. Researchers have conducted extensive studies on the medical and pharmaceutical sectors, public services, tourism, and hospitality. The number of citations varied greatly from 1 to 93, with a median value of 20. These studies have also used various research methods with different theoretical bases and produced different analyses. This finding enriches the perspective of talent identification.

Measuring the performance of healthcare organizations has become a crucial yet challenging task, which is the focus of this study. The paper’s primary goal is to identify the key factors that shape healthcare organizations’ performance management systems in Serbia, which can serve as useful guidelines for implementing sustainable solutions. Additionally, the aim is to emphasize the importance of a broad implementation of performance measurement systems to facilitate strategy implementation and enhance organizational effectiveness. The empirical research involved an online survey of 280 respondents, including managers, executives, and operational staff from both private and public healthcare organizations in Serbia. Statistical analysis was conducted using SPSS 20. The study identifies key challenges, including the lack of a developed performance measurement system, weak support from information and management systems for performance improvement, and an organizational structure that does not support performance enhancement. Furthermore, it has been found that a deeper understanding of the essence of measurement significantly contributes to identifying problems in its application in the healthcare sector. It was also observed that the more challenges identified in the measurement process, the less favourable the perception of the flexibility and adaptability of the system.

This research investigates the safety status of water transport in Lake Towuti, South Sulawesi, employing the MICMAC and MACTOR methodologies to discern the factors that affect navigation safety and the interactions among the relevant stakeholders. The MICMAC analysis reveals that the effectiveness of sustainable transportation in Lake Towuti is significantly dependent on technical elements such as vessel certification, maintenance practices, and safety monitoring, alongside robust relationships among key entities like The South Sulawesi Class II Land Transportation Management Center (BPTD), The East Luwu District Transportation Office (Dishub), and the Timampu Port Service Unit (Satpel). When implementing the MICMAC-MACTOR model, it is essential to consider the technical implications of the proposed recommendations from the perspectives of social justice, environmental sustainability, and economic feasibility. The outcomes derived from the MICMAC and MACTOR assessments in Lake Towuti provide critical insights that can be utilized in other lakes across Indonesia, especially those that exhibit deficiencies in safety measures and adherence to inland water transport safety regulations.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.