Dejan Ravšelj

Public administration increasingly relies on artificial intelligence (AI) to optimize processes, personalize services, and improve decision-making; however, evidence on human resources management (HRM) for AI readiness remains limited. This article examines how six HRM-related AI readiness factors relate to four AI readiness components (“Adaptability”, “Proactivity”, “Resilience”, “Ethical governance commitment”), with human resources (HR) development as a central capability-building lever. Data were collected through an online survey of 112 senior public managers in Slovenia conducted between May and July 2025 and analyzed using partial least squares structural equation modeling (PLS-SEM) to test the direct relationships between HRM factors and AI readiness components. “Human resources (HR) development” is the only factor positively associated with all AI readiness components, but the findings are exploratory due to modest effect sizes, low reliability for some constructs, and limited discriminant validity. “Leadership style” is associated with “Resilience” and “Ethical governance commitment,” while “Change management” shows a smaller association with “Proactivity.” Factors such as “HR planning, recruiting, hiring,” “Communication and collaboration,” and “Organizational culture” do not show statistically significant direct associations in the tested model. The findings support conceptualizing AI readiness as a human-centered, multidimensional organizational capability and suggest that capability building through “HR development”, supported by leadership and targeted change practices, may help strengthen AI readiness for AI-enabled change in public administration.

Background: Asset management has become crucial to organizational performance over the past three decades. Implementing an Asset Management System (AMS) can be pivotal in managing the operation, sustainability, and efficiency of both tangible and intangible organizational assets. However, many organizations still underappreciate AMSs, particularly in healthcare, where poor organization, unclear processes, and a lack of control contribute to long patient waiting times, financial losses, regulatory non-compliance, and diminished credibility. Methods: This study provides a comprehensive review of the existing body of research on AMSs, discusses AMSs in the context of healthcare, and identifies the specific healthcare areas that have most frequently been the focus of AMS research. This study applies bibliometric analysis of 16,667 documents on AMSs, complemented by a focused bibliometric analysis of a subset of 248 publications specifically addressing AMSs in healthcare. All documents, published up to the end of 2024 and indexed in the Scopus database, were analyzed to investigate the evolution of AMS research, with a particular emphasis on its applications within healthcare. The research employs several bibliometric approaches, utilizing the Python and VOSviewer software. Results: The findings highlight the rapid growth of AMS research, evolving from a niche topic into a strategic discipline that enhances predictive maintenance, efficiency, and sustainability. In healthcare, the adoption of AMSs has grown substantially, supported by the integration of artificial intelligence (AI) and the Internet of Things (IoT). Conclusions: The incorporation of these technologies has enabled more effective monitoring of medical equipment, improved oversight of critical infrastructure, and optimized the operational performance of healthcare providers. Nevertheless, significant research gaps remain concerning the direct impact of AMSs on the quality of patient care, provider coordination, and strategic decision-making. Addressing these gaps is essential not only for advancing academic knowledge but also for leveraging the full potential of AMSs to enhance healthcare delivery, improve outcomes, and support the evidence-based management of healthcare systems.

Asset Management Systems (AMS) are increasingly recognized for their potential to enhance operational efficiency, cost-effectiveness, and sustainability across asset-intensive sectors. In healthcare, however, the implementation of AMS remains fragmented, despite mounting pressures to improve medical equipment usage, infrastructure reliability, and service delivery. This study presents a literature review supported by a bibliometric analysis of 248 documents on AMS research in healthcare, published up to 2024 and indexed in the Scopus database, examining the evolution of AMS research with a specific focus on healthcare applications. Using Python and VOSviewer, the bibliometric analysis focuses on scientific production, geographical coverage, and major research clusters. The findings highlight a growing global interest in AMS and indicate a broader shift toward digitalization and strategic integration within healthcare systems. We synthesize these insights into a set of key areas and priorities, offering evidence-based recommendations to support more effective AMS adoption in healthcare.

Artificial intelligence (AI) is increasingly utilized in public administration as a strategic enabler for achieving environmental sustainability and advancing progress toward the Sustainable Development Goals (SDGs). This study presents an integrative review of 3,107 peer-reviewed publications indexed in Scopus and 98 documented use cases from European public administrations. It explores how AI contributes to environmental goals by synthesizing academic insights and practical implementations. Key themes include environmental monitoring and forecasting, sustainable urban and resource governance, and ethical AI use. Use cases are organized across six environmental SDGs with notable applications in water management, climate action, and biodiversity protection. Findings inform future research and support evidence-based environmental policymaking, while addressing regulatory, technical, and institutional challenges.

Recent societal challenges have highlighted the urgent need for public administration (PA) to harness the potential of disruptive technologies (DT) such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), virtual and augmented reality, 3D printing, robotics and drones. This paper aims to fill the research gap in comprehensive analyzes, especially with regard to the practical applications of AI in the context of DT in PA. The paper has two main objectives: first, to identify the role of DT, with particular attention to AI, in PA considering current research trends; second, to provide a comprehensive overview of the main practical applications of DT in PA worldwide. First, a bibliometric analysis of 5,927 documents in the Scopus database published between 2010 and 2023 is conducted. This is followed by a content analysis of prominent documents from institutions such as the World Bank, the OECD and the EU. The results show that DT, especially AI, are gaining importance in PA research and have concrete applications in various sectors. This study provides a refined conceptualization of DT in the context of PA with a focus on AI.

Background: The adoption of artificial intelligence (AI) in public administration (PA) has the potential to enhance transparency, efficiency, and responsiveness, ultimately creating greater public value. However, the integration of AI into PA faces challenges, including conceptual ambiguities and limited knowledge of the practical applications. This study addresses these gaps by offering an overview and categorization of AI research and applications in PA. Methods: Using a dataset of 3149 documents from the Scopus database, this study identifies the top 200 most-cited articles based on citation per year. It conducts descriptive and content analyses to identify the existing state, applications, and challenges regarding AI adoption. Additionally, selected AI use cases from the European Commission’s database are categorized, focusing on their contributions to public value. The analysis centers on three governance dimensions: internal processes, service delivery, and policymaking. Results: The findings provide a categorized understanding of AI concepts, types, and applications in PA, alongside a discussion of best practices and challenges. Conclusion: This study serves as a resource for researchers seeking a comprehensive overview of the current state of AI in PA and offers policymakers and practitioners insights into leveraging AI technologies to improve service delivery and operational efficiency.

The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students’ perceptions across different socio-demographic and geographic factors, with key factors influencing students’ use of ChatGPT also being identified. Higher education institutions’ managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.