This paper investigates how regulatory frameworks and financial incentives influence stakeholder engagement and the institutionalisation of grassroots innovations within the New European Bauhaus (NEB) initiative. Launched by the European Commission as a creative and interdisciplinary extension of the European Green Deal, the NEB aims to foster more inclusive, sustainable, and aesthetically engaging urban environments. Despite its transformative ambitions, limited empirical evidence exists on how NEB projects evolve from local experimental niches into institutionalised practices embedded within mainstream governance systems.
Building on Transition Theory and the Multi-Level Perspective (MLP), this study conceptualises NEB projects as niche innovations operating within broader socio-technical regimes. It explores how policy instruments shape the dynamics of stakeholder engagement, knowledge diffusion, and adaptive governance. By integrating governance studies into the MLP framework, the paper addresses a critical gap in the sustainability transitions literature: the institutional conditions necessary to scale bottom-up initiatives. Methodologically, the study adopts a qualitative embedded case study design. Data were collected through an online self-assessment form distributed to all NEB prize finalists, complemented by secondary document analysis and semi-structured interviews with project leaders across diverse socio-political contexts. The findings reveal that regulatory incentives are key enablers of stakeholder engagement, particularly when supported by strong institutional backing, clearly defined roles, and mechanisms for continuous co-creation. However, the effectiveness of these incentives is uneven. Ambiguities in policy implementation and disparities in resource accessibility limit participation for smaller or less institutionally connected actors. Some projects succeed in aligning local stakeholders around shared goals, facilitated by municipal support and EU funding, while others struggle with bureaucratic barriers and limited access to technical expertise.
Theoretically, the study extends the MLP by embedding stakeholder co-creation and adaptive governance mechanisms into transition analysis. Empirically, it provides one of the first grounded assessments of how NEB projects scale and institutionalise within multi-level governance structures. Practically, the research offers policy recommendations aimed at strengthening long-term engagement mechanisms, improving the accessibility of funding tools, and fostering cross-sectoral collaboration between local authorities, civil society, and industry.
Ultimately, this study contributes to a more comprehensive understanding of how participatory governance and regulatory design intersect in shaping urban sustainability transitions. It underscores the need for flexible, iterative governance models that allow experimental initiatives to influence policy landscapes and highlights the importance of inclusive and sustained stakeholder engagement for the institutional success of visionary policy agendas such as the NEB.

This study conducts a bibliometric analysis of the scientific literature addressing the intersection between Specific Learning Disabilities (SLDs) and Artificial Intelligence (AI), a field that has been gaining increasing relevance due to the transformative potential of AI-based interventions in education. SLDs, including dyslexia, dysgraphia, and dyscalculia, significantly impact learning processes and academic outcomes, making it crucial to explore innovative approaches for their early identification and personalized support. AI technologies, such as machine learning, natural language processing, and adaptive systems, offer promising tools to enhance educational inclusion and tailor interventions to individual learning needs. By leveraging data-driven methodologies, AI can provide dynamic and responsive learning environments, improving both engagement and outcomes for students with diverse cognitive profiles.
Using data retrieved exclusively from the Scopus database and analysed through the Bibliometrix R package, this research examines publication trends, citation patterns, international collaboration networks, and thematic evolutions within this interdisciplinary domain. The analysis reveals a substantial increase in research output since 2020, highlighting a growing academic and technological interest in leveraging AI to address SLD challenges. Countries such as the United States, Spain, Italy, and India emerge as key contributors, fostering an expanding global collaboration network. These international partnerships reflect a shared commitment to addressing the complex and multifaceted nature of learning disabilities through cross-disciplinary research efforts.
Thematic mapping identifies core topics like dyslexia, machine learning, and personalized learning systems, alongside emerging themes such as contrastive and adversarial machine learning approaches, which represent innovative frontiers for future exploration. In particular, these advanced AI techniques show potential in enhancing diagnostic precision and developing more adaptive educational tools. Despite this promising landscape, the study underscores the need for broader empirical validation and interdisciplinary cooperation involving educators, AI researchers, and healthcare professionals. Meta-analytic evidence also suggests the importance of integrating cross-disciplinary insights when developing AI tools tailored to the needs of students with disabilities (Zhang, Carter Jr., Liu, & Peng, 2024).
The findings emphasize the strategic role of AI in promoting educational equity and suggest future research directions to consolidate and expand this field, ultimately contributing to more inclusive and effective learning environments for individuals with SLDs. Such efforts are essential to bridge existing gaps in access to tailored educational resources, ensuring that technological innovation translates into tangible benefits for learners worldwide.

The increasing adoption of artificial intelligence (AI) and robotics is leading to a profound transformation of the healthcare sector (Raimo et al., 2022), affecting diagnostic accuracy, optimising clinical processes and redefining care practices (Comite, 2022). Collaboration between healthcare professionals and intelligent machines is a key element in the evolution of care, redefining roles and changing traditional organisational structures (Çetin, 2024).
However, large-scale implementation is hampered by acceptance barriers, behavioural resistance and ethical and regulatory issues. This integration presents an opportunity to increase efficiency and reduce the margin of error (Babashahi, 2024), yet questions remain about safety, reliability and the impact on professional skills (Nivethithitha et al., 2014). Adoption depends largely on the trust healthcare professionals place in these technologies, transparency in algorithmic decision-making, and the availability of resources and adequate training.
The study asks: “How does human-computer interaction affect the acceptance of digital technologies by healthcare professionals and what strategies can be implemented to improve their adoption?”
To answer this, the authors conducted a systematic literature review (Massaro et al., 2016), analysing articles from Web of Science (WoS) and Scopus. The selection process combined automated and manual searches, identifying 11 relevant studies. Inclusion criteria focused on works addressing acceptance factors, barriers, and implementation strategies of AI and robotics in healthcare. Technical studies without a human-computer interaction perspective were excluded. Recent literature also highlights the growing role of AI-powered assistants, where human-chatbot interactions redefine service delivery (Akpan et al., 2025).
The aim is to create a strategic agenda for adopting AI and robotics in healthcare, identifying strategies and methods, including empirical ones, to facilitate integration and improve perception among professionals.
Findings show that acceptance depends on trust, perceived usefulness, ease of use and training adequacy. Resistance stems from fear of a diminished human role, regulatory uncertainty and ethical concerns linked to automation. Effective strategies include transparency in decisions, involving healthcare workers in development, and specific training programmes.
Human-computer interaction is redefining healthcare, promoting a collaborative model between professionals and AI. For this transition to succeed, organisational, ethical and training challenges must be addressed. Institutions must invest in training and policies for responsible and sustainable integration (Murugan et al., 2024). Recent studies also emphasize AI-driven assistants as tools to enhance collaboration and bridge gaps in professional-patient interactions (Liu et al., 2024). The adoption of AI and robotics (Kaur, 2024) offers a unique chance to improve care, but requires a strategic, interdisciplinary approach. This contribution offers a foundation for future research and for defining effective, sustainable implementation models.

In recent years, the issue concerning digital security has become increasingly important. Academics within an interdisciplinary approach are debating the topic and how to ensure and implement digital information security from the perspective of both companies and consumer users. The issue affects both the public and private sectors and requires coordinated and effective policy action.
The risks associated with such attacks are closely linked to identity theft, cyber fraud, and money laundering.
In order to address the security-related emergency, the European legislator has enacted several legislative acts. The purpose of this paper is to explain, through a legal analysis, the current regulatory framework examining the new developments and identifying any remaining gaps.
From this analysis, it appears that the topic has recently been the subject of several legislative actions. In this scenario, the NIS 2 Directive (Directive EU 2022/2555) represents a crucial update in the European Union’s legislation for network and information security. This work points out the lack of remedies for individuals in the case where the parties addressed by all regulatory interventions fail to respect their obligations.
At the same time, there is no doubt that cyber incidents have a significant impact on the profits of companies. In this regard, the Corporate Sustainability Reporting Directive (CSRD 2022/2464) introduced the inclusion of cybersecurity information in the annual report as non-financial information. For this reason, it is useful to investigate how the cybersecurity obligations contribute to the quality of sustainability reporting.
To address the application problems of these regulations, in the final part of the paper, it could be proposed to use blockchain tools to implement the certainty of digital identity and the integrity of the entered information.

The advent of digitization has significantly changed how we live and work, and information society services – especially intermediate services – have become crucial components of our everyday lives. Platform capitalism, which is characterized by the dominance of major digital platforms that have substantial influence across different sectors, has emerged as a result of this digital transition. The widespread impact of these platforms raises serious concerns about their societal ramifications, especially in relation to individual rights, democratic governance, and geopolitical competition. This phenomenon has given rise to the term “state platform capitalism”, where governments increasingly leverage dominant digital platforms for their own political and economic agendas.
Aspects of this phenomenon have been examined in the literature to date, but there are still significant gaps in the extant research. Specifically, further research is required to fully comprehend how platform capitalism impacts public speech, accountability, and participation as well as how the power of digital platforms interacts with democratic processes. A small number of dominant digital platforms strongly affects international politics, data sovereignty, and technological competition. Additionally, given the growing competition between China and the United States in the digital sphere and the regulatory role of European Union, the geopolitical implications of platform capitalism demand further scholarly attention.
The purpose of this paper is to fill the literature gaps by carefully examining the complex interactions between platform capitalism and the State. With this aim, and applying a qualitative methodology, this work: first, analyzes the state of the art; second, proposes a definition and identifies the challenges of the platform infrastructural power; third, discusses the main models of the relationship between dominant platforms and the State. Eventually, further research paths are suggested.

The advent of generative Artificial Intelligence (AI) represents a significant challenge for contemporary copyright law. As the capacity of machines to generate content autonomously has increased, the distinction between human creation and automated production has become increasingly indistinct. This raises fundamental questions concerning the attribution of authorship and the ownership of rights. The prevailing legal framework, which is primarily anchored to the “human author” concept, is ill-equipped to resolve these issues uniformly and coherently. The intensity of the international debate has increased in recent years. Legislatively speaking, the adoption of EU Regulation 2024/1689 (AI Act) represented a significant turning point in the governance of AI. Although it does not directly regulate copyright, it introduces transparency for providers of general-purpose AI models, imposing, among other things, the obligation to document the content used for training algorithms and to comply with European copyright law, with particular reference to EU Directive 2019/790. Concurrently, the Italian legislator recently approved DDL no. 1146/2024, which proposes to extend copyright protection to works created with the help of AI, provided they are the result of human intellectual work.

This article investigates the role of stakeholder engagement in the development and deployment of GovTech solutions, using WiseTown—an Italian City Digital Twin (CDT) ecosystem of digital solutions—as a case study. Rooted in a socio-technical perspective, the study explores how engagement with diverse stakeholders—municipal leaders and technical officers—shapes the co-design, adaptation, and scaling of GovTech solutions in real-world contexts. Drawing on a semi-structured expert interview and supported by secondary materials, the analysis explores how WiseTown’s digital ecosystem evolves through collaborative practices. The findings are structured around four key facets of stakeholder engagement: entry points and layered engagement strategies; co-design and feedback mechanisms; handling institutional resistance; and inter-municipal scaling. The study highlights a set of mechanisms—such as co-design workshops, civic tech modules, and flexible deployment models—that foster both top-down and bottom-up stakeholder engagement. These approaches enable the co-evolution of digital tools with the needs of local administrations and communities. Lessons derived from the case underscore that stakeholder engagement is not just a participatory ideal but a practical enabler for adoption, usability, and scalability. The article contributes to the GovTech literature by offering empirical and actionable insights on how engagement can overcome institutional resistance, support collaboration, and ensure long-term relevance, while enabling the design and institutionalization of inclusive, scalable, and context-sensitive public sector innovation.

The conservation of ceramic materials in cultural heritage faces challenges due to their vulnerability to environmental factors such as humidity, extreme temperatures and natural wear. These materials are essential in architecture and historical artifacts, requiring specialized interventions to preserve their cultural and aesthetic value. This research explores the use of artificial intelligence (AI) to optimize the diagnostic and conservation processes of ceramic materials in cultural heritage, addressing the lack of effective methodologies that combine accurate analysis with sustainable interventions.
Through a methodology based on machine learning algorithms, high-resolution digital images and data obtained from advanced sensors, such as humidity, temperature and vibration, were analyzed. The data were also complemented with environmental monitoring techniques to obtain a comprehensive view of the factors affecting conservation. The objective is to identify and evaluate the state of conservation of ceramic materials through a predictive solution that facilitates early and accurate interventions. To this end, AI models were trained to detect deterioration patterns, such as microcracks and changes in molecular structure, which are imperceptible to the naked eye.
Preliminary findings indicate that AI can identify deterioration patterns in ceramic materials with an accuracy of over 90%. These results suggest that the implementation of AI not only improves diagnostic accuracy, but also the speed of interventions, optimizing decision making in conservation processes. The ability of AI to process large volumes of data and learn from previous patterns also offers new possibilities for preventive interventions, reducing the risk of irreparable damage.
In terms of qualitative results, it was observed that the integration of AI in conservation reduces the dependence on manual and observational methods, which often limit the efficiency of interventions. In terms of quantitative results, a significant improvement in the accuracy of deterioration detection was achieved, reaching levels of more than 90% in the identification of microcracks and structural alterations.
In conclusion, the research shows that AI has great potential to transform the conservation of ceramic materials in cultural heritage, improving the accuracy and efficiency of interventions. Integrating this technology facilitates a more sustainable and scalable approach, allowing the model to be replicated in different regions without the need for specialized experts, ensuring the long-term conservation of cultural heritage.

Despite rising healthcare costs, many health systems still fall short of patient and policymaker expectations for quality and efficiency. A major challenge is the integration of technological innovations, especially Artificial Intelligence (AI), into healthcare processes. AI has significant potential to improve clinical pathways, particularly in time-sensitive processes like stroke care, where every second counts. However, research on how to best utilize AI in these contexts remains limited. This paper examines the drivers for successful AI application in time-sensitive workflows, focusing on stroke care in Calabria, Italy. Using Business Process Model and Notation (BPMN), the study maps current workflows and analyzes potential AI interventions. Findings highlight the importance of systemic, integrated AI solutions that optimize entire workflows, offering actionable insights for the design of AI-enabled clinical processes that ensure timely, effective care.

Digital therapeutics (DTx) are an emerging class of evidence-based software interventions developed to prevent, manage, or treat medical conditions. Their increasing application across clinical settings reflects their potential to improve healthcare accessibility, enhance patient engagement, and support better treatment outcomes. When combined with artificial intelligence (AI), DTx can offer advanced capabilities such as real-time monitoring, personalised interventions, and predictive analytics, further strengthening its clinical impact. However, despite these technological advancements, widespread adoption remains limited due to persistent challenges in regulatory compliance, integration within clinical workflows, and the lack of standardised frameworks for evaluating effectiveness. This study presents a systematic literature review conducted according to PRISMA guidelines to explore the empirical landscape surrounding the implementation, ethical integration, and assessment of DTx within healthcare systems. From an initial pool of 1,951 records, 114 peer-reviewed studies met the inclusion criteria. The analysis identified three central research gaps: first, barriers to integration arising from limited acceptance by healthcare providers and patients; second, unresolved ethical and regulatory issues related to AI use, particularly concerning data privacy, algorithmic bias, and transparency; and third, the absence of consistent clinical benchmarks for evaluating DTx across different therapeutic areas. Bibliometric and thematic analyses reveal a rapidly expanding body of literature, with increasing attention to mental health and chronic disease management. AI integration stands out as a promising yet insufficiently validated DTx dimension. These findings highlight the need for unified regulatory approaches, robust ethical oversight, and interdisciplinary collaboration to support digital therapeutics’ safe and effective integration into routine clinical care.

Background: Physicians’ Dual Practice (PDP), defined as the concurrent provision of public and private healthcare services by the same physician, presents significant managerial challenges for public healthcare institutions. Its effective governance requires balancing public service obligations with private sector incentives.
Objective: This study aims to develop a comprehensive conceptual business model framework to support the development of PDP within public hospitals. The framework combines regulatory analysis, market evaluation, and the application of the Business Model Canvas (BMC) methodology.
Methods: An inductive approach was adopted, featuring a case study of Policlinico Tor Vergata (PTV), a major university hospital in Italy. The research integrates regulatory context analysis, K-means clustering for market segmentation, and the structured application of the BMC to assess and guide PDP management. Artificial Intelligence (AI) was examined as a strategic enabler to optimize operational processes.
Results: The findings underscore the centrality of regulatory frameworks, organizational governance models, and market dynamics in shaping PDP strategies. Clustering analysis revealed distinct strategic profiles among public healthcare organizations. Furthermore, the study highlights the pivotal role of AI technologies in optimizing waiting lists and balancing public-private service delivery.
Conclusion: By developing a structured and adaptable business model, this study provides actionable insights for policymakers and hospital administrators seeking to enhance the effectiveness of PDP initiatives. The integration of regulatory compliance, market responsiveness, and digital innovation emerges as essential for transforming PDP into a lever for strategic institutional development and improved healthcare delivery.

This study offers a bibliometric analysis of impression management (IM) within the evolving landscape of individual and organizational communication, shaped by emerging digital technologies. By analyzing 262 peer-reviewed journal articles indexed in Scopus between 2000 and 2024, the research examines the thematic structure of this interdisciplinary domain.
Performance analysis and co-occurrence network mapping findings reveal a growing interest in IM, particularly over the past decade. The study identifies four core thematic clusters: IM for identity construction through social media; technological augmentation of communicative credibility and authenticity; branding, self-presentation, and user engagement through IM in the age of digital intermediation; socio-psychological dimensions of IM in digital contexts.
The research shows that while IM has been widely explored in psychology, communication, and information science, it remains underrepresented in management and business studies.
The present study contributes to the literature on IM by providing a systematized, multidisciplinary overview of the topic and proposing directions for future research. It advocates for a deeper integration of IM into knowledge management and digital transformation frameworks, emphasizing the need for new communication paradigms to address current challenges.

The research conceptually outlines the nodal importance of narration as a crucial tool supporting stakeholders’ moral imagination in the conception of desirable alternatives to the critical existing status quo. This due to the fact that stories enable people to better understand reality decreasing, at the same time, the ethical pressure related to decisions and actions. Narrative is indeed a central and characterizing human activity orienting – through projections – moral choices, thanks to the evaluation of prospective effects. This is a pathway for value creation based on cooperative generation of knowledge to fully understand the big picture, thereby overcoming problems, crises and trade-offs.
Into this groove, Artificial Intelligence (AI) can play a valuable supporting function thanks to its computing and creative capacity becoming a convenient assistant for stakeholders during the genesis of alternate representations as possible “what if…”. AI can in fact contribute to envision further scenarios, making moral imagination even more inventive. Taking into account the doubts and risks connected to AI, control and management of technological tools is needed by the involved stakeholders, to orient the solutions without, of course, limiting the innovative potential. This represents a royal road to overcome a well-known, diffused and increasing resistance and opposition to the cooperation between human and non-human agents.
The paper thus contributes to the concept of moral imagination by adding stakeholders’ stories co-design as a creative form of moral imagination and investigating the ancillary role of AI.
The limit of the work is represented by the earlier stage of the still ongoing analysis.

Over the years, industries have increasingly implemented the transdisciplinary approach to research complex topics such as planetary and human health. Indeed, transdisciplinarity removes boundaries between disciplines to create new frameworks and approaches for solving real-world problems. Such an approach is particularly crucial in healthcare due to the systemic understanding required to solve its emerging challenges. At the same time, innovation literature affirms that the development of current technologies, which are also emerging in healthcare, is by nature transdisciplinary. Studies on digital health solutions reveal their central role in providing treatments, especially non-pharmaceutical ones. These are often developed by combining disciplines such as medicine, psychology, and art to treat diseases related to the emotional and behavioural sphere of the human being, while reducing healthcare costs. The emerging digital health solutions named “Digital Therapeutics” (DTx) are central in providing these care pathways. DTx are evidence-based, software-driven medical interventions providing cognitive-behavioural and emotional therapies. Despite the practical and theoretical relevance to transdisciplinary research, innovation and knowledge management literature still lack a comprehensive understanding of how DTx enhance a transdisciplinary approach to non-pharmaceutical treatments. The authors investigated two DTx providing music therapy by implementing a qualitative multiple case-study to explore the potential of such technology as a driver of new knowledge management strategies in healthcare. Three mechanisms emerged from a thematic analysis. The findings contribute to the current literature on knowledge and innovation and the emerging stream of research on DTx, which are presented as catalysts for integrating diverse forms of knowledge within therapeutic research. Moreover, this research supports practitioners in understanding how to approach digital health solutions when providing non-pharmaceutical treatments, increasing the effectiveness and accessibility of care, and reducing unnecessary costs.

The digital transformation has significantly reshaped organizational structures, particularly through the widespread adoption of remote working, which accelerated during the COVID-19 pandemic. While existing literature has explored internal impacts such as productivity and work-life balance, less attention has been paid to the effects on external relational dynamics, especially in the public sector. This paper investigates how remote working influences relational capital in public administration, focusing on the perceptions of external users such as citizens and institutional stakeholders. Relational capital (encompassing trust, reputation, and institutional networks) is critical for public entities delivering essential services. Using a qualitative approach based on semi-structured interviews within a medium-sized Italian municipality, the study explores users’ experiences and perceptions of PA following the shift to smart working. Findings contribute to a growing body of research by addressing a literature gap concerning the implications of remote work on trust and relationship quality in the public sector.

The pharmaceutical industry faces significant challenges, including prolonged drug development cycles (10-15 years), escalating costs (exceeding $1 billion per drug), and mounting pressures to align innovation with sustainability and transparency. This study investigates the transformative role of artificial intelligence (AI) in addressing these challenges, with a focus on value co-creation and environmental, social, and governance (ESG) integration (Prahalad and Ramaswamy, 2000, 2004; Vargo and Lusch, 2004). By analyzing sustainability reports and strategic documents from Johnson & Johnson (J&J) and NVIDIA through qualitative content analysis, this research identifies four thematic topics: AI in Sustainability Report, Value Co-Creation, AI and ESG and Transparency and Reporting. The study aims to find answers starting from how AI is represented in J&J and NVIDIA’s reports, which initiatives are linked to the co-creation of value in drug discovery, development and distribution, to what extent are references to AI in line with ESG criteria, and finally what elements emerge regarding transparency and communication of AI practices.
Findings reveal that AI accelerates drug discovery through machine learning (ML) and deep learning (DL), enabling rapid analysis of genomic, proteomic, and clinical trial data. Applications such as virtual screening and predictive modelling reduce trial failures and optimize resource allocation, aligning with ESG goals like waste reduction and energy efficiency. The strategic collaboration between J&J and NVIDIA exemplifies value co-creation, merging pharmaceutical expertise with advanced computing to enhance R&D efficiency and patient-centric outcomes. For instance, NVIDIA’s high-performance computing (HPC) infrastructure supports J&J’s drug pipeline, reducing time-to-market while fostering sustainable practices.
The analysis highlights AI’s dual role as a driver of operational innovation and a catalyst for stakeholder-driven sustainability. Transparency in AI reporting emerges as a critical factor, with both companies emphasizing ethical AI deployment and open communication. However, disparities exist in how AI’s societal and environmental impacts are framed, underscoring the need for standardized ESG-aligned reporting frameworks. By bridging value co-creation theory (Prahalad & Ramaswamy, 2004) with empirical insights, this study demonstrates how cross-sector partnerships and AI integration can reconcile economic objectives with global sustainability imperatives. The research contributes practical insights for policymakers and industry leaders, advocating for collaborative, transparent AI adoption to advance both innovation and accountability in the pharmaceutical sector.

This paper explores how Artificial Intelligence can transform microfinance to promote social inclusion and value creation for immigrant communities. Despite immigrants’ significant potential to contribute to local economies, they often encounter substantial barriers to accessing formal financial services, including limited credit histories, regulatory challenges, and cultural or linguistic differences. AI technologies, particularly machine learning, predictive analytics, and Natural Language Processing, provide innovative pathways to overcome these challenges by enabling inclusive credit scoring, automated multilingual support, and scalable, personalized financial services.
Employing a Systematic Literature Review guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses and structured through the Population, Intervention, Comparison, and Outcome framework, this study critically evaluates 52 peer-reviewed sources to assess the effectiveness and impacts of AI-driven microfinance compared to traditional microfinance methods. Findings indicate that AI significantly enhances financial accessibility, facilitates immigrant entrepreneurship, and fosters broader social inclusion through culturally and linguistically adapted financial solutions.
A central contribution of this research is the integration of the Intellectual Capital framework, comprising human, structural, and relational capital, to examine the essential conditions for successful AI adoption in microfinance institutions. Human capital is crucial for developing skills in ethical AI deployment, structural capital ensures robust technological infrastructures, and relational capital builds essential trust between institutions and immigrant communities, thus ensuring sustainable and inclusive outcomes.
This study aligns with Sustainable Development Goals 8 (Decent Work), 9 (Industry, Innovation, and Infrastructure), and particularly SDG 10 (Reduced Inequalities). It provides actionable insights and strategic recommendations for financial institutions, policymakers, and fintech firms. To realize AI’s full potential in immigrant social inclusion, the paper emphasizes the necessity of ethical AI frameworks, targeted capacity-building initiatives, and trust-based service models, ultimately contributing to more inclusive and equitable societies.

This paper explores the transition of operational human resource management (HRM) tasks to artificial intelligence (AI) and examines the resulting opportunities, challenges, and strategic pathways. We develop a theoretical analysis grounded in recent HRM literature and Tursunbayeva’s (2024) configurational framework, which views HRM practices as consisting of operational, relational, and transformational dimensions. The study is conceptual in nature, integrating insights from technology adoption (e.g., the technology-organization-people perspective) and responsible AI principles to extend and refine existing theory. We argue that delegating operational HRM activities to AI can significantly enhance efficiency and decision-making—such as through automation of repetitive administrative tasks and data-driven analytics—while freeing HR professionals to focus on higher-value relational and strategic roles. However, this shift also introduces notable challenges, including algorithmic bias, privacy and transparency concerns, workforce skill gaps, and employee resistance to AI-driven change. To address these issues, we propose strategic pathways for organizations, emphasizing the adoption of Responsible AI governance (ensuring fairness, accountability, and human oversight), proactive change management and upskilling of HR personnel, and alignment of AI initiatives with broader HR strategy (e.g., leveraging AI to support diversity and inclusion goals). In doing so, the paper contributes to HRM theory by extending Tursunbayeva’s framework with a more detailed examination of the operational HRM–AI interface and by incorporating multi-level and human-centric considerations. The resulting model offers a holistic view of AI integration in HRM, highlighting that AI’s benefits in operational HRM are maximized only when accompanied by ethical safeguards and strategic alignment. The paper concludes with theoretical implications and recommendations for HR practitioners seeking to responsibly navigate the AI transformation of HRM.

This study investigates the relationship between Top Management Teams’ (TMTs) educational diversity and enterprise performance within the Upper Echelons Theory (UET) framework. It integrates data from ten qualitative interviews with senior managers and a quantitative survey of 765 small and medium-sized Latvian enterprises (SMEs) representatives. The study explores how educational background—a form of deep-level diversity—affects strategic decision-making, innovation, and financial outcomes across organizational contexts.
Findings suggest educational diversity is a valuable strategic asset, particularly in medium to large enterprises and knowledge-intensive sectors. Respondents highlighted that diverse academic backgrounds support problem-solving, particularly in crisis situations. However, the perceived value of education varied by demographic and organizational factors, including age, gender, language group, company size, and industry sector. While 64% of survey participants rated educational background as “very important” or “extremely important” for financial decision-making, others emphasized the complementary role of practical experience and collective managerial tenure.
The analysis confirms that education is not always the dominant factor in executive performance. Meanwhile, when combined with other dynamic traits such as risk tolerance and shared values, it significantly influences organizational outcomes. Sector-specific findings underscore that in industries with high complexity and change, such as technology, services, and manufacturing, educational diversity in TMTs plays a more critical role. In smaller companies—particularly those with 10 to 49 employees or annual revenues below €300,000—hands-on experience is often viewed as more important than formal education, as shown by lower support for educational diversity in these groups. In contrast, 63.92% of all surveyed enterprise representatives rated educational diversity in Top Management Teams (TMTs) as “very” or “extremely important” for financial decision-making, with this figure rising significantly in firms with over 50 employees or annual revenues above €5 million. Using these findings for companies in practical terms, it means that developing their leadership teams and being willing to stay competitive, should consider not only formal education but also ensure a diverse blend of academic backgrounds among top managers. The study supports the development of a mathematical model that incorporates both the educational and behavioural characteristics of TMTs. It also contributes to the broader understanding of how diversity management impacts firm-level performance and innovation in a regional context.

This study explores the evolving profile of Human Resource (HR) professionals in Italy through a longitudinal analysis of the skills possessed by new entrants in the HR area from 2017 to 2024. In the face of rapid digital transformation, sustainability challenges, and post-pandemic organizational changes, the role of HR has shifted significantly from administrative support to strategic business partnership. Based on data from the Excelsior Information System, the research investigates the development of ten key competencies, grouped into four categories—social, intellectual, volitional, and managerial—based on the framework by Kolot et al. (2022). Findings reveal that the most developed competencies among new HR specialists are teamwork, problem-solving, and flexibility/adaptability, with consistently high scores and low standard deviation, indicating strong and homogeneous preparation in these areas. In contrast, the capacity to apply Industry 4.0 technologies and communication in foreign languages remain underdeveloped and highly variable, highlighting key areas for future improvement. Digital competencies, while relatively strong, still show moderate variation across candidates, suggesting that digital readiness is progressing but not yet universally embedded. The analysis also highlights important longitudinal trends. Volitional and managerial competencies show both high average performance and remarkable stability over time, while social and intellectual skills present more heterogeneous trends. The data reflect a clear need for HR professionals to enhance their technological and international communication capabilities, particularly given their strategic role in managing change, fostering cultural alignment, and driving organizational transformation. This research contributes to the understanding of how the HR function is adapting to contemporary demands, emphasizing the need for continuous upskilling and alignment between education, professional training, and labor market expectations. The findings support the call for stronger collaboration between academia and organizations, to ensure that HR professionals are equipped with the necessary skills.