The global energy sector is undergoing a deep transition to mitigate climate change and enhance energy security through sustainable sources (International Energy Agency, 2023). Green hydrogen (GH2) has emerged as a strategic solution for decarbonizing hard-to-abate sectors, aligning with Paris Agreement targets to limit global warming to 1.5°C (Deloitte, 2023; Fuel Cells and Hydrogen 2 Joint Undertaking, 2019). The GH2 value chain operates as a complex adaptive system, characterized by nonlinear relationships, emergent behaviors, and evolving stakeholder interactions (Peyerl & van der Zwaan, 2024; Zhang & Li, 2024). This work examines the economic, social, and environmental impacts of innovations in GH2 production within a European-funded project focused on developing a photocatalytic reactor for water splitting into hydrogen and high-value co-products. Using an Agent-Based Model (ABM) developed in NetLogo, the research simulates stakeholder interactions, capturing emergent dynamics often overlooked by traditional top-down models (Macal & North, 2010). The model is able to estimate the number of plants required to meet Spain’s GH2 demand by 2030 (Pawelec et al., 2023) and highlights how stricter sustainability criteria can promote best practices, stimulate innovation, and open economic opportunities. The model demonstrates the strategic potential of GH2 in driving decarbonization, technological advancement, and social inclusion. Moreover, by embedding sustainability across the value chain, the ABM provides policymakers and industry stakeholders with actionable insights to optimize GH2 deployment, lower costs, and support the broader transition to a low-carbon energy system (World Economic Forum, 2021). This research contributes to the growing body of knowledge needed to guide future investments, policy frameworks, and cross-sector collaborations.

Organisational resilience, defined as the capacity to absorb and recover from shocks affecting an organisation’s operations, becomes increasingly more important in today’s environment. While existing research has mainly explored structural and strategic factors contributing to organisational resilience, the role of managerial decision-making modes in this context remains underexamined. This paper addresses this gap by developing an agent-based model that simulates how different modes of decision-making affect resilience. The model captures stylised organisations as collections of interdependent departments operating on performance landscapes with varying complexity. The study compares silo-based, sequential, collaborative, and proposal-based decision-making across 144 simulated scenarios, incorporating shocks of varying severity. The results reveal that collaborative and proposal-based decision-making modes enhance shock absorption and recovery in complex task environments, while simpler modes perform well in settings where tasks are less complex. Proposal-based coordination offers balanced performance.

Entrepreneurial education ecosystems are increasingly recognized as strategic environments within universities that foster entrepreneurial knowledge, culture, and venture creation. However, the interplay between institutional factors and educational outcomes in these ecosystems remains underexplored, particularly from a dynamic, system-level perspective. This study addresses this gap by developing a theory-driven agent-based model—the Entrepreneurial education ecosystems Model: “3E Model”—to simulate how formal (e.g., corruption, bureaucracy, access to credit) and informal (e.g., social perception of entrepreneurship, cultural diversity) institutional factors influence entrepreneurial learning dynamics in university-based ecosystems.
Grounded in entrepreneurial ecosystem theory, stakeholder theory, and institutional theory, the model captures the interactions of heterogeneous agents including students, faculty, investors, and institutional actors. The simulation results reveal that institutional quality significantly shapes the accumulation of entrepreneurial knowledge, the formation of entrepreneurial culture, and the motivation to launch startups. In particular, corruption and bureaucratic burden emerge as major impediments, reducing both knowledge diffusion and entrepreneurial activity. Conversely, favorable social perceptions of entrepreneurship significantly enhance ecosystem performance, with evidence of threshold effects: only after surpassing a certain level of societal support does venture creation accelerate. Cultural diversity also contributes positively by enriching knowledge exchanges, though its impact is more gradual.
The model demonstrates the non-linear and interdependent nature of institutional influence in 3Es, confirming that both formal (structural) and informal (cultural) institutions jointly determine ecosystem vitality. Practically, the 3E Model serves as a decision-support tool for universities and policymakers, enabling the simulation of interventions to strengthen entrepreneurial education. The findings suggest that aligning institutional reforms with cultural initiatives—such as reducing administrative barriers while promoting entrepreneurship as a legitimate career path—can generate synergistic effects that boost entrepreneurial outcomes. The study concludes by outlining future research directions, including model refinement through empirical calibration and the inclusion of feedback loops between education and institutional change.

In this dynamic and full-opportunity world, firms must face challenges in markets and technologies. One of the main assets that firms can use to address these challenges is innovation, and an important element is to define the most appropriate innovation strategy. Inside innovation strategy, fundamental decisions are about selection, acquisition, and timing. The innovation selecting consists of the selection of the innovations to develop; the innovation acquiring consists of how firms acquire the innovation (the main strategies can be simplified with insourcing (make), outsourcing (buy), or collaborating (collaborate). In particular, with respect to timing, firms are heterogeneous and two main approaches can be identified: technological leaders and technological followers that work on the technological knowledge developed by the technological leaders. In this paper, the two approaches have been investigated to study the impact on the firm’s performance. To conduct this investigation, the Patent Agent-Based Innovation Model and Simulator (PABIM has been enhanced. The PABIM is characterized by heterogeneous firms that are organized as a directed random graph. The use of an agent-based model allows for the exploration of emergent dynamics and their subsequent effects on performance. The results indicate that when a firm can adopt all the acquisition strategies (make, buy, collaborate), the optimal strategy concerning timing for firm performance is the leaders’ approach, independently of the sector innovation intensity. The study recommends that managers and policymakers adopt a comprehensive approach to innovation management, integrating pertinent technical knowledge related to product or process development with the necessary resources for effective implementation.

Circular economy has emerged as an industrial approach that aims to overcome the traditional “take-make-dispose” economic model, which is based on the intensive extraction of raw materials and the design of products with a limited lifespan. The circular economy approach consists in a closed system in which products, materials and resources are continuously reused, remanufactured and recycled. The implementation of circular economy principles in companies requires a gradual redesign of products, which consists of a concurrent design of products and related manufacturing processes that enhance competitiveness measures, rationalize product/process/resource design decisions and improve operational efficiency in product development. Given this premise, the transition of companies towards a circular economy paradigm challenges companies to rethink their linear approach of doing business, leading to the research dilemma of answering: Is it economically convenient for a company, in terms of economic margin, to develop circular products starting from a linear positioning? To answer this research question, an agent-based model and simulator (ABM) has been developed characterized by four kinds of heterogeneous firms: the supplier, the manufacturer, the user and the recycler.
The adoption of an agent-based model, which is coherent with the approaches of complexity science, allows for investigating the phenomenon considering its complex nature, and the consequent dynamic non-linear relations among the various actors involved: manufacturers, suppliers, customers and consumers, recyclers. Results show that the manufacturer only partially follows consumer sentiment, as it retains a large proportion of linear products. Even when consumer sentiment is low, it is in the manufacturer’s interest to move even slightly towards circular products.

Artificial Intelligence (AI) is rapidly transforming organizational practices and enabling more sustainable business models, especially in the context of the circular economy. While its potential is widely acknowledged, empirical evidence on the factors that influence AI adoption and its actual impact on circular economy practices—particularly in the context of small and medium-sized enterprises (SMEs)—remains limited. This study investigates the drivers of AI adoption among innovative Italian SMEs and assesses how AI contributes to the implementation of circular economy strategies. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study explores the role of four key constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—in shaping AI adoption decisions. It also examines the extent to which AI adoption promotes circular economy practices. The empirical analysis is based on data collected through a structured online questionnaire distributed to 1,664 innovative SMEs across Italy, of which 52 provided complete responses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique particularly suitable for testing complex models with small to moderate sample sizes. The findings partially validate the proposed theoretical framework. Performance Expectancy, Social Influence, and Facilitating Conditions are found to significantly influence AI adoption, while Effort Expectancy does not emerge as a significant predictor—suggesting that ease of use is less critical, possibly due to growing digital literacy or available technical support. Most importantly, the results show that AI adoption positively affects circular economy practices, supporting the view of AI as not only a technological asset but also a strategic enabler of sustainability. This study contributes to the literature by bridging the gap between technology adoption theories and circular economy research. It also offers practical insights for managers and policymakers aiming to promote digital and sustainable transitions. Emphasizing AI’s strategic value, the study encourages investment in both technological infrastructure and supportive organizational environments to foster innovation and sustainable development.

The transition toward Industry 5.0 has brought forth a new paradigm within maritime logistics and port governance, where the focus shifts beyond automation to embrace resilience, sustainability, and above all, human-centric innovation. In this context, Port Authorities, particularly those operating under the Landlord model, face unprecedented demands to align infrastructural and technological transformation with comprehensive workforce development strategies. While infrastructure and digital systems have been historically prioritised, the capabilities and adaptability of the human workforce—especially blue-collar maritime workers—have received comparatively less strategic attention.
This study investigates the alignment of workforce development with Industry 5.0 principles, focusing on the evolving competencies, training gaps, and strategic responses of Port Authorities operating under the Landlord model. Framed by the Resource-Based View and the Dynamic Capabilities Theory, the research explores how human capital—particularly blue workers—functions as a core asset for competitive advantage, technological readiness, and organisational resilience.
Employing a qualitative methodology, the study draws on twelve semi-structured interviews with executives, HR managers, and operational staff from Italy’s maritime sector. Thematic content analysis revealed three core patterns: a widespread digital skills gap among blue workers, the fragmented nature of training initiatives across port authorities, and a misalignment between strategic HR objectives and operational governance.
Findings suggest that for Port Authorities to meet the expectations of Industry 5.0, workforce development must be fully integrated into port governance frameworks. Human-centric innovation, environmental sustainability, and digital transformation are not achievable without a skilled and empowered workforce.
The performed analysis additionally underscores the need for cultural and institutional shifts that reframe blue workers as, active agents of technological co-creation and environmental stewardship.
Ultimately, the study advocates for the establishment of structured, collaborative skilling frameworks, potentially through regional Port Skills Councils or transnational maritime academies, to harmonise training standards and close capability gaps. Such interventions would not only strengthen organisational performance but also affirm the role of Port Authorities as key enablers of a resilient and inclusive maritime ecosystem in line with Industry 5.0 values.

The transition to Industry 5.0 marks a paradigm shift towards innovation ecosystems that prioritize human-centricity, sustainability, and resilience. This paper advances the understanding of innovation ecosystem governance by introducing the concept of the “Bridge Maker” – a strategic intermediary that plays a pivotal role in aligning diverse stakeholders, fostering collaborative governance, and embedding Industry 5.0 values systemically across ecosystems. Grounded in insights from Complex Adaptive Systems (CAS) theory, Open Innovation (OI), and recent developments in ecosystem orchestration and governance, the Bridge Maker (BM) is positioned as a crucial enabler of ecosystem adaptability and success.
Through a conceptual analysis, this paper addresses two primary research questions: (1) How do Bridge Makers align stakeholders to drive collaboration and innovation within human-centered Industry 5.0 ecosystems? (2) What role do Bridge Makers play in embedding sustainability and human-centric values as systemic ecosystem features? Drawing from recent theoretical advancements (Chesbrough, 2003; Holgersson et al., 2022; Tedesco, 2019) and extending the Adaptive Open Innovation Systems (AOIS) framework, the study proposes a model highlighting the competencies, behaviors, and governance mechanisms required for effective Bridge Making.
The findings suggest that BM operates through trust-building, sense-making, strategic foresight, and adaptive orchestration practices that promote long-term ecosystem resilience. In doing so, they bridge institutional gaps, overcome organizational inertia, and create the conditions necessary for sustainable innovation trajectories. The paper offers practical implications for innovation managers, policymakers, and educators seeking to operationalize Industry 5.0 principles within complex multi-actor environments.
Finally, the study calls for further empirical research to explore the dynamic practices of BM across various industry contexts and to develop actionable guidelines for their identification, training, and support within innovation ecosystems.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.