Financial digitalisation represents a systemic transformation that affects the entire business lifecycle, emphasising trust, transparency, sustainability, and innovation. Digital tools and platforms, such as crowdfunding and online peer-to-peer lending, increase access to credit and mitigate entry barriers, particularly for startups and small and medium sized enterprises. New technologies, including blockchain and big data, reduce information asymmetries, improve risk assessment, and strengthen stakeholder trust. The transparency of these platforms, associated with the use of reputational signals (for instance credit ratings and reviews), plays a pivotal role in attracting investors and optimizing capital allocation. The democratisation of access to finance supports innovative business models that are not just profit-driven but also oriented towards social and environmental value.
In this context, our research aims to systematically conceptualise digital finance tools, their specific benefits and challenges. The integration of advanced digital technologies into financial services allows greater access to lending resources and innovative funding models. Moreover, several issues and trends emerge, such as, the regulatory uncertainty, the data quality for privacy and cybersecurity, increasing use of AI to customise financial services and risk assessment. Specifically, our study presents a detailed analysis of the state of the art on the connections between digital finance and entrepreneurship, focusing on credit rating in the digital context. In addition, it also provides propositions through which firms can gain competitive advantage and highlights the democratization of credit and financial inclusion, made possible by reducing geographical and socio-economic barriers. Several implications emerge from a managerial point of view and from a theoretical perspective.

Enterprise architecture (EA) is a fundamental framework for aligning technology, processes, and information systems with organizational strategy. In the context of knowledge assets and artificial intelligence (AI), EA provides the necessary structure for effective knowledge management and AI integration. However, traditional EA models face limitations in handling dynamic knowledge flows and AI-driven insights. This summary explores EA’s role in managing knowledge, the impact of AI on knowledge assets, and necessary modifications to improve AI integration.
EA plays a crucial role in structuring and governing knowledge within organizations. Frameworks like TOGAF and Zachman facilitate the creation of repositories, workflows, and governance structures to optimize knowledge storage, retrieval, and dissemination. In AI-driven organizations, EA ensures that AI initiatives align with knowledge management strategies, preventing disruptions while enhancing decision-making capabilities.
Knowledge serves as both an input and output in AI systems. High-quality, structured data is essential for training AI models, while AI-generated insights contribute to organizational knowledge. However, poorly curated or siloed knowledge assets can lead to biased models and unreliable results. Enterprise architects must ensure the integrity, accessibility, and interoperability of knowledge assets.
Conventional EA models struggle with:
• A lack of flexible standards for data pipelines, making it difficult to integrate AI-driven DataOps and MLOps.
• Siloed architectures that limit knowledge-sharing and AI effectiveness.
• Static frameworks that do not support real-time knowledge processing.
To enhance AI integration, organizations should:
• Adopt modular, dynamic EA metamodels supporting microservices and AI scalability.
• Implement knowledge-centric frameworks incorporating semantic models and ontologies.
• Integrate DataOps and MLOps pipelines to facilitate continuous AI model refinement.
• Foster a knowledge-sharing culture through training and transparent governance.
By evolving EA frameworks and fostering a collaborative organizational culture, businesses can optimize knowledge management and AI utilization for sustained performance.

Startups’ success is strongly influenced by the capabilities, traits, and dynamics of their founding teams. While the role of co-founders in shaping venture outcomes is well established in entrepreneurship literature, little is known about how they are selected in contexts where founding teams are externally assembled, such as within venture studios. Venture studios represent a distinct form of new venture creation (NVC), where organizations (rather than individuals) initiate and structure the entrepreneurial process. Central to this model is the strategic recruitment of external co-founders who are expected to lead ventures they did not originate. The goal of this paper is to investigate how co-founder selection processes are designed and implemented within venture studios, with a focus on the underlying decision criteria and timing strategies adopted in practice.
This research adopts a qualitative, exploratory design based on multi-case illustrative studies of three European venture studios: Rocket Internet, Hexa, and Mamazen. Findings reveal that the co-founder search in venture studios is a deliberate strategic activity. Rather than being recruited solely for executional tasks, co-founders are selected for their entrepreneurial mindset, risk appetite, and capacity to take full ownership of the venture. Venture Studios do not hire CEOs but rather recruit founder-like individuals to lead ventures alongside them, offering shared equity and full operational responsibility from day one. This inversion of traditional entrepreneurial logic positions venture builders as orchestrators of resources and talent, aligning with Gartner’s (1985) view of entrepreneurship as an organizing activity and the Resource-Based View’s emphasis on leveraging human capital.

This paper explores how AI startups articulate their value propositions—a critical element in defining and communicating value to customers. AI startups, in fact, struggle to align their technological capabilities with real market needs. Drawing on an exploratory multiple-case study of ten Italian AI startups, this research tries to investigate emerging patterns and main challenges AI startups face in articulating the value proposition. Preliminary findings reveal a fragmented landscape: while some startups adopt detailed use cases and visuals to support their claims, others rely on minimal communication and invite direct contact. Value propositions often emphasize efficiency and cost savings but lack quantification and substantiation. The study highlights the tension between technological sophistication and market clarity, especially in cross-industry applications. By focusing on how AI startups construct their value propositions, this research contributes to both AI entrepreneurship literature and broader discussions on value proposition strategy in emerging, high-tech markets.

The pursuit of sustainable and accelerated growth in small- and medium-sized enterprises (SMEs) can be effectively promoted through the application of Growth Hacking (GH) strategies. Improving the performance of GH practices within SMEs has therefore emerged as a critical objective for both practitioners and researchers. Drawing on the theoretical foundations of the Technology-Organization-Environment (TOE) framework, this article proposes an integrated analytical model to explore the determinants of GH performance in SMEs. Based on a sample of SMEs actively engaged in GH initiatives, the research will employ fuzzy-set qualitative comparative analysis (fsQCA) to examine the configurational relationships between technological, organizational, and environmental factors that influence GH outcomes. It is expected that: (1) no single condition will be sufficient on its own to achieve high GH performance; (2) multiple and equally effective combinations of internal and external factors will lead to successful outcomes, supporting the principle of equifinality; and (3) key technological capabilities, such as digital transformation and data analytics, along with organizational adaptability and responsiveness to environmental dynamism, will emerge as critical elements in successful configurations. Furthermore, relying solely on technology adoption or external market conditions may prove insufficient to maximize GH performance without appropriate organizational adaptation. These findings enrich the current understanding of the complex mechanisms influencing GH performance and offer practical insights for SMEs wishing to design more effective and resource-efficient growth strategies.
Overall, this research is expected to make a threefold contribution. First, it enriches the theoretical foundations of GH research by applying and extending the TOE framework in a configurational context. Second, it advances methodological practice by employing fsQCA to uncover complex causal relationships often missed by traditional methods. Third, it provides actionable insights for practitioners and policymakers by identifying strategic pathways to optimize GH practices according to different organizational contexts and resource profiles.

The advent of the Fourth Industrial Revolution has marked a profound shift in the global economy, driven by the integration of artificial intelligence (AI), the Internet of Things, and quantum computing. AI stands out as a transformative force, profoundly reshaping industries, labour markets, and economic interactions. Also, AI’s widespread implementation generates both opportunities and disruptions, with forecasts of the displacement of millions of jobs and the creation of new roles. However, these transformations vary significantly across regions and demographics, with countries like Italy facing unique challenges, including a substantial digital skills gap among the working population. Despite the growing interest in AI’s technological and economic implications, limited research has investigated how specifically entrepreneurs adopt and utilise AI technologies in their daily business practices. While existing literature has focused predominantly on sector-specific applications—most notably in healthcare—entrepreneurship as a dynamic and multifaceted phenomenon remains underexplored in this context. Indeed, our study investigates how entrepreneurs adopt AI into their professional routines, the digital competencies they display during this process, and the learning ecosystems that support their acquisition of AI-related knowledge. To reach our research aim, we adopt a qualitative approach, conducting semi-structured interviews with a sample of entrepreneurs operating in Southern Italy. Through thematic analysis, we aim to highlight the existing patterns of AI adoption and the support mechanisms within the entrepreneurial ecosystems. The findings contribute to a deeper understanding of the AI adoption for entrepreneurs and their main digital competencies gap, offering theoretical insights and practical implications for policymakers, educators, and innovation stakeholders.

The intersection of sustainability, inclusion, and entrepreneurship is increasingly recognised as a strategic priority for contemporary businesses and societies. Among underserved groups, people with disabilities (PwDs) present both a significant entrepreneurial potential and unique barriers to business creation and operation. While self-employment offers a promising pathway towards economic inclusion for PwDs, research into the specific mechanisms enabling this form of disadvantage entrepreneurship remains fragmented and under-theorised (Klangboonkrong and Baines, 2022; Bhardwaj et al., 2023). This paper critically reviews the business and management literature to examine how emerging digital technologies can act as affordances facilitating entrepreneurial activity among PwDs.
Drawing upon affordance theory (Gibson, 1979; Leonardi, 2011), this study employs a qualitative, problematising literature review methodology (Alvesson and Sandberg, 2011, 2020), sourcing peer-reviewed articles across entrepreneurship, business, management, and disability studies. A theoretical framework is developed by synthesising the findings, identifying four key technological affordances: communication and interaction, business creation and operation, learning and capability development, and community and identity formation.
The analysis demonstrates that digital technologies are not neutral enablers but dynamic socio-material actors whose affordances depend on user capability and institutional context. It reveals how digital solutions can meaningfully reduce systemic barriers, enhance market access, and foster entrepreneurial identity among PwDs. This study contributes to extending the understanding of inclusive entrepreneurship within a digital economy, proposing that inclusive technology strategies must be embedded in policy and education, digital platform design, and business support services.
The paper advocates for future empirical research to validate and refine the proposed framework across diverse PwD populations and socio-economic contexts. It emphasises the need to account for intersectional factors and to evaluate the long-term sustainability of technology-enabled ventures. Ultimately, enabling entrepreneurs with disabilities through technology is not merely an ethical imperative but a strategic investment in unlocking innovation, diversity, and sustainable growth.

The increasing integration of digital technologies (DT)—such as artificial intelligence, big data, and extended reality—is transforming the domain of academic entrepreneurship. Despite the growing interest in digital academic entrepreneurship, extant literature rarely distinguishes the unique institutional challenges faced by student entrepreneurs, whose ventures are typically less resource-intensive, more agile, and deeply embedded in digitally mediated learning environments. Moreover, while studies acknowledge the transformative potential of DT, few have examined their dual interaction with formal and informal university mechanisms, especially from a comparative perspective across institutional ecosystems. Finally, existing frameworks often overlook how students appropriate digital tools and platforms to navigate institutional constraints, thus underestimating student agency in shaping entrepreneurial outcomes in the digital age. This study addresses that gap by investigating how formal and informal university-based mechanisms influence the entrepreneurial motivations and capabilities of student founders in digitally evolving environments.
Grounded in the conceptual frameworks of Rippa and Secundo (2019) and Guerrero and Urbano (2012), the study adopts a qualitative, comparative case-study approach. Six student-led digital startups were analysed across two entrepreneurial universities—University of Naples Federico II (Italy) and Universitat Autònoma de Barcelona (Spain)—using in-depth interviews and documentary analysis. Findings reveal that while formal structures such as entrepreneurship education and technology transfer systems are widely available, they often lack practical alignment with the needs of digital ventures. Informal factors—like cultural perceptions of entrepreneurship, experiential learning practices, and exposure to role models—also play a crucial role, yet their effectiveness varies across institutional contexts.
The study advances two key propositions: first, that universities providing access to advanced digital skills and specialised technical resources are more likely to foster the successful development of digital startups; and second, that institutions facilitating access to online educational resources and entrepreneurial networks significantly enhance the success potential of student-led ventures. These findings demonstrate how DT reshape the interplay between institutional structures and entrepreneurial behaviour, offering theoretical contributions to digital academic entrepreneurship and practical insights for higher education institutions aiming to cultivate inclusive and future-ready innovation ecosystems.

The pervasive integration of artificial intelligence (AI) is fundamentally reshaping societal and business landscapes, presenting both significant opportunities and challenges, particularly within the entrepreneurial sphere. AI’s potential to alter venture conception, development, scaling, market entry, and competitive dynamics necessitates a re-evaluation of traditional entrepreneurship theories. As a key enabling technology, AI revolutionises core entrepreneurial processes, from ideation to market expansion, fostering novel forms of value creation and opportunity identification. However, despite acknowledging the influence of digital technologies, a comprehensive understanding of specific research trends concerning AI’s role, especially following the widespread adoption of generative AI tools like ChatGPT, remains underdeveloped. This study addresses this gap through a bibliometric systematic literature review. Utilising the Web of Science database and the “bibliometrix” R-package, we employ thematic evolution analysis to map the trajectory of research topics at the intersection of AI and entrepreneurship, specifically comparing the pre- and post-ChatGPT periods. We aim to visualise the field’s structure, identify influential contributions, and track thematic shifts. Anticipated findings include the emergence of research streams focused on generative AI’s impact on venture creation, ethical considerations, evolving decision-making processes, and the new skills required for AI-driven entrepreneurship. This analysis seeks to provide practical insights for entrepreneurs leveraging AI tools and highlight knowledge gaps and future research directions for academics, ultimately contributing to a deeper understanding of how AI is shaping the future of entrepreneurship.

In an era of rapid technological advancement, digital transformation has emerged as a critical driver reshaping human resource management in public sector organizations. While talent management has long been a cornerstone in the private sector, the public sector faces unique challenges—including bureaucratic rigidity, limited resources, and evolving workforce expectations—that complicate efforts to attract, retain, and develop skilled personnel. Today, talent management strategies must strike a balance between technological innovation and human-centric approaches that prioritize employee well-being, engagement, and ethical considerations. Despite growing scholarly interest, research on talent management in the context of digital transformation within the public sector remains fragmented across disciplines and methodological approaches.
To address this gap, the present study employs a bibliometric analysis, using the Bibliometrix R package, to systematically map the literature on digital transformation and talent management in public administration. Findings reveal a significant acceleration of academic output from 2020 onward, coinciding with the pandemic-driven digitalization of public services.
Thematic analysis highlights innovation, sustainable development, leadership, and perception as core emerging themes, alongside rising interest in AI and knowledge management. However, areas such as employee well-being, lifelong learning, and digital inclusion remain underexplored.
This paper offers a comprehensive overview of the evolving academic landscape, underscoring the need for future research to integrate both technological and human dimensions of talent management in the public sector. In doing so, it contributes to advancing a more resilient, agile, and inclusive public workforce capable of navigating the challenges of digital transformation while enhancing public value and citizen trust.

The adoption of artificial intelligence (AI) has become a strategic priority for organizations of all sizes, driven by pursuit of process optimization, efficiency gains, and enhanced decision-making. Yet, the effectiveness of AI depends not only on technological implementation but on how organizations integrate it with human-centric values –a challenge that remains underexplored in the context of corporate governance and innovation ecosystems.
This paper explores how corporate governance functions as an instrumental factor in institutionalizing human-centric AI adoption, comparing practices across large organizations, SMEs, and startups within the Rome Technopole (RT) innovation ecosystem. We argue that governance plays a critical role in fostering ethical AI adoption, yet human-centricity should not be treated merely as an afterthought. Instead, it must be a constitutive principle, embedded in the design of the innovation ecosystem itself. Using data from the Rome Technopole Observatory on AI, this exploratory study investigates how organizational size shapes AI strategies and perceived alignment with human-centric values. Findings reveal that structural changes (e.g., dedicated AI teams in large organizations) do not necessarily lead to more human-centric practices or perceptions that AI is aligned with human values. This underscores a critical gap: widespread AI adoption does not guarantee alignment with human-centric principles, which highlights the need for governance that fosters human-centricity at a foundational level.
To address this, based on Sangiovanni Vincentelli’s meet-in-the-middle principle, we outline the necessary conditions for developing an innovation ecosystem grounded in human-centric principles. Furthermore, we consider organizations as social, dynamic, and adaptable systems and the RT innovation ecosystem as a context-dependent ecosystem responsible for interactions between its organizations. This theoretical exploratory study paves the way for future empirical research into RT and others innovation ecosystems.
In conclusion, this study posits that corporate governance can be an instrumental mechanism for sustaining an innovation ecosystem in which human-centricity is constitutive. These insights are particularly relevant for organizations operating within collaborative innovation ecosystems, and the innovation ecosystem itself, as they emphasize the alignment between technological progress and societal values as a prerequisite for sustainable and integrative innovation.

This study proposes a systematic literature review aimed at analyzing three key thematic areas: the conceptual and operational adoption of new operational paradigms related to the use of technology in the business environment (Industry 5.0), sustainability strategies in new business models (sustainability) and the concept of well-being in the workplace. The study examined 21 scientific articles selected from the Scopus and Web of Science databases, published in the period 2020-2024, considering the following keywords: industry 5.0, sustainability and well-being. The results highlight the crucial role of advanced technologies – such as artificial intelligence, collaborative robotics and the Internet of Things – in fostering safer, more efficient and well-being-oriented production environments for workers, with a positive impact on the overall sustainability of industrial systems. The ‘Industry 5.0’ phase is an evolution of the previous Industry 4.0, shifting the focus from pure automation to the enhancement of human capital, the ethical integration of technologies and the promotion of human-machine cooperation. It moves towards a society that is based on the use of technology for a more human, inclusive and resilient society, where technology is at the service of human well-being (Society 5.0). Findings reveal that technology is configured not as an end, but as a means for the improvement of collective well-being and quality of life and lastly for production systems that evolve by placing the person at the center: the goal is to reach the ethical integration of technologies through the human-machine collaboration.

The integration of Artificial Intelligence (AI) and Knowledge Management (KM) in healthcare is transforming decision-making, resource allocation, and service efficiency, aligning with the United Nations’ Sustainable Development Goals (SDGs). AI-driven KM systems enhance clinical decision-making, optimize disease detection, and expand medical access, particularly in underserved areas, supporting SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities). Additionally, AI contributes to SDG 4 (Quality Education) by improving medical training and SDG 13 (Climate Action) through energy-efficient healthcare solutions. Despite its potential, the adoption of AI in healthcare faces significant challenges, including data privacy concerns, algorithmic biases, and regulatory uncertainties. The lack of comprehensive studies assessing AI’s measurable impact on sustainability further limits its large-scale implementation. This study addresses these gaps by analyzing how AI-driven KM systems support SDGs and identifying key obstacles to their integration. The research conducts a bibliometric and thematic analysis and examines 1,095 peer-reviewed papers from the Scopus database. Findings highlight AI’s role in enhancing efficiency, enabling knowledge-sharing, and improving resilience in healthcare systems. However, concerns regarding ethical governance, equitable access, and technological disparities underline the necessity of strong policy frameworks to ensure responsible AI deployment. This study contributes to the discourse on AI’s role in healthcare by providing a structured analysis of its impact on SDG achievement. Finally, the article addresses future research perspectives as joint scholars’ and practitioners’ analyses.

In recent years, Artificial Intelligence (AI) has gained significant attention across disciplines and industries, with the adoption of Generative Artificial Intelligence (GenAI) in organizations accelerating, particularly through the introduction of user-friendly conversational chatbots. While numerous studies have investigated either the factors influencing AI adoption within organizations or the effects of AI adoption on organizational performance, the role of human-centered factors in shaping its adoption and their subsequent impact on employee quality of working life remains largely unexplored. To address this gap, and drawing on socio-technical systems theory, this study proposes a theoretical model investigating the interplay between key human-centered factors, GenAI adoption, and employee quality of working life. Specifically, employee trust in GenAI is identified as a key human-centered antecedent of GenAI adoption. GenAI adoption is conceptualized through two main constructs: GenAI use and GenAI hedonic experience. Workplace well-being is used as the indicator of employee quality of working life. The model is tested using Partial Least Squares Structural Equation Modeling on survey data collected from 214 Italian professionals with experience in using GenAI in their work activities. The findings demonstrate that employee trust in AI positively influences both GenAI use and GenAI hedonic experience, and that these two factors, in turn, strongly impact workplace well-being. Additionally, while trust in AI also directly affects workplace well-being, its impact is weaker compared to the effects of GenAI use and GenAI hedonic experience. This study provides important practical implications for organizations aiming to improving employee well-being through the adoption of GenAI. Additionally, limitations of the study are discussed, along with suggestions for future research directions.

Innovative work behaviour (IWB) may be crucial to enable middle managers of public healthcare organizations (PHOs) to effectively fulfil their hybrid role of doctor-managers. However, psychological and cognitive boundaries associated with role hybridization can inhibit IWB. To overcome these criticalities, knowledge should be dynamically managed within PHOs. Grounded in the social identity theory, the present study aimed to explore the development of knowledge transfer at the individual-level of analysis by investigating the influence of doctor-managers’ organizational identification (OI) on their IWB, through the indirect effect of satisfaction with the managerial role. A survey was administered to doctor-managers working in Italian PHOs. A linear regression analysis was performed to test the research design. The findings demonstrated that doctor-managers’ OI address a greater satisfaction with the managerial role; in turn, this latter positively influences doctor-managers’ IWB. This study provides new insights into the individual determinants of IWB in knowledge-intensive public organizations like PHOs. In doing so, it contributes to the ongoing discussion on the pivotal role of knowledge transfer dynamics as a precursor of IWB by unleashing the potential of OI. PHOs’ top management should regulate doctor-managers’ identity by providing an environment fostering the transfer of knowledge supporting them in performing their hybrid role. This may be achieved through managerial practices that facilitate vertical and horizontal integration and knowledge exchange.

This paper aims to investigate how investment in artificial intelligence interacts with automation-related knowledge risks to shape the resilience of small- and medium-sized enterprises in the Italian context. The rapid advancement of digital transformation requires an extensive understanding of the link between innovative technologies, such as artificial intelligence, and the management of knowledge risks linked to automation, notably related to improving the competitive advantage of small- and medium-sized enterprises. This study employs knowledge risk theory to investigate how investments in artificial intelligence, alongside the challenges introduced by automation, collectively influence organizational resilience and innovation capacity. The empirical foundation of this research is based on secondary data collected across 20 Italian regions and various industries. The dataset was obtained from Confartigianato, a leading association of small- and medium-sized enterprises in Italy. The data were then analyzed using a quantitative approach. The results showed that areas with elevated automation-related knowledge risks are more inclined to invest in artificial intelligence, suggesting a reactive rather than proactive investment approach. The results also highlight varied geographical patterns related to investment levels, risk exposure, and digital readiness, with significant discrepancies in digital adoption and the critical role of knowledge risk as an intermediary factor in the link between artificial intelligence investment and organizational resilience, suggesting that beneficial outcomes are contingent on proficient risk management. This study aims to make a substantial contribution to the current literature on knowledge risk by investigating its application in the context of artificial intelligence and automation deployment in small- and medium-sized enterprises, thereby informing management and regulatory approaches.

Early-stage startups face unique challenges in Human Capital (HC) management, as traditional Human Resource Management (HRM) practices, designed for established firms, overlooking the unique organizational dynamics of nascent ventures. This exploratory qualitative case study examines HRM practices in early-stage startups through a cross-case analysis of eight cases: two each from France, Italy, and the United Kingdom, complemented by one case each from Switzerland and Tunisia. We conducted semi-structured interviews with eight startup founders and three early-hire employees, enabling comprehensive perspective triangulation through multiple stakeholder viewpoints within each case organization. Through the theoretical lenses of Resource-Based View (RBV) and contingency theory, this study examines how startups orchestrate their HR resources and adapt their management systems. The findings demonstrate the imperative for adaptive HRM strategies in early-stage startups’ dynamic environments during critical phases of hiring, onboarding, and retention. The study’s primary contribution is extending the startup HRM literature by introducing a flexible management of a fractional HR management model, which provides startups with a cost-effective, expertise-driven solution for managing essential HR functions, including talent acquisition, onboarding, development, and retention. The research advances both theoretical understanding of HRM in nascent ventures and provides actionable insights for startup founders in optimizing their HC management strategies within resource constraints, particularly during their founding years.

In recent years, growing societal attention to gender bias and inequality has prompted Universities to reconsider their institutional practices, including the use of inclusive language in formal communication. As central actors in the production and dissemination of knowledge, academic institutions bear a cultural responsibility to ensure that their communication practices reflect principles of equity and inclusion. This study explores the extent to which written communications addressed to students, either by university faculty or generated by artificial intelligence (AI), adhere to inclusive language practices.
The research adopts a qualitative design based on content analysis and evaluates 63 announcements authored by faculty members from two Italian universities, alongside 39 messages produced by three generative AI systems—ChatGPT Basic, Deepseek, and Gemini 2.0 Flash. The analysis is guided by 16 indicators of inclusive language use, drawn from the guidelines developed by Sabatini (1987) and revised by Somma and Maestri (2020). Each message is classified as inclusive, non-inclusive, or partially inclusive, based on the presence or absence of these indicators.
Findings show that inclusive communication practices are strongly gendered: female faculty members display a much higher rate of inclusive language use (48%) compared to their male colleagues (18%). Notably, no male full professor in the sample used inclusive language. Disciplinary differences also emerge, suggesting that the adoption of inclusive language is influenced by specific cultural norms within academic fields. Among AI systems, Gemini 2.0 Flash produced the highest share of inclusive outputs (38%), followed by ChatGPT (23%) and Deepseek (8%). However, the overall low performance of AI models indicates that inclusive principles are not yet systematically embedded in their training.
This paper contributes to the literature by combining human and AI communication analysis under a unified framework and offering empirical insights into how inclusive language is—or is not—implemented in practice. The results underscore the need for targeted interventions, both educational and technological, to promote inclusive communication in academia. By advancing this dual focus on human and AI-generated texts, the study highlights inclusivity not only as an ethical imperative, but also as a strategic priority for fostering institutional credibility and evolution.

The purpose of this research is to examine transformational leadership impacts open innovation through the mediating role of commitment-based human resource management practices. A quantitative survey was conducted to collect data from a sample of 169 SMEs. The findings indicate a considerable positive correlation between transformational leadership and inbound open innovation in SMEs, as demonstrated by the quantitative survey analysis conducted with SmartPLS version 4.1.0.9. Furthermore, commitment-based human resource management methods work as a significant mediator between transformational leadership towards and innovation and the acquisition of knowledge within a company, referred to as inbound open innovation. The results underscore the imperative of developing organizational leadership that promotes change, creativity, trust, and employee commitment to enhance organizational openness. Open innovation is a recognized organizational approach that promotes transformation, enhances the competitiveness of SMEs, and stimulates overall economic growth. The results compel policymakers and decision makers to concentrate more on a transformational leadership style that can facilitate transformation and boost employee loyalty to the firm. Consequently, a culture of openness will be fostered, resulting in a more favorable impact on the performance of SMEs.

The goal of the proposed paper is to provide a preliminary Artificial Intelligence (AI) approach addressing the analysis on the prevention of diabetes. The study is performed within the framework of the research project Telediabetolab and focuses on the check of the supervised Support Vector machine (SVM) algorithm to define a method to perform a data processing analysis based on predicted diabetes risk. The preliminary study highlights the possibility of defining a multi-parametric approach to prevent the chronic condition of diabetes by considering the pre-diabetes condition. The SVM data processing is executed by analyzing different parameters such as blood pressure, body weight, glucose, and blood ones. The predicted results show that there could be cases of diabetics who might not be diabetic and cases of non-risk that could degenerate.