This study investigates the dynamics of digital transformation in rural cultural tourism. It explores how rural territories can engage with digital innovation through context-specific strategies that enhance cultural heritage, territorial identity, and stakeholder collaboration.
The research adopts a qualitative methodology based on a SWOT analysis, drawing from semi structured interviews, participatory workshops, field observations, and desk research. This approach allows for a critical assessment of internal and external factors influencing digital transformation processes in rural tourism areas.
The analysis highlights a series of structural challenges, including limited digital infrastructure and skills gaps, but also identifies unique opportunities tied to community cohesion, authentic local narratives, and the potential for slow, sustainable tourism models. The findings emphasize the importance of bottom-up approaches and multi-actor engagement in shaping digital strategies that are both place-sensitive and innovation-oriented.
The paper contributes both to literature and to managerial and policy practices by offering a grounded perspective on rural digital transformation, moving beyond generalized frameworks to focus on localized innovation pathways. It positions cultural heritage not as a constraint but as a resource for co-creating digital solutions tailored to rural tourism contexts.

This study explores the role of models and tools in supporting innovation processes and digital transformation within cultural tourism ecosystems. It emphasizes the need for a shift for cultural tourism organizations towards digital innovation and innovation processes and highlights the importance of collaborative networks that actively engage stakeholders in the co-creation of smart tourism solutions. The research identifies participatory methodologies, particularly participatory events, as strategic levers to foster innovation and digital awareness in the sector.
A qualitative research design was adopted, combining desk-based analysis of existing literature with a multiple case study methodology. A series of thematic focus groups held in the Basilicata region provided empirical insights into how participatory formats can support digital transformation and innovation in cultural tourism.
The findings reveal key challenges and opportunities for fostering digital transformation and innovation in cultural tourism. The study outlines emerging models, tools, and participatory formats that enhance stakeholder engagement and co-design processes, while also identifying structural barriers and enablers critical to success.
The paper contributes to the literature by systematizing participatory practices as catalysts for digital transformation and innovation in cultural tourism. It offers practical guidelines and raises new questions for future research on innovation dynamics in rural tourism ecosystems.

Building on a previously developed Digital Maturity Model (DMM) tailored for cultural tourism, this paper explores the application of the model in rural tourism contexts. While digital transformation (DT) is widely recognized as a strategic imperative, rural areas often face unique barriers—including limited infrastructure, digital skill gaps, and organizational inertia—that hinder their digital evolution. This study aims to assess the digital maturity of rural tourism operators in selected pilot areas in the Basilicata region, offering insights to guide targeted DT strategies.
The research adopts an empirical approach by applying the seven-dimensional DMM—comprising Strategy, Organization, Culture, Employee, Tourists, Technology, and Operations—to a sample of rural tourism and cultural operators. Data were collected through surveys and interviews conducted within pilot areas engaged in the Tech4You PNRR initiative.
The assessment reveals heterogeneous levels of digital maturity across rural operators. While strengths emerged in cultural orientation and visitor engagement, significant gaps were observed in areas such as strategic planning, technological infrastructure, and operational data use. These findings suggest a need for differentiated support policies and context-sensitive digital transformation roadmaps.
This paper extends the applicability of the DMM to rural tourism ecosystems, demonstrating its relevance beyond urban or institutionalized cultural tourism settings. It contributes empirical evidence to the literature on digital transformation in marginal areas, helping bridge the gap between maturity models and real-world application.
The study provides a diagnostic tool for local authorities, innovation agencies, and tourism stakeholders to map digital readiness, prioritize interventions, and co-design tailored DT strategies in rural contexts. It also opens pathways for replicability in other regions with similar territorial challenges.

In this article, we will explore innovation management in such an important sector as tourism, focusing specifically on the hotel industry. Within the hotel industry, innovation has been a major focus for some years now, moving away from the popular phrase that traditionally circulates by word of mouth, “This has always been done this way.” Proof of this is that highly important organizations in the sector such as ITH (Hotel Technology Institute in Spanish) are holding highly interesting events such as the one held in 2020: “Balance between technology and investment, common sense and people, key in ITH Hotel Virtual Innovation Lab”. Because the current situation in the sector is changing, both for the hotelier and the customer, it is essential to be able to compare what has been studied so far with the current reality. In the past, guests would go to a hotel and find things they didn’t have at home: a larger TV, a better mattress, a jacuzzi… this meant that innovation took a backseat to customer attraction. Nowadays, it’s difficult to surprise guests with such basic items that they normally have at home that are just as good or better, so innovation has taken center stage and gained significant importance. We could say without fear of being wrong that today the hotel sector is one of the most interested in innovation in all its aspects (technological, marketing and processes) without a doubt. That’s why we consider it important to conduct a study and see if the reality of the sector is surpassing the academic studies conducted on it. To contrast this reality, it is important to obtain information directly from the most relevant players in the sector and conduct as many interviews as possible.

The rapid advancement of digital technologies has opened new possibilities for cultural heritage institutions (CHIs), enabling digitization and AI-supported initiatives that improve preservation, accessibility, and public engagement. However, CHIs often face significant challenges in evaluating and selecting appropriate projects due to fragmented resources, institutional complexity, and existing decision-support frameworks prioritize financial and technical feasibility while neglecting cultural and social dimensions. This study addresses a critical gap by presenting a micro-level decision-support framework specifically designed to guide CHIs in the evaluation of digitization and AI-supported projects. The framework was constructed through a three-stage mixed-method research design that combined a systematic literature review (SLR), focus group discussions, and the Delphi technique. It draws on the expertise of professionals from the European cultural and creative industries, including representatives from CH institutions and ethnic minority groups. Their diverse perspectives ensured that the framework reflects both technical and cultural priorities. The process resulted in a validated set of 43 criteria, grouped into six categories: finance and investment, employment and personnel, market, accessibility, social impact, and cultural heritage object. The framework offers CHIs a comprehensive but context-sensitive tool to guide structured, evidence-based decision-making. It captures both tangible and intangible project dimensions and supports strategic planning that aligns with institutional missions and stakeholder expectations. The study contributes theoretically by operationalizing cultural value and institutional priorities in micro-level evaluation, addressing the limitations of market-oriented models. It also offers practical value by enabling CHIs to navigate trade-offs between innovation, feasibility, and cultural integrity. While grounded in project-level application, the framework also serves as a foundation for the application of multi-criteria decision-making (MCDM) methods and future research on institutional-level decision making. The findings improve CHI’s ability to make informed, inclusive, and forward-looking investment decisions in a rapidly changing digital world.

The transition from linear to circular manufacturing systems (CMS) is essential for manufacturing firms aiming to enhance sustainability and mitigate resource depletion. Despite clear environmental and economic benefits, the adoption of circular economy (CE) principles within the manufacturing sector remains limited, largely due to significant technological, operational, and organisational barriers. This study addresses these issues by exploring the specific challenges that manufacturing companies face during their transition to CMS and proposes digital technologies to effectively overcome these barriers. Utilising an exploratory, qualitative, single-case study approach, we investigated a European white goods manufacturer implementing a refurbishment centre aimed at establishing circular practices within their laundry appliance segment. Through thematic analysis, we identified three primary challenges: uncertainty regarding the condition of returned appliances due to limited consumer data sharing, insufficient durability and longevity data from laboratory tests of new models, and operational inefficiencies due to inexperience with dismantling and refurbishing processes. To address these challenges, we propose advanced digital solutions leveraging Internet of Things (IoT), Artificial Intelligence (AI), and Augmented Reality (AR) technologies. Specifically, IoT and AI-based systems are recommended for comprehensive product tracking, real-time condition monitoring, predictive maintenance, and informed decision-making, significantly improving data availability and accuracy for refurbishment operations. Additionally, we suggest AR-enhanced workstations to assist workers in dismantling processes, enhancing operational efficiency, reducing error rates, and facilitating knowledge management across diverse product models. Our findings extend the current body of knowledge on CE barriers, particularly in relation to IoT capabilities and consumer data-sharing practices and highlight practical implications for manufacturers seeking effective digital interventions. This research underscores the critical role of technology integration and organisational readiness in achieving successful CMS transitions.

Recent developments of Generative Artificial Intelligence (GenAI) are profoundly impacting the creative contexts, offering innovative tools that redefine how artistic content is conceived, developed, and delivered. Across visual arts, design, music, and storytelling, GenAI is being embraced for its capacity to accelerate ideation, facilitate rapid prototyping, and democratize creative processes, so allowing both professionals and novices to produce high-quality outcomes. Its algorithmic creativity could augment human imagination and opens new frontiers for experimentation and collaborative creation.
However, while the GenAI tools are being celebrated, its adoption is not without reservations. Creatives express apprehension over issues such as loss of creative autonomy, potential homogenization of artistic outputs, and ethical concerns related to authorship and copyright infringement. The question of trust is pivotal: without transparency in AI processes and assurance over the originality of AI-generated content, users remain skeptical about relying on GenAI systems for meaningful creative work. Furthermore, aesthetic satisfaction plays a critical role, as artists seek not just functional outputs but final works that align with their personal and cultural values.
The study investigates the conceptual foundations underlying GenAI adoption in creative contexts. Existing theoretical frameworks such as the Technology Acceptance Model (TAM) provide useful foundations for examining user attitudes towards new technologies. However, these models originally developed in contexts of general-purpose technologies do not fully capture the emotional, aesthetic, and autonomy-related dimensions that characterize creative practices. As such, this paper critically explores their applicability to GenAI adoption in creative fields and identifies the need for further contextualization. Based on a systematic literature review of peer-reviewed articles sourced from Scopus and from existing AI governance frameworks (e.g. OECD and NIST), the paper identifies a set of constructs—including perceived usefulness, ease of use, trust, creative autonomy, aesthetic satisfaction, and behavioral intention—that can inform the future development of a tailored survey instrument. The relevance of the core constructs to education and artistic production is discussed. The contribution of the study is twofold: it offers an academically rigorous synthesis of current knowledge on GenAI adoption within creative domains and proposes a refined conceptual framework that can inform future empirical studies. By aligning established theoretical models with the distinctive needs of creative practice, the study provides a critical foundation for educators, researchers, and policymakers committed to supporting a responsible, ethical, and inspiring integration of AI technologies in the arts.

The centrality of data quality within data management, and more broadly in business decision-making processes, constitutes an essential prerequisite for the reliability of analyses and, consequently, for the effectiveness of business strategies. The growing complexity and heterogeneity of data flows make data quality management an increasingly critical challenge, aimed at preserving the accuracy and consistency of extracted information. In this context, efficiency and timeliness become fundamental requirements. However, data quality processes are often resource-intensive and require specialized expertise. In this perspective, the adoption of generative artificial intelligence models, particularly Large Language Models (LLMs), opens new opportunities to support data management and quality control activities. This study proposes a conceptual framework for modelling the use of an LLM Agent to assist Data Quality and Application Maintenance teams. The framework is designed to facilitate the analysis of anomalies and malfunctions detected within systems responsible for data quality controls. Furthermore, the agent can suggest a set of technical solutions, specifying the potential impacts on the existing infrastructure and scheduled processes. This approach provides a solid foundation for future applications, promoting more efficient anomaly management and a strategic use of existing technical knowledge.

The rapid advancement of Generative Artificial Intelligence (GenAI) has attracted considerable attention in industry and academia due to its transformative potential to automate the generation of text and multimedia content. However, concerns have emerged about privacy and information security implications in industrial settings, where sensitive data and intellectual property are essential assets.
While prior works have examined GenAI or information security in isolation, few have systematically analysed how GenAI is being integrated into privacy-preserving and security-enhancing practices in industrial sectors. By conducting a bibliometric review, this study fills this gap and provides a comprehensive mapping of how GenAI relates to information security and privacy issues in industry.
Leveraging a structured methodological framework, the bibliometric analysis conducted follows a two-phase approach to analyse a sample of 426 papers collected from Scopus and Web of Science (WoS): a preliminary analysis to assess publication trends, disciplinary contributions, document types, and geographic distributions; and a co-occurrence analysis, conducted using VOSviewer, to uncover thematic clusters. The bibliometric analysis identifies five dominant thematic clusters: (i) Generative Adversarial Networks (GANs) and Cybersecurity, focusing on adversarial attacks and industrial system protection; (ii) Industrial Cyber Protection, addressing federated learning and critical infrastructure defence; (iii) Disruptive GenAI, emphasizing Large Language Models and real-time automation; (iv) Privacy Preservation, covering anonymization and synthetic data generation; and (v) Ethical AI, exploring societal and governance implications. Overall, the study reveals critical research gaps such as the underrepresentation of medicine and energy in research landscape, limited decision sciences attention, regional asymmetries, and a lack of ethical discourse, which give rise to a forward-looking research agenda. This agenda advocates for interdisciplinary integration, regulatory alignment, and the development of context-sensitive and ethically robust GenAI systems in industry.
This study offers both theoretical contributions, advancing a more holistic understanding of GenAI’s role in information security, and practical implications aimed at ensuring the safe, effective, and socially responsible deployment of this technology.

Processing healthcare data presents significant challenges for clinicians, particularly in chronic disease management such as diabetes care. Traditional data aggregation and summarization methods often struggle to integrate the diverse data sources of health information, ranging from structured electronic health records to unstructured clinical notes. In response to this challenge, the paper proposes a reference framework that leverages generative Artificial Intelligence (AI) techniques and agentic AI architectures for enhancing health data management and decision support. By employing Large Language Models, the system is able to synthesize insights from collected data also deriving contextualized summaries and personalized recommendations useful for healthcare professionals. The paper outlines the architectural components of the framework and discusses key implementation strategies. Finally, a basic prototype has been developed to demonstrate the feasibility and usability of the proposed architecture in the context of telediabetology, supporting the management of diabetes patients during periodic clinical visits.

The emergence of the latest generations of generative artificial intelligence (GenAI) systems, particularly ChatGPT, has prompted knowledge management (KM) scholars to investigate how these tools can be effectively integrated into organizational KM systems. However, as this field of study is in its infancy, the literature remains scarce and provides fragmented results. Empirical evidence is especially limited, and numerous research questions awaiting answers. One such question is how work experience influences the use of AI in new knowledge creation. To address this issue, this paper examines how and for what purpose GenAI is employed to support the creation of new knowledge, specifically focusing on how its use differs according to users’ seniority levels. The hypothesis is that work experience, which shapes users’ knowledge pools, affects how they utilize GenAI to support knowledge creation. Past empirical research highlighted that there are differences in AI usage between more and less experienced workers. Given the exploratory nature of the study, we employed a qualitative research method, conducting interviews with 22 software engineers from various companies who use GenAI systems in their daily activities. We choose software engineers as they are knowledge workers and are currently among the early adopters of GenAI systems. Based on a common guideline developed from knowledge creation processes, the interviews were conducted online and in person between July and November 2024. Each interview was recorded, transcribed and translated into English for analysis. Empirical research shows differences between more and less experienced workers, particularly with respect to the complexity of the tasks performed and the type of knowledge created with GenAI support. The study’s results contribute to our understanding of GenAI’s impact on KM and offer managerial insights for introducing and using the new technology within business contexts.

The pharmaceutical industry, characterized by complex manufacturing systems and resource-intensive development cycles, generates significantly higher emissions and waste compared to other sectors—up to 55% more per revenue dollar than the automotive industry. Approximately 30% of raw materials are lost during drug production, highlighting urgent sustainability challenges. In response to growing environmental pressures, this study investigates the potential of Artificial Intelligence (AI) technologies to reduce waste and enhance sustainability within the pharmaceutical sector. Through a combination of quantitative data analysis and qualitative insights from major pharmaceutical companies—including Novo Nordisk, Pfizer, GSK, and AstraZeneca—this research explores how AI can transform various stages of the pharmaceutical value chain to improve efficiency and environmental performance. The study identifies three core applications of AI in the industry: business process optimization, waste classification and recycling infrastructure, and decision and policy support. Drawing from the principles of industrial ecology and circular economy, it evaluates how AI can be used to reduce defective drug batches, improve the recyclability of pharmaceutical waste, optimize resource flows, and enhance supply chain efficiency. Case study demonstrates AI’s ability to significantly reduce maintenance costs, avoid production losses, and cut emissions. AI’s role in optimizing pharmaceutical processes at different stages of the pharma value chain – from clinical trial design using digital twin simulations to forecast drug demand and reduce waste, to manufacturing optimization, predictive maintenance, and sustainable supply chain management – enables data-driven decisions, improves efficiency, and minimizes environmental impact. Despite these benefits, the adoption of AI faces numerous barriers including high implementation costs, regulatory challenges, data integration issues, and resistance to change within organizations. Technical limitations, cybersecurity concerns, and the lack of standardized industry frameworks further hinder widespread implementation. The study argues that overcoming these challenges will require coordinated efforts among stakeholders, including public institutions, to provide funding, develop digital skills, and establish policy frameworks that support the responsible use of AI for sustainability. While AI contributes positively to circular and climate-resilient healthcare systems, its energy demands and digital divides must be addressed. The research concludes that AI has the potential to be a pivotal enabler of sustainable transformation in Big Pharma, but its integration must be managed carefully to maximize benefits and minimize unintended consequences. Public investment should focus not only on AI innovation but also on building community readiness for the digital and green transition in healthcare

This study explores the propensity of cardiologists to adopt wearable electrocardiogram (ECG) devices in cardiovascular patient care. Using a qualitative exploratory design, we conducted e-focus group sessions to gather in-depth insights into cardiologists’ perspectives. This approach enabled both individual reflections and group interaction, revealing diverse opinions on the clinical usefulness and integration of wearable ECG technology. The findings revealed varying attitudes toward the use and perceived value of wearable ECG devices. Several key factors were identified as influencing cardiologists’ openness to adopting such technologies, including perceived usefulness, technical accuracy, the reliability of results, the availability of resources within healthcare organizations, the dynamics of the doctor-patient relationship, and individual patient characteristics.
The study underscores the importance of a multi-dimensional strategy to support the integration of wearable technologies in cardiovascular care. While wearable ECG devices hold significant potential to improve cardiovascular health monitoring, strategic planning and supportive infrastructure are vital to ensure their effective and sustainable implementation in clinical practice.

The 2030 Agenda for Sustainable Development highlights the transformative potential of digital technologies in accelerating human progress, reducing inequalities, and fostering sustainable knowledge societies. Within this context, Digital Health (DH) is recognized for enhancing healthcare systems’ effectiveness, resilience, and equity—especially in addressing environmental and climate-related challenges. While various assessment methodologies exist—such as Health Technology Assessment, Health Impact Assessment, Social Impact Assessment, and Environmental Impact Assessment—the literature lacks reviews that explore the social and environmental impacts of DH. These dimensions are critical for evaluating healthcare systems’ broader performance and alignment with long-term sustainability goals. This scoping review aims to map and synthesize existing frameworks and tools for evaluating the impact of DH, particularly in relation to social and environmental outcomes. The review is structured around the Sextuple Aim Framework, which extends the traditional Triple Aim by adding environmental impact. Following JBI methodology and PRISMA-ScR guidelines, searches were conducted in PubMed, Scopus, and the Cochrane Database. Eligible studies were published in English between 2014 and 2024 and had to address at least three of the six aims: Population Health, Experience of Care, Capital Cost, Care Team Well-Being, Health Equity, and Environmental Impact. Out of 8,243 identified records, 53 met the criteria and were included in the final review. None addressed all six aims comprehensively. Most studies focused on DH (30.19%) and m-Health (28.30%), with emerging technologies like AI and VR also represented. The most assessed aims were Experience of Care (98.11%) and Population Health (94.34%), while Environmental Impact appeared in only 11.32% of studies. Through thematic analysis, 157 unique items were identified, categorized into 17 domains, 62 topics, and 78 issues. The most developed topic was Experience of Care, particularly User Engagement and Process-related costs. In contrast, Environmental Impact was the least developed, with only two domains: carbon footprint and the clarity of environmental consequences of technologies. This review contributes to the development of a theoretical framework for evaluating the holistic impact of DH. By highlighting gaps—particularly in environmental and social dimensions—it supports more informed, balanced decision-making in DH strategies aligned with sustainability and equity principles.

The combination of economic crises, a growing shortage of public resources, socio-economic disparities, and demographic shifts has significantly amplified the strain on healthcare systems globally. Rising healthcare demand and the goal of universal coverage highlight the need for improved resource allocation, infrastructure, and workforce planning. AI offers significant opportunities for enhancing efficiency and service quality, yet its integration is limited by regulatory, organizational, ethical, and environmental challenges. Although the literature broadly emphasizes AI’s potential, empirical evidence on its actual impact on healthcare performance remains scarce.
Based on these premises, this study proposes a comprehensive set of key performance indicators (KPIs) to assess AI’s tangible effects. The proposed set of KPIs provides a practical and adaptable toolbox for healthcare decision-makers to evaluate AI-driven changes and monitor their implementation.
This paper employs a Causal Loop Diagram (CLD) approach to explore how AI influences healthcare systems, focusing on two key areas: (i) optimizing resource allocation to improve efficiency and (ii) enhanced personalized care to elevate service quality. CLDs provide a structured and dynamic framework for understanding the complex interdependencies shaping healthcare organizations. The CLD approach serves as a foundation for developing KPIs to guide managers in evaluating AI integration. By combining systems thinking with the development of KPIs, this research provides a novel methodological framework for assessing the tangible impact of AI technologies in healthcare organizations. The resulting set of KPIs measures and evaluates the effect of AI integration within healthcare organizations. It is intended to guide future research regarding AI applications to healthcare management, as well as help managers assess the impact of AI implementation in healthcare settings. This research addresses the lack of empirical validation regarding the impact of AI in healthcare organizations by providing a set of indicators for evidence-based decision-making. Thus, it contributes to both academic research and practical applications of AI in healthcare management.

This study contributes to the growing body of research on the phenomenon of zombie firms by adopting a novel perspective that links corporate zombification to CEO narcissism. In line with the behavioral corporate governance approach and the Upper Echelons Theory (Hambrick & Mason, 1984), the analysis explores how CEO narcissism can influence a firm’s likelihood of entering a state of zombification. In this study CEO narcissism is measured through a composite index based on unobtrusive indicators such as the size of the CEO’s photograph in the annual report, the frequency of personal pronouns in shareholder letters, and the dimensions of the CEO’s handwritten signature, following the methodologies proposed by Chatterjee and Hambrick (2007) and Ham et al. (2018). Zombie firms are identified according to McGowan, Andrews, and Millot (2018), using a dummy variable that takes the value of 1 if the Interest Coverage Ratio (ICR) is below 1 for at least three consecutive years during the observation period. Using a balanced panel dataset that includes 232 publicly listed firms across five European countries (United Kingdom, Italy, France, Germany, and Spain), covering more than 2,552 firm-year observations from 2013 to 2023, we find that CEO narcissism negatively impacts on zombie firms. Specifically, the results obtained support our hypothesis, highlighting a negative and statistically significant relationship between CEO narcissism and zombification, also confirmed by the positive relationships between CEO narcissism and indicators of financial strength such as Altman’s z-score, ICR and ICR2. Our results, despite all the weaknesses of the case, contribute to extend the literature that recognises CEO narcissism as a positive acceptance for the company, especially in relation to financial distress.

The urban development processes rely on cities and urban communities that make efforts to support initiatives and policies that enhance the aims and the implementation of sustainable issues within urban spaces. In particular, cities are going smart not only by investing human and organisational energies to make technology as a driver for sustainable urban growth, but also promoting cultural, civic and creative assets and inputs that make the city a culture-led and knowledge-driven socially innovative urban community, opening to creative urban spaces. Post-industrial cities increasingly are working to make and develop creative collaborative urban spaces. Cities of tomorrow will invest knowledge sources in driving creative-led and culture-driven initiatives that contribute to making collaborative urban spaces and driving sustainable and inclusive urban growth, leading to community development and engagement. The future of urban growth relies on promoting culture as an opportunity to develop inclusive and cohesive communities that contribute to making creative urban spaces. Creative hubs develop bottom-up initiatives, by involving artists and other creatives who are able to meet the needs of an artistic community, making creative meeting places as well as urban spaces. Creative urban spaces develop creativity-led processes and rely on creative cities and hubs as socially inclusive communities that rediscover the importance of collaborative innovation as a framework that helps shape wealthy urban spaces into engines of social innovation. The study aims at investigating the relationships between creative cities and hubs that are following a collaborative view to organisational as well as urban spaces, in order to drive social innovation and achieve social sustainability within creative urban spaces.

Over the past two decades, digital transformation has increasingly created links between human beings and technology (Nahavandi, 2019; Eriksson et al., 2024). We have moved from a company managed solely by human intelligence to a hybrid system where human talent is fused with artificial intelligence. At the same time, it is proven that the enhanced intellectual capital (eIC) helps intellectual property rights (IPR) intensive companies to innovate their business models (Trequattrini et al., 2022). Improving existing work and service experiences and enhancing sustainability are the goals of Industry 5.0 (Sindhwani et al., 2022). This led to a more efficient use of resources than Industry 4.0 (Demir et al., 2019) and a greater attention to human beings. While the Industry 4.0 model is firmly centred on technology, Industry 5.0 brings back the Human-Centric (HC) paradigm (Fani et al., 2024). In this Human-Centric (HC) perspective, human skills, interaction, critical thinking, and interpretation become a strong point again (Nahavandi, 2019). The lack of a Human-Centric (HC) paradigm can lose the potential positive effects of introducing new technologies (Carlsson, 2023; Olsson et al., 2025). Industry 5.0 is characterised by the importance of “concepts of sustainability, bioeconomy, and a collaborative environment of technology and human beings, thus establishing a resilient industry that incorporates human social values” (Sindhwani et al., 2022, p. 2).
This paper aims to study integration between artificial intelligence, knowledge management, and collaborative innovation for creating sustainable value, focusing on Industry 5.0. The interest is to see research progress on the Human-Centric (HC) paradigm.
The methodology used is content analysis (Krippendorff, 2018) on the final versions of demonstrators, pilots, and prototypes downloaded from the CORDIS EU website (https://cordis.europa.eu). The sample examined comprises six Effective Industrial-Human Collaboration Cluster Members projects. A quantitative analysis (counting the frequency of each category of codes, counting the words that make up each PDF file) and a qualitative (interpretation of the verbal expressions contained in the text) analysis were carried out. An analysis of the primary existing literature on the subject of artificial intelligence, Industry 5.0, and human intelligence was conducted before proceeding to the case study.
Among the limitations of this research is the availability of a small amount of information. It is crucial to notice that this is only the first step of the research on this subject. In the future, we want to have greater contact with companies operating in Industry 5.0 to carefully monitor the flow of knowledge in the internal system.

This study aims to model the relationships between the independent variables AI-implementation, techno-eustress and techno-distress and the dependent variable productivity, in business organizational context, to understand if an organization, that has, or not has, implemented AI-technologies, are more or less near the ideal organizational typology and to design the possible path to be competitive maximizing techno-eustress and minimizing techno-distress, deriving from the use of AI, impacting positively on productivity. The result is a Three-dimensional-Typology Model identifying 8 different organizational typologies that we named in a particular way for better to represent the situation identified and the distance from the ideal one of maximization of productivity. This work represents a framework for future research on the topic and offers a theoretical model for managers to understand which organizational typology is closest to their organization noting the level achieved by the productivity. In this way, they have the possibility to understand what actions to take to act on the independent variables to divert them towards the ideal typology of maximization of productivity identified as “Futuristic Oasis”.

Cities are identifying the smart city vision to develop the city an urban community and engine of responsible innovation. The analysis elucidates that European cities are rethinking on urban planning by adopting a smart city framework, leading to responsible urban innovation and future, fostering collaborative processes that contribute to sustainable and social urban growth and innovation to improve the quality of life. A smart city helps to shape the city a smart responsible innovative urban community. Cities are defining smart urban planning, enabling smartness as an ability to shape urban organisational, innovative and collaborative urban spaces. European cities are going smart, tracking a framework for collaborative innovation, leading to the urban community an engine of social innovation. Sustainable urban future relies on cities that will develop smart urban communities for responsible innovation and good life, promoting collaborative and multi-actor innovation, and following community-led and human-centred view to urban development and innovation.