The growing focus on sustainability and mitigation of environmental risks has made the ESG (Environmental, Social and Governance) framework a central element in the strategic and operational management of modern organizations. In a context marked by increasingly stringent regulatory pressures – such as the Corporate Sustainability Reporting Directive (CSRD) and the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD) – companies are now called upon to integrate ESG metrics into their decision-making processes, in order to address global challenges such as climate change, biodiversity loss and the necessary ecological transition. However, the fragmentation of available data and the lack of adequate predictive tools continue to hinder truly effective and future-oriented ESG management. This study proposes an innovative operating model based on the synergistic integration between Artificial Intelligence (AI) and Process Mining, with the aim of improving monitoring, automation and transparency in ESG processes. AI allows the processing of large volumes of heterogeneous and unstructured data, while Process Mining allows you to map and optimize business flows, detecting inefficiencies and ensuring traceability. The model proves to be particularly effective for financial institutions and companies operating in sectors with high climate exposure, providing concrete answers to the needs of regulatory compliance and strategic sustainability. The adoption of this approach makes it possible not only to anticipate environmental risks, but also to strengthen organizational resilience and support the transition to a “sustainability-first” paradigm. The study highlights the transformative potential of AI-driven Process Mining in ESG management, offering scalable and replicable solutions for sustainable innovation that combines efficiency, responsibility and competitiveness in the long term.

The global imperative for sustainable development is compelling industries to reassess operations, emphasizing Environmental, Social, and Governance (ESG) criteria. The energy sector, pivotal yet environmentally impactful, faces intense pressure to innovate sustainably. Artificial Intelligence (AI) offers transformative potential, particularly its application in Knowledge Management (KM) systems to enhance information handling for superior ESG performance. However, a literature gap exists concerning the interplay between AI-driven KM, ESG outcomes, stakeholder perceptions, and corporate competitiveness in this sector. This proposal outlines a study to investigate these dynamics: how AI-driven KM influences ESG outcomes, how stakeholders perceive these innovations, and how such practices might bolster energy companies’ competitive advantage. A mixed-methods design is planned (literature review, three Italian energy sector case studies, a survey of ~150 stakeholders). Findings are expected to show a positive AI-KM impact on ESG metrics, multifaceted stakeholder perceptions (more positive externally, varied internally with concerns like job displacement), and enhanced competitiveness through innovation and efficiency. The study aims for theoretical contributions to the AI-KM-ESG nexus and practical insights for energy executives.

Business success is no longer defined solely by financial performance or the quality of goods and services provided. It is imperative for companies to embed sustainability into their strategic frameworks, embracing socially and environmentally responsible practices. Businesses must now prioritize aligning profit with ethical and sustainable practices that enhance lives, protect the environment, and foster inclusive communities.
In this new mindset, work focuses on growth as well as output, redefining productivity around well-being and sustainability in a people-centered “productive revolution.”
Emerging technologies such as artificial intelligence (AI) offer new ways of organizing work, helping companies use their resources more effectively. It is through intelligent automation that many repetitive, low-value tasks can be eliminated, freeing up time for more strategic and creative activities. This makes it possible to reduce working hours without compromising, and indeed improving, both productivity and competitiveness.
At the same time, AI plays a fundamental role in knowledge management (KM), due to its ability to collect, analyze, and organize large volumes of data, enabling the creation, sharing, and preservation of knowledge within companies.
The reduction of working hours is increasingly central to the debate on the future of work, closely linked to the issue of work-life balance. Improving the balance between private and professional life has become a priority for many companies. What experiences such as the “4 Days Week” have demonstrated in various European countries is that they can bring tangible benefits: greater well-being, motivation, productivity, and improved talent retention.
The present study aims to explore the role of AI in transforming work models, focusing in particular on the effects of reducing working hours on productivity and knowledge management. The analysis will be carried out through a theoretical-documentary research approach, based on an integrated review of scientific literature, institutional reports, and national and international business cases, with particular reference to the themes of artificial intelligence, productivity, and knowledge management.

Social sustainability is a fundamental pillar for the balanced and inclusive development of contemporary societies. It embodies the commitment to social equity, inclusion, respect for human rights, and the promotion of collective well-being. Artificial intelligence (AI), with its increasing deployment, is emerging as a key tool for addressing social challenges and promoting sustainability goals, which, to date, also involve education, following the provisions of Sustainable Development Goal 4 (SDGs 4) of the UN 2030 Agenda. The integration of artificial intelligence (AI) technology into education has introduced many possibilities. However, it also raises ethical issues that require careful consideration. This paper aims to highlight the importance of considering AI as a driver for educational inclusion, starting with an analysis of its potential. The study delves into the benefits and challenges of the introduction of a generative AI chatbot by Multiversity Group, Italy’s largest group operating in the Digital Education sector and Europe’s second largest group, which, driven by its vision of AI-enhanced education, collaborated with strategic consulting firm Bain & Company to build such a generative chatbot, leveraging OpenAI technology, to provide a better learning experience for students by fostering engagement and interaction with professors, and to combine didactic delivery and interactive assistance to learners. This paper aims to explore the emerging contribution of AI in education to promote innovative and inclusive educational delivery to reduce possible educational inequalities, considering the benefits, challenges and ethical implications of implementing the technology. AI can be an enabler for social progress, but it requires attention relative to the risks of discrimination and risks related to privacy and data security. The paper analyzes the high-impact challenges faced in developing the generative AI project to ensure that student data is securely managed in compliance with legal standards, such as GDPR 2016/679, the qualitative value of responses, the need for continuous monitoring, and overcoming any technological limitations.

The integration of Artificial Intelligence (AI) into sustainability practices is transforming industries by enabling innovative solutions to complex challenges. However, while the nexus between AI and economic or environmental sustainability has been the subject of substantial academic inquiry, its potential role in advancing social sustainability remains comparatively underexplored. Social sustainability, encompassing critical aspects such as health and well-being, equitable access to education, and community resilience, is fundamental to the development of sustainable societies and is explicitly linked to Sustainable Development Goals (SDGs) such as SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). In the energy sector, where innovation is a primary driver, AI is increasingly employed to address issues including energy equity, workforce safety, and community development. The success of such initiatives, however, critically hinges on stakeholder perception, which influences the legitimacy and acceptance of AI-driven practices. This study aims to bridge the gap between AI applications and their social implications by examining how AI-driven innovative practices in the energy sector can enhance social sustainability and Environmental, Social, and Governance (ESG) performance. Specifically, it focuses on the role of stakeholder perception as a key determinant of the success of socially sustainable initiatives. By integrating concepts from stakeholder theory and knowledge management frameworks, this research seeks to advance theoretical understanding while offering practical strategies for achieving sustainability goals in knowledge-intensive and innovation-driven industries. The paper presents a review of the extant literature and outlines a research proposal predicated on a mixed-methods approach, focusing on case studies of energy companies implementing AI for social sustainability.

The European Union plays an important role in promoting the Sustainable Development Goals (SDGs) of the 2030 Agenda through the advancement of sustainable finance. The aim of sustainable finance is to direct financial resources toward initiatives aligned with ESG (Environmental, Social, and Governance) principles. However, unlike financial ratings based on recognized accounting standards, a globally shared framework for assessing ESG activities and investments is still lacking.
To fill this gap, the EU has launched a process to develop common standards for identifying sustainable economic activities, not only from an environmental but also from a social perspective.
In this context, the proposal for a European Social Taxonomy has emerged, developed by the Platform on Sustainable Finance (PSF). Its goal is to classify projects, investments, and policies based on their social impact. The three main objectives identified, decent work, well-being and adequate living conditions, and inclusive societies, are supported by sub-goals to facilitate implementation and reporting.
Nonetheless, the analysis of social variables remains particularly complex due to their qualitative, multidimensional, and often subjective nature, which makes objective and standardized measurement challenging. In the absence of a global social reporting standard, practices remain heterogeneous and fragmented, undermining data comparability and the development of evidence-based policies.
In this scenario, Public Administrations (PAs) are called upon to adopt effective tools to make their social actions visible, measurable, and communicable. The Final social balance sheet serves as a key tool for accountability and transparency, useful for strengthening the relationship with citizens and evaluating the public value generated.
The integration of the Social Report, the Social Taxonomy, and Artificial Intelligence (AI) represents a strategic opportunity to improve the analysis, monitoring, and implementation of social policies in alignment with the 2030 Agenda. This study analyzes the approach adopted by local administrations in social reporting and the potential of AI to enhance these processes, promoting more effective and sustainable public governance.

The adoption of innovative technologies can help agrifood companies face crucial challenges related to meeting the food needs of a growing world population, adapting to and mitigating the effects of climate change, and increasing the resilience of supply chains against sudden disruptions. In fact, several studies highlight the advantages that technological innovation can bring to agrifood supply chains. At the same time, few studies examine the connections between technological innovation and risk management. This study aims to help bridge this gap by reviewing and organizing previous findings. Specifically, this paper provides a classification of the main risks affecting agrifood supply chains and investigates the role that technological innovations may play in helping agrifood companies address key risks. The results lead to the identification of five main risk categories: food safety, information management, environmental, geopolitical, and supply chain management risks. Furthermore, the literature review focuses on six key technologies, including blockchain, Internet of Things, big data, artificial intelligence, drones, and digital twins. From this perspective, the results show that blockchain tracking systems allow companies to monitor product conditions throughout the supply chain. This helps ensure product quality, combat counterfeiting, and support the management of information and product flows. IoT systems are especially useful for mitigating food safety risks by enabling real-time monitoring during cultivation, transport, and distribution. Big data and AI-based solutions mainly support precision agriculture activities, contributing to the mitigation of environmental risks. Finally, the study highlights emerging applications related to drones and digital twins. Ultimately, this study provides several relevant contributions. First, it helps organize the fragmented contributions on the impact of technologies on risk management in the agrifood industry. Second, it offers a solid theoretical foundation for analysing the implications of technological applications on different risk categories. Third, it provides useful insights for practitioners to assess how technological innovation may help manage and mitigate key risks.

The study aims to investigate the digital transformation processes within the agri-food sector and their influence on business model innovation (BMI) as a pathway toward sustainability. Specifically, it analyses how agri-food companies are exploiting the potential of digital technologies (DTs) to reshape their value creation processes, enhance competitiveness, and address environmental and economic challenges. In order to exploit DTs and integrate them into business operations, it is essential to innovate the companies’ business model by rethinking traditional operational and strategic logics. This underscores the necessity of both the adoption of new technologies and the transformation of the business model. Despite the multiple opportunities offered by DTs, their introduction in agri-food companies presents significant obstacles due mainly to a lack of knowledge, limited digital skills or to the high costs associated with their implementation. Addressing these barriers, requires a growing emphasis on knowledge co-creation-oriented approaches, that integrate diverse disciplines and sectors. In order to pursue this objective, the study adopts a theoretical approach, based on an analysis of the existing literature on DTs and their role in fostering BMI and sustainability, and boundary management within the agri-food sector. The research focuses on the role of company boundaries as collaborative spaces that can facilitate the adoption of DTs and promote co-creation dynamics. The findings of the research lead to the development of a theoretical framework aimed at guiding agri-food companies in their transition toward more innovative and sustainable business models. This study offers theoretical contributions to support companies in leveraging DTs for sustainable development.

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.

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”.

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.

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.

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 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.

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.

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 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

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 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.

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.