The need for user-centered and agile solutions has driven the adoption of methodologies such as Lean UX in knowledge engineering projects. While Lean UX focuses on thinking, making, and checking to enhance user experience, knowledge engineering is dedicated to building intelligent systems grounded in formalized expert knowledge. Despite the conceptual alignment between these approaches, the integration of Generative Artificial into this context remains underexplored. This study proposes a model that incorporates generative AI tools into the Lean UX process to accelerate and enhance the development of knowledge-based systems. Based on the Design Science Research (DSR) methodology, a narrative literature review and exploratory research were conducted to identify generative AI techniques applicable to each Lean UX phase: Think, Make and Check. A total of 10 generative AI approaches were mapped and organized into a model that supports small and agile teams in knowledge engineering projects. The model was instantiated through the development of GPT-based agents customized for specific tasks, such as persona generation, empathy mapping, requirements analysis, and prototype creation. These agents leverage prompts containing transcripts, observations, or interview data to generate detailed and realistic outputs, streamlining processes that are traditionally manual and time-consuming. One example presented is the Persona Generation Agent, which creates structured user profiles and illustrative images from simple textual input. This integration contributes both theoretically and practically by demonstrating how generative AI can be used to enhance user experience (UX) focused design processes in knowledge engineering. The proposed model promotes more efficient workflows, while keeping the user at the center of development. It also supports interdisciplinary collaboration, faster iteration cycles, and the creation of intelligent systems that are more aligned with user needs. Future work includes empirical validation of the model in diverse application contexts, aiming to refine its use and encourage widespread adoption in knowledge engineering and related fields.

The wine industry generates different waste and by-products that can be converted into high-value goods, such as biogenic products or energy, offering new market opportunities and reducing the environmental implications. However, the variety of technologies available complicates the selection of the best valorisation option. An effective evaluation system integrating economic, technical, environmental, and social factors is essential for choosing appropriate valorisation strategies. In this study, we apply the Analytic Hierarchy Process and Fuzzy Set Theory to comprehensively assess food waste valorisation within the wine industry. Our results indicate that anaerobic digestion technology is the best option to valorise winery waste. This framework helps decision-makers evaluate waste valorisation strategies holistically, highlighting the need for investments in sustainable technologies tailored to specific company characteristics.

The growing awareness of social and environmental topics such as climate change, social inequality, and poverty from humanity has driven global efforts toward sustainable development through concrete policy initiatives. In this changed socio-economic context and with the rise of IT technologies, the Sharing Economy model has spread thanks to digital economy platforms. The growth of digital sharing platforms has raised concerns about their role in supporting social and environmentally sustainable consumption. In line with this, the evaluation of the governance of these platforms also raises questions regarding the management of power and the balance between the interests of users and owners. In this context, current literature lacks a comprehensive framework for assessing the social-environmental sustainability of these platforms and the form of governance that characterises them. To tackle this issue, the paper proposes an innovative holistic framework to assess the sustainability of digital platforms, with a focus on Peer-to-Peer platforms. The framework is created adapting and integrating protocols, variables and framings already presents in the literature. The study aims to provide a comprehensive evaluation of the social, environmental impacts and types of governance of these platforms. Then the framework obtained is applied to a pool of forty P2P platforms, enabling a detailed assessment of their current sustainability practices and governance structures. The findings contribute to the literature by proposing a holistic framework to evaluate the sustainability of digital platforms in the sharing economy and provide a systematic approach to understanding their role in the global transition towards sustainability.

Innovative start-ups play a crucial role in fostering innovation and driving economic growth while enhancing technological competitiveness. In Italy, the emergence of these start-ups has significantly influenced the landscape of economic development and technological advancement, acting as a key engine for generating and integrating knowledge into new products and services. This introduction sets the stage for a deeper exploration of the dynamics surrounding Italian innovative start-ups and their impact on the broader economic and technological landscape. This study analyzes the factors affecting the economic performance of these start-ups, with a focus on governance aspects and corporate resources, particularly team composition, company size, and the impact of gender diversity. It also investigates how sector and geographic location may influence access to resources, often linked to public policies and local innovation ecosystems. Using the AIDA (Analisi Informatizzata delle Aziende) database, managed by Bureau van Dijk—a leading global provider of financial information specializing in the collection and storage of economic-financial data on Italian companies—we studied a sample of 5,669 innovative start-ups across Italy. Through OLS regression models, the article reveals how regional context and policies can serve as either a competitive advantage or a barrier for firms. The findings indicate a positive effect of female leadership on business growth in terms of profitability and sustainability, suggesting that diverse governance and locations within developed entrepreneurial ecosystems can significantly enhance business performance. These insights are valuable for policymakers and entrepreneurs, contributing to a deeper understanding of the economic dynamics of innovative start-ups in Italy. To our knowledge, this is the first attempt to examine regional differences in the performance of Italian start-ups by including all Italian regions.

The development of human skills through technology, knowledge co-creation, and meaningful work drives successful change, well-being, and societal cohesion. Technological advancements have raised concerns about job displacement and skills shortages, necessitating a human-centric approach to leadership. The study explores the relationship between meaningfulness and meaninglessness (m&m) experiences at work, emphasizing that meaningfulness is not tied to formal job descriptions and can be supported by leadership. Transformational leadership, with its four dimensions Idealized influence, Inspirational motivation, Intellectual stimulation, and Individual consideration, is examined for its potential to foster meaningfulness in education and elderly and caregiving sectors. The research, based on the “The m&m of work” project, aims to enhance organizational practices by leveraging employees’ experiences of meaningfulness and meaninglessness. This research focuses particularly on transformational leadership (TL) entity of the survey and the identification of four dimensions of TL (visioning, challenging, engaging, and leading by example) in the real-life experiences of the education and elderly and caregiving sectors at both individual and organizational levels. The study found that Transformational Leadership (TL) had significant influence in the education and caregiving sectors. At the organizational level, TL influenced areas such as responsibility and ethics, work environment safety, and discussions about the meaningfulness of work. At the individual level, TL fostered trust within the work community, encouraged the use of employees’ creativity, and supported their success. Additionally, a method was co-created during the research to enhance leadership, facilitate knowledge sharing, and promote discussions within organizations, addressing even challenging experiences of meaninglessness and their effects.

The UN Agenda 2030 and its 17 Sustainable Development Goals (SDGs) represent the most comprehensive framework ever formulated to address the global environmental, social, and governance (ESG) challenges. Prioritizing SDGs is crucial for companies to integrate SDGs into corporate decision-making by identifying the most relevant goals and making informed strategic decisions for corporate sustainability. Materiality analysis, as identifying the sustainability issues most relevant to a company and its stakeholders, becomes thus a valid tool to determine priority SDGs. Nevertheless, the existing international managerial guidelines that promote materiality analysis for aligning corporate initiatives with SDGs are often too generic difficult to implement, and open to ambiguous interpretation, which hampers its practical adoption. The present paper proposes an exploratory study by adopting the qualitative approach of semi-structured interviews to gather insights from strategic decision-makers on the relevance, applicability, and potential challenges concerning the adoption of a Multi-Criteria Decision Making (MCDM) method for performing materiality analysis in the process of SDG prioritization. As a result, the multi-criteria approach to materiality analysis is widely accepted by all involved interviewees, who agree on its usefulness in providing a standardized procedure for the prioritization of SDGs. In addition, as highlighted by the suggestions of the experts involved in the study, integrating the potentialities of Artificial Intelligence (AI) technologies into materiality analysis appears to be a promising direction for shaping future developments in the application of materiality analysis to SDG prioritization decisions.

Entomophagy, so the consumption of insects as food for humans, is a contemporary topic of discussions on food security, sustainability, and cultural adaptation. Scientific literature has shown growing interest in the acceptance of insect-based foods but also a marketing potential, also with reference to the 4Ps model, for example Specifically, high prices have been identified as a significant deterrent to the purchase of insect-based food products while, in terms of products consumer responses are strongly influenced by packaging visuals and wording. In this paper, which represents an ongoing research, we further show the first results of a survey between Italian consumers.

Retrieval-Augmented Generation (RAG) systems address critical limitations of large language models (LLMs) such as hallucinations, static knowledge, and context constraints-by dynamically integrating organizational data to enhance accuracy and relevance in knowledge management (KM). This RAG LLM development integrates the generative strengths of large language models with the precision of real-time information retrieval, enabling responses grounded in both structured and unstructured organizational data. The development process involves data collection, preprocessing, embedding, and indexing, followed by retrieval and prompt augmentation, ensuring that the model can access and utilize up-to-date, domain-specific knowledge at response time. By leveraging authoritative internal sources and advanced vector search, the RAG system overcomes the static knowledge and hallucination limitations of traditional LLMs, delivering more accurate, contextually relevant, and transparent answers. This approach not only enhances the reliability of generative AI in business scenarios but also offers a scalable, low-code framework adaptable to diverse enterprise needs. Our research employs low code approach for RAG system development and open source LLM.
Research results demonstrate how a low-code RAG architecture, leveraging open-source LLMs and real-time data retrieval, transforms KM through three key contributions: (i) enhanced accuracy and operational efficiency, (ii) cost-effective scalability and customization, (iii) managerial and strategic impact. The RAG system reduced hallucination rates compared to standalone LLMs, achieving higher accuracy in various tasks.
Future work aims to integrate multimodal data and hybrid architectures, further advancing collaborative human-AI knowledge ecosystems. Addressing these challenges involves robust system architecture, scalable data pipelines, advanced retrieval and ranking techniques, and strong governance over data quality and security.