For companies, it is critical that their white-collar employees develop the necessary skills and knowledge to leverage the potential of Generative Artificial Intelligence (GAI) to improve productivity and organizational performance. To explore how employees perceive and use GAI in their daily work, and what measures companies are taking to support skills development, we conducted a practice-oriented empirical study.
The literature discusses the impacts of GAI on the economy and employment, particularly regarding efficiency gains, job transformation, and evolving skill requirements (Budhwar et al. 2023; Gmyrek et al. 2023; Haase 2024; Bremen 2023; McKinsey 2023). Several studies highlight productivity benefits and the dissemination of best practices (Brynjolfsson et al. 2023), while others, such as Slack (2025), emphasize employee skepticism as a barrier to realizing these gains. A central focus across the literature is the need for skills and knowledge referring GAI models as such (Al Naqbi et al. 2024; Cardon et al. 2024; Chandra et al. 2024).
Against this backdrop, we surveyed white-collar employees in Switzerland to assess the actual state concerning skills and knowledge in the following three areas: (1) perception of GAI-relevant skills and knowledge, (2) application of GAI in daily tasks and corporate functions, and (3) employer measures for supporting skill development.
The survey was distributed among members of Switzerland’s largest white-collar professional associations. After data cleaning, responses from 1,843 participants were analyzed. As participation was based on self-selection (opt-in), the sample is not fully representative; distortions were mitigated using iterative proportional fitting (Kolenikov, 2014; Choupani et al., 2016).
A key finding shows that 80% of participants perceive a need for more GAI knowledge (25% report to almost no knowledge; 55% to have only basic knowledge), while only 15% consider themselves sufficiently knowledgeable. A second key finding show that GAI models are most used for writing, editing, and translating texts (48%). The most cited reason for non-use was a perceived lack of necessity (24%), though it remains unclear whether this reflects actual need or inexperience. Only 23% reported that their employer has a GAI strategy. Practical implications, including offering task-oriented GAI training, are discussed in the full paper.

Background: The European Medical Devices Regulation (MDR) 2017/745 seeks to minimize clinical uncertainty – especially regarding the generation, dissemination, and quality of clinical evidence – across all phases of the medical device lifecycle: pre-market, market approval, post-market, and disinvestment. Although the MDR promotes using gold-standard methodologies, such as Randomized Controlled Trials to address clinical uncertainty, healthcare organizations face significant challenges in applying these methods. These challenges affect the effectiveness of evidence generation, dissemination, and quality. As a result, healthcare systems must incorporate technological, organizational, and managerial tools to manage these difficulties throughout the medical device lifecycle. This study aims to identify the tools that can reduce clinical uncertainty associated with medical devices.
Methods: The research adopts a case study approach, focusing on the Tuscany Regional Healthcare System, which is recognized nationally for its effectiveness, efficiency, and appropriate care delivery.
Results: The findings indicate that the majority of the identified tools are concentrated in the pre-market and post-market stages. Examples include study coordinators (organizational tools), standardized templates (technological tools), and case report forms (managerial tools), all of which improve the quality of clinical evidence. In addition, tools such as patient diaries (managerial) and surgical trainers (organizational) play a crucial role in enhancing both the generation and dissemination of evidence during these phases.
Discussion and Implications: This research provides useful insights for policymakers in guiding healthcare organizations on how to implement the MDR effectively. A key takeaway is the importance of introducing clinical registries, surveillance systems, and data collection platforms to gather real-world evidence, which are essential in the post-market phase. The study contributes to the ongoing discourse surrounding medical devices’ clinical uncertainty by identifying technological, organizational, and managerial tools that can strengthen clinical evidence for medical devices.

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.