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.

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.

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.

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.

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 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.

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 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.

This paper explores the growing significance of frugal and reverse innovation, two key concepts that address challenges in resource-constrained environments, particularly in developing economies. As businesses expand globally, these innovation models offer cost-effective and sustainable solutions. Frugal innovation focuses on creating affordable, efficient solutions in developing countries, while reverse innovation involves adapting innovations from emerging markets to developed economies. Though both aim for low-cost solutions, they stem from different contexts and raise important strategic questions about their integration into corporate strategies for growth, sustainability, and competitive advantage.
The paper investigates how the intersection of frugal and reverse innovation can enhance the understanding of global business strategies in emerging markets. By examining both conceptual foundations and practical applications, this study highlights their roles in global business dynamics and their potential to transform industries such as healthcare, energy, and education.
The study reviews existing literature through a bibliometric analysis. Out of 554 publications, 365 are bibliographically coupled and assigned to 6 clusters. These interrelated clusters capture the multifaceted nature of frugal and reverse innovation, via the exploration of conceptual frameworks, organizational strategies, social impacts, real-world applications, product-market challenges, and specific sectors like water and sanitation. The findings reveal that frugal and reverse innovations are not isolated but interconnected processes that shape global innovation trajectories.
The paper contributes to both academic and practical knowledge, offering a synthesized understanding of the field, identifying gaps in research, and providing insights for businesses and policymakers. It calls for future research on the co-evolution of North-South innovation flows and empirical studies on user perceptions and adoption dynamics in reverse innovation pathways. Despite its limitations, this study provides a solid foundation for advancing the theory and practice of frugal and reverse innovation.

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.

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.

This conceptual paper aims to identify and present the potential impacts of employee withdrawal on knowledge management processes in organizations i.e.: knowledge sharing & transfer, knowledge creation & development, knowledge application, and storage. Employee withdrawal is a subtype of counterproductive work behaviour and a critical issue for organisations. Specifically, it refers to a set of attitudes and behaviours used by employees when they stay at the job but for some reason decide to be less participative in duty engagement. Employee withdrawal may take either a physical or a psychological form and may begin with small and irrelevant actions like being late, or daydreaming, but they might culminate in more serious and significant behaviours like absenteeism, missing meetings, cyberloafing, or quitting that can be detrimental to the organization. Most research frames employee withdrawal behaviours as negative behaviours from the organizational point of view, mostly due to cost. However, a large proportion of organizational knowledge is personalized and resides in individuals’ heads, therefore researching employee withdrawal behaviours is particularly critical in the context of knowledge management. To the best of the authors’ knowledge, there are no publications that comprehensively describe the potential impact of employee withdrawal on different knowledge management processes. In the face of the above, there is a clear need to investigate how employee withdrawal behaviours may potentially harm knowledge sharing & transfer,, knowledge creation & development, knowledge application, and storage in organisations.
To do so, the authors of this paper related the found literature and described the potential impact of employee withdrawal behaviours on different knowledge management processes. Furthermore, the selected sources have been analysed in relation to each other, identifying conceptual overlaps which help in understanding how employee withdrawal behaviours may impact various knowledge processes.
Originality/value – The paper contributes to the understanding of the relationship between employee withdrawal behaviours and knowledge management processes.
Practical implications – The study provides food for thought for managers and owners on how the withdrawal behaviours of their employees might potentially hinder knowledge sharing, transfer, creation, development, application and knowledge storage in their organizations.

This work has investigated the convergence of the business paradigms of artificial intelligence, knowledge management, and emerging technologies. This work uses a relevant new database that considers the scientific literature on the field. The bibliometric methods used were related to the multiple correspondence analysis, which helped construct a conceptual map, and ensemble community detection clustering, which helped identify the relevant “cores” of the literature. As a relevant result, we found the key role of data-driven strategies but also the necessity of rethinking education and training. In fact, the rise of AI as a very relevant technology in knowledge management can impact how firms create and store knowledge. Of course, these changes necessitate adequate organizational changes to facilitate the integration with the systems, process, and, most, people.

The concept of Diversity in the Workplace (DiW) and the Knowledge Management (KM) concept have been the subject of extensive analysis from a wide range of perspectives, typically at the micro, meso or macro level. However, there remains a paucity of research addressing the interrelation between these two concepts. The present study adopts an exploratory approach, utilising a bibliometric co-citation and coupling technique in conjunction with a comprehensive literature review. This approach offers novel insights into the past and present dynamics of the intellectual structure. The co-citation study reveals three predominant clusters within the intellectual structure, two of which are associated with the primary variables, i.e. KM and DiW, while a third serves as a theoretical framework for the interrelation between KM and DiW. In contrast, contemporary research trends are oriented towards the role of the TMT (or upper echelons in general) in KM, the significance of inter-organizational collaboration, and the management of “distance” due to heterogeneity. This study aims to explore the interrelation between DiW and KM. The findings reveal that the intellectual evolution of research commences with the concept of DiW, progressing to KM, and culminating in the theoretical nexus between them.

In an increasingly digitized and hypercompetitive market landscape, pricing is evolving from a static, cost-based process into a dynamic, strategic function powered by Artificial Intelligence (AI). Organizations are moving beyond traditional pricing models by leveraging real-time analytics, predictive modelling, and behavioural insights to enhance pricing decisions. AI enables large-scale automation and personalization of prices, allowing firms to adapt continuously to changes in demand, consumer behavior, and competitive signals. However, this shift also raises critical concerns regarding transparency, fairness, and algorithmic accountability—especially in consumer-facing markets, where price sensitivity and perceptions of justice directly influence trust and loyalty. This study shows how AI reshapes pricing across four impact areas—predictive modelling, real-time optimisation, behavioural adaptation, explainability and fairness—and, through a systematic literature review, distils a two-level taxonomy of AI applications in pricing. The findings demonstrate that AI-powered pricing enhances forecasting accuracy, enables granular and dynamic price optimization, and improves behavioural targeting through the integration of cognitive heuristics such as anchoring and loss aversion. These benefits, however, must be balanced against the heightened risks of opacity, unfair discrimination, and regulatory non-compliance, which can erode consumer trust. The study emphasises that AI is not merely a technological tool, but a transformative agent that reshapes market dynamics and redefines consumer expectations. As such, the adoption of transparent, explainable, and ethically aligned AI models is essential—not only to ensure compliance with emerging regulatory frameworks, but also to foster long-term consumer confidence and sustainable competitive advantage. The paper concludes by calling for interdisciplinary research to explore the longitudinal effects of AI-based pricing, ethical governance mechanisms, and the integration of social responsibility into algorithmic pricing strategies.

This paper investigates the evolving role of users in Virtual Worlds (VWs), emphasizing their transition from passive consumers to active creators of immersive experiences. In the context of platforms like Roblox, Decentraland, and Fortnite, the study explores the dynamics of User-Generated Content (UGC) and User-Generated Experiences (UGEs), drawing on principles from Open Innovation and User-Centered Design, the research develops a design-driven framework that integrates co-creation, co-design, and co-production processes within the Double Diamond model and expands the theory with tailored design phases. Through a comparative analysis of three leading VWs, the study identifies key challenges and proposes a structured framework to enhance creativity, inclusivity, and business opportunities. The framework is validated against academic literature and case study insights, offering an approach to participatory innovation in digital environments. The paper concludes by outlining future directions for ethical integration and hybrid physical-virtual applications.

Crises in tourism have become increasingly common. Yet, COVID-19 stands out as the most disruptive event, causing significant damages to the global economy, severely impacting the tourism sectors across the regions. Even though it has been five years since the crisis started, the pandemic’s influence on global tourism can still be noticeable. This study focuses on Polish tourism organisations with the purpose to explore in-depth the role of knowledge sharing processes and their usefulness in mitigating the effects of crisis in tourism.
Analysis of the case suggests that the organisation’s key role during a crisis was to serve as a knowledge broker. The analysed case indicates that different types of information and knowledge were significant during that period. For the industry, key knowledge concerned the information related to legislation and the proposed changes. For municipal and governmental level, quantitative data from tourist traffic was important. For tourists, a focus was put on navigating the restrictions. Given the numerous constraints imposed by the pandemics, the organisation adapted to the new reality by increasing its dependence on ICT systems. This includes the creation of a dedicated subpage, ongoing e-mail and newsletters communication, video chats with training, regular online team meetings and employees’ conversations via online chats.
This study is based on qualitative in-depth analysis and the semi-structured interviews with representatives of a local tourism organisation in Poland. This research employs a single case study design, focusing on the organisation as the unit of analysis.
This study contributes to the body of literature by expanding the understanding of the role of knowledge sharing processes within the context of the tourism crisis. It offers valuable insights for researchers exploring the application of knowledge management to crisis management.
Findings of this study deliver food for thought for both the academic community and tourism organisations eager to facilitate their operational activity during hazardous events. The results offer practical guidance for managers regarding the knowledge sharing practices on how to enhance the resilience and performance of their organisations during crisis, while simultaneously supporting the tourism industry.
Research output is limited to the analysis of a single organisation (illustrated through a case study example) and thus, the analysis presented may not fully capture the complexity of the entire sector. The organisation is operating in Poland, and this can additionally limit the usefulness of analysis to one country specifics.

Do mentors influence the effects of the entrepreneurial programs of the early-stage start-ups pivot decisions? We address our research question investigating the role of mentors supporting entrepreneurs’ decisions in an early-stage training program, receiving the scientific method or the effectuation approach. In order to achieve our goal, we based our research on Kram’s Mentor Role theory (Kram, 1985) and we applied it to the scientific method, according to the theory-and-evidence-based approach (Agarwal et al., 2024), and to Saras Sarasvathy’s theory of Effectuation (Sarasvathy, 2008), related to the evidence-based approach. Previous research, in fact, has considered mentors as experienced people who can support mentees’ decisions, career-development and activities within organizations, by coaching, challenging, and directing (Kram, 1985; Wilson and Elman, 1990). Moreover, mentorship is an important building block of educational programs within entrepreneurial support programs (Yitshaki, 2025). Nevertheless, little is known about the role of mentors in start-ups that have received an Entrepreneurial training (ET) program specifically based on the scientific and effectuation approach to decision-making. In this vein, we discuss how mentors’ support can change decision-making processes, in terms of radical and incremental pivot decisions, of entrepreneurs adopting the scientific approach and the effectuation approach. To investigate our question, we collected evidence via a randomized control trial (RCT) in which 308 early-stage start-ups from Italy of a pre-acceleration program were randomly assigned to “treatment” (scientific, effectuation) and “control” group. Each start-up in treatment and control groups had from 0 to 7 mentors, from the beginning of the program. We observed firms throughout and post-training and we compared differences concerning radical and incremental pivot decisions between firms with mentors and without mentors in the treatment and control group, focusing on mentors’ support. We find that start-ups with mentors are not more likely to pivot radically, on the other hand they make more incremental pivots than firms with no mentors. In particular, firms with mentors trained by scientific method are more likely to pivot incrementally than firms in the effectuation and control group. We suggest that it is due to mentoring that reinforces the scientific approach and realigns what entrepreneurs have learnt during the training program. These findings highlight important insights for mentorship research, showing how mentors can shape decision-making processes in ET programs, for the literature on entrepreneurial decision-making and for stakeholders involved in designing training programs that support entrepreneurs.

This study tackles the absence of a unified framework for categorizing knowledge workers, a gap that constrains effective management and strategic alignment in knowledge-driven organizations. While these workers are central to innovation and value creation, existing definitions and taxonomies remain inconsistent and fragmented.
A systematic literature review of 26 peer-reviewed articles from Scopus and Web of Science was conducted. The study applied structured keyword searches and inclusion criteria to identify thematic patterns, classification criteria, and conceptual models relevant to knowledge workers and their roles.
The analysis uncovers a broad set of classification dimensions, including task complexity, educational background, organizational roles, and engagement with knowledge processes. It introduces a typology of eleven functional roles—knowledge Handlers—anchored in how individuals create, apply, and transfer knowledge. The study also delineates ten core processes and thirteen activities integral to knowledge work. These findings reflect a shift from static categorizations toward dynamic, context-sensitive interpretations of knowledge workers’ roles.
The scope is limited to published literature, which may omit practitioner insights and sector-specific nuances. The absence of empirical validation also constrains immediate practical application.
The synthesized framework offers a structured basis for human resource management, knowledge management, and leadership strategies, enabling more precise role alignment, task design, and integration with digital tools in diverse organizational settings.
By consolidating dispersed research into an integrative taxonomy, this study advances theory and practice in knowledge management. It provides a foundation for future empirical studies and supports the development of adaptive classification systems for managing knowledge-intensive roles.

Disproportion in the level of economic development is more of a historical fact than a recent phenomenon. The empirical analysis in this study, based on ten selected countries, six of which are the high-income and innovation leaders and four middle-income countries (of which one is the regional innovation leader), shows that in the last decades, the income gap has increased in absolute terms while decreasing slightly in relative terms. The income gap (absolute) increased less for middle-income countries ranked as more innovative. This study investigates whether the difference in knowledge becomes a more relevant determinant of income convergence than capital growth. This study’s empirical analysis is based on two samples. To evaluate the impact of innovations on economic growth and income convergence, we used a sample of ten economies, which were grouped into two categories (high-income and middle-income), and compared their average income over time. It spans the period from 2004 to 2023. The income gap between the two groups of countries has increased from 41,330 (constant 2015 US$) in 2004 to 49,989 (constant 2015 US$) in 2023. If we compare individual middle-income countries with a group of high-income countries, we see that the gap has increased less in those more innovative. The analysis also shows a strong positive relationship between the average value of four Global Innovation Index components (Human capital and research, Business sophistication, Knowledge and technology outputs, and Creative outputs) and GERD per capita. Based on an evidenced positive relationship between innovativeness and expenditures for R&D, we estimated aggregate production function on a sample of 35 European economies from 2000 to 2023 (annual data). Estimation shows that gross fixed capital formation growth of 1% leads to output growth of 0.32%. Employment growth leads to output growth of 0.214% and R&D investment growth of 0.157%. Growth in other exogenous factors (TFP) contributes to output growth by 0.02%. The estimation results show a highly positive impact of expenditures on R&D on economic growth. The country’s educational outcomes strongly determine research and innovative activities, especially regarding science education. Observing the strong relevance of the expenditures for R&D and the innovative potentials for economic growth, what are the options if a country cannot provide innovative growth internally? Technology transfer is one possibility, therefore, less innovative countries should encourage and support technology transfers through FDI or other forms to narrow the knowledge and income gap if they cannot increase R&D investments.