This study explores visitor satisfaction at Bourbon cultural heritage sites in Campania, with a focus on the Royal Palace of Naples and the National Archaeological Museum (MANN). Adopting a mixed-methods approach, researchers carried out 97 interviews to assess eight critical dimensions of satisfaction. Through the application of the expectancy-disconfirmation model, the study contrasts perceived quality with the importance attributed by visitors. Findings show strong appreciation for the cultural content offered but point to notable shortcomings in infrastructure, digital accessibility, and post-visit engagement. A clear mismatch between visitor expectations and the actual services provided—especially regarding online communication and complementary offerings—emerges.
The research incorporates a pilot phase conducted by the Siti Reali organization, which represents a foundational step in satisfaction measurement. This phase serves as a valuable input for future collaborations with innovative startups that could enhance and scale similar evaluation processes using digital and data-driven tools.
Demographic and behavioral data indicate a growing interest in thematic experiences and customized itineraries, underscoring the need for more responsive and flexible site management. Overall, the study contributes to the field of cultural tourism by aligning satisfaction analytics with heritage site operations. Strategic proposals aim to strengthen visitor engagement and reposition Bourbon sites within the global tourism landscape, supporting sustainable and visitor-focused growth for Southern Italy’s cultural tourism sector.

The museum sector is undergoing a significant digital transformation, accelerated by the COVID-19 pandemic, which exposed vulnerabilities such as revenue losses and reduced visitor engagement. Emerging Web3 technologies, including blockchain and Non-Fungible Tokens (NFTs), offer potential solutions for monetization, visitor engagement, and accessibility. However, adoption in Italian museums remains limited. This study explores the barriers to Web3 technology adoption from an institutional perspective, using qualitative interviews with 23 museum professionals, including directors and curators.
Key findings reveal multifaceted challenges, including skepticism about the added value of NFTs, perceived technological complexity, financial constraints, and regulatory uncertainties related to copyright and data protection. Cultural concerns also emerged, with some stakeholders fearing that digital innovations might compromise museums’ traditional roles or lead to excessive commercialization. The study aligns these themes with the Unified Theory of Acceptance and Use of Technology (UTAUT), extending the model to include sector-specific factors.
The research highlights the tension between innovation and tradition in museums, emphasizing the need for compelling use cases, robust regulatory guidelines, and dedicated funding to facilitate adoption. By contextualizing Web3 adoption within UTAUT, this study provides a theoretical foundation for future empirical investigations and practical insights for museums navigating digital transformation. The findings underscore the importance of balancing technological advancement with cultural preservation, ensuring that digital tools enhance rather than overshadow the core mission of museums as custodians of heritage and education. This study contributes to a broader debate on technology adoption in cultural institutions, particularly in contexts with strong historical traditions such as Italy.

The present study investigates the potential of Artificial Intelligence (AI) to foster inclusive cultural value in Italian museums, focusing on addressing the physical, sensory and cognitive barriers encountered by visitors with disabilities. The research employed a multiple-case qualitative design, engaging more than twenty museums through a semi-structured online questionnaire administered to accessibility managers. The museums varied in governance, location, and thematic focus, but all were committed to accessibility. The findings demonstrate a robust theoretical understanding of AI’s potential to personalise experiences. However, its practical implementation remains predominantly experimental, constrained by infrastructural limitations, organisational rigidity and financial constraints. A significant proportion of institutions engaging with AI, specifically 87.5%, reported incorporating co-design processes with individuals living with disabilities. This finding serves to emphasise the pivotal role that participatory approaches play in ensuring technological solutions are aligned with the needs of the end-users. From a governance perspective, the study posits that truly inclusive innovation necessitates a systemic strategy: museums must invest not only in technological infrastructure but also in interdisciplinary processes that facilitate collaboration among curators, technical specialists and disability-community representatives. Furthermore, the sustainable integration of AI tools necessitates a coordinated commitment across multiple institutional levels, encompassing individual museums, governmental bodies, and disability-rights organisations, to ensure the availability of adequate resources, expertise, and policy support. To strengthen the evidence base, it is recommended that future research should expand the empirical scope by testing the proposed framework on a larger, more diverse sample of cultural institutions and by conducting longitudinal studies to track the long-term impacts of AI-driven accessibility initiatives. The work under discussion here establishes the foundations for human-centred technological innovation that extends beyond the scope of pilot projects and integrates inclusivity as a fundamental element of museum practice.

This ongoing research investigates how Generative Artificial Intelligence (GAI) technologies are reshaping the roles and competencies of middle managers. While much of the existing literature emphasizes macro-level impacts of AI—such as productivity gains and strategic transformation—this study focuses on micro-level changes, particularly managerial adaptation, decision-making, and skill reconfiguration.
Grounded in activity theory (Engeström, 1987, 2001) and informed by a critical realist epistemology, the study adopts a qualitative, inductive approach. Data is being collected through semi-structured interviews with middle managers from three contrasting organizational contexts: a large enterprise in the energy sector (30 interviews), a medium-sized digital services firm (15 interviews), and a small telecommunications company (15 interviews). This multi-site design enables the analysis of systemic tensions across different governance models and organizational cultures.
Preliminary findings reveal that middle managers are often at the forefront of GAI experimentation, initiating Bottom-Up innovation processes outside official IT channels. These informal practices—frequently associated with Shadow IT—allow for agile problem-solving but also pose significant risks in terms of data security and strategic misalignment. Managers report that GAI tools not only automate routine tasks but also support cognitive structuring, thereby transforming how they plan, communicate, and lead.
The integration of GAI calls for hybrid competencies that go beyond technical know-how. Conceptual skills (e.g., sense-making and system thinking), human skills (e.g., empathy, leadership, conflict mediation), and ethical awareness are increasingly vital. Middle managers thus emerge as key facilitators of digital transformation, mediating between frontline innovation and top-down strategic frameworks.
This paper contributes to the literature by offering a multi-dimensional reading of managerial transformation through the lens of activity theory. It proposes actionable insights for organizations aiming to responsibly integrate GAI, emphasizing the need for adaptive governance, targeted upskilling, and the creation of experimental safe zones. Future work will extend the dataset and explore longitudinal trajectories of GAI adoption and governance.

Think of a funnel. What is its shape? What does it do? It has a large end, larger and larger that allows many elements to pass through, such as grains of sand or kernels of rice; and it has a narrow end, narrowing to block the passage to most of them; funnel has a simple but useful function: it serves to pour a big mass of parts into a container with a small opening. This is, when you think about it, the same principle behind human information processing and knowledge management. This paper proposes a funnel-shaped conceptual framework, that integrates three dimensions of knowledge: complexity; intelligibility; innovability; as the three dimensions of the funnel. Such framework provides a more comprehensive form of understanding complex institutions, both political and economic. It highlights, also, the interaction between information and institutions, and how it can impact human behaviour.

The fragmentation of data on the existing built environment is a critical obstacle to the governance of urban spaces. Although a wide range of data sources is available, from satellite imagery and environmental sensors to socio-demographic and cadastral datasets, these resources often lack interoperability and integration.
This deficiency limits the ability of governments and stakeholders to conduct accurate monitoring, implement informed real estate development strategies, and promote sustainable urban regeneration practices.
The research aims to explore how the integration of advanced Artificial Intelligence (AI) models, particularly Generative Adversarial Networks (GAN) based technologies, can contribute to the development of innovative approaches for urban management and the enhancement of disused building stock. In particular, the analysis aims to investigate the role of AI in Due Diligence (DD) processes, through a literature review in the areas of Smart Cities (SC) and Urban Management (UM), to define a theoretical-methodological framework to support data-driven urban regeneration strategies.
These technologies enable the generation of predictive urban models by combining heterogeneous inputs, such as geospatial frameworks, land-use data, environmental performance, and socioeconomic indicators. The research adopts a multidimensional approach based on a systematic literature review, which identified more than 1,200 academic contributions. Through a multi-stage filtering process, the most relevant analyses were classified into two main areas: “Artificial Intelligence and Smart Cities” and “Artificial Intelligence and Urban Management”, with a focus on “Due Diligence” and models for “Architectural Heritage Enhancement.” Integrating AI capabilities into the development of urban regeneration strategies has the potential to create resilient and smart cities. This can optimise resources while minimising land consumption and enhance inclusive and collaborative governance. Despite growing scholarly interest, analysis has revealed significant gaps in the development of these technologies.
The proposed approach is in line with emerging urban agendas and promotes a transition to resilient, circular, and smart cities. This study advocates for digital innovation that is not only technologically advanced but also ethically grounded and socially inclusive.

This research aims at exploring and understanding the complex contemporary phenomena of socially sustainable Experience economy. It contributes to the academic discussion around Experience economy research, strategy and policy by developing understanding about managing social sustainability within Experience economy ecosystems in Finland. The core question of this doctoral thesis is how to ensure everyone equal access to Experience Economy Ecosystems?
The context of this research is the new Experience Economy research program conducted at Tampere University. This article is part of a doctoral thesis, focused in specifically on industry phenomenas Accessibility of Experience and Paths and Communities of Experience. The Experience economy research program generates a rich empirical context and metadatabase highlighting the ongoing transformation and market development within the uprising Experience Economy ecosystems in Finland.
In this article experience economy is studied through social sustainability and in specifically inclusivity, to identify and analyse the mechanisms for managing social development goal SDG implementation within the experience economy ecosystems. Empirical data is drawn from OAF Outsider Art Festival. OAF is a new multidiscipline art festival, organized in conjunction with the largest Nordic art festival Helsinki Festival. OAF promotes diversity, equity and inclusion (DEI) and contributes to Social Development Goal implementation (SDG) by managing innovative collaborations between industry outsiders and insiders. OAF provides an interesting case – an industry specific critical “node” -within the EE ecosystem, providing new knowledge from the perspective of industry outsiders.
In result, by identifying, analysing and mapping the critical nodes, access points and stages where social transformation and inclusivity actualise and create socially sustainable, experience driven business models, this research creates new knowledge of managing social sustainability within Experience economy ecosystems and networks, in research and practice. This research contributes to Sustainable Development Goals (SDG): Sustainable cities and communities (11), reduced inequalities (10), partnerships for the goals (17), industry, innovation and infrastructure (9), decent work and economic growth (8).

The aim of the current study is to examine the perception of cultural diversity among German STEM students. Previous research has mainly focused on the attitudes towards gender in the STEM field. This study undertakes an innovative approach and explores the opinions of German STEM students with and without a migration background on cultural diversity and educational barriers. To test this, we conducted semi-structured interviews with 90 STEM students from two German cities. The data was analysed with a qualitative content analysis. The results show that both students with and without a migration background are to a large extent unfamiliar with the topic of cultural diversity. Most STEM students think that cultural diversity has a positive impact on work performance. Some of the barriers which students with a migration background face are unfair university structures, lack of support in the educational system, challenges of cultural adaptation, and insensitivity or hostility from host-culture members. This study shows the need for a more inclusive university culture in the STEM field. It also emphasizes that diversity trainings can successfully prepare students for intercultural experiences.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.