Purpose The Fourth Industrial Revolution has accelerated technological advancements across industries, necessitating that countries, research institutions and enterprises enhance their technological competitiveness. A key challenge in this process is the ability to predict promising technologies and integrate them into strategic decision-making. However, existing methods predominantly rely on expert-driven qualitative assessments, which can be subjective and inconsistent. This study aims to address these limitations by proposing a quantitative, data-driven framework for technology foresight and strategic development in Korea’s railway industry, with a specific focus on emerging digital and control systems. Design/methodology/approach This research integrates autoregressive integrated moving average (ARIMA) time-series forecasting and weighs social network analysis (SNA) to systematically identify emerging technological trends. Using 4,352 railway-related patents from the Korean Intellectual Property Office (KIPO) from 1990 to 2023, technology keywords were extracted through text mining using TF-IDF scores. Promising technologies were identified by analyzing their temporal growth patterns (including forecast confidence intervals) and network influence, enabling a data-driven approach to forecasting technological developments and informing strategic planning. Findings The analysis demonstrates that the synergistic use of ARIMA-based forecasting and SNA-driven influence assessment provides a robust and systematic methodology for identifying emerging technologies. The results highlight that core technologies related to “control,” “signal,” “sensor,” “device” and “speed” are poised for significant growth and hold central positions within the technology network. This quantitative approach enhances technology management by reducing reliance on subjective expert opinions and providing objective, data-driven insights. Practical implications This study offers a structured methodology for organizations to enhance technology foresight and strategic planning. By leveraging predictive analytics, policymakers and industry leaders can proactively identify high-potential technologies, optimize resource allocation and foster innovation in the railway sector, particularly in the transition toward automated and intelligent transportation systems. Originality/value This research contributes to the field of technology forecasting by introducing a reproducible, quantitative framework that combines time-series analysis with network theory. By justifying the methodological choices and demonstrating their synergy, this framework offers a novel and robust alternative to traditional methods for strategic decision-making and technology development, particularly in mature, high-tech industries like the railway sector.

Work-life balance has emerged as a central theme in modern medicine, particularly in radiology, where high burnout rates underscore the urgency for systemic change. This narrative review explores how perceptions of work-life balance vary across generations-Baby Boomers, Generation X, Millennials, and Generation Z-and how these differences shape workplace expectations and cultural evolution within healthcare. Baby Boomers often view medicine as a vocation requiring sacrifice and long hours, while Gen X emphasises flexibility and independence. Millennials prioritise purpose, inclusivity, and work-life integration, favouring fluid schedules and value-driven environments. Gen Z, as digital natives, seeks ethical workplaces, diversity, and clearly defined personal-professional boundaries. That paper started from a dedicated session at the European Congress of Radiology (ECR) 2025, combining literature references with reflections on evolving professional values. It highlights that while generational perspectives differ, common ground exists: across all groups, well-being, fulfilment, and supportive workplace structures are increasingly seen as essential rather than optional. The paper emphasises the importance of adapting institutional policies to accommodate generational needs through flexible scheduling, mentorship, protected time, and inclusive leadership. Ultimately, we aim for the embracing of intergenerational collaboration and recognition of the diverse definitions of professional success, which are key to building resilient radiology teams. Sustainable solutions must move beyond one-size-fits-all models to foster innovation, prevent burnout, and retain talent across all career stages. It is also calling for healthcare institutions to proactively integrate these perspectives to shape a more supportive and effective professional culture. CRITICAL RELEVANCE STATEMENT: This paper offers a narrative overview of generational perspectives on work-life balance in radiology, highlighting both shared values and evolving priorities across age groups KEY POINTS: Burnout remains a widespread issue in radiology, with high prevalence across all career stages, emphasising the need for systemic solutions rather than individual resilience alone. Generational views on work-life balance vary: Boomers value duty, Millennials seek purpose and flexibility, while Gen Z demands ethics, diversity, and personal sustainability. Intergenerational collaboration and adaptability are essential for building resilient teams, requiring healthcare institutions to embrace diverse expectations and implement inclusive, flexible work models.

Unseen seeds, green minds in the digital era: How SMEs foster innovation through green intellectual capital, AI, and resource commitment

ABSTRACT This paper utilizes a unique dataset to examine the impact that the research and development (R&D) tax credit under the Statute for Industrial Innovation has on firm innovation in Taiwan. Using propensity score matching, the results suggest that the firms with R&D tax credits have R&D expenditures that are 12.3% higher than those without them. Compared to direct R&D subsidies, the R&D tax credit appears to be slightly more cost-effective. The policy effect is stronger for smaller firms and ICT (information and communication technology) firms. However, we find no evidence that the R&D tax credits increase the number of patent applications. These findings support the continued implementation of R&D tax credits as an instrument to stimulate R&D investment, while also highlighting the need for policymakers to consider whether additional or alternative policy tools are required to foster innovation outputs such as patent applications.

The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial-and-error approaches, AI-empowered data-driven technologies, particularly large AI models such as universal machine learning interatomic potentials (MLIPs) and large language models (LLMs), offer unprecedented capabilities in exploring complex spaces, predicting catalytic performance, and accelerating rational design. Standing at the forefront of data-driven science, we underscore how databases, universal MLIPs, and LLMs are revolutionizing the traditional catalysis paradigm and bridging the ontology-concept-computation-experiment continuum. We then demonstrate significant recent progress, and discuss their potential and challenges in the catalytic field. By leveraging cutting-edge universal MLIPs and LLMs, researchers can conduct large-scale simulations, highly efficient data acquisition, training, and prediction, and even self-directed research in the field of catalysis. Looking ahead, these advantages enable the rapid development of target catalysts, which will be propelled by integrated universal MLIPs, multimodal LLMs, and automation systems. Developments in these domains will pave the way toward AI-empowered closed-loop platforms and cross-disciplinary Digital Materials Ecosystems that broaden the discovery landscape and foster cross-materials innovation, marking the dawn of a new era in which catalyst materials discovery is perpetually accelerating.

ABSTRACT New quality productivity serves as the core engine driving high-quality development of China's manufacturing industry. Grounded in the strategic triangle framework, this study employs dynamic QCA and multi-period fsQCA to deconstruct the generative logic, spatiotemporal evolution, and configuration paths of new quality productivity within China's advanced manufacturing. The findings reveal that: (1) High-level new quality productivity is driven by causal complexity rather than a single factor, with the ‘Strategic Triangle Synergy’ identified as the high-order path for radical productivity leaps. (2) Spatiotemporal analysis uncovers a significant asymmetric pattern, characterised by an ‘Eastern leadership, Central catch-up, and Western and Northeastern structural lag’ distribution. (3) Multi-period analysis indicates a dynamic transformation in new quality productivity drivers from ‘resource accumulation’ during the 13th Five-Year Plan to ‘institutional innovation’ in the 14th Five-Year Plan. This research advances the theoretical understanding of new quality productivity and provides policymakers with adaptive, configuration strategies to foster continuous innovation and industrial upgrading.

Introduction & Purpose: Ultrasonography is widely used to assess skeletal muscle and tendon properties, such as architecture, cross-sectional area, and tissue stiffness (Sarto et al., 2021). Despite its growing application in different scenarios, and the increasing call for open data access and sharing in clinical research, there remains a significant scarcity of public datasets in this field. This lack of accessible and standardized public datasets limits large-scale analysis algorithm studies, trainee training and the development of image analysis algorithms. To address this, we developed the Universal Musculoskeletal Ultrasonography Database (UMUD), a web application designed to facilitate access to these datasets and foster standardization and innovation in musculoskeletal ultrasonography imaging research. Methods: UMUD is an online repository that aggregates and indexes metadata from publicly available musculoskeletal ultrasonography datasets hosted on platforms like the Open Science Framework and Zenodo. The web application (https://universalmuscledatabase.streamlit.app/) is built using a streamlit (v1.35.0) frontend and mongoDB for its database infrastructure. Standardized metadata descriptors, i.e., muscle group, ultrasound device, participant demographics, are implemented using a combination of pydantic models (v1.10.0) and json schemata to ensure reproducibility and ease of usage for contributing data. UMUD provides detailed instructions for community contributions, including tools for data anonymization. Results: Currently, UMUD hosts 11 datasets from 10 studies, comprising 75,569 images and 2,573 videos from 1,769 participants. The database covers nine lower-limb muscles and one muscle–tendon junction (distal triceps surae and Achilles tendon), captured using various modalities including static imaging, dynamic video, and 3D reconstructions. Datasets include measurements across proximal, middle, and distal regions of each muscle. Benchmark datasets are provided for trainee training and algorithm evaluation, including multi-expert annotated images, fascicle and cross-sectional area overlays, and fully labeled datasets for deep-learning model training. Additionally, UMUD lists available automated image analysis algorithms as a reference for community use. Discussion: UMUD provides an initial foundation for open, standardized, and community-driven musculoskeletal ultrasound research. By centralizing datasets and metadata, it facilitates reproducible research, algorithm benchmarking, and operator education. The inclusion of multi-expert and labeled benchmark datasets supports both training and the development of automated analysis methods. Future directions include expanding dataset coverage, enhancing interactive visualization tools, and launching community challenges for algorithm benchmarking to accelerate innovation in the field. Conclusions: In conclusion, UMUD addresses relevant challenges in musculoskeletal ultrasonography by providing a centralized, standardized repository of datasets and tools. It promotes transparency and innovation in the field, supporting reproducible research and advancements in automated image analysis. Future developments include adding datasets, expanding functionalities and introducing community-driven algorithm development challenges.

This study explores how leadership style and village institutional support contribute to the performance of Village-Owned Enterprises (BUM Desa), with innovation capability acting as a mediating mechanism in West Java. The research is grounded in the observed decline in BUM Desa performance rankings during the 2023–2024 period, which signals persistent limitations in sustaining and improving long-term BUM Desa performance. Existing empirical studies indicate that the performance of BUM Desa is closely linked to internal organizational dynamics, particularly leadership effectiveness, institutional arrangements at the village level, and the capacity to foster innovation. To empirically investigate these relationships, this study employs a quantitative approach with a causal explanatory design. Survey data were collected from 540 employees of legally established BUM Desa in West Java, with the sample size determined using the Slovin formula. The data were obtained through a structured questionnaire using a five-point Likert scale and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 3. The findings reveal that leadership style and village institutional support have a positive and statistically significant effect on BUM Desa performance. In addition, innovation capability directly enhances BUM Desa performance and partially mediates the effects of leadership style and institutional support on performance outcomes. These results underscore the strategic role of innovation capability as a key pathway through which leadership and institutional support translate into improved BUM Desa performance. Consequently, efforts to strengthen BUM Desa performance in West Java should prioritize the development of effective leadership practices and consistent institutional support to reinforce innovation capability and promote sustainable BUM Desa performance.

Universities across the world are urged to carefully reconsider their role in society and to evaluate their relationship with the community through innovation. Despite its importance for national competitiveness, few papers examine the impact of National Policy Interventions licensing on Intellectual Property (IP) protections for innovativeness in universities, yet patents and copyrights are often considered to be the determinants of innovation. Our goal in this qualitative research article was to examine, evaluate, and summarise IP management publications, institutional reports, legislation, and policies. The case studies used were Kyambogo and Mbarara University of Science and Technology. This essay aims to address two basic IP-related questions: First, are there operational IP management policies in Uganda's public universities? Second, do national IP regulations foster university innovation? To structure the study, we used the Endogenous Growth Model of Innovation. We discovered that many university academics appear to be unaware of the current IP regulations in areas like patents, copyright, and licensing, despite the fact that several laws have been passed to protect IP and foster innovation and competition. The study also discovered that there are insufficient policy choices for the protection of intellectual property produced by public universities, based on the capacities and gaps mentioned in policy documents and their recommended priority areas of intervention. In order to guarantee IP protection and redress functions of Uganda's public universities, the report suggests that the government create a national innovation council.

The field of biomaterials development is undergoing a fundamental paradigm shift, moving from empirical, trial-and-error approaches to data-driven, intelligent design strategies powered by Artificial Intelligence (AI). This review systematically synthesizes recent progress in applying AI and Machine Learning (ML) technologies to the preparation of high-quality biomaterials. It begins by outlining core AI methodologiesincluding foundational learning paradigms and advanced architectures such as Graph Neural Networks (GNNs) and Transformersand discusses their alignment with specific types of biomaterials data. The article then details AI's transformative role across three critical stages of the biomaterials R&D pipeline: (1) precision prediction of properties via high-throughput screening and virtual data analysis; (2) inverse design driven by target performance requirements; and (3) rapid multiobjective optimization of both material formulations and synthesis process parameters. Illustrative case studies demonstrate how these AI-enhanced approaches significantly accelerate design efficiency, expand discovery space, and foster innovation. Furthermore, the review critically examines persistent challenges, such as data scarcity and heterogeneity, model interpretability and reliability, rigor in validation, and ethical-regulatory concerns. Finally, we present a forward-looking perspective on emerging directions, including the evolution toward autonomous intelligent design, end-to-end smart manufacturing, cross-disciplinary integrated applications, and a transition to sustainable development. The deep integration of AI is positioned to fundamentally accelerate the discovery, optimization, and clinical translation of next-generation, high-performance biomaterials for regenerative and precision medicine.

From a knowledge-based perspective, monopolizing knowledge is getting difficult and is an emerging source for innovation and organizational performance. This study explores to what extent knowledge sources stimulate green innovation to enhance organizational performance, with moderation affecting resource orchestrion capability. Data were collected from Chinese manufacturing industries during February and April 2025. This study employed SmartPLS 4.1.12, which is widely used to analyze complex models of mediation and moderation for first- and second-order constructs. The results show that knowledge sources, both internal and external, act as stimulants to promote green innovation and improve organizational performance. In addition, green innovation also positively mediates between knowledge sources and organizational performance. Furthermore, resource orchestration capability strengthens the relationship between knowledge sources and green innovation. Future research may broaden the concept beyond other industrial businesses. Green innovation, tacit knowledge management, affective trust, and task efficiency can also be explored to stimulate organizational performance. This study provides proof that knowledge sources are essential for green innovation. Managers and organization leaders should encourage knowledge sharing initiatives inside and outside the company to improve organizational performance and green innovation. This study fills existing research areas that have received limited empirical attention and improve understanding of knowledge sources, inside and outside organizations, to boost green innovation and overall performance. Mediation and moderation research explores knowledge sources to foster green innovation and organizational performance.

Abstract Firm innovation and corporate social responsibility (CSR) are key strategic considerations that shape a firm's competitiveness and sustainability. However, studies exploring the relationship between the two are heterogeneous and sometimes obtain contradictory results, making it difficult to draw clear conclusions. This systematic literature review addresses this gap by examining 72 peer‐reviewed studies spanning 2000–2024 to assess the type of relationship (direct, indirect, other), direction (causality), sign (positive/negative), and statistical significance of the innovation‐CSR relationship. The results generally support a positive relationship – especially from CSR to firm innovation‐ although the outcomes vary depending on measurement approach, sector, geographical context, and period. Beyond direct effects, we identify more complex dynamics, notably mediation/moderation processes, which can involve both firm innovation and CSR. Academically, this study clarifies theoretical trends, highlights underexplored frameworks, and contributes methodologically by applying the Scientific Procedures and Rationales for Systematic Literature Review protocol and the theories‐contexts‐methods framework, which could enrich future research. For managers, we offer guidance with regard to leveraging CSR to foster innovation and in turn improve firm performance. For policymakers, our findings can inform designing frameworks to stimulate sustainable business practices. Finally, for society at large, this study can contribute to aligning corporate behavior with social and environmental objectives.

Ontario's primary care system faces challenges from fragmented digital infrastructure, where electronic medical records (EMR) operate in silos. This contributes to administrative inefficiencies, delayed care, and provider burnout. This evaluation examined the feasibility and potential impact of the Health Application Lightweight Protocol (HALO), a vendor-neutral middleware framework designed to enhance interoperability and automate high-volume tasks across disparate EMRs. Data was collected through an online survey of primary care providers and key stakeholder interviews. Findings revealed strong support for HALO's potential to streamline workflows and improve care coordination. HALO met key requirements such as single sign-on, task automation, and middleware integration. However, critical gaps including vendor alignment, governance, and organizational readiness exist. Key recommendations to address these gaps included unified governance, a certified vendor marketplace, and clinical champions networks. HALO offers a scalable pathway to unify Ontario's fragmented primary care digital systems and foster patient-centered innovation. This evaluation provides actionable insights for policymakers and healthcare leaders aiming to advance interoperable health IT integration.

This article examines the challenges that blockchain-based digital currencies pose to state monetary sovereignty in the digital era. As technological advancements transform monetary systems, decentralized peer-to-peer transactions threaten to erode state control over monetary policy. We distinguish between wholesale and retail central bank digital currencies (CBDCs), assessing their respective roles in mitigating sovereignty risks. While wholesale CBDCs reinforce traditional monetary authority, retail CBDCs redefine sovereignty by countering the rise of decentralized crypto-assets and foreign CBDCs. We argue for a dual-CBDC system that integrates both models, with a particular emphasis on an indirect retail CBDC framework to enhance financial stability, ensure public accountability, and foster innovation.

ABSTRACT To address the social and environmental challenges (also called ‘grand challenges’) faced by our society, new forms of collaborative and innovative processes are needed to support profound transformations of socioeconomic ecosystems. In such contexts, actors are likely to be highly distant from each other, not only geographically but also cognitively (having heterogeneous knowledge and expertise), organisationally (being scattered across different organisations and possibly sectors) and institutionally (not sharing the same rules and norms). This paper defines these types of contexts as situations of ‘grand distance’ where collaborative innovation is hindered by too large a degree of cognitive, organisational and institutional distance. This paper thus aims to shed light on a specific class of co‐design methods, called ‘resilient‐fit co‐design methods’, which help manage the distance between highly heterogeneous actors to foster collaborative innovation. An example of grand‐distance situations can be seen in the field of Earth observation (EO), where data‐based solution designers undertake dedicated co‐design efforts to integrate data into multiple ecosystems with the specific aim of addressing contemporary socioenvironmental challenges. Based on an in‐depth empirical investigation of two case studies in this field, this paper describes how resilient‐fit co‐design methods can be built in such contexts, revealing their similarities and specificities with respect to existing co‐design methods. This paper also highlights several original forms of ecosystem dynamics, which are driven by external actors and oriented towards locally enhancing the resilience of ecosystems. As such, this research offers theoretical and practical contributions that enrich the pool of available co‐design methods necessary for the transformation of socioeconomic ecosystems to address grand challenges.

Purpose This study examines the impact of ethical leadership and the physical work environment on the creativity and innovative behaviour of employees in the same organizational context, with a particular emphasis on implications for international business settings. Design/methodology/approach A quantitative method (survey) was used for this study, with 396 questionnaires completed online. Data analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4, following the two-stage approach. The research sample was generated using a simple, non-probability, convenience sampling technique. Findings This study challenges prevailing theoretical frameworks by examining the interrelations between ethical leadership (EL), physical work environment (PWE), employee creativity (EC) and innovative behaviours (IB) within a unified framework. EC emerges as a key mediator linking EL and PWE to IB. The PWE, modelled as a second-order construct, exerts both direct and indirect effects on IB, indicating partial mediation through EC. Supportive and aesthetically pleasing environments stimulate EC and directly promote IB. These findings are particularly relevant for multinational organizations managing culturally diverse teams. Research limitations/implications The present study is subject to a number of limitations that may inform the design of future research. One such limitation is that the study included a relatively younger cohort of respondents, with an average age range of 29–30. This may have influenced the overall findings of the study. In subsequent research, the age range of respondents should be broader to gain a more representative sample and to ensure a greater distribution of the questionnaire to respondents in older age categories. Another potential limitation of the study that could be addressed in future research is the voluntary participation of many study participants, which may have introduced demographic biases into the sample. Subsequent studies could consider including a more diverse set of organizations or activities in the scope of investigation. Practical implications This study offers several managerial implications. First, it highlights ethical leadership's role in fostering employee innovation. As leadership shapes behaviour, organizations should prioritize creativity and support its development. Ethical leaders inspire creative employees to act innovatively, enhancing innovation outcomes. Second, the physical work environment significantly affects behaviour and performance. Visually engaging spaces can boost satisfaction, productivity and creativity. For multinational enterprises, aligning leadership and workplace design with local cultural norms is essential. Perceptions of supportive or aesthetic environments, and interpretations of ethical leadership, vary across regions. Thus, culturally sensitive leadership and design strategies are vital for local effectiveness and a cohesive global innovation strategy. Originality/value Among physical work environment (PWE) dimensions, Focus and Sense of Beauty influence innovative behaviours (IB) entirely through employee creativity (EC), indicating full mediation. Connectedness does not significantly affect EC but has a modest direct effect on IB, suggesting social connection may independently foster innovation. These results affirm that leadership and environmental factors shape innovation primarily via EC, while Connectedness may play a distinct role. The findings guide global leaders in aligning leadership and workplace design across cultures.

Abstract Central Bank Digital Currencies (CBDCs) are being currently developed by most central banks in the world as a promising development mechanism: they may foster innovation in a somehow stagnant financial sector, stimulate diversification in products and services offered to consumers, and grant citizens broader access to sophisticated market products that are up to this moment available only to professional investors. All these potential benefits, however, come along with raising concerns. CBDCs are looked down by those who see relevant risks for data privacy. Critics also highlight the underpinning risks for financial stability, considering that radical changes in money issuance may affect the most prominent actors of the financial system – the banks, which may no longer play such a relevant role in the economic process. Though sometimes far-fetched, these issues are still relevant in the current debate. When supporters and critics brandish their perspectives in general terms, however, the high level of experimentalism involved in designing CBDCs is overlooked, in spite of its importance to understanding monetary innovation. This paper proposes a more empirical and less abstract approach to institutional aspects of CBDCs. The main goal of this work is to emphasize how official digital currencies should consider the regulatory framework under which they will effectively operate. This regulatory perspective reveals not only the varied legal landscape of official digital money but also how the potential contribution of CBDCs to development is conditioned by their institutional design, reflecting policy ambitions and also deep political and economic constraints. Within this empirical agenda, the paper analyzes one particular case of CBDC implementation, the Brazilian “Drex,” exploring its main strategic decisions as well as the policy choices for regulatory design.

The incorporation of generative artificial intelligence (AI) in doctoral supervision signifies a transformative evolution in higher education. This has been significant, particularly within intricate and emotionally complex research such as sexuality studies. This reflective, collaborative autoethnographic study investigates the experiences of a doctoral student and her supervisor. They explored AI generative tools to enhance research processes, quality of supervision and intellectual inquiry. Anchored in Kolb’s Experiential Learning Theory and reconceptualised through an augmented experiential learning framework, the study elucidates how AI tools like ChatGPT encourage critical thinking. These tools were also used to foster methodological innovation and facilitate ethical reflexivity. Through iterative engagements, AI supported the formulation of sophisticated research questions and bolstered academic writing. It also aided emotional resilience in traversing heteronormative and interdisciplinary landscapes. The study highlights the evolving role of supervisors, not as gatekeepers but as collaborators in AI-informed learning. Significant emphasis was placed on prompt engineering, scholarly scrutiny and academic integrity. Ethical guidelines and rigorous documentation practices ensured a responsible AI application without sacrificing originality. Contribution: The findings reveal that AI-augmented supervision promotes deeper theoretical engagement and enhances self-directed learning. It also introduces new pedagogical possibilities for complex research endeavours. Nonetheless, the study also underscores the challenges of bias, overreliance and contextual insensitivity inherent in AI outputs. By suggesting actionable strategies for ethical integration, this paper contributes to emerging global discussions on AI in higher education. It presents a framework for inclusive, transformative and contextually aware supervision practices.

Abstract The purpose of this paper is to explore the literature on knowledge management for innovation and sustainable development, to unveil the main research trends developed over time. To contextualize the field under study, bibliometric techniques are used, while traditional methods of systematic literature review are employed to deepen the study and discover the main trends developed over time. The software chosen for this research is Bibliometrix ® , which has been combined with an innovative three-level systematic analysis of keywords, co-occurrence of keywords and analysis of the abstracts of the 793 peer-reviewed articles that make up the database obtained from Web of Science. The findings reveal six relevant subtopics focused on the interconnectedness of knowledge management with some key factors for firms’ competitiveness. It is concluded that effective knowledge management practices disseminate knowledge within and between organizations, teams, and departments, nurturing a culture of continuous learning and cross-functional collaboration. This research significantly contributes to the understanding of the interplay between knowledge management, innovation and sustainable development, as well as providing guidance to policymakers in developing regulations and incentives that support the development of sustainable business models, eco-innovation and efficient knowledge management, and to organizations on how they can apply knowledge management practices to foster innovation, streamline operations and adapt to changing markets and environmental conditions.

This study stems from the clear need to understand why and how organizations in Romania integrate emerging digital technologies into circular economy (CE) practices, given the critical role of this integration in improving resource efficiency and supporting sustainable business models. Data were collected through a structured questionnaire applied to 149 organizations of different sizes, ranging from SMEs (fewer than 50 employees) to large corporations (over 500 employees), operating across multiple sectors, including agriculture, construction, security, services and research. The questionnaire assessed organizations’ familiarity with CE principles, their stage of CE implementation, and their adoption of digital technologies, including artificial intelligence (AI), Internet of Things (IoT), blockchain, cloud computing and robotics. The results indicate that most organizations are aware of the potential benefits of digital technologies, particularly in terms of resource efficiency, enhanced product traceability and support for sustainability goals. However, effective implementation remains quite limited in many cases due to inadequate or outdated infrastructure, lack of technical skills, and organizational resistance to changes. At the same time, the findings further reveal a growing strategic interest in digitalization: approximately 41% of SMEs and 59% of large organizations plan to increase investments in digitalization, primarily to improve sustainability performance and foster innovation. Overall, the study provides a comprehensive overview of the current state of digitalization in support of CE in Romania and proposes practical recommendations for organizations and decision-makers, highlighting both emerging opportunities and persistent barriers.