Efficiency and value creation for citizens, communities, and societies are becoming increasingly important for the public sector. Public sector is continuously seeking for innovative ways to create and provide services, and despite acknowledging the key role of knowledge in innovation processes, the relationship between publicly available knowledge and innovation is still poorly understood. A more unified view of OI and the public sector originated knowledge ecosystem, alongside public-private collaboration, is crucial. The private sector’s creation of open knowledge-based innovations, such as products or services, for private markets appears to have minimal influence. To achieve successful knowledge-based innovation, functional systems are crucial for addressing the identified barriers within public knowledge and innovation. This paper investigates the potential of public sector originated knowledge to drive open innovation in partnership with public and private organizations. The potential to gather and use openly available knowledge is now greater than ever due to the possibilities provided by AI. Challenges in innovation are widely explored in existing research. Future studies should focus on developing models for successful open knowledge initiatives, including strategic planning (value realization and resource-based strategic analysis), technical enablers (new digital technologies as AI), sharing platforms (design of public knowledge sources with appropriate APIs), enabling functional public knowledge ecosystems, and legal frameworks to support open public knowledge sharing and utilization (e.g., knowledge privacy). Research needs to consider social aspects alongside technological and business ones. This includes management models for public knowledge and innovation, knowledge processes and its management to support OI, and organizational cultures that reflect experiences of control or safety, risk management, attitudes and engagement in OI processes. To better comprehend how the private sector can benefit from public knowledge, additional empirical studies are crucial.

This paper examines the transformative impact of AI-enhanced data platforms on knowledge management (KM) within waste management organizations, focusing on the case of Alia Servizi Ambientali SpA in Tuscany, Italy. Utilizing a qualitative case study approach that combines on-site observations, stakeholder interviews, and system data analysis, our research demonstrates that AI integration significantly optimizes operational efficiency by consolidating diverse data streams, from IoT sensors monitoring waste receptacles to vehicle fleet metrics, into a unified, high-quality repository. The platform employs a medallion architecture to ensure data quality, enabling predictive analytics that improve route optimization, reduce vehicle movements, and lower carbon emissions. Beyond these practical benefits, the study advances theoretical insights by proposing a framework that situates AI-driven KM within broader governance and ethical contexts, contrasting traditional approaches with the dynamic capabilities of AI technologies. Despite the inherent limitations of a single-case design, our findings provide a strategic blueprint for leveraging AI-enhanced data platforms in waste management. They underscore the critical role of robust governance frameworks, leadership commitment, and targeted training in aligning technological capabilities with evolving KM practices, ensuring the sustainability and scalability of digital transformation initiatives.

Knowledge is one of the most valuable assets in modern organizations. However, its intangible and evolving nature makes it challenging to capture and leverage. Knowledge management (KM) is the organized, systematic process that acquires, refines, organizes, and applies this knowledge to improve organizational success and secure long-term competitive advantage. As data volume and complexity continue to grow, conventional systems have proven insufficient to harness the full potential of knowledge assets. As a result, emerging technologies have become indispensable, providing agile, scalable, and intelligent solutions. However, much of the existing research often examines these technologies in isolation, considering artificial intelligence (AI) and others as standalone entities, failing to reveal the synergies that emerge from combining them. This limitation is further complicated by the nature of common research approaches: literature reviews, while offering broad theoretical insights, are typically too abstract to inform practical application; meanwhile, case studies, though grounded in real-world scenarios, often lack generalizability due to their strong context-specific focus. This study addresses these gaps by investigating how AI and complementary, emerging technologies redefine KM areas. Drawing on 1,487 documented business practices—comprising case studies, pilot projects, and simulation models—derived from literature, this research focuses on five critical KM phases: knowledge creation, acquisition, organization, transfer, and application. Conceptually, knowledge is created or acquired, then captured, organized, and preserved for the long term, transferred to those who need it, and finally applied to produce value. Finally, these practices are analyzed through association analysis via Cramér’s V to quantify the strength of relationships between AI (whether used alone or alongside other technologies), business functions and the different KM areas. Theoretically, the findings advance KM research by demonstrating which technological combinations are more effective in each KM phase. From a managerial perspective, the emerging practices examined in this study offer real-world examples of how these integrated solutions can be successfully deployed, allowing managers to draw from documented practices rather than starting from scratch.

Previous studies have highlighted the numerous benefits of artificial intelligence (AI) models in enhancing the management of organizational knowledge assets. However, the adoption of AI in organizational settings often encounters significant barriers that hinder its optimal implementation. This paper presents preliminary findings from a timely study that uniquely focuses on the perceived obstacles at the intersection of AI and knowledge management (KM) in organizations. Our objective was to understand the perspectives of employees and managers regarding four dimensions of barriers and challenges in integrating AI technologies into knowledge management systems (KMSs) in business: human, technological, financial, and ethical-regulatory. A voluntary and anonymous online questionnaire was completed by 378 respondents from various industries. The results reveal that financial barriers were reported to be the least significant by both regular employees and managers. Instead, nearly half of the participants expressed concerns about technical barriers, particularly the inadequacy of their organizations’ technological infrastructure to support AI applications effectively. A significant percentage of 82.28% of the sample mentioned organizational barriers, specifically noting that employees lack the necessary skills to leverage AI for enhancing organizational KM. Furthermore, nine out of ten respondents indicated that a substantial cultural shift is essential for facilitating AI adoption within their organizations. Concerns about the potential leakage of sensitive information due to AI usage were significant, with approximately two-thirds of respondents highlighting this issue. Additional ethical barriers were prominent, with three out of four participants reporting a lack of clear organizational procedures to ensure information security and privacy in AI applications. These findings have significant theoretical and practical implications for the discipline of KM in general, and for KMSs in particular. These findings lay a fertile ground for future empirical investigations into the relationship between AI and KM.

In the era of Industry 5.0, artificial intelligence offers new opportunities for co-creation, personalization, collaboration and integration. These affect companies’ Business Model Innovation strategies, supporting progress in the interactions between sets of actors, populations, activities, institutions and networks impacting the entire innovation ecosystems. Positioning itself in the research stream focused on analysing the impact generated by emerging technologies in the innovation of business ecosystems, this study aims of discussing the impact generated by artificial intelligence in business model innovation, reflecting on the salience of the involved stakeholders. A three-phase methodological approach was used, integrating a systematic literature review with a case study analysis. The related findings were discussed according the stakeholder theory basing on the salience attributes of power, legitimacy and urgency. This approach allowed us to establish a strong theoretical foundation while anchoring our findings in real-world example. Seven artificial intelligence-based value creation innovations were identified, demonstrating how artificial intelligence-based applications are transforming value creation mechanisms. Findings indicate that artificial intelligence has the potential to generate significant added value, primarily by enhancing value creation through data exploitation and the high degree of services customization tailored to customer needs. Moreover, new value streams emerge from artificial intelligence-based applications, driven by the widespread adoption of the technology across innovation ecosystems. This diffusion influences and engages stakeholders at every layer of the ecosystem’s structure. Stakeholders are classified as Discretionary (Standards Bodies, Public Bodies), Demanding (Distribution Channels, Research Institutes), Dangerous (other suppliers), Dependent (Suppliers), Definitive (Core Organization, Customers, Complementors). Notably, no stakeholders were identified that align with the Dormant or Dominant categories.

Mathematical modeling of complex problems, with particular attention to multi-criteria and optimization models, is particularly effective for analyzing critical issues in the agri-food supply chain. These tools allow analyzing problems of different nature through the use of qualitative and quantitative criteria, often conflicting with each other, and decision alternatives. Their versatility allows adapting solid theoretical constructs to different contexts and application areas, ensuring a modeling that takes into account the peculiarities of the problem under examination. In fact, multi-criteria analysis tools and optimization models allow supporting decision makers in the different phases that characterize the agri-food supply chain. This work addresses in particular the decision problem related to the transition of agri-food products from the storage to the marketing phase, using the Analytic Hierarchy Process (AHP) method. Once the products have undergone transformation processes, they are stored in one or more warehouses for distribution to commercial activities. There are several choices to be made in this phase, including the choice of the commercial establishments in which to distribute the products. In particular, the use of the AHP method is proposed for the analysis of a decision problem relating to the choice of the warehouse distribution with respect to four different commercial establishments (alternatives) that sell directly to the final consumer. A sensitivity analysis is conducted to test the robustness of the method. Through the analysis of a practical case, it is highlighted how the agri-food supply chain is a very complex context, in which the competitiveness of organizations is based on the ability of the decision maker to identify problems, imagine alternatives and adopt solutions. It is also highlighted that these tools, if correctly implemented in organizations, allow the start of an organizational learning process, reducing the costs associated with decision-making errors.

This study explores how consumers perceive and respond to smart packaging technologies as a practical solution to the growing issue of food waste. While traditional food packaging plays a role in preservation, it often lacks the ability to communicate real-time information about freshness or quality.
Smart packaging, which includes technologies such as sensors, freshness indicators and traceability systems, allows for real-time monitoring of product quality, providing consumers with essential information to make more informed decisions and extend the shelf life of food products.
To understand the factors influencing consumers’ intention to adopt these technologies, the research adopts an extended version of the Technology Acceptance Model (TAM). In addition to the classic variables: Perceived Usefulness (PU), Perceived Ease of Use (PEU) and Attitude toward Smart Packaging (ATT), the study introduces a novel variable such as Willingness to Pay (WP).
Data were collected through a structured online survey using realistic, scenario-based situations, and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM).
The preliminary findings show that Perceived Usefulness (PU), Perceived Ease of Use (PEU) and Attitude toward Smart Packaging (ATT) significantly influence consumers’ intentions to adopt such innovative solutions. Conversely, Willingness to Pay (WTP) does not show a statistically significant relationship with consumer intention.
This research contributes to the academic and practical discourse on sustainable innovation in food systems, offering insights for policymakers, businesses and technology developers aiming to promote smart packaging solutions. The study emphasizes the importance of consumer engagement and targeted strategies to support sustainable consumption patterns and reduce food loss across the supply chain.

The agrifood business system, in terms of sector turnover and employment, assumes significant weight for creating economic value within local socio-economic systems, with a direct impact on population health. Furthermore, the agri-food business system is a sector that has direct repercussions in terms of public health and well-being of people. In recent years, the agrifood sector has undergone numerous changes, both regulatory and technological view. These recent changes along with growing societal awareness, have shifted the focus towards combating food waste in the agrifood supply chain. As an effect, the agrifood business system has garnered increasing attention from policymakers and scholars. Consolidated studies on agri-food business systems have focused their attention on historical and evolutionary aspects, leaving out managerial aspects such as those linked to inter-organizational relationships and the supply chain.
This preliminary study aims to offer a first analysis of the main regulatory and organizational elements within the agri-food supply chain business system. This study, through a qualitative approach, analyzes organizational actors, interorganizational relationships and sustainability-oriented strategies along the entire supply chain. The theoretical approach used is based on knowledge management studies. Adopting an exploratory research design, this study employs qualitative methods to gather in-depth insights into the dynamics of the agrifood supply chain. Given the complexity and diversity of organizational actors within the agri-food business system, this exploratory study aims to identify the main evolutionary trends—both regulatory and managerial—of the agri-food supply chain. The research adopts a qualitative methodology, drawing on secondary data sources such as industry reports, regulatory documents, and academic literature.
This study contributes to the academic and practical discourse on agri-food business system by focusing on the organizational dimensions of the issue. Unlike existing literature, it integrates these additional drivers for change, beginning with the regulatory context. The findings provide initial observations and reflections for practitioners seeking to reduce inefficiency through organizational innovation and for scholars in organizational theory. Furthermore, the proposed regulatory framework emphasizes the importance of integrating data-driven technologies and predictive analytics into decision-making processes, enabling more precise inventory management, better coordination among supply chain actors, and the development of reactive strategies to mitigate waste. These practical implications also extend to policymakers, offering pathways for creating supportive regulatory environments and incentivizing sustainable practices in the agrifood sector.

The agri-food industry is currently facing significant global challenges, including climate change, resource depletion, and growing demands for food security. In response to these challenges, sustainable innovation has become a critical focus for the industry. Sustainable innovation refers to the development and adoption of new technologies, practices, and business models that aim to improve environmental, social, and economic outcomes. One of the most promising technologies for supporting sustainable innovation is artificial intelligence (AI), which has the potential to enhance various aspects of sustainability within the agri-food sector. This study explores the role of AI in supporting sustainable innovation strategies and practices in the agri-food industry, with a focus on understanding how AI can help optimize processes, reduce waste, improve resource management, and foster more sustainable business models.
To achieve this, a comprehensive systematic literature review was conducted, analyzing seventy-three peer-reviewed articles related to AI applications in the agri-food industry. Through this analysis, the study identifies six main clusters of strategies that are currently being employed to drive sustainability in the sector. These strategies include optimizing production processes, improving supply chain efficiency, enhancing product traceability, and advancing precision agriculture practices. Additionally, the study highlights the role of AI in supporting twenty-four specific practices within these strategies, such as predictive analytics for crop yield forecasting, AI-driven waste reduction systems, and AI-based monitoring tools for environmental impacts.
However, the findings also reveal significant gaps in the application of AI, particularly the lack of support for circular economy approaches, which focus on reducing waste and reusing resources throughout the supply chain. This gap presents an opportunity for further research into how AI can be leveraged to support circular practices within the agri-food industry. The study provides a foundation for future exploration into the integration of AI and sustainable innovation, offering insights for both researchers and practitioners seeking to improve sustainability in the agri-food industry.

The traceability of product sourcing and production processes has become a crucial need for the Agri-food Industry, where stakeholders – particularly consumers – increasingly demand guarantees on product quality and authenticity. Blockchain technology, given its characteristics of immutability and transparency, can play a key role in solving issues related to traceability and information asymmetry, which undermine security and stakeholder trust in companies, as well as in promoting corporate social sustainability. The present study aims to fill a gap in literature by focusing on the operational application of this technology, with particular attention to the issue of credibility of the raw data entered into the system. To this end, a qualitative approach is adopted based on a multiple case study conducted through secondary data, integrated with primary data collected through a focus group. The work has a twofold objective: on the one hand, to explore the motives and goals that drive companies to implement Blockchain technology; on the other hand, to identify the preliminary conditions that ensure its operational effectiveness and credibility in the eyes of stakeholders. The results suggest that traceability is the main reason why companies decide to implement Blockchain technology, with the goal of ensuring product authenticity and transparency to the consumer. In addition, two sets of factors – operational and validation – were identified as key preconditions for effectively implementing a credible Blockchain information system. The study contributes to a deeper understanding of the operational application of Blockchain in the agri-food sector, providing relevant theoretical and practical implications.

The distribution of fresh products presents unique challenges. Kilowatt specifically aims to: optimize and improve the logistics management of the fresh product supply chain; support processes that ensure the quality and organoleptic and nutritional properties of food; reduce food waste; maximize the energy recovery of waste.
The key points of the project can be summarized as follows: business model innovation, enhanced environmental and social sustainability, optimization of the distribution chain, Smart Packaging design, and waste recycling.
The development of the new business model will focus on offering high-quality products certified for safety, authenticity, environmental impact, and strong market orientation. These products will be strategically placed in various outlets based on real-time demand analysis. A sustainable and ethical approach to food marketing will be promoted, with an emphasis on recovering waste materials to produce alternative energy, as well as redistributing unsold products to the third sector to benefit vulnerable groups in society. Logistical activities for goods distribution will be optimized to reduce travel distances, enabling the planning of deliveries over a longer time horizon, minimizing store visits, and reducing the risk of unsold goods.
Finally, smart packaging systems, composed of active wrappers designed to extend product shelf life and integrated with IoT sensors for monitoring, will be developed. Additionally, digestion matrices will be defined to maximize the energy produced from food waste.

This research aims to analyze how operational sustainable business strategies and modern technologies, including employee training influence performance. The methodology is a quantitative approach with a cross-sectional design. Data was collected from 207 SMEs in Aguascalientes, central region of Mexico, through a survey administered to managers or those responsible for operations (INEGI, 2024). The data will be analyzed using EQS software to assess the relationships between the variables. This research aims to provide valuable insights into the factors contributing to firms’ operational performance success. The research model will be assessed by the reliability and validity of the measurement instruments using Cronbach’s Alpha, Dijkstra-Henseler’s rho, Composite Reliability Index, and Average Variance Extracted (AVE). So, the analysis will ensure that the constructs are distinct and not overlapping. The good fit for the proposed theoretical model will be evaluated to suggest that the relationships between the variables were adequately represented (Henseler et al., 2015). The study focuses on the crucial role of sustainable business strategies in enhancing the operational performance of SME (Buttle & Maklan, 2019; Marr, 2016; Marston et al., 2011; Sutherland, 2019). The results obtained in this study indicate that, on the one hand, the factor loading of the variables is within the norm and, on the other hand, sustainable business strategies do have a significant impact on the performance of operational activities in this type of company. This means that for manufacturing SMEs, it is very important to have a sustainable environment that allows all of their operations to be focused on the performance of their internal operating systems. The data collection period was between March and May 2024, considering a stratified random sample of companies within the state of Aguascalientes.

The healthcare system in Mexico faces persistent and multifaceted challenges, such as the exclusion of vulnerable populations, limited access to medical services in rural and marginalized areas, and increasing pressure due to an aging population. The COVID-19 pandemic exacerbated these structural weaknesses, highlighting the urgent need to integrate innovative technological solutions that not only optimize available resources but also expand and improve access to healthcare services.
In response to these needs, a sustainable and inclusive telemedicine ecosystem was proposed, grounded in methodologies from Exponential Organizations (ExO), to ensure long-term scalability, adaptability, and effectiveness.
A diagnostic assessment using the Pan American Health Organization (PAHO) tool revealed a low level of technological maturity at the Centro Médico Puentecillas. To address these gaps, the CANVAS model was applied to redesign the center’s business structure, incorporating ExO SCALE principles: staff autonomy, active community participation, and continuous user engagement.
Additionally, the proposal included the development of a digital platform for managing teleconsultations, telemonitoring, and teleassistance, integrating real-time clinical data collection to support evidence-based medical decision-making and preventive health monitoring.
The methodology focused on identifying key technological and organizational deficiencies, prioritizing intervention strategies such as digital literacy training for medical personnel and infrastructure enhancement to improve system performance and reliability.

Small and Medium Enterprises (SMEs) are fundamental to Mexico’s economy, representing over 70% of formal employment. Despite their importance, many SMEs, particularly in traditional industries such as leather-footwear, struggle to adopt digital technologies due to outdated infrastructure, limited resources, and cultural resistance to change. The COVID-19 pandemic intensified these challenges, underscoring the need for digital transformation to ensure competitiveness and sustainability.
This study explores how SMEs in Guanajuato’s leather-footwear sector can leverage digital innovation and human capital to overcome transformation barriers, enhance competitiveness, and align with sustainability goals. It examines the intersection between technology, knowledge management, and organizational culture in the context of the Fourth Industrial Revolution. A qualitative, multi-case methodology was employed, using semi-structured interviews with managers from 13 SMEs. These interviews explored four key dimensions: technological infrastructure, digital culture, challenges to adoption, and future strategies. Triangulation with academic literature ensured analytical rigor and contextual relevance.
Findings reveal that only 15% of the companies have sufficient technological infrastructure to support comprehensive digital transformation, while 70% possess only basic tools such as computers and internet access—often underutilized. A significant 85% of managers cited lack of employee training as a major obstacle. Nonetheless, companies with a strong culture of learning and openness to collaboration showed higher adaptability and up to 20% increases in operational efficiency. The pandemic also acted as a catalyst, pushing many firms toward digitalization, although often reactively.
A conceptual model was developed integrating the four dimensions of transformation, offering a framework for SMEs to navigate digital change. The study further proposes knowledge management strategies including training programs, the integration of digital tools, and collaboration with startups and government agencies. These were identified by 60% of interviewees as key enablers of sustainable innovation.
This research contributes to academic understanding of digital transformation in traditional sectors and provides actionable insights for entrepreneurs and policymakers. It aligns with IFKAD’s vision of integrating human expertise and digital innovation to foster socially responsible businesses. By focusing on knowledge as a strategic asset, the study highlights the importance of human capital in driving innovation, sustainability, and long-term resilience in SMEs.

The impact of Artificial Intelligence (AI) on Human Capital (HC) responsible for designing, reengineering, and developing Business Models (BM) has not been extensively investigated. Latest research seems to diverge on whether AI has a negative or positive impact on HC competences, HC capabilities, and Business Model Innovation (BMI) processes. Studies indicate that HC is at risk of being wasted or underutilized due to AI, as research verifies that if the human brain is not utilized as much as AI enables today, where AI takes over HC tasks and HC will be wasted, spilled or lost. Other research shows that AI can release unused HC and potential when AI designs and reengineers Business Models (BMs) and Business Model Ecosystems (BMES). In some cases, AI is even shown to be able to release unused HC when downloading, analyzing BMs, and sensing disruptive, radical, and/or incremental BMs.
The paper investigates AI´s impact on HC and HC´s capabilities (HCC) in 7 different Multi Business Model Innovation (MBMI) processes, seeking to determine whether AI can enhance the use and potential of HC, or, conversely, hinder and reduce HCC in MBMI.
The impact of AI and MBMI was studied in seven MBMI projects in different BMESs that are influenced by AI. The effect of AI on HCC was analyzed as a key finding and output to the research question: Does AI lead to HCC Waste, spill, loos or does it increase the grow, increase and release HCC potential?

The present study examines the impact of technology diffusion on energy consumption and efficiency in small and medium enterprises (SMEs) in Montenegro. The focus of this study is a project proposal submitted by a specific SME. The project entails the procurement of the machine for heating and waste collection, as well as the implementation of a digital key for purpose of productivity and energy monitoring. The objective of the project is threefold: firstly, to reduce electricity consumption; secondly, to increase revenue; and thirdly, to promote sustainable practices. This is an empirical study, and as such the results obtained from the real-life consumption of a company can be representative. It was demonstrated that undertaking of project activities would result in the company accruing benefits in both the short and long term. It has been indicated that a reduction in both energy consumption and CO₂ emissions will be observed. However, it is anticipated that the project activities will, over time, lead to the adoption of even more sustainable practices within the company. The paper provides a comprehensive exposition of the demonstration of the Rogers model (TOE and IDP framework) using empirical evidence. Project of this nature are of great benefit to SMEs in Montenegro and the wider region. The provision of financial assistance is but one of the functions of this calls, another is the raising of awareness of various issues. The concept of energy efficiency should not be perceived as disadvantageous, rather, it should be regarded as a concept that is universally accessible and demonstrably manageable in a variety of ways.

As environmental and social challenges intensify, regenerative practices in supply chain management (SCM) are emerging as a transformative approach, shifting focus from sustainability to the active restoration of socio-ecological systems. The present study analyses how regenerative principles, including poly-rhythmicity, proportionality, and reciprocity, enhance supply chain resilience through systemic renewal. A comprehensive review of 33 high-impact articles (2010–2024) has identified six key regenerative practices: supply chain plasticity, ecological restoration, circular measures, sustainable resource management (SRM), reshoring, and restorative topophilia. These practices address ecological degradation, economic volatility, and social inequities, all while promoting adaptability. Whilst circularity and SRM predominate in industry applications, there is considerable potential in niche practices such as restorative topophilia, which combines place attachment with ecological restoration. The combination of reshoring and circular principles has the potential to reduce emissions by 30% and stimulate local economies. However, it is important to note that 40% of reshoring initiatives are unsuccessful, resulting in the relocation of production activities that are associated with increased environmental impact. Supply chain plasticity has been demonstrated to exhibit operational resilience; however, it frequently neglects ecological reciprocity, thereby highlighting an evident implementation gap. It is imperative to emphasise the pivotal role that stakeholder engagement, notably from communities and non-governmental organisations (NGOs), plays in transcending the divide between sectors. The present study proposes a framework for “regenerative intelligence” that integrates ecological, social, and supply chain objectives. The study validates institutional redundancy (policy-market synergy) and emphasises poly-rhythmicity as an unexplored lever for resilience. The study provides practical insights for transitioning from protective to proactive regeneration, and it notes two limitations: potential interpretive biases and a focus on academic literature. The study suggests that future research should explore metrics, cultural contexts, and decision-support tools. This work reframes supply chains as catalysts for systemic renewal, advocating for strategies that restore rather than merely sustain.

This article analyses the green aspects of technologies embedded within the Fintech sector, providing insights into the intersection of financial innovation and environmental sustainability. The technological advancements introduced in the financial sector over the past decades hold considerable potential to facilitate the ecological transition and promote environmental sustainability – a potential that has been widely acknowledge in the literature. However, there is still little research on the green attributes of the technologies underlying the Fintech sector, particularly through the lens of patents, which serve as concrete indicators of technological innovation.
Our findings contribute to the understanding of technology, finance, and sustainability, emphasising the value of patent data in tracking progress and guiding future developments.
Through the use of statistical analyses and the Social Network Analysis techniques, we define a multi-step methodology and a set of new measures that allow us to analyse Fintech technologies, as described by the underlying patents, in the light of the environmental and eco-friendly perspective, as proposed by the WIPO IPC Green Inventory classification. The statistical analysis of the green Fintech patents reveals several notable trends. The relative frequency of patents containing a number of green technology classifications within the complete dataset exhibits an increasing trends, although this has stabilised in recent years.
Furthermore, the data indicate a modest deceleration in the overall growth of this segment, suggesting a potential plateau in the expansion of green-oriented innovations in the Fintech domain.
Our results offer an original perspective on the idea of green, expanding the analysis to encompass the collective behaviour of the Fintech ecosystem of technologies and, eventually, any other industrial sector.

This paper investigates how people’s perceptions of socio-economic indicators in Romania and the Republic of Moldova influence the development of the business environment at local and regional level, in the context of growing challenges to economic and social resilience. Based on a questionnaire applied to a sample of 3228 respondents, the study analyzes eight key indicators, grouped into two composite indices: “Economic and social policies” (I1) and ” Access to basic services/resources (critical infrastructures) services” (I2). These two indices were then combined into a third, entitled ” Overall socio-economic perceptions” (I3), to provide an integrated picture of the well-being of the population. During the course of the work the K-means clustering algorithm and the Silhouette indicator were applied to validate the optimal number of clusters, using the RStudio software the research identified different relevant county-level clusters. In the case of the I1 index, Romanian counties clustered in a distinct cluster from those in the Republic of Moldova, suggesting significant differences in income satisfaction, job security and access to public services. In contrast, for I2, the analysis revealed five clusters, reflecting greater variation in perceptions of access to utilities, energy, education and health. Thus, even though the majority of counties in Romania received high scores, there were also a few counties that scored lower, for which some improvements should be made. The results provide a useful tool for policy makers and business actors, suggesting areas with different needs and varying investment potential. The study highlights the importance of understanding local perceptions in formulating development and intervention policies.

Twin transition —integrating digitalisation and ecological sustainability— has become a structural priority for European regional development, as reflected in major frameworks such as NextGenerationEU and the 2021–2027 Cohesion Policy. These programmes aim not only to support recovery but also to reshape the technological and institutional foundations of regional economies. However, a significant knowledge gap remains: while the twin transition requires coordinated and systemic change, we still lack a clear understanding of how it unfolds across different regional contexts —particularly given persistent disparities in structural capacity and institutional agency. This paper addresses that gap by investigating the relationship between regional industrial path development and the capacity of regions to engage in twin transitions. Drawing on an established typology of development paths —including extension, upgrading, branching, diversification, creation and importation—and on a review of regional case studies, we develop a configurational framework that links these development paths to a set of structural and institutional causal conditions. These conditions are operationalised into indicators grounded in the literature, and applied to the Italian regional context through a fuzzy-set Qualitative Comparative Analysis covering the period 2007–2017. The findings show that regional engagement in twin transitions could result from distinct combinations of development paths and enabling conditions. Extension, upgrading, and importation path —when coupled with enabling regional conditions— are the most conducive to twin specialisation.
The study contributes to debates on bridging evolutionary economic geography and transition-oriented policy, and provides a scalable diagnostic tool to inform place-based strategies tailored to structurally diverse regional contexts.