Search Results (425)

Action bias, the cognitive tendency to favor action over inaction regardless of its necessity, has been extensively studied across domains such as behavioral economics, organizational behavior, and policy development. However, its manifestation in educational contexts remains critically underexplored. In the digital age, with an abundance of both factual and misleading information, the persistence of action bias within education jeopardizes the cultivation of initial critical thinking capable of addressing multifaceted global challenges. The analysis indicates how institutional norms may foster a performative academic identity that conflates speed and compliance with intellectual competence. Through workshops conducted with university students ranging from undergraduate to PhD levels, participants were tasked with solving a practical yet ambiguous problem to highlight potential cognitive differences across educational stages. Despite prior training in critical thinking, participants consistently defaulted to immediate ideation, bypassing fundamental inquiries into the problem’s legitimacy or broader implications. Using a sensemaking approach, this study demonstrates that reflexive actions are not interpreted as merely cognitive shortcuts but behaviors shaped by educational systems prioritizing visible outputs over critical inquiry. The findings reveal how institutional norms foster a performative academic identity, conflating speed and compliance with intellectual competence. This research challenges traditional pedagogical models, advocating for educational reforms that emphasize assessing the process of learning. By situating action bias within the broader framework of active learning, this study offers actionable insights for educators, policy makers and researchers to foster critical innovative thinking, essential in an increasingly digital future. Full article

This study presents a bibliometric and thematic analysis of economic convergence analysis from 1982 to 2025, based on a corpus of 2924 Scopus-indexed articles. Using VOSviewer and the bibliometrix R package, this research maps the field’s intellectual structure, identifying five main thematic clusters: (1) formal statistical models, (2) institutional-contextual approaches, (3) theoretical–statistical foundations, (4) nonlinear historical dynamics, and (5) normative and policy assessments. These reflect a shift from descriptive to explanatory and prescriptive frameworks, with growing integration of sustainability, spatial analysis, and institutional factors. The most productive journals include Journal of Econometrics (121 articles), Applied Economics (117), and Journal of Cleaner Production (81), while seminal contributions by Quah, Im et al., and Levin et al. anchor the co-citation network. International collaboration is significant, with 25.99% of publications involving cross-country co-authorship, particularly in European and North American networks. The field has grown at a compound annual rate of 14.4%, accelerating after 2000 and peaking in 2022–2024, indicating sustained academic interest. These findings highlight the maturation of convergence analysis as a multidisciplinary domain. Practically, this study underscores the value of composite indicators and spatial econometric models for monitoring regional, environmental, and technological convergence—offering policymakers tools for inclusive growth, climate resilience, and innovation strategies. Moreover, the emergence of clusters around sustainability and digital transformation reveals fertile ground for future research at the intersection of transitions in energy, digital, and institutional domains and sustainable development (a broader sense of structural change). Full article

This paper offers an innovative exploration of variational informational principles by incorporating complexification and studying norms of arbitrary order, thereby surpassing the limitations of the conventional $L^2$ norm. For years, variational principles have been vital for deriving fundamental results in both physics and information theory; however, our proposed framework represents a significant advancement by utilizing complex variables to enhance our understanding of information measures. By employing complex numbers, we introduce a sophisticated structure that captures phase information, thereby significantly improving the potential applicability and scope of variational principles. The inclusion of norms of arbitrary order further expands the scope of optimization problems in information theory, leading to the potential for more creative solutions. Our findings indicate that this extended framework not only maintains the essential characteristics of traditional variational principles but also reveals valuable insights into the complex interplay between complexity, information, and optimization. We conclude with a thoughtful discussion of potential applications and future research directions, emphasizing the transformative impact that complexified variational principles, together with norms of arbitrary order, could have on the study of quantum dynamics. Full article

Continuous technological, ecological, and digital transformations reshape urban mobility systems. While sustainable mobility has become a dominant keyword, there are many different approaches and policies to help achieve lasting and properly functioning change. This study applies a comprehensive qualitative policy analysis to influential and leading sustainable mobility approaches (i.a. Mobility Justice, Avoid–Shift–Improve, spatial models like the 15-Minute City and Superblocks, governance frameworks such as SUMPs, and tools ranging from economic incentives to service architectures like MaaS and others). Each was assessed across structural barriers, psychological resistance, governance constraints, and affective dimensions. The results show that, although these approaches provide clear normative direction, measurable impacts, and scalable applicability, their implementation is often undermined by fragmentation, Policy Layering, limited intermodality, weak Future-Readiness, and insufficient participatory engagement. Particularly, the lack of sequencing and pacing mechanisms leads to policy silos and societal resistance. The analysis highlights that the main challenge is not the absence of solutions but the absence of a unifying paradigm. To address this gap, the paper introduces CalmMobility, a conceptual framework that integrates existing strengths while emphasizing comprehensiveness, pacing–sequencing–inclusion, and Future-Readiness. CalmMobility offers adaptive and co-created pathways for mobility transitions, grounded in education, open innovation, and a calm, deliberate approach. Rather than being driven by hasty or disruptive change, it seeks to align technological and spatial innovations with societal expectations, building trust, legitimacy, and long-term resilience of sustainable mobility. Full article

This study examines the evolution and current challenges of Catholic Higher Education (CHE) in South Korea within the context of globalization and neoliberal educational reform. It explores how Korean Catholic universities balance their distinctive mission with intensifying pressures for competitiveness, external validation, and adaptation to secular academic norms. Drawing on P. Boudieu’s field theory and H. Richard Niebuhr’s typology of Christian responses to culture, the analysis frames the ways institutions of Korean CHE navigate the sometimes contrary currents of their institutional aims—simultaneously striving for academic excellence and maintaining Catholic identity. Case studies of three major Korean Catholic universities illustrate how leadership and curricular programs reflect the ongoing negotiation between mission-driven imperatives and market demands. The paper contends that living within this tension is not a sign of deficiency. It can actually be a source of resilience and innovation. The Korean experience of CHE offers insights for Catholic universities facing similar dilemmas globally, suggesting that engaging proactively with the seemingly contrary paradoxical demands can sustain the integrity of mission while fostering adaptive capacity amid the rapidly changing landscape of higher education. Full article

Artificial intelligence (AI) is an innovation in science and technology designed to make computer systems capable of imitating human intellectual abilities. In the legal world, the advancement of artificial intelligence (AI) often causes debate, which has changed the way humans work, interact, and make decisions, one of which is whether AI can replace the role of judges. The purpose of this study is to determine the role of AI in the world of justice and whether AI-based court decisions can provide substantive justice for justice seekers. This study is based on normative legal research that uses a statute and conceptual approach. The results indicate that the use of AI must be carried out carefully, considering ethical aspects, and maintaining the role of judges in deciding cases based on deep legal and moral considerations. The system that uses AI in the decisions of the Panel of Judges must be able to balance efficiency and justice, ensuring that human rights, legal principles, and applicable social and cultural values are maintained. Full article

Since its articulation in 2009, Computational Social Science (CSS) has grown into a mature interdisciplinary paradigm, shaped first by mobile media-generated digital traces and more recently by generative AI. With over a decade of development, CSS has expanded its scope across data, methods, and theory: data sources have evolved from mobile traces to multimodal records; methods have diversified from surveys and experiments to agent-based modeling, network analysis, and computer vision; and theory has advanced by revisiting classical questions and modeling emergent digital phenomena. Generative AI further enhances CSS through scalable annotation, experimental design, and simulation, while raising challenges of validity, reproducibility, and ethics. The evolutionary logic of CSS lies in coupling theory, models, and data, balancing innovation with normative safeguards to build cumulative knowledge and support responsible digital governance. Full article

This paper examines the current implementation of Artificial Intelligence (AI) in higher education and its implications for inclusivity, particularly for minority groups. Using a rapid review methodology, it synthesises academic literature, policy reports, and case studies to explore how AI is reshaping educational environments. The analysis reveals that although AI technologies—such as adaptive learning systems, intelligent tutoring, and predictive analytics—are increasingly adopted, their primary aim remains institutional efficiency rather than fostering equity. Initiatives explicitly designed to support underrepresented students are rare, exposing a gap between technological innovation and inclusive practice. The study identifies key barriers, including socioeconomic inequality, cultural and linguistic bias, and limited institutional capacity, which are often compounded by AI systems trained on non-representative data. While isolated case studies demonstrate that (e.g., culturally) responsive AI can enhance educational access for marginalised learners, these remain exceptions rather than norms. The findings suggest that without deliberate efforts to embed inclusivity in AI design and deployment, existing inequalities may be perpetuated or worsened. The paper concludes that realising AI’s inclusive potential requires ethical frameworks, diverse development teams, and equitable access strategies. It calls for future empirical research focused on practical interventions that reduce disparities, contributing to a more just and inclusive higher education landscape. Full article

Open-source generative AI technologies offer transparent and customizable alternatives to proprietary AI systems, the concept of which closely aligns with the principles of open innovation. Organizations with strong open-source orientations may have greater absorptive capacity to adopt open-source generative AI technologies. However, adopting such technologies into the organizations is not always guaranteed because ethical, privacy, and regulatory concerns on open-source generative AI usage create normative uncertainty that can reduce organizations’ willingness to adopt the technology, particularly when it is used in customer-facing products or services rather than integrated into internal processes. This study draws on organizational learning theory and open innovation literature to examine how open-source orientation affects open-source generative AI adoption under normative uncertainty, and how this relationship depends on organizational exploiting and exploring behaviors. Using global survey data from the Linux Foundation, we test our hypotheses with ordered logistic regression and interaction effects. The results show that open-source oriented organizations are more likely to adopt open-source generative AI, but this effect weakens when normative uncertainty is high, especially in product-related use cases. These findings extend absorptive capacity theory by highlighting ethical ambiguity as a key moderating factor and provide practical insights into how organizations can responsibly approach open-source generative AI adoption. Full article

Emerging hybrid governance models are transforming conventional approaches to land-use regulation by simultaneously enabling urban–rural development and enforcing ecological safeguards. This study investigates the regulatory mechanisms underpinning China’s urban–rural land-use policies through an innovative mixed-methods approach, integrating systematic text analysis and the Institutional Grammar Tool (IGT). Drawing on a comprehensive dataset of 62 national policy documents (2012–2024), we employ textual coding and thematic clustering to identify seven core policy pathways, ranging from territorial spatial planning to ecological protection. These pathways are further deconstructed using IGT to assess their regulatory intensity, revealing a tripartite governance model: (1) flexible AIC-strategies (e.g., land market mechanisms), which enable local experimentation by specifying actors, aims, and conditions without rigid obligations; (2) adaptive ADIC-norms (e.g., collective land reforms), which balance central directives with localized discretion through conditional deontic rules; and (3) rigid ADICO-rules (e.g., ecological redlines), which enforce absolute compliance through binding sanctions. Through systematic analysis of land use policy regulations, we reveal how China’s hybrid governance system operationalizes a tripartite institutional logic—maintaining rigid regulatory control (ADICO-rules) in ecologically critical zones, adaptive policy experimentation (ADIC-norms) in transitional areas, and flexible market-based instruments (AIC-strategies) in development zones—thereby dynamically reconciling environmental conservation with socioeconomic diversification. The study advances both institutional theory through its grammatical analysis of policy instruments and governance theory by transcending the traditional command-and-control versus flexible governance dichotomy. Practically, the research offers actionable insights for policymakers in emerging economies, emphasizing spatially differentiated regulation, dynamic monitoring system, and strategic coupling of binding rules with flexible implementation mechanisms. Full article

This study examines how Batik Ciprat functions as a vehicle for inclusive creative tourism in Karangpatihan Village, Indonesia, whereby individuals with intellectual disabilities contribute to a socially impactful rural tourism model. Grounded in social capital theory, we analyse how trust, social networks, and inclusive norms enable empowerment and help establish a distinctive, inclusion-oriented tourism identity. Using a qualitatively led mixed-methods (sequential exploratory) design with partial least squares-structural equation modelling (PLS-SEM), we identify key pathways through which art-based entrepreneurship supports village branding and visitor engagement. Our findings indicate that when social innovation is embedded in cultural tourism, it catalyses local pride, participation, and quality of life gains. The study contributes to debates on inclusive/accessible tourism, demonstrating that creative industries can act as engines of equitable, community-based development in rural settings. Full article

Confucian ethics, as a form of virtue ethics, focuses on moral practices and ritual norms, which differ significantly from utilitarian and deontological theories. Confucian ethics emphasize that moral norms are not only theoretically prescribed, but are also deeply embedded in etiquette practice, which is manifested through actual social behaviors. By integrating Confucian ethics into the design of social robots to form artificial intelligence with the function of etiquette practice, it provides an innovative solution to improve the quality of human–robot relationships and the morality of the society. Full article

Financial performance research has increasingly intersected with philosophical debates on ethics, sustainability, and stakeholder value, yet a clear framework linking these perspectives to actionable financial metrics remains underdeveloped. This study aims to explore how philosophical perspectives (normative, epistemological, and behavioral) inform the evolving concept of financial performance, using bibliometric and science mapping techniques to analyze key research trends from 2006 to 2023. The analysis identifies four dominant thematic areas: corporate social responsibility (CSR), organizational performance, ethical governance, and circular economy innovation. We synthesize these into a practical framework that connects each theme to measurable financial indicators, enabling managers to refine capital allocation, investors to incorporate non-financial drivers into valuation models, and policymakers to design sustainability reporting standards that integrate both economic and ethical considerations. By bridging philosophical insights and financial decision-making tools, this study contributes to both the theoretical development and applied practice of performance assessment in finance. Full article

This study examines the impact of job commitment on the adoption of strategic entrepreneurship within organizations, with leadership support considered as a moderating variable. Focusing on information technology companies in Jordan, we integrate perspectives from organizational behavior and strategic management to explore how employees’ commitment (affective, normative, continuance) influences their engagement in entrepreneurial initiatives, and whether supportive leadership environments amplify this effect. This study draws on social exchange theory and organizational support theory to propose that committed employees will reciprocate the organization’s support by innovating and taking initiative, especially when they feel backed by leadership. A quantitative survey was conducted, gathering 384 valid responses from employees across Jordan’s IT sector. Data were analyzed using structural equation modeling. The findings reveal that all three forms of commitment positively affect the propensity to engage in strategic entrepreneurship, with affective commitment showing the strongest link. Notably, leadership support significantly moderates these relationships: in high-support contexts, committed employees exhibit substantially greater entrepreneurial behavior. These results indicate that committed employees are more likely to pursue innovative ideas and strategic opportunities, especially when leaders encourage and back their efforts. Theoretical implications include an enhanced understanding of commitment’s role in corporate entrepreneurship and the contingent value of leadership, while practical implications suggest actionable steps for IT firms and others in emerging economies to stimulate innovation. This research contributes to the literature by highlighting human and leadership factors as key drivers of strategic entrepreneurship in organizational settings, and by providing empirical evidence from the Middle East context. Full article

The integration of nanotechnology into drug repurposing strategies is redefining the development landscape for diagnostic, therapeutic, and theranostic agents. In radiopharmacy, nanoplatforms are increasingly being explored to enhance or extend the use of existing radiopharmaceuticals, complementing earlier applications in other biomedical fields. Many of these nanoplatforms evolve into multifunctional systems by incorporating additional imaging modalities (e.g., MRI, fluorescence) or non-radioactive therapies (e.g., photodynamic therapy, chemotherapy). These hybrid constructs often emerge from the reformulation, repositioning, or revival of previously approved or abandoned compounds, generating entities with novel pharmacological, pharmacokinetic, and biodistribution profiles. However, their translational potential faces significant regulatory hurdles. Existing frameworks—typically designed for single-modality drugs or devices—struggle to accommodate the combined complexity of nanoengineering, radioactive components, and integrated functionalities. This review examines how these systems challenge current norms in classification, safety assessment, preclinical modeling, and regulatory coordination. It also addresses emerging concerns around digital adjuncts such as AI-assisted dosimetry and software-based therapy planning. Finally, the article outlines international initiatives aimed at closing regulatory gaps and provides future directions for building harmonized, risk-adapted frameworks that support innovation while ensuring safety and efficacy. Full article