Search Results (779)

Using data from Chinese listed non-financial firms from 2015 to 2023, this study examines how environmental, social, and governance (ESG) rating divergence affects banks’ loan limit decisions and the underlying decision-making mechanisms within the sustainable finance system. Drawing on cognitive psychology, we conceptualize bank lending as an organizational decision-making process in which conflicting ESG signals create information ambiguity and influence banks’ risk assessments. Greater ESG rating divergence is associated with significantly lower total annual bank loan limits. Mechanism analyses provide evidence consistent with heightened bank concerns about firm default risk, whereas we find no supporting evidence for the information-quality channel captured by discretionary accruals. Further analysis reveals an asymmetric response to conflicting ESG information: banks appear to place greater weight on relatively unfavorable ESG ratings, while favorable ratings do not exert a comparable moderating effect. The negative association between ESG rating divergence and bank loan limits is more evident among non-state-owned enterprises, firms with weaker repayment capacity, firms with lower financial information disclosure quality, and firms without third-party ESG assurance. These findings extend the literature on ESG rating divergence and sustainable finance by showing how conflicting ESG information is associated with contractual credit allocation through banks’ risk assessment and asymmetric information-processing behavior. They also highlight the importance of improving ESG information governance and banks’ capacity to evaluate inconsistent sustainability signals. Full article

Generative artificial intelligence (AI) complicates academic integrity by blurring the boundary between assistance and authorship, enabling cognitive delegation, and introducing algorithmic mediation into assessment and institutional decisions. This article presents a critical conceptual synthesis, not a systematic review. A purposive corpus of 64 academic and policy sources was examined through comparative coding, negative-case analysis, source-to-concept tracing, and normative interpretation. The synthesis distinguishes integrity of academic work, integrity of assessment, and institutional procedural integrity. It proposes an integrated governance framework connecting preventive governance, ethical AI literacy, governed verification, restorative accountability, and proportionate discipline. The framework argues that policy clarity must reach the assessed task; AI literacy supports judgement but cannot neutralize strategic misconduct; automated indicators require corroboration, competent human review, reasons, and appeal; and restorative processes are appropriate only when harm, affected parties, voluntary participation, responsibility, repair, and reintegration are substantively addressed. Its principal contribution is a case-to-governance feedback mechanism through which integrity cases generate institutional learning and trigger revision of policy, assessment design, literacy provision, and technology oversight. Seven testable conceptual propositions, a response-selection pathway, role-specific duties, staged implementation responsibilities, and evaluation indicators are provided. The framework remains a normative and empirically testable proposal rather than a validated intervention or universally effective solution. Full article

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

Akerlof’s classical theory of the “Market for Lemons,” which conceptualizes adverse selection driven by information asymmetry, established the foundation of information economics. While the traditional model assumes static equilibria among a limited number of agents, analyzing its behavioral dynamics within large-scale, complex network environments remains a highly relevant task in computational social science. This study extends the classical lemon market model into a nonlinear dynamical system on adaptive networks. We mathematically elucidate macro-level social phase transitions—specifically structural exclusion such as scapegoating and collective ostracism—induced by computational cognitive limits, and evaluate optimal intervention strategies to mitigate these systemic failures. Multi-agent simulations utilizing large-scale tensor operations demonstrate that autonomous edge rewiring under incomplete information does not merely result in the uniform displacement of high-quality goods as predicted by static theory. Instead, the network self-organizes into an irreversible structural division: a core group of influential agents monopolizes high-quality information, while marginalized agents are isolated into a peripheral “lemon echo chamber” where only low-quality information circulates. To address this structural pathology under a resource constraint limiting intervention to 10% of the total agents, we evaluated two distinct approaches. The results indicate that providing informational support to influential hubs functions as a trap that exacerbates systemic inequality, superficially elevating the overall market evaluation but permanently fixing the exclusion gap. Conversely, the forced maintenance and protection of “weak ties” bridging disconnected clusters constitutes the mathematically optimal solution to dissolve fragmentation, effectively eliminating the price gap and facilitating social inclusion. Furthermore, this study demonstrates that the mechanism of social exclusion exhibits strong hysteresis effects. A distinct tipping point governs the progression toward a fragmented lemon echo chamber. Interventions implemented after crossing this critical threshold fail to restore the system to its baseline state despite identical resource expenditure, confirming the presence of an irreversible phase transition. These findings establish that the collapse dynamics outlined in the classical lemon market serve as a generalized model for explaining contemporary collective ostracism driven by information cascades. Consequently, the analysis highlights the necessity of early intervention prior to critical thresholds and the systemic preservation of structural bypasses rather than post-hoc remediation. Full article

Conventional furniture design predominantly hinges on empirical expertise and subjective appraisal, absent systematic quantitative validation of users’ visual cognition. This research establishes a multimodal quantitative evaluation framework for round-back armchairs that integrates subjective weighting via AHP and objective eye tracking measurements. Four ocular physiological metrics are aggregated using the CRITIC–entropy composite weighting approach to characterize visual attention allocation. PCA distills three core perceptual dimensions, and Quantification Theory Type I constructs a mapping function linking component morphology to perceptual responses. Notable systematic divergence is identified between expert aesthetic preferences and users’ fixation patterns. Backrests govern volume perception, chair rings dominate concise shape perception, and foot stretchers determine stylistic elegance. Low-relief backrests, three-segment curved chair rings and inward horse hoof stretchers yield superior perceptual performance, whereas gooseneck front posts and connecting balusters exhibit trivial marginal optimization gains. A model-derived candidate morphological configuration, A2-B3-C1-D1-E2-F2, is identified with corresponding effect size estimation. This study provides an exploratory analytical framework and methodological reference for promoting the transition from experience-oriented to data-driven traditional furniture design. Full article

The rapid growth of organisational textual data has increased the value of Natural Language Processing (NLP) and semantic analytics for strategic decision support. However, many employees still lack the knowledge and skills needed to evaluate AI-generated information critically. This study develops an explainable Management Information Systems (MIS) framework that integrates NLP-based semantic analytics with PLS-SEM to examine the relationship between AI illiteracy and strategic decision quality. A convergent mixed-methods design with sequential analytical integration was used with a valid sample of 200 knowledge workers from public organisations in Saudi Arabia across six industries. The sample included employees from Saudi Arabia, Egypt, Jordan, Sudan, Syria, India, and the Philippines. Quantitative data were analysed using PLS-SEM, while textual data were analysed using Sentence-BERT, BERTopic, semantic network analysis, and Aspect-Based Sentiment Analysis. The results showed that higher AI illiteracy was negatively associated with strategic decision quality and positively associated with automation bias, uncritical trust in AI, and cognitive offloading. Digital proficiency and AI governance awareness weakened the negative association between AI illiteracy and decision quality, while functional-background differences were examined through multigroup analysis. The semantic analysis identified six themes: AI competency, decision trust, AI governance, decision support, organisational learning, and risk awareness. Sentiment analysis showed positive views of productivity and decision support, together with concerns about algorithmic bias, explainability, transparency, and AI governance. The study contributes an integrated human–AI decision vulnerability framework in which semantic evidence complements structural modelling and provides a clearer understanding of AI-related competency, reliance, governance, and decision-support issues. Full article

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

Industry 4.0 has produced one of the fastest-growing bodies of engineering and management research; much of this output remains siloed by technology domain. This study addresses that fragmentation through a structured critical review (a review-of-reviews), synthesising 70 peer-reviewed review articles and foundational sources (2003–2026) spanning 14 technology domains. The review introduces the I4.0-STS framework, an original four-layer structure organising evidence across physical, cyber, cognitive, and socio-organisational dimensions. Five principal findings emerge. The physical and cyber layers show consistent evidence of maturity. Industry-reported lighthouse IIoT deployments show 20–30% energy and up to 39% lead-time reductions. AI-driven predictive maintenance shows 30–50% unplanned-downtime reductions. The cognitive layer (LLM-augmented digital twins and generative AI interfaces) is technically feasible but outpaces its governance frameworks. Cybersecurity remains insufficiently governed, with documented ransomware incidents in manufacturing OT environments underscoring the risks of OT–IT convergence. SME adoption and developing-economy manufacturing transformation remain comparatively under-addressed. Finally, 12 research gaps are assessed as of June 2026, five rated Open, with future research directions proposed for each, framed against the emerging Industry 5.0 agenda. All findings are second-order interpretations from the source reviews, and their limitations are stated explicitly. Full article

Large Language Models (LLMs) are increasingly integrated into agentic workflows that require extended reasoning, persistent state management, coordinated tool use, and controlled execution. As this operational scope expands, a central question emerges: whether probabilistic generation alone can reliably support coherent behavior across interacting system components. This paper addresses that question through a structural diagnostic review of contemporary agentic systems. Starting from LLM-based tutoring as an analytically demanding entry point and extending toward structurally related agent architectures, the paper draws on a five-phase review of $N=145$ research records. The analysis is organized through the Agentic Structure Taxonomy (AST), which structures the literature across four dimensions: Cognition, Interaction, Orchestration, and Governance. The review identifies five recurrent empirical problem patterns and uses them as abductive diagnostic cues for formulating seven cross-dimensional transition gaps that capture recurrent discontinuities at the boundaries between reasoning, state, control, and execution. From these gaps, fourteen structural constraints are derived across three control domains: state isolation, control alignment, and execution governance. These constraints are interpreted not as prescriptive design mandates, but as analytically derived conditions associated with reducing error propagation across subsystem transitions. The paper argues that reliability in agentic systems is shaped not only by model performance or prompt design, but also by whether the boundaries linking probabilistic reasoning to persistent state, orchestration, and execution are governed by explicit structural conditions. Full article

The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Académie nationale de chirurgie, which proposes not only a concept but a method for the safe adoption of artificial intelligence (AI) in surgery. At its core is the C40 Maturity Model of the Operating Room, a human-governed “surgical world model” describing the transition from the Smart OR to the Learning OR across six levels, from the conventional operating room to a sovereign, federated network of surgical world models. We situate surgical autonomy on an explicit six-level scale, show that autonomous devices are already an accepted clinical reality in fields such as interventional cardiology, ophthalmology, neuro- and orthopedic surgery, and argue that governance must be native rather than retrofitted, through a Cognitive Governance Layer resting on human oversight, explainability, auditability and agent governance. We describe the economic and sovereignty stakes specific to intelligent surgical technologies and set out the design of the 2026 C40 field survey, whose results will feed a Livre Blanc for public decision-makers. C40 offers five steps that can genuinely be climbed, and a method for climbing them safely, with the surgeon retaining final clinical authority at every step. Full article

Consumer food waste is a major challenge for food system sustainability and a governance problem that cannot be addressed by individual behavioral change alone. Previous reviews have examined the causes of food waste or evaluated specific intervention strategies, but less attention has been paid to how behavioral drivers, intervention strategies, and the roles of governance actors can be examined within an integrated analytical framework. Against this background, this review aims to synthesize the behavioral drivers of consumer food waste, classify the main types of behavioral interventions, and develop a multi-level governance perspective that connects these elements. Using a narrative review approach supported by a structured literature search, the review identifies three broad categories of behavioral drivers, namely cognitive biases, motivational drivers, and environmental and situational influences. It then classifies behavioral interventions into five types, namely information-based interventions, social norm interventions, choice architecture and environmental design interventions, incentive-based interventions, and education and capacity-building interventions. The review indicates that no single intervention type is likely to be effective across all settings. Instead, intervention outcomes are closely linked to the alignment among targeted behavioral drivers, intervention strategies, consumption contexts, and governance support. From a multi-level governance perspective, this review views consumers as the actors engaged in everyday food-related practices, businesses as the designers of the consumption environments and operational routines, and policymakers as the providers of institutional, informational, and economic support. By bringing behavioral drivers, intervention strategies, and governance roles into a shared analytical framework, this review moves beyond fragmented, individual-centered approaches to reducing consumer food waste. The framework suggests that food waste reduction efforts may be more effectively implemented and maintained when consumer practices, business environments, and policy conditions are mutually supportive. Full article

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

The rapid advancement of artificial intelligence (AI) and growing global fragmentation are reshaping how multinational enterprises manage cross-border strategic alliances. Prior research has focused on alliance formation and resource access, yet less attention has been paid to how AI transforms the managerial capabilities required for effective alliance orchestration. This study addresses this gap by examining how AI augmentation may alter the microfoundations of dynamic managerial capabilities (DMC) in cross-border alliances. Building on DMC theory, the paper proposes an AI-augmented framework with four interrelated microfoundations: AI-enhanced managerial cognition, algorithmic social capital, hybrid human–AI capital, and AI-enabled political capital. Using an abductive research design and longitudinal case analyses of Carrefour–Google, Walmart–IBM, and Tesco–Carrefour, the study illustrates how AI may support the reshaping of sensing, seizing, and reconfiguring processes. It uses a Gioia-informed coding procedure to enhance transparency in the elaboration of theory. The findings suggest that AI may improve data-driven sensing, enable scalable coordination through algorithmic trust, enhance ambidexterity through human–AI collaboration, and support proactive regulatory positioning. However, these benefits are expected to be contingent on institutional conditions, including data governance constraints and power asymmetries. The study contributes by extending DMC theory and highlighting how AI-enabled capabilities could drive alliance performance in complex global environments. Full article

Environmental regulation is widely regarded as an institutional driver of corporate green transformation, yet how it operates on firms and for whom remains unsettled. Treating the 2015 entry into force of China’s revised Environmental Protection Law as a quasi-natural experiment, this study estimates a difference-in-differences (DID) model on 38,910 firm-year observations covering 4447 Shanghai and Shenzhen A-share firms over 2010–2024. Corporate green transformation is measured along three dimensions—the length-normalized intensity of green-transformation language in annual reports, green patent output, and green total factor productivity—and combined into a composite index. The regulation raises green-transformation intensity by 0.156 (about 14.8% of the sample mean) and green total factor productivity by 0.0021; pre-reform event-study coefficients are jointly insignificant, no randomized placebo reaches the observed estimate, and the result survives propensity-score matching and province-by-year fixed effects. It is accompanied by falling greenwashing and rising disclosure specificity, indicating that the additional green language is not merely talk. Bootstrap mediation tests support transmission through executive green cognition and through green exploratory, but not exploitative, innovation. Contrary to the received view, the law did not loosen financing constraints; instead, pre-reform financial capacity bounds the response, and firms that were more constrained respond substantially less. Effects are larger for digitally capable, better-governed firms and in more marketized regions, and command-and-control instruments outperform market-based pilots over this window. Full article