Search Results (285)

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context. Full article

Background/Objectives: The effective prevention and control of healthcare-associated infections (HAIs) require the active engagement of clinical staff, which depends on strong relationships between the Infection Prevention and Control Team (IPCT) and frontline healthcare personnel. The role of the Infection Control Physician (ICP) as a clinical leader is essential for supporting evidence-based practice and fostering collaboration. This study aimed to demonstrate the impact of leadership-oriented interventions—particularly the introduction of ICP consultations in hospital wards—on HAI surveillance quality. Methods: A retrospective observational quasi-experimental study was conducted in a single hospital in southern Poland between 2017 and 2024, excluding 2020–2021 due to the COVID-19 pandemic. HAI surveillance followed the ECDC HAI-Net methodology. The study included all hospitalized patients in wards where invasive medical devices or invasive procedures were used. The intervention consisted of expanding the IPCT, increasing managerial support, extending infection control nurses’ competencies, and implementing routine ICP medical consultations. Changes in HAI incidence rates between the pre-intervention (pre-IP) and post-intervention (post-IP) periods were analyzed for catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and central line-associated bloodstream infections (CLABSI), expressed per 1000 device-days. Results: The overall device utilization increased from 0.44 to 0.54 per 1000 patient-days in the post-IP period. The utilization of microbiological diagnostic tests more than doubled, with marked increases in blood cultures (6.4% vs. 15.5%) and urine cultures (7.7% vs. 11.0%). No IPCT consultations occurred in the pre-IP period, while 874 consultations were recorded in the post-IP period. Th incidence rates for CAUTI and VAP increased (1.4 to 3.1 and 11.7 to 24.6 per 1000 device-days, respectively). The CLABSI incidence showed no significant overall change. Conclusions: Structural and functional changes in the IPCT, combined with the introduction of ICP consultations, substantially enhanced the quality and completeness of HAI surveillance in the analyzed hospital. The findings highlight the importance of leadership-driven engagement in improving infection prevention and control systems. Full article

This study investigates the dynamics of information diffusion and uncertainty evolution in online public opinion systems under human-made disasters. A variant of the SIR model considering individual psychological involvement and group herd behavior is proposed. The theoretical analysis derives the propagation equilibrium points and the propagation threshold and further examines the stability of the system. The results indicate that the transmission rate, immunity rate, and herd behavior coefficient are key parameters influencing the dynamics of public opinion propagation. The simulation results validate the theoretical findings and provide a visualization of the sensitivity of the key parameters. Finally, an empirical case study is conducted to verify the effectiveness and applicability of the proposed model. The results indicate that controlling contact rate, reducing herd behavior, and lowering psychological involvement can effectively suppress opinion diffusion, with herd behavior and psychological involvement exerting a greater influence than contact rate on spreaders of the public opinion system. Consequently, mitigating public emotional resonance and herd effects constitutes an effective strategy for managing public opinion in human-made disasters, but reducing herd behavior makes the system relatively more uncertain compared with other scenarios. Finally, managerial implications for public opinion governance in human-made disasters are proposed. The findings enrich the theoretical system of information evolution modeling for complex social systems based on entropy and information theory, offer practical guidance for governments in developing scientific public opinion management strategies, and realize the transformation of public opinion systems from high-entropy disorder to low-entropy order. Full article

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers. Full article

Digital twin technology (DTT) creates a virtual replica of a physical object, system, or process and uses real-time data to support monitoring, analysis, and control. Although DTT is increasingly discussed as a means to enhance supply-chain resilience, prior evidence is fragmented and lacks an integrated view across disruption stages. This study conducts a systematic literature review of 89 peer-reviewed articles on DTT and supply-chain resilience, applying relevance-based screening to retain studies with substantive theoretical and practical implications. The review indicates that DTT applications for resilience are emergent but gaining momentum, and that their contribution differs by resilience stage. Specifically, DTT capabilities support preparedness through enhanced visibility, risk sensing, and scenario testing; resistance through real-time monitoring, early warning, and evaluation of mitigation options; rebound through response coordination, recovery planning, and adaptive reconfiguration; and growth through post-disruption learning and network redesign. The synthesis also identifies key barriers to adoption, including data quality limitations, high implementation costs, shortages of specialised skills, and governance challenges, and suggests that integration with complementary digital technologies often enables more advanced functionality. Overall, the study provides a stage-based consolidation of DTT capabilities, benefits, and barriers to guide research and managerial deployment. Full article

The paper investigates the relationship between ESG transparency/performance and financial performance in tourism, with a focus on profitability (ROA), capital structure (D/E), and cost of capital (WACC). The empirical analysis uses a 2019–2024 panel for 10 listed tourism companies—Booking Holdings, Expedia Group, Airbnb, Marriott International, Hilton Worldwide, Hyatt Hotels, InterContinental Hotels Group, Wyndham Hotels & Resorts, TUI Group, and Carnival Corporation—covering distinct sub-sectors (OTA/Platform, Hotels, Tour Operator, Cruise). The study is based on a quantitative methodology that includes descriptive analyses and the application of advanced econometric models. Methodologically, the paper applies panel econometric models with fixed effects (firm and year), sectoral controls and robustness tests (ESG × Sector interactions, alternative size specifications). The results indicate, on average, a positive association between ESG and profitability (ROA) scores, as well as a negative relationship with WACC (indicating a lower cost of capital for firms with higher ESG), after controlling for size, country and sector. The effects are heterogeneous across sub-sectors, with the ESG–performance relationship more pronounced in hotels (where capital intensity and operational exposure are higher) and less pronounced for OTA platforms, but remain directional and statistically significant in most specifications. Overall, ESG compliance and performance emerge not only as reporting obligations, but also as strategic tools associated with sustainable competitive advantage in tourism. Therefore, the CSRD is not just a reporting obligation, but also a strategic tool that boosts financial performance and managerial innovation. The study provides directions for future research on the use of artificial intelligence in the evaluation of ESG reporting and the expansion of the analysis to other economic branches. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Business Intelligence (BI) comprises methods and technologies for collecting, organizing, and analyzing data to support managerial decision-making. This study presents a systematic literature review with a two-tier scope: first, identifying the most widely adopted BI tools across organizational contexts, and second, examining the specific application of Microsoft Power BI within the civil construction sector. The review followed the PRISMA guidelines and was complemented by the snowball sampling technique. A total of 81 articles published between 2015 and 2025 were analyzed to identify the most used tools, main application sectors, benefits, and challenges in BI adoption. The analysis combines descriptive bibliometric techniques with qualitative content analysis to examine publication trends, tools, application domains, and reported challenges. Results indicate that Power BI, Tableau, and Qlik Sense are the most frequent BI tools, with Power BI standing out for its integration with diverse data sources such as spreadsheets, databases, management software, and cloud platforms, enabling the creation of dashboards. The civil construction, business management, and manufacturing industries show the highest adoption rates, mainly for cost control, performance monitoring, and sustainability indicators. Reported benefits include operational efficiency, process automation, and improved decision-making. However, gaps remain regarding data standardization, interoperability, technological infrastructure, and user resistance. As a contribution, this review advances the existing literature by explicitly distinguishing general BI tool adoption from the sector-specific use of Power BI in civil construction, systematically classifying application domains and revealing limitations in maturity that remain underexplored in prior reviews. Full article

To address the challenge of complex quality control in shared manufacturing arising from loose “partner” relationships, a quality disclosure mechanism is incorporated into a shared manufacturing supply chain. By developing a platform-led game-theoretic model, it compares four quality disclosure strategies under third-party and self-built shared manufacturing platforms, filling a theoretical gap on how quality disclosure aligns with different platform models. The findings indicate that: (1) Quality disclosure always increases platform profit, providing theoretical support for the economic incentives for platforms to promote quality transparency. (2) Under third-party shared manufacturing platforms, all manufacturers prefer unilateral disclosure by the high-quality manufacturer, indicating that this platform model naturally generates a high-quality-led signaling mechanism and reduces coordination costs. (3) Under self-built shared manufacturing platforms, strategy choice is conditional: when the disclosure level is very high, the high-quality manufacturer counter-intuitively induces the low-quality manufacturer to disclose in order to avoid excessive guarantee risk; when the market quality gap is large, bilateral disclosure is the equilibrium, jointly building market trust; when the quality gap narrows, the equilibrium returns to unilateral disclosure by the high-quality manufacturer to strengthen the quality signal.This study provides a new theoretical framework for understanding quality signaling in multi-actor collaborative settings and offers managerial insights for shared manufacturing platforms to design disclosure mechanisms and for manufacturers to choose cooperation modes. Full article

This study examines how employee benefit practices link employee well-being with financial sustainability in sustainable organization management. Focusing on Generation Z, it investigates the intersection between meaning attributed to employee benefits and managerial decision-making guided by financial rationality. Drawing on human resources management (HRM) and finance perspectives, employee benefits are conceptualized as mechanisms for balancing human-centered value creation and economic resilience. A qualitative design was used, based on semi-structured interviews with 15 Generation Z employees and 20 human resources (HR) and finance managers in Türkiye. Data were analyzed through thematic analysis and the Gioia methodology to develop an inductive, multi-level framework. The findings indicate that Generation Z employees view employee benefits as psychosocial resources reflecting justice, autonomy, psychological safety, and value alignment—core components of subjective and eudaimonic well-being—while managers assess them primarily through financial sustainability logics such as cost control and return on investment. Overall, meaning- and cost-oriented perspectives emerge as mutually reinforcing within sustainable organizational systems. The study proposes the Meaning–Cost Balance (MCB) Framework, conceptualizing employee benefits as a strategic management mechanism aligning employee well-being with financial resilience. Positioned at the intersection of HRM and financial sustainability, the framework contributes to sustainable organization management and offers a transferable basis for future comparative research. Full article

In the context of strategies for the promotion of a sustainable wine industry, the utilization of production regulations under the European Geographical Indications system is seldom contemplated. Furthermore, when such texts are considered, the focus is typically on rules for viticulture or winemaking, rather than on articles governing the boundaries of a PDO or PGI. The present study examines the manner in which regulatory innovation, when viewed from a strictly geographical perspective, can promote the sustainable growth of the sparkling wine districts of Franciacorta and Oltrepò Pavese, which are located in the Italian Lombardy region. Through a comparative analysis of Franciacorta and Oltrepò Pavese, we explore how regulatory frameworks, land-use constraints, and production capacities interact to shape environmental, social, and economic sustainability. Franciacorta’s premium positioning and global reputation are constrained by its limited geographic area, making expansion environmentally and socially challenging. In contrast, Oltrepò Pavese has substantial production potential, particularly for Pinot Noir-based classic-method sparkling wines but suffers from a fragmented identity and weak market recognition. Benchmarking the Prosecco PDO evolution, we propose a sustainability-oriented growth model integrating multiple territories under harmonized rules, termed “Grande Franciacorta”. This framework would enable controlled growth, reduce land pressure in high-density areas, enhance regional competitiveness, and support long-term ecological stewardship. This study outlines managerial implications for producers, emphasizing multi-tier product architectures, dynamic capabilities, and coordinated governance mechanisms. Policy recommendations highlight the need for regulatory frameworks that embed sustainability criteria, optimize land use, and consolidate regional reputation to ensure the long-term viability of high-quality sparkling wine production. Full article

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone. Full article

Corporate fraud remains a persistent problem that highlights the need for improved internal control and governance. Research on corporate governance (CG) and forensic accounting (FA) has been largely performed as separate studies. Little has been done to look at how accountants’ knowledge and the specific training of accountants in fraud awareness for their company’s leaders affect preventing fraud (FP). The study surveyed 150 accountants in India from April 2023 to May 2024. The results are based on Chi-Square testing and binary logistic regression. The study investigated how companies in India incorporate CG policy understanding and FG use for KMP and boards and how these factors affect FP. The findings indicate that understanding CG, using FA, and having specific training on fraud awareness for KMPs and boards of directors are all significant factors in reducing the occurrence of fraud. In addition, general employee training has no impact on FP. The theories of agency, stakeholder, and fraud triangle were combined to create one model to provide guidance to both organizations and regulators on how to institutionalize FG and to improve transparency in governance. Full article

Artificial intelligence (AI) is transforming customer experience management (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generative AI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining how AI-enabled personalization, privacy concerns, and customer value interact within AI-mediated customer experiences. Drawing on the Personalization–Privacy–Value (PPV) framework, the review synthesizes evidence on how AI-driven personalization enhances utilitarian, hedonic, experiential, relational, and emotional value, thereby strengthening satisfaction, engagement, loyalty, and behavioral intentions. At the same time, the findings reveal persistent tensions, as privacy concerns, perceived surveillance, algorithmic bias, and contextual moderators—including generational differences, cultural expectations, and technological literacy—frequently constrain value creation and erode trust. The review highlights that personalization benefits are highly contingent on transparency, perceived control, and ethical alignment, rather than personalization intensity alone. The study contributes by integrating ethical AI considerations into CXM research and clarifying conditions under which AI-enabled personalization leads to value creation versus value destruction. Managerially, the findings underscore the importance of ethical governance, transparent data practices, and customer-centered AI design to sustain trust and long-term customer relationships. Future research should prioritize longitudinal analyses of trust development, demographic heterogeneity, and cross-sector comparisons of AI governance as AI technologies become increasingly embedded in service ecosystems. Full article

Geothermal energy is a crucial component of climate adaptation and sustainability transitions, as it provides a dependable, low-carbon source of baseload power that can accelerate sustainable energy transitions and enhance climate resilience. Yet, in East Africa—one of the world’s most promising geothermal regions, with the East African Rift—a unique climate-energy opportunity zone—the harnessing of geothermal power remains slow and uneven. This study examines the contextual conditions that facilitate the successful and sustainable development of geothermal power in the region. Drawing on semi-structured interviews with 17 experienced professionals who have worked extensively on geothermal projects across East Africa, the analysis identifies how technical, institutional, managerial, and relational circumstances interact to shape outcomes. The findings indicate an interdependent configuration of success conditions, with structural, institutional, managerial, and meta-conditions jointly influencing project trajectories rather than operating in isolation. The most frequently emphasised enablers were resource confirmation and technical design, leadership and team competence, long-term stakeholder commitment, professional project management and control, and collaboration across institutions and communities. A co-occurrence analysis reinforces these insights by showing strong patterns of overlap between core domains—particularly between structural and managerial factors and between managerial and meta-conditions, highlighting the mediating role of managerial capability in translating contextual conditions into operational performance. Together, these interrelated circumstances form a system in which structural and institutional foundations create the enabling context, managerial capabilities operationalise this context under uncertainty, and meta-conditions sustain cooperation, learning, and adaptation over time. The study contributes to sustainability research by providing a context-sensitive interpretation of how project success conditions manifest in geothermal development under climate transition pressures, and it offers practical guidance for policymakers and partners working to advance SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action) in Africa. Full article