Search Results (3,702)

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail under highly heterogeneous naming conventions, while single statistical models tend to overfit and degrade sharply under distribution shifts between training and deployment domains. These limitations stem from the weak semantic signals and distributional heterogeneity of structured data, which make it difficult to simultaneously capture explicit rules and latent, variant-sensitive attributes. To address these challenges, we propose a detection framework based on multi-view complementary features and a Hierarchical Gated Residual Network (HGRN). The framework first constructs a full-spectrum feature system that integrates explicit rules and implicit statistical fingerprints (e.g., entropy and character texture) to fill the semantic gap. It then introduces a decision mechanism combining robust priors with dynamic residual calibration: a random forest provides a stable probabilistic anchor, which is further nonlinearly corrected by a learnable gating-and-expert network. This design explicitly resolves the cognitive conflict between rule-dominated regions and complex distributional regions. Experiments on multiple real-world datasets—including DeSSI, CMS Open Payments and Home Credit—show that the proposed method achieves a Macro-F1 of 0.9408 on DeSSI and exhibits strong in-domain performance. Under strict frozen-model cross-domain transfer, HGRN mitigates the catastrophic collapse observed in pure neural baselines and maintains moderate detection capability, offering interpretable trust allocation between rule-based priors and data-driven correction in both financial and healthcare scenarios. Full article

Large language models (LLMs) are being adopted rapidly in financial institutions for applications including customer communication, compliance review, fraud detection, and agentic workflows, but without bias evaluation, they risk reinforcing systemic biases that may lead to unethical or unlawful decisions. To address potential systemic bias in LLMs in regulated settings like financial services, we present a statistical analysis framework and structured, reproducible methodology for evaluating whether LLM outputs vary significantly across demographic groups. Using financial fraud stories from the CNN/DailyMail dataset, we employ substitution-based identity variations across protected demographic classes, generate summaries via three proprietary language models, and perform statistical analysis on common metrics (ROUGE, BERTScore, Adverse Impact Ratio (AIR), and Standardized Mean Difference (SMD)). Statistical approaches such as MANOVA and ANOVA reveal small but significant differences in output metric values (e.g., for White female, Black male, and Asian male identities in our analysis), while sentiment analysis and human evaluation confirm disparities in tone and framing. Our results also indicate that measured disparities appear to decrease across subsequent model generations. Full article

This study sought to examine the evolution of financial technology (FinTech) and its huge influence on traditional financial systems, with particular attention to InsurTech, regulatory technology, robo-advisory services, and advertising technology. Focusing on the intersection of FinTech and small- and medium-sized enterprises (SMEs), the study employed a bibliometric analysis of 365 publications indexed in the Scopus database from 2007 to 2023. Scientific mapping techniques were used to identify key research domains, leading institutions, influential authors, and major contributing countries. The findings revealed a strong and growing interconnection between FinTech and SMEs, emphasizing the critical role of SMEs in economic development and financial inclusion. The analysis also highlighted the dominance of China in global FinTech research and identified emerging thematic trends that appeared to have shaped the field. The study concluded that FinTech innovations significantly contribute to enhancing financial system efficiency, resilience, and accessibility, thereby supporting sustainable economic growth. The insights obtained from this study may be found to be useful for policymakers, financial institutions, and SMEs whose interest is to leverage digital financial innovations for strategic decision making. This research offers a comprehensive overview of FinTech’s evolution and provides a foundation for future empirical and qualitative studies. Full article

The main challenge of hydrogen electrolysis lies in the high cost of hydrogen production. Achieving a decarbonized energy sector requires substantial investment to shift from carbon-intensive technologies to more sustainable alternatives. However, investment decisions in this context remain complex and uncertain. Currently, green hydrogen projects account for more than 500 initiatives worldwide and are expected to expand rapidly in the coming years. Evidence from feasibility studies suggests that green hydrogen produced from renewable energy is already technically viable and is approaching economic competitiveness. The current emphasis is on large-scale deployment and learning-by-doing processes to reduce electrolyzer costs and improve supply chain efficiency. This transition requires appropriate funding mechanisms, often involving significant public sector participation alongside private investment. This study analyzes the financing structures of green hydrogen projects in Germany, Australia, and Brazil using Principal Component Analysis (PCA) to identify the most relevant combinations of technical, economic, and financial variables. Unlike previous studies that address technical, economic, and financial dimensions in isolation, this study offers an integrated, empirically grounded analysis at the project level, combining cross-country comparison with a multivariate approach. The results indicate that project characteristics are strongly associated with capital intensity and financing structures, while cost variables such as levelized cost of hydrogen (LCOH) play a secondary role in explaining variation across projects. These findings suggest that financing arrangements—particularly those involving public support mechanisms—are closely associated with project configuration in this emerging sector. However, these results should be interpreted as patterns of statistical association rather than evidence of causal relationships. Overall, the analysis highlights the importance of coordinated financing strategies in supporting the development of green hydrogen and its potential contribution to emissions reduction in line with the Paris Agreement and the transition toward climate neutrality. Full article

The basic renovation of old urban communities is an important livelihood project for urban renewal, but there are many problems in the decision-making of renovation schemes, such as strong dependence on experience, lack of quantitative basis for multi-objective trade-off, and difficulty in describing residents’ risk attitude. Combining Case-Based Reasoning (CBR) and utility theory, this paper constructs a set of intelligent decision support models driven by data and knowledge. First of all, through literature analysis and expert investigation, a decision-making index system is established, which includes four dimensions and 16 quantitative indicators: policy and financial support, residential conditions and needs, residents’ consensus and social coordination, and implementation management and long-term maintenance. Secondly, the framework representation method is used to describe the reconstruction case, a hybrid retrieval strategy combining inductive retrieval and nearest-neighbor retrieval is designed, and the subjective and objective data combination weights are calculated by using AHP and the entropy method. On this basis, a loss utility function and risk aversion coefficient based on accident and public opinion data (a = 0.02) are introduced to modify the similarity calculation results to describe the risk avoidance behavior of decision-makers. Through 40 real renovation projects, a case base is built, and two types of target cases, “typical inclusive” (F5) and “key renovation” (F35), are selected for empirical verification. The results show that the model can effectively retrieve similar cases, and the similarity ranking changes in line with risk aversion expectations after utility correction. Taking F5 as an example, by reusing and revising the reconstruction scheme of a similar case, targeted suggestions are generated, which give consideration to safety, economy and operability. This model provides a new quantifiable and reusable method for scientific decision-making in basic renovation of old residential areas. Full article

This study examines the relationship between managerial overconfidence and environmental, social, and governance (ESG) performance through firm-level financial policy channels in an emerging-market context. Using panel data from non-financial firms listed on the Indonesia Stock Exchange during 2015–2024, this study adopts a multidimensional channel-based perspective in which managerial overconfidence is indirectly reflected through financing, liquidity, and investment decisions. Fixed-effects estimation with Driscoll–Kraay standard errors is employed as the baseline approach and complemented by lagged specifications, system GMM estimation, alternative measurements, and quantile regressions to assess robustness. The findings suggest that managerial overconfidence does not exert a direct and uniform influence on ESG performance but operates indirectly through heterogeneous financial policy behavior. The financing channel provides weak and unstable evidence, whereas the liquidity channel shows a relatively stronger positive association with ESG performance. The investment channel appears most sensitive to measurement and model specification, indicating that different operationalizations may capture distinct dimensions of managerial overconfidence. This study contributes to the behavioral corporate finance and ESG literature by showing that managerial overconfidence influences sustainability outcomes indirectly through heterogeneous financial policy mechanisms in an emerging market setting while highlighting the importance of temporal dynamics, endogeneity, and measurement sensitivity. Full article

Against the backdrop of the ongoing advancement of the “dual carbon” goals and the carbon emission trading system, green innovation in small and medium-sized manufacturing enterprises faces multiple practical constraints, including financing constraints, technological commercialization risk, and market recognition costs. To examine the mechanism through which government regulation affects firms’ green innovation behavior, this study develops a four-party evolutionary game model involving government, small and medium-sized manufacturing enterprises, consumers, and investment institutions, and analyzes the strategic interactions and dynamic evolution of these actors. The results show that regulatory intensity, consumer green preference, and financial support from investment institutions all exert significant effects on green innovation decisions in small and medium-sized manufacturing enterprises. Whether firms choose substantive green innovation depends primarily on such key factors as financing uncertainty, technological commercialization risk, the intensity of government penalties, and the level of policy incentives. Further stability analysis and numerical simulations indicate that stronger administrative penalties significantly increase the likelihood that firms adopt substantive green innovation and also promote green consumption among consumers. This effect becomes more pronounced when financing uncertainty declines. At the same time, stronger policy incentives for green investment enhance the willingness of investment institutions to participate in green projects, and this effect is further reinforced when technological commercialization risk is reduced. The findings suggest that green innovation in small and medium-sized manufacturing enterprises is characterized by strong multi-actor interdependence. Its evolutionary outcome is shaped not only by regulatory pressure, but also by green financial support, the conditions for technological commercialization, and market demand. Accordingly, sustained green innovation in small and medium-sized manufacturing enterprises requires coordinated efforts to improve regulatory arrangements, strengthen green finance support systems, reduce the cost of technological commercialization, and cultivate green consumer markets. Full article

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks in shaping barriers to cross-border investment. In this regard, this paper attempts to analyze the effects of ESG risks on foreign direct investment (FDI) inflows based on an unbalanced panel dataset for up to 250 countries spanning the years 2000 to 2024, coupled with cross-sectional data for 2020. This study uses a two-dimensional approach, whereby structural ESG risks are evaluated using panel FMOLS regression, while ESG risk exposures are assessed using cross-sectional models. This research also considers moderating factors such as economic development, industrial composition, and innovation capabilities. Based on the use of the national-level ESG risk, it is evident that ESG risks considerably reduce inward foreign direct investment. Full article

Climate-related financial risks have become a central concern for financial institutions and regulators, particularly within the European financial system. This paper examines how climate-related risks are integrated into governance, risk assessment, and regulatory practices in European financial institutions. Using a structured narrative literature review of academic and institutional sources published between 2015 and 2026, the study synthesizes evidence on physical, transition, and liability risks, as well as the frameworks and tools used to assess them, including climate stress testing, scenario analysis, and climate value-at-risk models. The findings indicate that climate considerations are increasingly embedded within governance structures and supervisory frameworks; however, implementation remains fragmented due to inconsistent data, methodological limitations, and institutional barriers. The review further highlights that existing risk models often struggle to capture the long-term and non-linear nature of climate-related uncertainty. This paper contributes to the literature by linking financial stability theory and institutional theory to explain the persistent gap between regulatory ambition and institutional practice within the European context. The study concludes by discussing implications for supervisory policy, disclosure standardization, and climate-risk integration in financial decision-making. Full article

Regular physical activity is essential for physical and mental health, yet participation among Norwegian university students remains below nationally recommended levels. This study explored facilitators and barriers for physical activity among first-year students, using the COM-B model as a conceptual framework. Fifteen physically active first-year students from two higher education campuses in Bodø were interviewed in spring 2025, and the data were analysed using inductive thematic analysis. Analysis showed that students’ activity behaviours were shaped by a dynamic interaction between physical and psychological capabilities, particularly in relation to technical competence, previous injuries, and self-regulation strategies. Opportunity-related factors—such as time constraints, financial limitations, commuting distance, and access to facilities—substantially influenced students’ ability to maintain regular activity, while social support from friends, family, and peers functioned as an important facilitator. Motivation emerged through a mixture of automatic processes—including stress reduction, enjoyment, and habits—and reflective processes such as goal-setting and health-oriented decision-making. For students in physically demanding study programmes, professional identity and body-related expectations also contributed to their engagement. Overall, this study highlights the need for institutional strategies that simultaneously address structural, social, and psychological factors to support sustainable physical activity habits during the transition to university life. Full article

Gastrointestinal nematodes (GIN) continue to impose substantial health and productivity losses in grazing ruminants, and the accelerating emergence of anthelmintic resistance (AR) underscores the need for SWC strategies. Although multiple SWC approaches have been validated experimentally, their implementation across European livestock systems remains inconsistent, and limited evidence exists regarding the stakeholders’ perceptions that affect decision-making. This study conducted a multilingual cross-sectional survey of 1261 respondents, including farmers, veterinarians, advisors, and other professionals, across 13 European countries to evaluate perceived worm-control cost burdens and the economic feasibility of seven SWC strategies. Descriptive and regression analyses revealed that a majority of respondents (56.7%) considered diagnostic testing to be financially reasonable, although perceptions varied significantly between countries. Sustainable anthelmintic use, quarantine and strategic screening, and grazing management were perceived as the most viable strategies, whereas biological control and bioactive compound-based approaches elicited greater uncertainty. An aggregated SWC Attitude Score demonstrated systematically higher acceptance among veterinarians compared to farmers, while male and older respondents exhibited lower levels of agreement across practices. The overall findings suggest that economic considerations may not be perceived as the primary barrier to sustainable worm control adoption, but other practical factors may potentially limit implementation. Full article

Sustainable (ESG) investments have gained significant interest, prompting renewed attention to retail investors’ decision-making processes. ESG investing is motivated by both financial concerns and psychological factors. However, despite growing interest, the motivational underpinnings of sustainable asset allocation remain underexplored. This study bridges economic psychology and sustainable finance to examine drivers of ESG investment intentions and choices in the Italian market. Drawing on the Theory of Planned Behavior, it explores how attitudes, subjective norms, perceived behavioral control, and trust shape ESG investing intentions and choices. Results show that each factor significantly influences investing intentions when considered independently. In particular, the affective dimension of attitudes emerges as especially relevant. These findings challenge traditional views of financial rationality in ESG contexts, suggesting that the motivations of sustainability-oriented investors may differ meaningfully from those of traditional investors. Practical implications are that ESG communication should appeal to emotional and ethical dimensions of decisions, while educational initiatives should enhance investors’ ability to critically assess ESG-related information. Full article

Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article

This study examines the causal impact of environmental, social, and governance (ESG) ratings on corporate green technology innovation using a panel of Saudi listed firms over the period 2015–2024. Adopting a multi-period difference-in-differences (DID) framework, the analysis evaluates three dimensions of innovation outcomes—quantity, quality, and efficiency (CGTI1–CGTI3). The results show that ESG ratings significantly enhance green technology innovation. Dynamic evidence indicates that these effects strengthen over time, reflecting gradual adjustment in firms’ innovation strategies. Mechanism analysis reveals that ESG ratings promote innovation primarily by alleviating financial constraints and mitigating agency problems. These effects are driven by improvements in the information environment, as ESG ratings reduce information asymmetry and enhance monitoring. From a theoretical perspective, ESG ratings are conceptualized as a digital information infrastructure that reduces informational entropy and provides algorithmic evaluation signals, thereby guiding managerial decision-making in R&D investment and project selection. Robustness tests, including propensity score matching difference-in-differences (PSM-DID), confirm that the results are not driven by selection bias. Focusing on Saudi Arabia under Vision 2030, the findings highlight the role of ESG information systems in shaping green innovation in an emerging market context. Full article