ABSTRACT The adoption of social media has significantly propelled the development of the tourism industry, with an increasing number of tourists sharing positive travel experiences online. Based on the Emotional Contagion Theory and the Broaden-and-Build Theory of Positive Emotions, this study examines the mechanisms through which emotional valence and arousal in tourism experiences influence comment volume, while exploring the moderating roles of emoji and hashtag usage. Employing an approach that combines the BERT model with the Chinese EMObank to conduct sentiment analysis on large-scale field data from Weibo and construct a negative binomial regression model. Empirical results reveal that both emotional valence and arousal positively impact comment volume, with valence demonstrating a stronger effect. Emojis positively moderate the influence of emotional valence rather than arousal, whereas hashtags amplify the effect of emotional arousal. This study contributes to understanding the potential spillover effects of sharing tourism experiences on social media and provides managerial insights for tourism stakeholders.

Purpose Drawing on knowledge-based theory and from the perspective of dynamic capabilities, this study aims to investigate the impact of digital and intelligent (D&I) transformation on both exploitative and explorative technological innovation, and further examines how knowledge recombinant capabilities, namely, reuse and creation, moderate these effects. Design/methodology/approach The research model was empirically examined by using a panel data set of Chinese listed firms in Shanghai and Shenzhen Stock Exchanges from 2000 to 2022. To address potential endogeneity concerns and strengthen the validity of the findings, we further conducted a series of robustness checks, including two-stage least squares estimation with the instrumental variable, a firm-level fixed-effect model that controls for unobserved heterogeneity across firms and over time and the panel regression models using robust and clustered standard errors. Findings The empirical results demonstrate that firm D&I transformation exerts a significantly positive impact on both exploitative and explorative technological innovation, highlighting the dual roles of D&I transformation in reinforcing incremental improvements as well as fostering breakthrough advances. Moreover, the analysis reveals that firms with stronger knowledge recombination capabilities, including both recombinant reuse capability and recombinant creation capability, could gain more technological innovation benefits from D&I transformation. Originality/value The findings highlight that firms’ benefits from D&I transformation vary, with knowledge recombination capabilities critically shaping how such transformation drives technological innovation, offering insights into how firms can strategically manage and reconfigure knowledge to fully unlock the innovative potential of technological changes.

ABSTRACT Based on a sample of Chinese A‐share listed enterprises from 2012 to 2021, this study examines the effects and mechanisms of regional digitalization on corporate greenwashing. The findings indicate that regional digitalization initially exacerbates corporate greenwashing, but in the long run, it inhibits this behavior. The results from the mechanism analysis reveal that regional digitalization exacerbates corporate greenwashing by reducing information disclosure quality. Furthermore, our empirical results reveal heterogeneity in the impact of digitalization on greenwashing among different types of enterprises. The effect is significant for firms with non‐tech background CEOs, non‐tech firms, digital transformation firms, and firms within the Yangtze River Economic Belt and low firm level of corporate digitalization, likely because these firms are more inclined to use digitalization for image‐building or face stronger external pressures. Besides, the moderating roles of financing cost and cross‐listing status are tested. The study reveals that higher equity financing costs help reduce greenwashing. However, digitalization tends to amplify greenwashing behavior when equity financing costs are high. In contrast, debt financing costs show no significant impact on greenwashing. For cross‐listing, firms listed in both Mainland China and Hong Kong are generally less prone to greenwashing due to stricter disclosure requirements, but digitalization amplifies their potential for greenwashing behavior under dual regulatory systems that require compliance with both markets. This research contributes to understanding how digitalization can mitigate greenwashing and promote genuine environmental responsibility in the corporate sector.

ABSTRACT The paper examines the regionally very different prevalence of private law disputes and criminal cases before courts in the late Habsburg Empire. The hypothesis is pursued that these two phenomena partly occur in parallel and are conditioned by common social causes. Based on an analysis of judicial and census data for the Austrian half of the Monarchy from around 1900, the paper shows that ‘everyday conflicts’ brought to court, such as small claims disputes, actions of trespass and convictions for petty thefts and violent acts, actually become significantly less frequent the more industrialized a region is – even if this pattern is mainly determined by the rather special situation in the eastern Crownlands. Embezzlement, trade-related disputes and lawsuits over larger sums of money, on the other hand, are more common where commerce, transport and services are more prevalent, and the density of lawyers is higher. The rate of convictions for more serious crimes, however, is difficult to explain in terms of socio-economic indicators, but points to cultural patterns of criminalization that could be linked to migration phenomena. Different social theoretical interpretations of these empirical results are contrasted and discussed. On the one hand, the correlations found could point to states of social disintegration, as they can be described with the classic sociological-criminological concept of ‘anomie’; on the other, institutions of justice are strategically used ‘from below’ for the interests of different groups of the population. Insofar as civil litigation requires communicative actions and enables conflict resolution, the courts also provide a means of social integration to a certain extent. Patterns of harsher punishment of foreigners, by contrast, point to mechanisms of social exclusion. The article argues that research on uses of justice can benefit from considering the macrosocial framework conditions that can be described for the late Habsburg Monarchy with rich statistical sources.

This paper develops and applies an integrated risk-based counterparty due diligence framework for Pertamina’s crude segment, combining a structured questionnaire with Fuzzy Analytical Hierarchy Process Fuzzy AHP and TOPSIS. The framework operationalizes four risk domains, legal compliance, corporate image, operational performance, and financial management, by converting qualitative judgments from experts and counterparties into probability–impact scores and normalized risk indices. Expert judgements gathered via a Focus Group Discussion are modeled using Fuzzy AHP to derive uncertainty-aware weights that emphasize operational 0.45 and legal compliance 0.30 risks, with financial management 0.20 and corporate image 0.05 playing supporting roles. These weights are applied to indicator-level indices from five crude counterparties CRD-01–CRD-05 to obtain composite risk scores, which are then processed using TOPSIS to compute closeness coefficients and produce a transparent ranking relative to an ideal low-risk profile. The empirical results show that all assessed counterparties pass minimum compliance screening and fall predominantly within Low to Moderate and Moderate risk bands, with operational and financial dimensions emerging as the dominant sources of residual exposure. The resulting Counterparty Risk Ratings provide a direct link to Pertamina’s risk appetite, enabling differentiated decision rules such as standard approval, conditional approval with enhanced covenants, or restricted engagement. The study demonstrates that integrating Fuzzy AHP and TOPSIS into counterparty due diligence can enhance the rigor, transparency, and defensibility of crude supplier evaluations, while also highlighting limitations related to sample size, reliance on subjective judgments, and opportunities for incorporating longitudinal and more objective performance data in future applications. The integrated use of Fuzzy AHP for deriving domain and sub‑criterion weights, combined with TOPSIS for ranking crude counterparties, allows Pertamina to move from qualitative checklists to a traceable, quantitative risk rating. This integration strengthens methodological rigor (through consistency‑checked expert weights), transparency (through explicit criteria and weights), and defensibility (through a documented link from raw scores to final ratings).

Abstract Narratives on social media emerge through the interaction of content, users, and platform-specific structures, yet identifying cohesive narrative formations across platforms remains challenging. This paper introduces Weighted Contextual Focal Structures Analysis (W–CFSA), a graph-based method for discovering structurally embedded narrative groups in multiplex networks. We construct a three-layer multiplex network integrating user–user interactions, user–narrative associations, and narrative–narrative similarity, with edge weights derived from semantic alignment. The method is evaluated on over 70,000 cross-platform data collected from YouTube, TikTok, X (formerly Twitter), and Instagram between January and May 2025, comprising posts related to U.S.-China trade and tariff policies. Empirical results show that W–CFSA identifies focal sets with higher internal cohesion, greater internal density, and more substantial impact on global clustering than a size-matched eigenvector-centrality baseline. Qualitative analysis further indicates that the recovered focal sets correspond to distinct narrative architectures rather than simple stance groupings, demonstrating the value of integrating narrative context with network structure for cross-platform analysis.

With the continuous advancement of green development strategies, environmental regulation has gradually shifted from administrative control to market-based governance mechanisms. The environmental protection tax, as an important institutional arrangement in China’s environmental regulatory system, internalizes pollution costs through taxation and significantly influences corporate production decisions and resource allocation. This study takes the implementation of China’s Environmental Protection Tax Law in 2018 as a quasi-natural experiment and uses data from Chinese A-share listed companies from 2015 to 2022. A difference-in-differences (DID) model is constructed to examine the impact of the environmental protection tax on the green transformation of heavy polluting enterprises. The empirical results show that the environmental protection tax significantly promotes the green transformation of heavily polluting firms. Further analysis indicates that the policy encourages enterprises to increase environmental investment and adopt cleaner production technologies, thereby improving environmental performance and innovation capability. Robustness tests confirm the reliability of the findings. The results provide empirical evidence for improving green taxation systems and offer policy insights for promoting coordinated development between economic growth and environmental protection.

ABSTRACT In the context of rapid urbanisation and climate change, initiatives like Singapore’s One Million Trees movement offer promising solutions to the challenges facing urban life. Co-production is a key focus in governance reform agendas, emphasising increased citizen participation in shaping their environments. This study explores how co-production manifests and is experienced in Singapore’s context. This article examines the experiential dynamics of public participation in Singapore’s One Million Trees movement, drawing on original empirical results from a survey, semi-structured interviews and journaling with participants in that program. Our analysis highlight’s themes of belonging, responsibility and empowerment while also showing how participants’ desires for ownership and belonging in urban greening processes can sit together uneasily, marking the difference between feeling ‘responsible’ and being ‘responsibilised’. We argue that understanding these tensions are critical for overcoming barriers to participation, internal or personal confliction, and the differences between government and personal values in urban greening.

This qualitative study explores the induction experiences of Palestinian Arab novice teachers in Jewish-majority schools in Israel during a period of intense national tension (2023–2025). Amid ongoing teacher shortages in the Jewish sector and a surplus of qualified teachers in the Arab sector, Boundary-Crossing Teaching (BCT) has become a notable phenomenon. Using semi-structured interviews and reflective journals of 23 beginning teachers and eight mentors, the study investigates how minority educators navigate cultural and political divides in a conflict-affected society. The findings reveal that during periods of heightened tension, teachers’ professional identity is often overshadowed by ethnic suspicion, leading to a “dual burden” of professional and national representation. The data shows that teachers navigate national ceremonies through “strategic ambiguity”—performing outward compliance (e.g., standing for the siren) while maintaining internal identity boundaries. Furthermore, the study identifies a paradox in language dynamics: while Palestinian Arabic is often “securitized” and viewed with suspicion in staffrooms, teachers successfully leverage their linguistic background as “intercultural capital” to build empathy with students. The research finds that shared-identity mentors provide an essential “third space” for processing experiences of racism that are otherwise silenced within the school hierarchy. These empirical results demonstrate that teacher retention in conflict zones requires active institutional protection to prevent professional status from collapsing into national categorization.

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes in bullish conditions and the other in bearish conditions, while a trading decision selector dynamically predicts market regimes and allocates execution accordingly. This design enables the system to adapt to regime shifts and mitigate risks arising from market volatility and extreme events. Using Mini Taiwan Stock Exchange Index Futures (MTX) as a case study, a four-year historical backtest is conducted covering multiple disruptive periods, including the tax adjustment and the Russia–Ukraine conflict. The empirical results show that, under a monthly capital reset and loss-compensation rule with a fixed investment of TWD 500,000 per month, the proposed framework achieves an average cumulative return of 2240%, an annualized return of 109%, and a Sharpe ratio of 0.31, with the cumulative ROI exceeding twice the MTX index growth over the same period. Although the Sharpe ratio remains moderate, this outcome reflects the framework’s emphasis on directional trading and absolute return maximization, where profitable trades outweigh intermittent losses despite higher short-term volatility. These findings suggest that adaptive, regime-aware DRL architectures are particularly effective for futures trading in markets characterized by frequent trend reversals, offering both methodological innovation and practical applicability under realistic market conditions, with strong returns achieved at a moderate risk-adjusted level.

The rapid convergence of Artificial Intelligence (AI) and Metaverse technologies is reshaping the higher education landscape by enabling immersive, personalized, and adaptive learning experiences. However, the long-term sustainability of such innovations remains uncertain without addressing environmental, social, and governance (ESG) considerations. This study develops and empirically validates an ESG-informed framework for Sustainable AI–Metaverse Adoption (SAAM) in higher education. A quantitative research design was employed, collecting data from 280 university students across diverse disciplines through a structured survey. Structural Equation Modeling (SEM-PLS) was applied to assess measurement reliability, convergent and discriminant validity, and to test the proposed hypotheses. The empirical results demonstrate that ESG dimensions exert differential effects on sustainable adoption, with environmental and social factors showing stronger direct associations than governance-related variables. Environmental sustainability, through energy-efficient AI systems, significantly enhances SAAM. Similarly, social dimensions, particularly inclusive AI access and student acceptance, exert robust positive effects on sustainable adoption, whereas faculty readiness influences adoption indirectly. Conversely, governance-related factors exhibit comparatively weaker direct effects: institutional policy support enhances digital infrastructure but does not directly influence SAAM, whereas ethical AI use has a limited impact, reflecting student prioritization of usability over ethics in early stages of adoption. Importantly, the outcomes highlight that SAAM substantially fosters digital pedagogical innovation (DPI) and enhanced student learning outcomes (ESLO), confirming its transformative potential. The study contributes theoretically by integrating ESG principles into technology adoption research, offering a multidimensional lens that enriches the understanding of sustainable digital transformation in higher education. Practically, it provides institutions and policymakers with evidence-based insights to design environmentally conscious, socially inclusive, and governance-supported strategies for AI–Metaverse integration. Future research should expand to cross-cultural contexts, larger samples, and longitudinal designs to validate and generalize these findings.

How does digital regulations influence mode 1 services trade? cross-country empirical results

Purpose Urban agriculture (UA) is a key strategy for fostering sustainable cities and university campuses offer ideal settings for innovative and demonstrative initiatives. This study investigates UA as regenerative infrastructure on a university campus in Brazil. It aims to provide a conceptual and methodological foundation for integrating UA into the campus, through assessing challenges and opportunities before the project’s implementation; and proposing design requirements to guide future projects. Design/methodology/approach Design Science Research is adopted, grounded in the university context. Data were collected through a questionnaire (n = 120) and interviews (n = 9), and analyzed using non-parametric statistics (SPSS). Findings Key challenges include maintenance, stakeholder engagement, risk of discontinuity and lack of institutional support. The academic setting presents opportunities through integration with teaching, research and outreach. Fourteen design requirements were developed, and an institutional UA policy is recommended. The campus community views UA positively, associating it with innovation. The results suggest that university campuses are strategic spaces for promoting UA as a regenerative infrastructure system. Research limitations/implications The proposed method for diagnosing challenges/opportunities and developing design requirements can be adapted to similar contexts. However, empirical results are not generalizable without additional studies in comparable settings. Originality/value This research offers theoretical insights for incorporating UA into regenerative infrastructure planning on university campuses, contributing to regenerative development and the Sustainable Development Goals.

Trajectory planning is a central problem in autonomous driving, requiring long-horizon reasoning, strict safety guarantees, and robustness to rare but critical events. Recent learning-based planners increasingly formulate planning as an autoregressive sequence generation problem, analogous to large language models, where future motions are discretized into action tokens and predicted by Transformer-based neural sequence models. Despite promising empirical results, most existing approaches adopt time-domain action representations, in which consecutive actions are highly correlated. When combined with autoregressive decoding, this design induces degenerate generation behavior in learning-based planners, encouraging local action continuation and leading to rapid error accumulation during closed-loop execution, particularly in safety-critical corner cases such as sudden pedestrian emergence. To address this limitation of time-domain autoregressive planning, we propose a unified trajectory planning framework built upon three core ideas: (1) explicit action tokenization for long-horizon planning, (2) transformation of the action space from the time domain to the frequency domain, and (3) a hybrid learning paradigm that combines imitation learning with reinforcement learning. By representing future motion using compact frequency-domain action coefficients rather than per-timestep actions, the proposed planner is encouraged to reason about global motion intent before refining local details. This change in action representation fundamentally alters the inductive bias of learning-based autoregressive planning, mitigates exposure bias, and enables earlier and more decisive responses in complex and safety-critical environments. We present the model formulation, learning objectives, and training strategy, and outline a comprehensive experimental protocol.

The proliferation of digital commerce platforms has transformed consumer behavior while simultaneously elevating environmental consciousness among Indonesian consumers. This research investigates the influence of digital eco-labels on consumer willingness to pay premium prices on marketplace platforms, specifically Shopee and Tokopedia, within Indonesian urban contexts. Employing a quantitative associative research design, this study collected data from 50 active marketplace users through purposive sampling techniques using structured questionnaires with five-point Likert scales. Multiple linear regression analysis was conducted to examine the relationships between digital eco-labels and willingness to pay premium prices, with perceived credibility and environmental knowledge as moderating variables. The findings reveal that digital eco-labels significantly and positively influence willingness to pay premium prices with a beta coefficient of 0.438 and significance level of 0.000. Perceived credibility effectively moderates this relationship with a coefficient of 0.312, while environmental knowledge demonstrates a moderating effect with a coefficient of 0.276. The research model explains 64.7 percent of the variance in willingness to pay premium prices. These empirical results confirm the applicability of Theory of Planned Behavior and signaling theory in the context of sustainable digital consumption in Indonesia. The study contributes theoretical insights into green consumer behavior within emerging market e-commerce platforms and provides practical implications for marketplace operators to develop transparent third-party verification systems for eco-labels, integrate educational content regarding sustainability, and optimize search algorithms to enhance visibility of verified sustainable products. Policy implications emphasize the necessity for governmental regulation standardizing digital sustainability labels and monitoring environmental claims in e-commerce to prevent greenwashing practices.

The concept of high intellectual ability (HIA) is being redefined to incorporate its transformational dimension. The high intellectual potential that characterizes it should not only be directed towards personal achievement, but also providing a transformational response involving positive social responsibility and the ethical regulation of high potential. Research suggests that schoolchildren and adolescents with HIA demonstrate higher and earlier ethical sensitivity than their peers, but academic excellence does not necessarily lead to social responsibility. Therefore, it is necessary to educate their executive and ethical regulation and other functional traits, such as critical thinking and wisdom. This work aims to understand how ethical sensitivity contributes to the transformational expression of HIA. A comparative theoretical review of scientific articles on the subject was carried out, combined with empirical results from previous research on the subject. Two fundamental ideas are incorporated into the concept of HIA: its development and the importance that this development is not only directed towards individual achievements but is also transformational, that is, giving rise to socially beneficial behaviors. The discussion focus on the need for educating the executive and ethical regulation of high intellectual potential in a transformational HIA in order to offer effective solutions to the complexities of the 21st century.

This paper examines the impact of digital transformation on the financial performance of retail banking at the Joint Stock Commercial Bank for Investment and Development of Vietnam (BIDV) over the period from 2012Q1 to 2025Q2. Given the time-series properties of the data, the study employs the Autoregressive Distributed Lag (ARDL) model in conjunction with the Error Correction Model (ECM) to assess both short-run and long-run effects of digital transformation. The empirical results indicate that digital transformation exhibits a robust long-run relationship with cost efficiency (proxied by CIR). In addition, digital transformation effects on profitability indicators (ROA and ROE) significantly during the Covid-19 pandemic because banks have strong motives to digitalize. These results suggest that the financial benefits of digital transformation at BIDV are primarily realized through cost optimization, whereas improvements in profitability require a longer time horizon and a higher level of digital maturity. Based on these findings, the paper provides managerial implications emphasizing a digitization-for-efficiency approach, highlighting the importance of process optimization, data-driven governance, and channel restructuring to enhance the financial performance of retail banking.

Sequential Model-Based Optimization (SMBO) is a highly effective strategy for hyperparameter search in machine learning. It utilizes a surrogate model that fits previous trials and approximates the hyperparameter response surface (performance). This surrogate model primarily guides the decision-making process for selecting the next set of hyperparameters. Existing classic surrogates, such as Gaussian processes and random forests, focus solely on the current task of interest and cannot incorporate trials from historical tasks. This limitation hinders their efficacy in various applications. Inspired by the state-of-the-art convolutional neural process, this paper proposes a novel meta-learning-based surrogate model for efficient and effective hyperparameter optimization. Our surrogate is trained on the meta-knowledge from a range of historical tasks, enabling it to accurately predict the hyperparameter response surface even with a limited number of trials on a new task. We tested our approach on the hyperparameter selection problem for the well-known support vector machine (SVM), residual neural network (ResNet), and vision transformer (ViT) across hundreds of real-world classification datasets. The empirical results demonstrate its superiority over existing surrogate models, highlighting the effectiveness of meta-learning in hyperparameter optimization.

Temporal rich-club phenomenon and its formation mechanisms in international trade: Evidence from new energy minerals.

The research article presents an overall comparative study of multivariate GARCH M-GARCH in portfolio risk management, where three prevailing specifications VEC, CCC and DCC models are considered. We take daily closing prices of four major assets of the year 2018 through to 2023; S&P 500, NASDAQ-100, gold futures, and US Treasury Bonds, to estimate conditional variances, covariances, and dynamic correlations using maximum likelihood estimation. The descriptive statistics indicate that volatility is highly concentrated in clustering and time varying across assets with NASDAQ having the highest volatility (2.08) and significant negative skewness that shows non-normal returns. Comparison on models based on information criteria indicates that the Dynamic Conditional Correlation (DCC) specification has better performance with less computation need, fewer 8 parameters as compared to 21 (VEC) and greater log-likelihood with high improvement (164.67 units). Adequate model specification is shown through diagnostic testing using Ljung-Box test and ARCH-LM tests. Empirical results indicate that the volatility persistence (alpha + - 0 = 0.98) and the dynamics of high correlation (beta = 0.9321) are high which indicates long-memory properties and mean-reverting behavior which does not support constant correlation assumptions.