In recent years, the application of gray models for forecasting has gained significant attention in economic research. Grey system theory provides a robust methodology for analyzing and predicting time-series data characterized by uncertainty, limited information, and small sample sizes. This is particularly relevant for GDP forecasting, where macroeconomic data may be incomplete or noisy. This study introduces an optimized multivariable Hausdorff fractional gray Bernoulli forecasting approach to predict the real GDP using the variables in Cobb-Douglas production function. The proposed model's effectiveness is tested with a case study on forecasting real GDP in five Eastern European transition economies: Bulgaria, Czech Republic, Hungary, Poland, and Romania. The empirical analysis reveals heterogeneous growth patterns: capital accumulation and significantly drives output in four countries. The proposed forecasting model achieves superior predictive accuracy significantly outperforms traditional forecasting models. Rolling window validation confirms the model's robustness across varying forecast horizons. The projections indicate a steady, though moderating, growth trajectory across five countries through 2030. The methodology demonstrates its potential as a valuable tool for policymakers and economic planners who need more dependable methods to predict GDP trends in transition economies.
Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodologies and theoretical frameworks to examine retention factors, these approaches lack predictive capability and fail to provide actionable, employee-specific insights. This study advances beyond descriptive and correlational analyses by employing explainable artificial intelligence (XAI) to develop a transparent, data-driven framework for identifying attrition drivers and quantitatively evaluating retention strategies. Unlike existing studies that rely on self-reported perceptions, our approach leverages objective HR data and machine learning to predict individual-level attrition risk with calibrated probabilities. Leveraging the IBM HR Analytics dataset as a proxy for sustainability-focused roles, we construct an interpretable logistic regression model with strong predictive performance and isotonic regression calibration. Global and local interpretability techniques, including SHAP, LIME, and permutation importance, show that non-monetary factors, such as excessive overtime, frequent business travel, and limited promotion opportunities, have a greater impact on turnover risk than salary levels. These findings align with Green Human Management (Green HRM) principles, which emphasize work-life balance and employee well-being. Crucially, our policy simulation framework, absent from prior Green HRM studies, demonstrates that eliminating overtime could reduce predicted attrition probability by 17.35% for affected employees, potentially retaining 31 staff members, substantially outperforming modest salary adjustments. This work expands the value of predictive AI into HR analytics by consolidating HR analytics with Green HRM through a novel methodology that bridges the gap between prediction and actionable intervention. It represents the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation in environmentally conscious sustainable organizations.
This article examines the elevated risk of sexual violence experienced by US servicewomen during deployment and the implications of internal military sexual violence for the prevention of conflict-related sexual violence against civilians. Drawing on qualitative interviews with servicewomen, this study highlights how deployment environments amplify harassment and assault, perpetuated by a culture of warrior masculinity and institutional tolerance for gendered aggression. I show that predeployment briefings and informal mentorships warn women of sexual danger on deployment. In deployment settings, women are instructed to rely on hypervigilant individual strategies, such as walking in pairs or living near other women, rather than being supported by systemic institutional safeguards. These informal and gendered risk-management practices reflect an organizational awareness of sexual threat that coexists with institutional inaction or dismissal when violence occurs. The article situates these findings within the framework of the United Nations' women, peace, and security agenda and the Beijing Declaration and Platform for Action, emphasizing that militaries' capacity to protect civilians from conflict-related sexual violence should account for how they address sexual violence within their own ranks. It argues that internal sexual violence is not distinct from conflict-related sexual violence but interconnected through shared environments of militarized aggression. The article calls for a systemic lens of sexual safety to be integrated into leadership, operations, practices, and policies, finding this is essential to ensure institutional accountability for sexual violence against servicemembers and civilians. Cet article examine le risque & eacute;lev & eacute; de violences sexuelles auquel est expos & eacute; le personnel f & eacute;minin des forces arm & eacute;es des & Eacute;tats-Unis pendant son d & eacute;ploiement, et les implications des violences sexuelles internes & agrave; l'arm & eacute;e pour la pr & eacute;vention des violences sexuelles li & eacute;es aux conflits exerc & eacute;es & agrave; l'encontre des civils. & Agrave; partir d'entretiens qualitatifs avec des femmes militaires, l'& eacute;tude met en & eacute;vidence la mani & egrave;re dont les contextes de d & eacute;ploiement militaire amplifient les ph & eacute;nom & egrave;nes de harc & egrave;lement et les agressions, perp & eacute;tu & eacute;s par une culture de la masculinit & eacute; guerri & egrave;re et une tol & eacute;rance institutionnelle & agrave; l'& eacute;gard des agressions sexistes. Je montre que les s & eacute;ances d'information qui pr & eacute;c & egrave;dent le d & eacute;ploiement des forces arm & eacute;es et les conseils informels donn & eacute;s aux femmes militaires sont utilis & eacute;s pour les avertir des dangers sexuels lors du d & eacute;ploiement des forces arm & eacute;es. Dans les contextes de d & eacute;ploiement militaire, les femmes se voient expliquer qu'elles doivent s'appuyer sur des strat & eacute;gies individuelles d'hypervigilance, telles que marcher par groupes de deux ou vivre & agrave; proximit & eacute; d'autres femmes, au lieu d'& ecirc;tre soutenues par des mesures de protection institutionnelles syst & eacute;miques. Ces pratiques informelles et genr & eacute;es de gestion des risques d & eacute;notent une conscience organisationnelle des menaces sexuelles qui coexiste avec l'inaction ou le d & eacute;ni institutionnels lorsque celles-ci se produisent. J'inscris ces conclusions dans le cadre du programme & laquo; Femmes, paix et s & eacute;curit & eacute; & raquo; des Nations unies et de la D & eacute;claration et du Programme d'action de Beijing, en soulignant que la capacit & eacute; des forces arm & eacute;es & agrave; prot & eacute;ger les civils contre les violences sexuelles li & eacute;es aux conflits devrait & ecirc;tre le reflet de la mani & egrave;re dont elles traitent les violences sexuelles au sein de leurs propres rangs. Il ressort en effet que les violences sexuelles internes ne sont pas distinctes des violences sexuelles li & eacute;es aux conflits, mais sont au contraire & eacute;troitement reli & eacute;es par des contextes communs d'agression militaris & eacute;e. J'estime qu'une approche syst & eacute;mique de la s & eacute;curit & eacute; sexuelle int & eacute;gr & eacute;e dans l'exercice des fonctions d'encadrement, dans les op & eacute;rations et dans les mesures & agrave; adopter est essentielle pour garantir la responsabilit & eacute; institutionnelle en mati & egrave;re de violences sexuelles & agrave; l'encontre des militaires et des civils. Este art & iacute;culo examina el elevado riesgo de violencia sexual que experimentan las mujeres militares estadounidenses durante los despliegues y las implicaciones de la violencia sexual militar interna para la prevenci & oacute;n de la violencia sexual relacionada con los conflictos armados contra civiles. A partir de entrevistas cualitativas con mujeres militares, este estudio destaca c & oacute;mo los entornos de los despliegues amplifican el acoso y la agresi & oacute;n, perpetuados por una cultura de masculinidad guerrera y una tolerancia institucional hacia la violencia de g & eacute;nero. Se muestra que las sesiones informativas previas al despliegue y las mentor & iacute;as informales advierten a las mujeres sobre el peligro de agresi & oacute;n sexual durante el despliegue. En entornos de despliegue, se instruye a las mujeres a confiar en estrategias individuales de hipervigilancia, como caminar en parejas o vivir cerca de otras mujeres, en lugar de contar con el apoyo de salvaguardas institucionales sist & eacute;micas. Estas pr & aacute;cticas informales y 'generizadas' de gesti & oacute;n de riesgos reflejan una conciencia organizacional de la amenaza sexual que coexiste con la inacci & oacute;n o la negaci & oacute;n institucional cuando ocurre. El art & iacute;culo contextualiza estos hallazgos en el marco de la agenda de las Naciones Unidas sobre Mujeres, Paz y Seguridad y la Declaraci & oacute;n y Plataforma de Acci & oacute;n de Beijing, enfatizando que la capacidad de las fuerzas armadas para proteger a la poblaci & oacute;n civil de la violencia sexual relacionada con los conflictos armados tendr & iacute;a que ser un reflejo de c & oacute;mo abordan la violencia sexual dentro de sus propias filas. Se argumenta que la violencia sexual interna no es distinta de la violencia sexual relacionada con los conflictos armados, sino que est & aacute; interconectada a trav & eacute;s de entornos compartidos de agresi & oacute;n militarizada. Se argumenta tambi & eacute;n que una perspectiva sist & eacute;mica de la seguridad sexual integrada en el liderazgo, las operaciones y las pol & iacute;ticas es esencial para garantizar la rendici & oacute;n de cuentas institucional por la violencia sexual hacia militares y civiles.
PurposeEconomic Policy Uncertainty (EPU) introduces significant challenges to business decision-making, often dampening investment activity and exerting adverse effects on financial markets. Beyond equities, EPU may also influence alternative asset classes such as gold and cryptocurrencies. This study aims to investigate the impact of EPU on the US stock, gold and Bitcoin markets and these asset classes on each other using monthly time series data from October 2015 to September 2025.Design/methodology/approachFor this study, a vector autoregressive (VAR) framework is initially developed using monthly time series data from October 2015 to September 2025. The time series properties of the data are first examined to assess stationarity, followed by cointegration tests to evaluate the presence of long-run relationships among the variables. As all series are stationary in first differences and the null hypothesis of no cointegration is rejected, a Vector Error Correction Model (VECM) is subsequently used.FindingsThe estimated impulse response functions and variance decomposition results from the VECM reveal that EPU exerts a negative effect on the stock market while positively influencing gold prices, indicating that investors use gold as a hedge against heightened policy uncertainty. In contrast, the effect of EPU on the Bitcoin market is comparatively weaker than that observed in the stock and gold markets.Originality/valueThis study addresses a notable gap in the literature by jointly examining the effects of economic policy uncertainty (EPU) on stock, gold and Bitcoin prices within a unified analytical framework. Using U.S. data, the paper investigates how EPU influences these asset classes during periods of heightened uncertainty and explores the cross-market dynamics through which price movements in one market affect the others. Additionally, the study analyzes the feedback effects of the stock market on EPU, as well as on gold and Bitcoin markets, thereby offering a more comprehensive understanding of the interconnected relationships among traditional and alternative assets under uncertainty.
While AI ethics ensures fairness, accountability, and protection of user rights, dark patterns manipulate users to take unintended actions on digital interfaces. Related studies uncover limited insights into how reliably; human experts and AI models can detect dark patterns within a specific taxonomy. Our research fills this gap by asymmetrically examining cross-origin detection performance of human and AI/LLM evaluators (each evaluator’s ability to detect dark patterns generated by the opposite source) to understand their limitations and future potentials. Using GPT-4.1, we generated 200 UI images (with matched dark and non-dark pattern pairs) and selected 200 UI images collected 200 human-created UI screenshots from the ContextDP/AidUI dataset, based on computational, methodological, and statistical considerations. We calculated inter-rater reliability, recall, and error distribution. The results show that UX experts achieved substantial agreement (k = 0.75) and significantly higher recall (r = 0.99) over AI/LLMs. We present a novel study which explore the performance of AI/LLMs and UX experts in detecting dark patterns in UI images, and provide a benchmark dataset that could be useful to future research, while discussing empirical insights into the role, limitations, and promise of AI/LLMs in UI/UX design ethics and auditing, in realistic deployment scenarios.
