This paper explores the evolving nature of geopolitical threats and introduces a scenario-based framework for businesses to navigate an increasingly uncertain international environment. Drawing on the theoretical underpinnings of scenario planning, particularly as articulated by Scoblic (2020), this study suggest that firms must shift from traditional risk-based models to uncertainty-driven anticipatory strategies. The research highlights that geopolitical uncertainty differs fundamentally from geopolitical risk due to the inability to assign probabilities to complex and often unprecedented global events. Using real-world case studies and a structured methodological framework, the authors illustrate how scenario planning can be institutionalized within organizations to improve strategic foresight and resilience. The paper concludes that by embracing scenario-based methodologies, businesses can move beyond reactive risk assessments toward proactive, strategic engagement with geopolitical uncertainty. This transformation enables organizations to "learn from the future," enhancing their capacity to anticipate disruptions and safeguard long-term operations.

With emailing as a strategic tool, promotional emails need to be optimized, notably in how they encourage customer reaction through the call-to-action (CTA) button. The effects of promotional emailing especially remain unclear when tourism services are embracing the sharing economy and new forms of digitalized accessibility to propose access-based ‘moments of luxury’. This study uses two research context studies and a large-scale randomized field experiment conducted with a peer-to-peer boat rental company to investigate the effects of a promotional CTA button in emails from a sharing economy platform proposing access to ‘moments of luxury’ in the tourism industry. We find a detrimental impact of promotional CTA on click-through rates and a positive impact of prior engagement and multichannel use of customers on email metrics. This research thereby contributes to tourism-related access-based ‘moments of luxury’, and promotional email CTA from both theoretical and managerial perspectives.

Cet article contribue à la littérature sur l’entrepreneuriat féminin en rouvrant le débat sur les raisons de la sous-performance des entreprises dirigées par des femmes. Il explore un aspect sous-estimé de l’entrepreneuriat : la conciliation vie personnelle – vie professionnelle et, plus précisément, le soutien conjugal. Une analyse inductive de 24 cas de femmes entrepreneures issues de deux cultures plutôt opposées : celle du Nord de la France et la culture tunisienne. nous permet de dresser un modèle qui pourrait expliquer la définition que les femmes donnent à la performance de leur entreprise, en fonction de leur situation familiale et matrimoniale. Nous finissons par une discussion sur le rôle de la famille dans la performance des entreprises, et en particulier des entreprises féminines.

Value at Risk (VaR) and Average Value at Risk (AVaR) are among the most widely-used risk measures by market participants to assess the risk of individual financial firms and institutions. Despite their popularity, both measures fail to account for spillover effects between firms. To address this limitation, the CoVaR (Conditional Value at Risk) measure was introduced, which defines the VaR of a financial system conditional on the state of another institution. The traditional approach to estimating CoVaR involves a regression model combined with a quantile method to estimate the model’s parameters. This paper proposes a composite quantile regression method to enhance the accuracy of CoVaR estimation. We apply this methodology to several U.S. companies across various sectors, including finance, consumer goods, energy, industry, and technology. An analysis of the out-of-sample forecast accuracy using two popular backtesting criteria demonstrates that the composite quantile method provides more accurate CoVaR estimates than the standard quantile method. All computation codes are freely available in both R and MATLAB.

Wearable devices provide reliable biometric measurements in different contexts, and AI algorithms are increasingly being used to analyze this data. The objective of this review is to examine the use of wearable devices to collect biometric data combined with AI algorithms in an educational setting. A systematic review was conducted through the PRISMA methodology, by searching the Scopus database for works that included wearables, biometrics, and AI algorithms. A total of 43 studies were included and examined. The objectives, the type of collected data, and the methodologies of the included studies were investigated. Most articles utilized machine learning and deep learning algorithms for classification tasks, such as detecting stress or attention. Other applications included human activity recognition (HAR) for classroom orchestration and emotional or cognitive state detection. Many of the studies applied knowledge from previous works to the educational context, resembling exploratory research. Conversely, some authors developed tasks and methodologies tailored to the educational context. The strengths and weaknesses of the presented studies were discussed to propose future research directions. The main findings of this review highlight the advantages of the combination of multimodal sensing and predictive modeling in education with the eventual prospect of personalization. The absence of standardized acquisition and reporting remains the main barrier to replication, benchmarking, and synthesis across studies.