This study examines how firms operating in emerging-economy contexts build organizational resilience, emphasizing the entrepreneurial mechanisms through which strategic resources are transformed into adaptive capacity. Drawing on perspectives from international entrepreneurship and dynamic capabilities, the study argues that entrepreneurial ambidexterity is a core factor enabling firms to navigate uncertainty and technological turbulence. Using survey data from 588 firms across multiple industries in Middle Eastern countries, the study employs structural equation modeling to test mediation and moderation effects. The findings show that organizational capabilities and resource alignment enhance organizational resilience primarily through entrepreneurial ambidexterity, which facilitates opportunity exploration and exploitation under volatile conditions. Technological turbulence further conditions these relationships, shaping the effectiveness of entrepreneurial responses to environmental change. By theorizing resilience as an entrepreneurial outcome rather than a static organizational attribute, this study advances international entrepreneurship research by explaining how firms in emerging markets enact resilience through ambidextrous entrepreneurial action in turbulent international environments. The findings extend dynamic capabilities theory by demonstrating how international and technological turbulence jointly influence entrepreneurial adaptation beyond developed-economy settings.
Heat stress is a growing concern for public human health systems, underscoring the need for reliable thermal stress forecasting tools. This study introduces a novel approach to estimating the Universal Thermal Climate Index (UTCI) for southern South America using a reduced subset of variables from data-driven weather forecast models. These data-driven models offer rapid, accurate, and publicly accessible forecasts of meteorological variables. However, they do not provide all the variables required to calculate the UTCI. To address this limitation, this study provides a method to estimate heat stress in southern South America using advanced feature selection techniques and regression/classification models. We apply a wrapper evolutionary approach based on the Probabilistic Coral-Reef Optimization with Substrate Layers algorithm (PCRO-SL), previously tested in complex optimization problems, to identify key meteorological variables at both individual grid points and within homogeneous UTCI regions defined through K-means clustering. Various regression and classification models are then constructed and evaluated against reanalysis ground-truth UTCI data. The combination of PCRO-SL and Light Gradient Boosting Machine emerges as the most effective approach. The method is successfully implemented to estimate UTCI during three heat waves using forecasts from data-driven models. The results demonstrate a good predictive skill for forecasts up to three days in advance, outperforming a traditional numerical weather prediction model. This research represents a significant advance towards the development of a thermal stress early warning system for the region, potentially enhancing public health interventions through timely preventive measures against extreme thermal conditions. In this study, we present an alternative approach to estimate the Universal Climate Thermal Index (UTCI), a key indicator of heat stress, using a refined subset of meteorological variables available across multiple datasets, including emerging data-driven weather models. Using southern South America as a pilot region, our methodology expands the applicability of UTCI estimation and establishes a foundation for an early-warning system for heat stress. The method integrates advanced feature selection techniques to identify an optimal set of inputs for non-linear regression models. We further assess the ability of state-of-the-art data-driven forecasting systems to estimate both meteorological inputs and the UTCI during heat wave events. These models, powered by machine learning and data science, remain underexplored in the South American context. Our findings show that their performance is competitive with traditional physics-based forecasting systems in predicting meteorological variables and thermal stress. Beyond its scientific contribution, this research addresses an urgent societal challenge: the escalating risk of heat stress driven by climate change, with direct implications for human health and well-being. The work is inherently interdisciplinary, bridging meteorology, climate science, environmental epidemiology, and machine learning to advance innovative forecasting tools. Nonlinear methods outperform linear ones in estimating heat stress by capturing more complex relationships between factors Data-driven models surpass traditional physical models in forecasting weather variables and heat stress during three heat wave events. Feature selection by regions proves to enhance interpretability and aligns with the physical processes governing each climate zone.
This study investigates whether cumulative Adverse Childhood Experiences (ACEs) and specific exposure to child sexual abuse (CSA) are associated with parent reports of closeness and conflict in their relationships with their children. Parents (N = 426; 85.9
This paper investigates how household educational attainment and labor market specialization shape both expenditures on domestic services and the mode of hiring domestic workers. Using microdata from the Spanish Family Income Survey (2016–2023), we analyze the likelihood of hiring domestic help directly and formally, as well as the amounts spent on services and social security contributions. Our results show that educational level and occupational status are strong predictors of both domestic service expenditure and formal direct hiring. Households headed by self-employed individuals or those with university degrees are significantly more likely to outsource domestic work and spend considerably more when they do. These findings suggest that domestic service consumption reflects not only income or preferences, but also a reallocation of household labor, whereby specialized, highly skilled members substitute unpaid domestic tasks with market services.
Machine learning methods are increasingly used in economics, yet most applications rely on single-task approaches that model outcomes, markets, or regions in isolation. In this study, we adopt a multi-task learning (MTL) framework for economic and tabular data to analyze interdependencies across related economic units. Using benchmark housing datasets representing different economic environments, we define each task as a regional or categorical subgroup. The MTL approach exploits shared representations while allowing for task-specific effects, leading to improved predictive performance and revealing latent relationships across subgroups. An empirical evaluation across three datasets demonstrates the suitability of MTL for structured economic data. Overall, the results suggest that MTL provides a transparent and flexible framework for jointly modeling related entities such as regions, industries, or firms.
