This study presents a hybrid decision-support framework for crisis-induced rescheduling in a large-scale infrastructure project. Using a real-world case involving the insolvency of a critical architectural subcontractor, the study examines how project-continuation alternatives can be compared based on net financial gain, resource demand, and operational constraints. The deterministic component represents project activities and dependencies in a directed graph and calculates financial and resource indicators for the evaluated continuation alternatives. A generative artificial intelligence interface is then applied as an interaction layer over these deterministic outputs, enabling decision-makers to query and compare calculated scenarios in natural language. The empirical application illustrates how the framework can support the identification of a financially preferable continuation alternative and facilitate scenario comparison in a time-sensitive crisis setting. Since the evidence is derived from a single project structure and a single focal disruption type, the proposed framework is presented as a case-based methodological proof of concept that requires further validation across additional project and crisis contexts.
Despite the growing body of research on female entrepreneurship, previous studies have predominantly focused on individual attitudinal variables rather than on their underlying structure in the context of business venture creation. This study aims to identify latent dimensions of entrepreneurial attitudes and to examine how these dimensions vary among non-entrepreneurs, individuals with entrepreneurial intentions, and active entrepreneurs. Using Global Entrepreneurship Monitor (GEM) Adult Population Survey data from Hungary (2021–2024), the analysis is based on a representative sample of 4,080 women. An exploratory Principal Component Analysis (PCA) was applied to identify underlying attitudinal dimensions. Subsequently, factor scores were compared across (1) non-entrepreneurs, (2) individuals with entrepreneurial intentions, and (3) entrepreneurs using one-way analysis of variance (ANOVA). The results reveal four latent factors – risk aversion, entrepreneurial environment perception, entrepreneurial self-perception, and perceived readiness – which explain 60.39% of the total variance. Significant differences are observed across the analysed groups, with non-entrepreneurs exhibiting higher risk aversion and lower levels of entrepreneurial self-perception and readiness. These findings highlight the relevance of examining attitudinal structures in understanding differences in entrepreneurial engagement among women. The results suggest that targeted interventions – particularly those aimed at reducing risk aversion and strengthening perceived capabilities – may support the development of entrepreneurial potential.
Technology and Forced Migration – Ukrainian Migrants in Central and Eastern Europe is a timely and highly relevant edited volume that examines the role of information and communication technologies (ICTs) in the migration trajec-tories, decision-making processes, and integration experiences of Ukrainian forced migrants in Central and Eastern Europe (CEE). With a particular focus on the Visegrad Four (V4) countries of Czechia, Hungary, Poland, and Slovakia, the book makes an important contribution to the growing literature on digital migration, forced displacement, and migration governance by situating these debates in a region that has been underexplored in both migration studies and science and technology studies.
This study examines how the COVID-19 pandemic affected the relationship between educational outcomes and regional economic indicators in Bulgaria and the Central and Eastern European (CEE) region. Using panel data from all 28 Bulgarian Nomenclature of Territorial Units of Statistics (NUTS) 3 regions between 2015 and 2024, we analyse weighted matriculation exam scores in con-junction with macroeconomic variables, including Gross Domestic Product per capita, internet access and share of educated population. The post-pandemic learning loss weakened the previously strong correlation between regional economic wealth and academic achievement, exposing structural inequalities. We find limited evidence that digital infrastructure improved outcomes, especially in marginalised regions. Furthermore, our findings indicate a persistent learning loss following the COVID-19 pandemic, with exam performance declining by up to 0.29 points (approximately 0.38 to 0.447 standard deviations) in 2021–2022, followed by a full recovery in 2024. We argue that learning losses were already happening before COVID-19, with an average magnitude of 0.01–0.03 standard deviations, with some years reaching as much as 0.1 standard deviations.
This study examines questionnaire breakoff – respondents’ early survey discontinuation – and its implications for data quality in marketing research. Using telephone interviews with three independent random sub-samples of Hungarian adults (n = 1040; 1028; 988), we applied K-means cluster analysis to segment respondents based on prior breakoff experiences and atti-tudes toward questionnaire characteristics. Chi-square, ANOVA, and Friedman tests identify the key drivers of discontinuation. Findings show that question-naire length, perceived topic irrelevance, and poorly structured items signif-icantly increase breakoff risk. Based on the results, three distinct respondent segments emerged – Discerning Evaluators, Experience Seekers, and Noncha-lant Responders – each exhibiting different engagement preferences and tol-erance thresholds. Trust in research, age, and educational attainment further shape breakoff propensities across segments. Practically, the results support segment-specific survey design strategies that optimize length, structure, and topic salience while incorporating trust-building elements. Conceptually, the study extends leverage–saliency theory by introducing a segmentation frame-work that accounts for heterogeneity in survey discontinuation risk and is adaptable to multinational research settings.
