Objective: The article aims to explore the potential differences between the two phases of entrepreneurship, i.e., total early-stage entrepreneurial activity and established business, as defined by the Global Entrepreneurship Monitor (GEM). The study aimed to classify entrepreneurs using various machine learning models and to evaluate their classification performance comparatively. Research Design & Methods: Using the Hungarian GEM datasets from 2021 to 2023, we analysed a subsample of 964 entrepreneurs. Due to inconsistent results from traditional analyses (e.g., correlations, regressions, principal component analyses), we employed machine learning approaches (supervised learning classification methods) to uncover latent relationships between variables. Findings: The study utilized seven machine learning classification methods to examine the feasibility of grouping companies within the sample using Hungarian GEM data. Findings indicate that machine learning techniques are particularly effective for classifying businesses, although the performance of each method varies significantly. Implications & Recommendations: These results provide valuable insights for researchers in selecting methodologies to identify various business phases. Moreover, they offer practical benefits for market research professionals, suggesting that machine learning techniques can enhance the classification and understanding of entrepreneurial phases. Contribution & Value Added: The study adds to the existing body of knowledge by demonstrating the effectiveness of machine learning methods in classifying business phases. It highlights the variability in performance across different machine learning techniques, thereby guiding future research and practical applications in market research and entrepreneurship studies.

This paper examines the spatial distribution of chemical startups in the Visegrad Countries (Czech Republic, Slovakia, Poland, and Hungary), highlighting their potential to drive technological innovation by creating new products or services under conditions of high uncertainty. The study focuses on the proximity of these startups to medical or chemical universities and those with biotechnological research fields to better understand their geographical patterns and potential knowledge spillovers. Data were drawn from Crunchbase, a comprehensive startup database, resulting in a final sample of approximately 333 operational chemical, pharmaceutical, or biotechnological startups. Companies were identified using keyword-based searches, while startup locations and distances to the nearest medical universities were recorded. Statistical methods were applied to assess spatial patterns. Results indicate that these startups are frequently located in cities with biotechnological, chemical, or medical universities. Our findings highlight different types of startup activities and levels of financial support across the Visegrad countries, emphasising the role of chemical startups in fostering technological advancement and sustainable development.

Universities have a wealth of new digital tools and methodologies at their disposal for educational processes. It is difficult to know which of the many options to use, but it makes sense to combine methodologies to increase student satisfaction and, above all, to reduce drop-out rates. The study used a questionnaire survey in a mass course to see how satisfied students are with the technical services of Moodle, the quality of teaching, and its usability. The students’ learning habits and what content they use on the Moodle LMS (MLMS) platform of our own institution in Hungary is also examined. The use of MLMS as an educational tool, not only in distance learning but also in full-time education, is significant at our university, and its strengths have been successfully translated into benefits for students. The results confirmed our preliminary assumptions. The analysis suggests that the MLMS was a good choice as course outcomes improved, drop-out rates decreased, and student satisfaction increased.

Sustainability is a contemporary global challenge that could be resolved only with the active and effective contribution of businesses. Thus, this paper aims to shed light on factors influencing entrepreneurs’ responsible behaviour. The analysis is based on the Hungarian merged dataset of the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) 2021 and 2022 (n=697). The results are based on statistical analyses, namely non-parametric correlation analyses and factor analysis. The findings show that variables concerning entrepreneurs’ responsible attitudes and behaviours significantly correlate with each other – except for two variables concerning directly with the SDGs, namely SDG awareness and considering SDG in KPIs. Using the five correlated variables, two factors can be created, where variables concerning intentions decouple from those concerning taking any steps towards minimising environmental or maximising social impacts. These results implicate that although entrepreneurs tend to consider environmental and/or societal aspects of their business decisions, they come short of taking steps towards them. Thus, responsible actions should be incentivised with education or targeted aids.