In our paper, we used factor analysis and artificial intelligence-based modeling to reveal how video gamers living in an economically peripheric European Union member state can be segmented based on gaming time. We performed a causal analysis of two groups—occasional and frequent players—based on the data of a 2022 representative survey. The results show that in a semi-peripheral country, the adult population plays video games in a similar proportion as in the central countries (every second person), but at the same time, a high degree of polarization can be demonstrated in the time spent gaming. Based on our results, players can be predicted with high reliability based on player preferences, that is, game types, motivations, and shopping habits. However, it is worth supplementing these categories with players' socio-demographic characteristics. Our research contributes to a deeper understanding of the factors and causal relationships that determine video gamers in semi-peripheral countries; moreover, it sheds light on the distinguishing variables that function as group builders.

In our study, we examined the characteristics of nascent entrepreneurs using the 2021 Global Entrepreneurship Monitor national representative data in Hungary. We examined our topic based on Arenius and Minitti’s four-category theory framework. In our research, we examined system-level feature sets with four machine learning modeling algorithms: multivariate adaptive regression spline (MARS), support vector machine (SVM), random forest (RF), and AdaBoost. Our results show that each machine algorithm can predict nascent entrepreneurs with over 90% adaptive cruise control (ACC) accuracy. Furthermore, the adaptation of the categories of variables based on the theory of Arenius and Minitti provides an appropriate framework for obtaining reliable predictions. Based on our results, it can be concluded that perceptual factors have different importance and weight along the optimal models, and if we include further reliability measures in the model validation, we cannot pinpoint only one algorithm that can adequately identify nascent entrepreneurs. Accurate forecasting requires a careful and predictor-level analysis of the algorithms’ models, which also includes the systemic relationship between the affecting factors. An important but unexpected result of our study is that we identified that Hungarian NEs have very specific previous entrepreneurial and business ownership experience; thus, they can be defined not as a beginner but as a novice enterprise.

In the economies of Eastern and Central Europe, startups are expected to become the engine of economic development. Based on interviews with the founders, we examine the extent to which startup life cycle theories (SLCT) can be synthesized and what different or identical narrative conceptual spaces can be drawn. Based on our natural language processing results, it can be concluded that SLCTs cannot be clearly synthesized in the case of semi-peripheral countries, as they draw different narrative spaces. SLCTs can adequately capture enterprises in the initial stages, but in the later stages, the practical applicability of the theories is reduced.

A vállalkozási környezet és a vállalkozással kapcsolatos attitűdök megismerése az egész gazdaság és társadalom számára fontos téma, hiszen a vállalkozások olyan pénzügyi és nem pénzügyi értéket teremtenek, amire nemzetgazdaságunknak és közösségeinknek nagy szüksége van. A vállalkozások fejlődése, valamint innovációs potenciálja számos gazdasági, környezeti és társadalmi probléma megoldásához járulhat hozzá. A vállalkozók és a vállalkozási környezet aktuális állapotának mélyebb megismerése amellett, hogy hasznos látképet ad, forrásként szolgálhat a döntéshozók számára is. A Global Entrepreneurship Monitor (GEM), a világ legnagyobb vállalkozáskutatása 1999 óta biztosít megbízható adatokat a felmérésben részt vevő gazdaságokra jellemző vállalkozói aktivitásról, a vállalkozási ökoszisztéma helyzetéről. A globális felmérésben, amelyben világszerte neves egyetemek vesznek részt, 2020-tól a Budapesti Gazdasági Egyetem képviseli Magyarországot. A 2016 óta az első, hazai, már a BGE Budapest LAB Vállalkozásfejlesztési Iroda irányításával zajló adatgyűjtésre 2021-ben került sor. A kutatás során a felnőtt, 18-64 éves lakosság körében 2014 fő bevonásával zajlott reprezentatív kérdőíves felmérés, továbbá 36 kiválasztott szakértő megkérdezésére került sor. Jelen riport a kutatás magyar eredményeit foglalja össze, információt biztosítva a vállalkozói atmoszféra, aktivitás és ökoszisztéma hazai helyzetéről.

The study discusses the reasons why entrepreneurs in the semi-peripheral country of Hungary see a positive and new opportunity in COVID-19. The study is about the factors that determine entrepreneurs’ ideas about starting a new business as a result of a pandemic. In our study, the Partial Least Squares Structural Equation Modelling (PLS-SEM) method is applied to a nationally representative sample, the data of which were provided by the data of the Global Entrepreneurship Monitor (GEM) international survey on Hungary. Our results show that the willingness of small and medium-sized enterprises in Hungary to start a business was not significantly affected by the COVID-19 pandemic. This is because positive perception can be traced back to past influences, such as individual characteristics, positive perceptions of the majority society, and the respondents’ perception of the Hungarian government’s COVID-19 related measures.