Vol 18, No 4 (2024)

While the economic transition from a planned economy to a market economy seems to be over for most countries after 25 years, a socialist heritage could have long lasting effects. In this paper we aim to answer to the following two research questions: (1) How deeply have Central and Eastern European (CEE) countries proceeded in digital entrepreneurship? (2) Are there some specific digital entrepreneurship characteristics of the CEE countries that can be explained by their socialist heritage? We applied the Digital Entrepreneurship Ecosystem (DEE) Index methodology that relies upon a dataset for 170 countries to evaluate the former socialist CEE countries’ performance in the development of a digital entrepreneurship ecosystem. The non-EU Western countries are the best performers in Europe, but Western EU member states are close behind. The Southern European country group’s performance is close to the EU CEE country cluster, implying that these countries have caught up with most Southern European countries in their DEE development. The former SU country group and the non-EU Balkan country groups are very similar to each other. We also examined the four sub-indices and the twelve pillars and concluded that DEE scores vary significantly among European countries, but these differences can be explained by economic development and not the long-lasting effects of the socialist system. We also provided a detailed DEE profile for Russia, which explains Russia’s modest performance in the development of a digital entrepreneurship ecosystem.

Over the past three decades, entrepreneurship and related processes and institutions have been widely discussed in Russian academic literature. In order to understand the achievements, thematic gaps, and methodological problems that must be solved in subsequent studies, this article provides a systematic analysis of research papers on the topic of Russian entrepreneurship considering publications from leading Russian academic journals published in the period of 1991–2023. The analysis enabled the identification of the most elaborated topics, revealing the advances in the theoretical understanding of Russian entrepreneurship, as well as contradictions in research programs and empirical methods within publications on this topic in Russian and international journals. As a result of the analysis, promising scientific research areas for further investigation of entrepreneurship are proposed: (1) the reconceptualization of standard definitions/concepts of the theory of entrepreneurship, considering the Russian context; (2) building new theories and concepts of the middle level based on the investigation of unique phenomena and institutions in the Russian business environment.

Generative AI (GenAI) or large language models (LLMs) have been running the world since 2022, but despite all the trends surrounding the use of generative models, these cannot yet be used professionally. While they are most valued for ‘knowing everything’, nonetheless GenAI models cannot explain and prove. In this way we conceptualize the most recent problem of LLMs as the general trend of mistakes even in the core of knowledge and non-causality of mistake via the complexity of question, as the mistake can be named as an accident and be everywhere as the most limitation of professionalism. At their current stage of development, LLMs are not widely used in a professional context, nor have they replaced human workers. They do not event extend workers’ professional abilities.. These limitations of GenAI have one general: non-repayment. This article seeks to analyze GenAI’s professional viability by examining two models (GigaChatPro, GPT-4) in three fields of knowledge (economics, law, education) based on our unique Bloom’s taxonomy benchmark. To prove our assumption concerning the low possibility of its professional usage, we test three hypotheses: 1) the number of parameters of models have low elasticity regarding difficulty and taxonomy with even the right answer; 2) difficulty and taxonomy jointly have no effect on the correctness of an answer, 3) multiple choice is a factor that decreases the number of right answers of a model. We also present the results of GPT-4 and GigaChat MAX on our benchmark. Finally, we suggest what can be done about the limitations of GenAI’s architecture to reach at least a quasi-professional use.