Support through the corporate tax system is a very specific form of funding to promote the functioning of team sports. The basic idea of the mechanism is that profit-oriented companies can donate a larger part of their corporate tax to sports organisations. The scheme has been in operation in Hungary since 2011. Its introduction and fine-tuning required several legislative changes and EU approval. Its importance is reflected in the increase in the number of sports organisations in the respective sports. While funding is available to many sports organisations, in some cases it is quite concentrated. In our empirical research we sought to find out how the degree of concentration has changed over time. The degree of concentration has an impact on how balanced the competition is. One of the key values for sports services is the requirement of an uncertain output. The data reveal that over time the distribution has become more evenly balanced across all sport operators. The amount of funding for sports organisations has started to converge. According to these figures, there are several sports organisations with equivalent subsidies participating in the competition system. However, the majority of clubs with the highest subsidies tend to be the same from year to year. The allocation of grants is determined by the sports federation of the given sport according to the submitted applications. Decision-makers should pay particular attention to maintaining the balance of competition over a long period of time. To this end, the list of sporting organisations with the highest subsidies should be continuously assessed and revised.

This study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.