The author puts forward the idea that decentralized finance doesn’t act without managerial influence. The management moves from the external circuit to the internal one, there occurs self-ruling and “self-regulation” of the financial system. This indicates the appearance of a new type of financial intermediation—a cyber-social one. The potential of using decentralized finance in post-Soviet countries are formulated the following: freeing up the time of transaction participants due to the autonomy of transactions; a superior degree of information security compared to traditional forms of financial intermediation; financial intermediation cost saving, freeing up human resources; reduction in the speed of transactions; increasing accuracy in contractual relations due to the elimination of the human factor influence; stimulating the development of new business areas expands the competitive environment; information safety due to the constant creation of a large number of backup copies. At the same time, the author identified and substantiated the risks associated with decentralized financial flows, which may have an impact on the well-being of the population of post-Soviet countries. The purpose of this study is to determine the prospects for applying decentralized finance as a growth factor in the well-being of the population in post-Soviet countries.

Choosing a university is a crucial decision for each field of study, as it significantly influences the quality of graduates. An important factor in this decision is the university’s annual benchmark scores. The benchmark score represents the minimum score required for admission. This study evaluates the benchmark scores in the logistics sector for several prominent universities in Vietnam during the period 2021–2023. The research process utilized data on the benchmark scores for the years 2021, 2022, and 2023. The weights of these benchmark scores were calculated using the Rank Order Centroid (ROC) method, and the Probability method was employed to compare the benchmark scores of the universities. The analysis identified C3 as the criterion with the highest importance, while U3 emerged as the top-ranked alternative. The two-stage comprehensive sensitivity analysis revealed that universities consistently ranked high or low regardless of the method used to calculate benchmark score weights or the method employed for ranking. Additionally, the smallest weight change that affected the overall Probability ranking was 4.61%. This study provides significant guidance for students in selecting a university for logistics studies and serves as a foundational reference for universities to assess their capabilities in logistics education, thereby fostering healthy competition among institutions.