The key goal of the study is to identify aspects of the implementation of blockchain technologies in human resource management and argue for the moderating role of institutional support. The need to introduce new technologies at both the tactical and strategic levels is substantiated. It is highlighted that the key core of modern organizations is the human resource management system. The role of integration of blockchain technologies in human resource management, which ensures the effective training of qualified personnel at the right time and in the right place, is argued. It has been determined that the introduction of blockchain technologies in human resource management facilitates the organization of cooperation between countries in updating skills and knowledge based on compliance with competency standards and corporate governance rules. A survey of 300 employees of the pharmaceutical industry in Jordan was conducted, which served as the basis for a multivariate analysis to confirm reasonable hypotheses. The results obtained are valuable and can be applied in practice in terms of determining the impact of the implementation of blockchain technology in the human resource management system and on the UTAUT structure, which in turn provides institutional support.

This study aims to examine the evolution of the system of support sources in Hungary, focusing on the specific goals supporting higher education in the development programs Széchenyi 2020 (2014–2020) and Széchenyi Plan Plus (2021–2027). The study provides insights into development program evolution and changes, aiming to inform EU funding opportunities for Hungarian higher education institutions over a nearly 10-year period. By focusing on the operational programs that are the basis for the upcoming tenders, the study will display the target system of EU funds that can be utilized to bolster higher education institutions in Hungary. The study is based on document analysis, examining the Hungarian policy tools of the development programs and the operational program strategies of the ten-year time period from 2014 to 2024. By analyzing the support landscape for higher education institutions in Hungary, this study contributes to a better understanding of how the key objectives and criteria of strategic programs have evolved. It also examines the aspects and elements defined in two different development programs over the last ten years. The result of the study can contribute to anticipate the types of funding opportunities that may be available in the future and inform future decision-making processes.

The aim of this study is to examine the contributions of the components of employee engagement on knowledge-sharing behavior alongside possible mediating effect of management support. This study collected data from 395 respondents purposively selected from pharmaceutical organizations in Bangladesh. For input and incorporation of sample data, SPSS version 26 was used, whereas the PLS-SEM (version-4) tool was used to test the hypotheses relationships. The findings reveal significant positive effects of adaptation, devotion, and vitality on both knowledge sharing behavior and management support. Adaptation to new technologies and processes enhances employees’ ability and intention to share knowledge, facilitated by robust management support. Similarly, devotion and vitality among employees fosters a supportive environment that is conducive for knowledge exchange. Management support emerges as a critical mediator, amplifying the positive impacts of adaptation, devotion, and vitality on organizational outcomes. These findings address a critical gap in understanding the conditions that enhance knowledge-sharing behaviors in highly regulated industries and provides a valuable framework for organizations to nurture knowledge-sharing cultures that will drive innovation and resilience within emerging markets.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.