Martina Rašticová

In modern business, the phenomena of “nepotism” and “cronyism” are often observed, which are usually associated with corruption in the public sector and abuse of public resources. However, these phenomena are international in scale, and no country or sector of the economy is free from them. Existing research does not identify shared and contradictory interests of individuals based on nepotism or cronyism. This study aims to fill this research gap. A research hypothesis was developed and tested in order to determine a point from which nepotism and cronyism are not beneficial to their perpetrators. The research data included Poles and Albanians. According to Transparency International (2022), the obtained results showed that Polish society is currently less vulnerable to corrupt practices than Albanians. Moreover, it was substantiated that relative altruism operates as the mechanism that explains nepotism or cronyism practices. Individuals involved in nepotism or cronyism perceive these practices as a tool to reduce the risk of their operations failing. However, the phenomena lead to long-lasting reciprocity, like in the case of corruption. This study contributes to a better contextual diagnosis of organizations and helps develop preventive strategies.

Technostress (TS) has previously been addressed mainly from a broader organizational perspective, leaving more specific salient settings in the background. This paper bridges this gap by exploring TS dynamics in consulting companies in Germany, a setting that was previously little investigated but is highly TS-prone. This study uses an exploratory mixed-methods approach (MMR) with three components: 1) quantitative validation of the TS test-battery, 2) qualitative exploration of workplace TS through employee experiences, and 3) analysis of the relationship between TS experiences and demographics (age, gender, rank). Quantitative data representativeness is achieved through context-specific test-battery validation and a tailored questionnaire. 702 consulting company employees (based in Germany, aged 18-65) of a renowned management consulting firm participated (m = 417, f = 275). Qualitative data representativeness was ensured through in-depth interviews with 15 employees of different ages, genders, and ranks and company affiliations (Accenture, Boston Consulting Group, Deloitte, Ernst & Young, Roland Berger). Quantitative results indicated that female employees and those above 35 experienced higher levels of Techno-Complexity. Female employees experienced higher TS overall, reflected in their higher Techno-Overload, Techno-Invasion, and Techno-Complexity scores. This applied even to female employees who disagreed with the gender-difference tendency. Additional findings indicated that senior-ranking employees experienced more Techno-Overload and Techno-Invasion. Qualitative results highlighted three themes that further shape the psychological profile of TS in the investigated workplace: a) factors influencing TS, b) TS impact on workplace habits, and c) coping strategies. These findings emphasize that understanding the relationship between creators and demographics is crucial for mitigating consulting workplace TS.

This paper aims to examine the crucial role of employee engagement in the operational success and resilience of small and medium-sized enterprises in the European Union, with a specific focus on the impact of age diversity on engagement strategies. Tailored engagement strategies significantly improve job satisfaction, decrease turnover rates, and promote a cohesive organizational culture. The study is based on an extensive analysis of previous research and scientific publications, providing a comprehensive overview of the current state of employee engagement practices within small and medium-sized enterprises. It explores how various age groups within the workforce perceive and react to different engagement initiatives, emphasizing the necessity for customized approaches. The paper also delves into the specific engagement tactics that have proven effective across different age demographics, such as digital tools for younger employees and recognition programs for older employees. Moreover, the paper discusses the broader implications of these findings for human resources managers and business leaders, highlighting the strategic importance of implementing age-aware engagement practices. It calls for further research to explore the long-term effects of these strategies, particularly in the context of evolving workplace dynamics and technological advancements. By adopting such tailored strategies, small and medium-sized enterprises can not only enhance employee satisfaction and retention but also foster a more innovative and adaptable organizational culture. In conclusion, this study underscores the importance of a nuanced and flexible approach to employee engagement in small and medium-sized enterprises.

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used.

An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis.

The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals.

Acknowledgment
This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’