National-Louis University

This study analyses the long-run relationship between governance quality and renewable energy development using a global panel of 174 countries over the period 2000–2023. The objective is to assess whether institutional quality systematically influences renewable energy deployment across heterogeneous development contexts. The empirical analysis employs a panel autoregressive distributed lag (PMG-ARDL) framework, which accommodates mixed integration orders and allows for heterogeneous short-run dynamics while imposing homogeneity on long-run coefficients. Renewable energy consumption, measured as the share of renewable energy in total final energy consumption, is modelled as a function of governance quality indicators, economic development, and environmental pressure, with trade openness and foreign direct investment included as control variables. Panel unit root tests indicate a mixture of I(0) and I(1) variables, supporting the use of the ARDL framework, while panel cointegration tests provide strong evidence of a stable long-run relationship in the estimated model. The results reveal a statistically significant long-run association between governance quality and renewable energy development, although the magnitude and direction of the effects vary across governance dimensions and development levels. In contrast, short-run effects are generally weak, suggesting that governance primarily shapes renewable energy outcomes through gradual, structural channels. These findings highlight the importance of institutional quality for long-term energy transition processes and provide empirically grounded insights for the design of energy and governance policies. The analysis reveals significant heterogeneity across development contexts: governance improvements yield positive effects on renewable energy adoption in low-income countries (β = +3.77), where institutional deficits constitute binding constraints, whilst the effect becomes negative in high-income economies (β = −11.87), reflecting diminishing returns and infrastructure lock-in. These findings suggest that developing countries should prioritise governance reforms—particularly Regulatory Quality and Political Stability—to accelerate energy transitions, whereas advanced economies should shift policy attention toward grid modernisation and market design. International organisations should adopt differentiated climate finance strategies matching institutional support to the development stage.

Academic recruitment announcements are the primary public source of information on competitive procedures. However, their unstructured nature significantly complicates the systematic analysis of procedural characteristics and comparison of academic recruitment announcements published by different universities and in different scientific disciplines. This study aims to develop an AI-assisted research methodology to analyze unstructured texts in academic recruitment announcements, grounded in procedural and ethical criteria. The methodology enables the formation of a standardized procedural profile for each academic recruitment announcement, the identification of procedural and ethical risks, and the presentation of the results in a unified analytical format. The assigned scores are verified by the researcher for consistency with the original text and the uniform application of the criteria. Additionally, independent expert validation of 40 analytical decisions from five disciplinary corpora showed that 38 of 40 classifications (95.0%) were confirmed without changes. To provide an integral characteristic of an individual academic recruitment announcement, the Procedural Openness Score (POS) indicator is proposed to reflect the proportion of criteria classified as low risk. The application of the methodology is demonstrated on five independent samples of academic recruitment announcements from various scientific disciplines. The empirical part demonstrates the applicability of a unified analytical architecture to various corpora of academic recruitment announcements. The interpretability of the results is ensured by comparing the original fragments of academic recruitment announcements with the assigned procedural risk scores. The proposed methodology expands the potential for using artificial intelligence in research practice by combining transparent analysis rules, a standardized procedural academic recruitment announcement profile, and a scalable procedural transparency metric. Although the methodology was tested on academic recruitment announcements, its architecture allows for application to the analysis of other types of organizational and regulatory documents.

The rapid expansion of artificial intelligence (AI) research does not automatically imply its structural integration into industry governance systems. In the energy sector, this raises the question of whether a policy-relevant AI regime has already emerged or whether a structural gap persists between technological development and institutional integration. This study is based on a dataset of 792,417 publications indexed in Scopus (1981–2025). Using the AI-Assisted Research Methodology, a piecewise linear phase segmentation of the AI corpus publications was applied. A matrix model was developed to analyze the distribution of energy relevance (Y) and policy relevance (X) in X–Y coordinates. The results indicate that AI research entered a phase of unstable growth after 2017 and a phase of methodological acceleration after 2021. Despite the growth of both indicators (X, Y), the structural concentration of research related to energy and policy remains moderate (zone 2). The adoption of AI in policy is significantly faster than its integration into energy, suggesting an institutional lag. This study introduces the concept of a “synchronization zone” as an indicator of structural convergence and proposes a framework for assessing the degree of AI integration in energy governance. The findings shift the analytical focus from the growth of publications to the structural configuration and contribute to the development of more coordinated strategies in digital and energy policy.

Purpose: This paper examines the role of employee motivation in effective business management. It also explores the impact of financial and non-financial motivators on employee engagement. Design/methodology/approach: The research used a quantitative methodology and an online survey of 102 individuals. Statistical analyses, including variance analysis and correlation analysis, were conducted to identify significant patterns and differences in motivation levels. Findings: Financial motivators, particularly bonuses for achieving targets, were identified as the most effective. Non-financial motivators, such as flexible work schedules and additional days off, also showed high effectiveness in enhancing motivation. Significant differences in motivation levels were observed by gender, age, and length of service. Research limitations/implications: The study is limited to a specific demographic and geographic scope. Future research could explore diverse cultural and occupational contexts. Practical implications: Combining financial and non-financial motivators can effectively increase employee satisfaction and engagement. Social implications: Fostering effective motivation practices contributes to stronger family company relationships. Companies adopting such strategies set benchmarks for the best workplace. Originality/value: This study provides insights into the balance of financial and non-financial motivators in shaping employee motivation. It offers actionable recommendations for HR managers and organisational leaders.

This study draws on the experience of selected European micro-regions in Germany, Poland, and Romania, representing different stages of a just transition, to identify applicable strategies for the Ukrainian context. The research aims to assess the spatial and economic preconditions for transformation, compare them across regions, and propose adaptation pathways. Methodologically, it combines spatial–economic analysis, comparative assessment, and critical evaluation of EU strategic approaches. The results reveal substantial disparities: European coal regions generally benefit from high population density, diversified economies dominated by the tertiary sector, strong research and education infrastructure, and cross-border advantages. In contrast, Ukrainian micro-regions are marked by demographic decline, low population density, rural settlement patterns, and complex security conditions. Based on these findings, the study recommends a localized transformation model emphasizing targeted investments, the strategic use of cross-border location, and the repurposing of existing specialized logistics and production infrastructure for new economic activities. The proposed approach contributes to the discourse on just transition by aligning regional development strategies with local structural capacities and constraints. The results obtained may be applicable in other European countries.