In a labour market where competition for the workforce is growing, how an organisation positions itself as an employer can influence its ability to attract candidates and keep employees within the company. This study analyzes the perceptions of potential employees on the employer brand and the factors that influence their choice of a job, through qualitative research based on focus groups. The results show that the reputation of the organization contributes to its attractiveness, but the final decision depends on several criteria. Remuneration has been highlighted as one of the most important factors, along with professional development opportunities, work environment, internal communication and benefits offered. Thus, employer branding strategies should combine a positive reputation with concrete benefits and an attractive professional experience for employees.
Adoption of AI varies across the EU, even though the differences in ICT training and employment of ICT specialists are smaller. This shows that the simple presence of digital resources does not ensure the effective integration of the artificial intelligence into enterprises' processes. The study analyses the relation between digital capability and AI adoption in medium-sized enterprises in the EU. Digital capability is evaluated through two elements: employee training in ICT and the employment of ICT specialists. Romania's position is also analysed separately. The analysis uses Eurostat data for 2024 on enterprises with 50–249 employees. The main sample included 26 EU Member States. Descriptive statistics, Pearson correlations and five linear regression models were used. Diagnostic and sensitivity checks were also performed, and countries were grouped descriptively according to their digital profile. Romania’s position was evaluated by comparing it with European averages, indicator rankings and the values estimated by the main model. Both ICT training and employment of ICT specialists are positively correlated with AI adoption. The connection is stronger for employee training, with a correlation coefficient of r =0.708, compared to r =0.642 for ICT specialists. When the two variables are analysed together, training remains statistically significant (B=0.418, p=0.023), while ICT specialists are no longer significant (B=0.273, p=0.225). The sample explains 53.3% of differences between countries. Romania has an AI adoption rate of 3.88%, the lowest in the sample The results show that the development of digital skills among employees is more closely connected to the adoption of AI than the simple employment of ICT specialists. Technical expertise remains important but is more effective when supported by constant employee training. Because the analysis uses aggregated data for a single year, the results indicate associations across countries, but do not demonstrate causal relationships.
Smart economic growth emphasizes development that simultaneously promotes productivity, social inclusion, and long-term sustainability. Urban housing systems play a critical role in this process because access to affordable housing determines labor mobility, human capital formation, and spatial economic balance. This study examines housing affordability as a structural constraint on smart economic growth using evidence from New York City. Combining a housing cost burden model, hedonic pricing framework, and neighborhood affordability index, the analysis evaluates how income–rent mismatch, educational accessibility, demographic composition, and density pressures interact to shape urban economic outcomes. The results indicate that rising rents relative to stagnant incomes significantly increase housing cost burdens and restrict residential mobility, thereby weakening labor market efficiency and reinforcing spatial inequality. The findings further show that school quality capitalization and housing scarcity create localized productivity barriers, limiting inclusive participation in high-opportunity areas. Overcrowding and demographic concentration intensify these effects by amplifying economic vulnerability in dense urban neighborhoods. The study demonstrates that housing affordability operates not merely as a social welfare concern but also as a measurable growth determinant that affects economic resilience and long-run development. Policy implications suggest that aligning wage growth, expanding affordable housing supply, and improving access to education constitute complementary mechanisms for achieving smart economic growth. The paper contributes to interdisciplinary growth literature by integrating housing market dynamics into the broader framework of sustainable and inclusive economic development.
As digital communication technologies have expanded, they have reshaped the way consumers connect with cultural products, with a notable impact on how books are discovered and consumed. Social media platforms now function as key environments where discovery, evaluation, and purchasing decisions are shaped through interaction, visibility, and collective feedback. As a result, consumer behaviour is increasingly influenced by socially embedded digital experiences rather than solely by individual preferences or traditional sources of information. This paper examines the mechanisms through which social media shapes book purchasing behavior, focusing on perceived authenticity, emotional engagement, and social validation. The findings highlight the growing complexity of decision-making in algorithm-driven environments, suggesting that purchasing choices are embedded within broader systems of influence that operate through both cognitive and affective processes.
This article analyses the determinants of youth graduate unemployment in Tunisia by combining classical econometric methods (logistic regression) with three machine learning algorithms (Random Forest, XGBoost, RBF-kernel SVM) applied to an original survey of 1,200 Tunisian graduates. The econometric results reveal that female gender, belonging to the engineering field, and education employment mismatch are the most significant determinants. The machine learning analysis confirms the predominance of gender in discriminating between unemployed and employed individuals, and uncovers non-linear relationships that parametric models fail to capture. XGBoost and SVM offer the best predictive performance. These findings call for a deep reform of the university system, targeted policies against gender discrimination, and improved recruitment transparency.
