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.
This article follows the evolution of the consumer concept from traditional models based on the observation of human behavior to advanced digital representations based on artificial intelligence. Through a conceptual analysis of the literature on consumer behavior, digital footprints, algorithmic consumers, digital twins and synthetic consumers, the article proposes an evolutionary framework that explains the progressive transformation of the consumer into a digital entity capable of being modeled, simulated and anticipated. The findings highlight that the development of Big Data, Machine Learning and Large Language Models is fundamentally changing the way researchers and organizations understand, measure and predict consumer behavior. The article discusses the theoretical, managerial and ethical implications of this transformation, including issues of decision autonomy, data privacy, and algorithmic transparency.
Gender disparities and segregation in the context of higher education manifest in unequal patterns of enrollment across different academic fields, being women more concentrated in areas such as education, health and social sciences and, on the other hand, the men in engineering, technology as well as other STEM-related disciplines. The main objective of this study is to examine patterns of gender disparity and segregation across disciplines while providing an updated empirical overview of the Romanian context by analyzing statistical reports and presenting gender-disaggregated enrollment data across different fields of study in higher education from the academic years 2018–2019 to 2022–2023. The results reveal a consistent, stable and persistent pattern of gender segregation across the different fields of study in Romanian higher education and highlight the continued gendered field selection in this context, which demonstrates a persistent horizontal segregation within the system.
This paper evaluates the macroeconomic performance of the Cuban economy during the 2015–2024 period, a decade marked by the impact of the COVID-19 pandemic, the tightening of U.S. sanctions, and the accumulation of internal imbalances. Through a descriptive analysis based on official statistics and the estimation of the output gap using the Hodrick-Prescott filter, the dynamics of the real sector, the monetary sector, and public finances are examined. The results reveal a context of stagflation: GDP contracted by 10.9% in 2020 and has not managed to recover to pre-pandemic levels, with signs of a loss of installed productive capacity. In the monetary sphere, the 2021 exchange rate unification (Tarea Ordenamiento) triggered an unprecedented inflationary process. Public finances exhibit persistent fiscal deficits, and fiscal policy still lacks the institutional arrangements to limit quasi-fiscal operations and enable it to fulfill its stabilization function. It is concluded that economic recovery, price stabilization, and the transformation of the regulatory framework require interdependent reforms in the fiscal, monetary, and investment spheres, the proper sequencing of which is critical to their success.
The removal of fuel subsidies has significantly exacerbated inflationary pressures in Nigeria. According to the National Bureau of Statistics (2024), Nigeria’s headline inflation rose from 28.9% in December 2023 to 29.9% in January 2024. It continued its upward trajectory, reaching 34.19% by June 2024, marking the seventh consecutive monthly increase and the highest level recorded in nearly two decades. This inflationary trend has been largely driven by rising fuel and food prices, coupled with currency depreciation, and has deepened the cost-of-living crisis. This study aims to simulate the impact of removing the petrol subsidy, as reflected in the increase in PMS prices, on Nigeria’s inflation trajectory. We hypothesize that the removal of petroleum subsidies does not generate inflationary pressures and test this hypothesis using the innovative Dynamic Simulated Autoregressive Distributed Lag (DS-ARDL) framework. The outcome of the study shows that fuel-subsidy removal has a significant impact on the cost of living and inflation level in Nigeria, indicating that as 1 unit of subsidy removal increases, the cost of living and inflation level increase by 73.8%. The government should therefore carefully consider the impact of removing fuel subsidies on citizens and provide palliatives and other welfare-enhancing initiatives to cushion the effect on individuals, households, and firms.
The purpose of the article is to provide an overview of the six largest ASEAN-5 economies by economic criteria, in terms of financial resilience. The financial resilience challenges facing these major economies could be categorized into: the ability to create and maintain a strong financial system capable of absorbing external shocks such as economic crises or natural disasters; diversification of the economy with investments in new and innovative sectors; social stability; and the ability to adapt to rapid changes in the economic and social environment. Both financial resilience and financial sustainability at country level implies: efficient resource management through responsible use of natural and human resources; economic equilibrium by maintaining a balance between growth, employment, inflation and public debt; allocation of resources to education, research, innovation and infrastructure to ensure long-term growth; reducing imbalances by adopting policies that reduce social and regional inequalities, ensuring an equitable distribution of wealth; protecting the environment by implementing policies that reduce pollution, conserve biodiversity and promote the use of renewable resources. The paper also focuses on the comparative analysis, of the six studied countries, examining the status of key indicators that measure financial resilience and sustainability based on data collected between 2020 and 2025.
The ability to make decisions in crisis situations has always been an extremely important quality for leadership. Moreover, the ability to make good decisions is the element that makes the difference between a company that thrives and one that fails. For a long time, it has been assumed that this decision-making ability is exclusively human orientated, and it relies on available data and intuition. The purpose of this article is to show how, in the years ahead, artificial intelligence can be used to make informed and predictive decisions. Design thinking programmes and agile working methods are already being used to improve decision-making processes, but given the VUCA environment we live in, proper decisions require a much more complex information base, which can be provided by artificial intelligence. This article will show how specific predictors can be combined to create predefined sets that help determine key information for management decisions, but more importantly, what kind of predictors can and should be considered. Too many predictors complicate the analysis process and can sabotage the result, while a small number of predictors can exclude the most important factors in the area under study. Therefore, the proper selection of predictors is very important and depends on the macroeconomic and microeconomic knowledge of the analyst processing the data. Predictor analysis is performed using artificial intelligence programs so that the decisions generated take into account a much larger number of factors than the human brain can process. The analysis is based on scenarios that span a longer period of time and takes into account the interdependence between the causative factors. Business decisions are already made based on various predictors or parameters, but the use of artificial intelligence can significantly improve the accuracy and reliability of decisions. However, artificial intelligence alone does not guarantee high prediction accuracy; the business knowledge and skills of the programmer are a key factor in achieving high accuracy. To reduce the risk of poor accuracy even though artificial intelligence is used for prediction, we have developed a scheme for selecting the right parameters. In addition, the use of artificial intelligence in this area will enhance business managers' understanding of the impact of various predictive factors on their business.
This paper evaluates the hypothesis that there is an insignificant relationship between net exports (NX) and import tariffs. By analyzing the relationship between import tariffs and NX for 78 countries from 1970 to 2024, I find that there is a negative and significant relationship between import tariffs and net exports for a global sample and a sample that excludes Africa, Europe, and Latin America and the Caribbean. Two empirical methods are utilized to deal with intellectual uncertainties about data adequacy and parameter estimates: (i) a frequentist regression model that is less suitable for sampling adequacy and stable parameter estimates, and (ii) a Bayesian alternative for credible intervals and sampling adequacy. The paper finds that scatter plots have the potential of generating credible economic misperceptions by diachronic transmissions. The paper concludes that the hypothesis fails to provide general theoretical appeal.
In this paper, we make an original contribution by measuring the impact of R&D capital and knowledge accumulation on economic growth in a lower-medium income African country. For this purpose, we make use of time series data from 1996-2022 and investigate the potential causality relationship between R&D and Economic growth in Tunisia. Using ARDL model, the cointegration analysis suggests that there is a long run relationship between the two factors. However, the R&D returns to growth is relatively weak. Moreover, the results of causality test confirm only unidirectional causality from R&D expenditure to growth. Specifically, when a country experiences relatively low level of growth, no benefits of growth will be directed towards R&D and there is no feedback effect from growth to R&D. Keywords: R&D, Economic growth, Cointegration, ARDL estimation JEL Classification: O30, O47, O11,C23
This study examines the effects of interest rates, corruption of control, and infrastructure on Foreign Direct Investment (FDI) in emerging Asia. This study took samples from Indonesia, Malaysia, Thailand, India, South Korea, Oman, and Qatar. By using secondary data sourced from the World Bank with the period 2004 to 2021. This study uses a fixed effect model type of panel data regression. The results show that interest rates have a negative and significant effect on FDI, while control of corruption and infrastructure are able to significantly increase FDI. Foreign investors tend to look for a cheaper and more stable investment environment, therefore high interest rates make it less attractive. Developing Asian countries need to invest in infrastructure projects and ensure that transparent and clean business practices are implemented.
This article provides an overview of the evolution of public procurement procedures on the Electronic Public Procurement System (SEAP) used in Romania over the last ten years, identifying the trend and the patterns in their structure. The research method used is the analysis of secondary data from statistical reports on public procurement procedures carried out within the SEAP electronic platform in Romania, collected from the official website of the National Agency for Public Procurement (ANAP). According to the results obtained, public spending has increased significantly in recent years. This evolution in the growth rate of budgetary expenditure has caused large budget deficits, which have raised numerous problems in attracting the funds needed to cover expenditure.
In the the past few years, the Mauritian government has prioritised the development of the blue economy, launching initiatives to establish a robust maritime industry and the dedication of a ministry to handle the activities of the blue economy. In spite of these efforts, financing remains a significant hurdle to expansion with strategic plans put in place, and numerous conferences held to identify workable solutions. With this background, the study sought to assess the impact of the expenditure of blue economy on tax revenue in Mauritius for the period commencing 2016 and ending 2024. A quantitative analytical framework was employed, using the pooled least squares estimation regression model. It has been noted that the pooled least squares estimation regression model may be limitation to the study and not fully account for time dynamics. In future research, panel or time series models such as fixed effects and Autoregressive Distributed Lag model could be adopted because of their robust nature. Also, the analysis could be disaggregated into sub-sectors such as fisheries, shipping, aquaculture and ocean energy. The empirical results revealed that the nature of the nexus between each of the independent variable and the dependent variable. The impact of the expenditure on tax revenue showed a significant positive relationship and some of the variables and negative relationship for other variables. The details have been presented in the body of the article. As a result of the research findings, it was recommended among others that, access to financial resources to targeted groups in the blue economy like small-scale fishers and aquaculture operators is essential for achieving sustainable blue economy at a reduced cost. The development of specialised financial instruments, such as low-interest loans and micro-finance can enhance the growth of the sector and minimise the dependence on revenue from the central government.
This paper identifies the challenges of local multifunctional agriculture and ways to develop it. A review of the relevant literature shows that the competitiveness and sustainability of farms are the main issues in adopting a multifunctional agricultural model. In addition, financial assistance granted to farmers by the local public authority in the form of flexible contracts, and the development of specific markets could well be the two credible economic instruments for financing such agriculture. The deployment of these instruments is constrained by the characteristics of externalities and/or public goods, recognised in the non-market functions of agriculture.
While conducting business, including in the management activity within an organization, the rules underlying business are mainly of a legal nature. Interesting are the cases/situations in which the law is not limited to legal norm, but also includes ethical norms, which it protects, through the sanction of the legal norm. Labor Code stipulates: "it is prohibited, under the sanction of absolute nullity, to conclude an individual employment contract for the purpose of performing an illicit or immoral job or activity". Immoral work or activity means that it does not comply with moral norms. This article refers to the activity that the employee, the human resource of the organization, is obliged to provide within the legal relation of labor law, a relation established between it and the employing organization. A correct and complete human resource management should prioritize aspects regarding the legal protection of the employee, at all stages of management activity. This represents a correct valorization of human resources by the employing organization.
Laws "function" on the territory of Romania and must be known and observed by individuals or organizations operating on the Romanian market The "life" of normative acts lasts, in time, from the moment they come into force until the moment they cease to be in force. The only ones that are established in the Romanian legal mechanism, through Romanian legislation, and are also stated in the Romanian Constitution, are the immediate effects of the normative act, i.e. a normative act produces effects from the moment of its entry into force. The provisions of the normative act must not and cannot be known and observed by citizens and organizations before the entry into force of this act, i.e. the respective normative act does not retroactively apply in time, before its entry into force. The provisions of the normative act must not and cannot be respected by citizens and organizations after the expiry of the respective normative act, i.e. the normative act in question does not have ultra-active effects in time, after its expiry. In Romania, one notes an implementation of the requirements of social responsibility also in the management strategies of business operators. The next step in the evolution of social responsibility is currently finding these requirements in the activity of public institutions in Romania, in a socially responsible management. A socially responsible management at the level of the highest public institutions in Romania, would have created trust in the image of the Romanian Government and would have had a positive impact, both regarding the stakeholder citizens, and before the European/international stakeholder forums, in order to obtain various financing/support.
Women living in rural, conflict-affected areas of Sub-Saharan Africa face significant financial resource constraints, limiting their ability to participate in sustainable agribusiness activities. This study looks into the impact of local savings systems (Asusu), microfinance institutions, and government initiatives on the empowerment of rural female farmers in northeastern Nigeria. The study, which is based on feminist empowerment and institutional voids theories, takes a convergent mixed-methods approach, combining survey data from 1,146 participants with qualitative interviews conducted in Adamawa, Bauchi, and Gombe. The financial mechanisms and empowerment outcomes were evaluated using structural equation modeling (SEM) and thematic analysis. The study found that Asusu is the most effective catalyst for empowerment (β = 0.72, p < 0.001), acting as social collateral.
