Lately, the global banking services industry has faced numerous challenges: the digitalization, increased competition, the instability of monetary and foreign exchange markets, and the volatility of exchange rates. However, at present, banks are facing the greatest challenge of all that is, placing the customers at the centre of business and the systematic follow-up of customer satisfaction. The present paper aims at assessing the influence of a series of determinants and socio-demographic factors on customer satisfaction with banking services in Romania, using the probit and logit models. The research focused on the Romanian banking market due to its distinctiveness within the European context – performance indicators above the European average during the past five years and a concentration level that discloses a significant growth potential. The results of the two models employed revealed similar results, with the most influential variables on customer satisfaction being convenience, e-banking, quality, and revenues. First published online 14 March 2022 Keyword : customer satisfaction, banking industry development, banking services, satisfaction determinants, socio-demographic factors, probit and logit models

This study presents a hybrid modeling framework aimed at enhancing climate mitigation strategies by evaluating the predictive power of sustainability indicators using both statistical analysis—correlation metrics, regression modeling, distribution tests—and artificial neural networks (ANNs). The analysis centers on variables critical to climate outcomes, including renewable energy use in transport and electricity, greenhouse gas emissions from production, and aggregated target completion values. The findings identify renewable energy usage in transport as the primary predictor of improved performance in the Sustainable Development Report (SDR), followed by overall target completeness, electricity-based renewables, and production-related emissions. Multidimensional interaction analyses highlight a synergetic link between transport renewables and target achievement, underscoring their strategic relevance for climate mitigation efforts. The ANN models demonstrate high predictive accuracy and minimal error, affirming the model’s suitability for scenario-based climate forecasting. Results offer actionable intelligence for policymakers and climate stakeholders to optimize resource allocation and accelerate low-carbon transitions. The study acknowledges limitations, namely, the relatively small dataset and EU-centric analysis, and recommends future extensions to more geographically diverse datasets and the incorporation of advanced econometric techniques and AI frameworks to improve generalizability and predictive potency.

Understanding entrepreneurial intention among female students has become increasingly important for addressing gender disparities in business creation and fostering economic development. Pursuing to promote inclusive entrepreneurship and reduce gender gaps in business creation aligns with Sustainable Development Goals, particularly SDG 5 (gender equality). This study examines how demographic and social variables influence entrepreneurial intentions, with perceived gender discrimination as a potential mediating factor. Data were collected through an online survey employing a structured questionnaire and analyzed using logistic regression models incorporating mediation analysis. The sample consisted of 360 female students from a university in the South–East region of Romania. Among the six socio-demographic variables examined, marital status and income satisfaction emerge as significant predictors. The results indicated that married students expressed higher entrepreneurial intentions, while the participants with higher income satisfaction reported lower entrepreneurial intentions. Perceived gender discrimination was not a significant mediator in the tested model, and all calculated indirect effects were statistically non-significant. The findings of the study offer valuable insights for the design and implementation of local entrepreneurship policies as well as for university strategies and curricula adjustments to better support young women in their entrepreneurship endeavors.

Understanding the factors that influence the Consumption Footprint (CF) is essential for advancing sustainable development within the European Union. This study investigates the most impactful indicators affecting CF, aligning the analysis with the 17 Sustainable Development Goals (SDGs), which are grouped into five thematic clusters: economic conditions, globalization, health, environmental awareness, and cultural factors. To identify key drivers, the research employs a dual-method approach: Graphical representations and correlation analyses and machine learning via Artificial Neural Networks (ANNs), supported by statistical analysis using non-parametric tests. Data from Romania (2012–2023) were used to evaluate the influence of variables such as Gross Domestic Product (GDP), Price Level Indices (CPI), Unemployment Rate (UNE), and Circular Material Use Rate (CMU) on CF. The results reveal that GDP and CPI are the most influential variables, together accounting for over 64% of the impact on CF, followed by UNE and CMU. The study concludes that economic indicators play a dominant role in shaping consumption-related environmental impact. The proposed framework is replicable and adaptable, offering valuable insights for policymakers and researchers aiming to accelerate progress toward EU sustainability targets.