Silvia Ghiță-Mitrescu

This cross-sectional mixed-methods study examines psychological, economic, and social factors associated with sustainable food choices and receptivity to behavioral interventions among adults in Romania. Conducted within the Erasmus+ FRAME project, the study used an online survey of 244 participants recruited through convenience sampling in May 2026. The questionnaire assessed cognitive biases, perceived cost barriers, social influences, information gaps and trust, and receptivity to interventions involving default portion sizes, social-norm messages, and choice-preserving nudges. Quantitative analyses identified positive associations between cognitive biases and perceived cost barriers, social influences and intervention receptivity, and information gaps and trust-related perceptions and intervention receptivity. Overall receptivity was slightly above the neutral midpoint, with greater support for autonomy-preserving nudges and default portion sizes than for plate-finishing norm messages. Qualitative findings highlighted interconnected economic, behavioral, cultural, and informational barriers. The study contributes evidence on how these dimensions coexist in a Romanian sample and identifies differences in the perceived acceptability of behavioral interventions. The findings may inform context-sensitive intervention development and pilot testing, while highlighting the need for representative and experimental research before conclusions about behavioral effectiveness can be drawn.

Research on digital banking has largely examined technology adoption, while less is known about how perceived digital capability enters the prior decision of selecting a banking institution. This study adopts a system-based modelling perspective to examine bank selection as a non-linear decision system in which digitalization acceptance, perceived technology level, usage behaviour, and financial education interact to influence the perceived importance of digitalization in bank choice. Using survey data collected from Romanian university students, we develop and evaluate multiple Artificial Neural Network (ANN) configurations to model these relationships and identify the most effective predictive architecture. University students were selected because they represent a highly digitally exposed consumer segment whose banking choices are particularly relevant for examining technology-oriented decision patterns. The results indicate that the optimal ANN configuration achieves strong predictive performance (MAE = 0.1487; MSE = 0.0314) while maintaining efficient convergence behaviour. Variable importance analysis, computed using both a Random Forest measure and permutation importance on the ANN itself, consistently identifies perceived technology level as the dominant component of the decision system and frequency of use as the least influential, while the relative ranking of digitalization acceptance and financial education varies between the two importance measures. The findings demonstrate the suitability of ANN-based approaches for modelling complex behavioural decision systems characterized by non-linear interactions and heterogeneous component influence. The study contributes to the digital banking literature by extending technology acceptance research from adoption intentions toward bank selection behaviour. From a practical perspective, the findings suggest that banks targeting digitally oriented young consumers should prioritize perceived technological capability and the quality of the digital banking experience, including usability, trust, transparency, and usefulness. The comparatively lower predictive of usage frequency and financial education further indicates that strategies focused on strengthening perceived digital value may be more relevant to bank selection than those aimed primarily at increasing routine service usage.

The integration of quantitative mathematical methods and artificial intelligence into agricultural monitoring systems represents a critical pathway toward data-driven decision-making in the contemporary precision agriculture economy. This study applies mathematical modeling and quantitative analysis to temporal NDVI (Normalized Difference Vegetation Index) raster datasets from six agricultural parcels in the Dobrogea region of Romania (2017 growing season), with the objective of supporting agronomic performance evaluation and operational decision-making. Higher-order statistical descriptors—variance, kurtosis, and skewness—were extracted from XML raster files and subjected to comprehensive visual analytics using kernel density estimation, three-dimensional surface modeling, and polynomial regression in Python. A feedforward Artificial Neural Network (ANN) with a 4-15-9-3-1 architecture was trained under four activation function and solver combinations (tanh/ReLU × Adam/SGD) to classify satellite sensing-date authenticity (is_sensing_date), a key data-quality indicator for operational crop monitoring workflows. Permutation-based feature importance analysis confirmed that variance is the dominant mathematical predictor (~35.8%), followed by kurtosis (~31.5%) and skewness (~26.6%), while the temporal month variable contributed least (~6.1%). The tanh–SGD configuration yielded the best training–test error balance for most individual datasets, while tanh–Adam performed optimally on the combined dataset. The inverse mathematical relationship between variance and kurtosis, and the direct co-variation between kurtosis and skewness, were consistent across all parcels, demonstrating the universality of these quantitative patterns in agricultural remote sensing data. These findings establish a replicable mathematical modeling framework applicable to predictive analytics, risk assessment of data quality, and performance evaluation in agricultural decision-making systems, with direct relevance to digital transformation strategies in the agri-economy sector.

The digital transformation of financial services has changed how customers interact with banks through electronic channels, yet the factors influencing channel choice between branch-based and digital banking are not entirely understood, especially in emerging European markets. This study investigates banking channel preferences over three consecutive years (2023–2025) in Constanta County, Romania, quantifying how perceived bank technologization and sociodemographic characteristics are associated with the likelihood of digital banking adoption in the e-commerce context. Using repeated cross-sectional survey data from 785 respondents, we applied pooled and year-specific logistic regression models to evaluate temporal effects and estimate the predictive contribution of a composite perception measure (TechScore). Results show that although digital banking usage increased from 87.7% to 92.4%, time alone did not significantly predict adoption. Technologization perceptions consistently increased the odds of digital banking use, with stronger effects in 2025. Age and living environment were significant determinants, while gender and relationship length were not. As digital financial services mature, perceived bank technologization becomes increasingly influential in channel-use decisions. The study contributes to the electronic commerce and technology acceptance literature by demonstrating the importance of perception-based predictors in digital banking contexts and highlights how perception-based evaluation shapes channel choice in digital service platforms, offering insights applicable to electronic commerce contexts where providers compete across physical and digital channels.

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.

This paper examines Romania’s progress in adopting the EU Circular Economy Monitoring Framework, assessing key performance indicators and identifying challenges in its transition towards a sustainable economic model. By analyzing data on the five main areas of the Monitoring Framework, the study highlights Romania’s strengths and areas for improvement compared to the EU averages. The findings indicate an improvement in almost all categories of indicators but also underline the need for enhanced efforts.

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