Monica Maria Dobrescu

Foreign direct investment (FDI) has often been cast as a straightforward engine of growth, yet its record across Central and Eastern Europe tells a more tangled story where outcomes hinge on the interplay of education, governance, and the timing of external shocks. This study embeds fixed effects panel econometrics within a systems framework, treating FDI as a subsystem of socio-economic dynamics. Using a long-run panel of eleven economies from 2000 to 2023, the analysis models path dependence and regime shifts through interaction terms and period-specific dummies set against a systems-thinking backdrop. The analysis shows that for the average CEE economy, FDI’s contribution has waxed and waned: it dragged on growth during the early transition years (2000–2007), settled into a neutral role after the global financial crisis, and proved unpredictable in the pandemic era. Romania stands out, however, with a marked “FDI premium” quantified as approximately 0.7 pp of growth per pp of FDI that seems to stem from reinforcing loops between rising tertiary enrolment and productivity spillovers. Mapping these feedbacks brings to light virtuous circles where human capital and resilience make or break the benefits of foreign capital. The policy message is plain: nurture the positive loops through investment in skills and firm linkages, keep institutions nimble enough to adapt, and watch for early warning signs of systemic strain.

Policymakers in small open economies need reliable signals of incipient private consumption downturns, yet traditional indicators are revised, noisy, and often arrive too late. This study develops a Romanian-specific early warning system that combines a time-varying parameter VAR with stochastic volatility and exogenous drivers (TVP-SV-VARX) with modern machine learning classifiers. The structural layer extracts regime-dependent anomalies in the macro-financial transmission to household demand, while the learning layer transforms these anomalies into calibrated probabilities of short-term consumption declines. A strictly time-based evaluation design with rolling blocks, purge and embargo periods, and rare-event metrics (precision–recall area under the curve, PR-AUC, and Brier score) underpins the assessment. The best-performing specification, a TVP-filtered random forest, attains a PR-AUC of 0.87, a ROC-AUC of 0.89, a median warning lead of one quarter, and no false positives at the chosen operating point. A sparse logistic calibration model improves probability reliability and supports transparent communication of risk bands. The time-varying anomaly layer is critical: ablation experiments that remove it lead to marked losses in discrimination and recall. For implementation, the paper proposes a three-tier WATCH–AMBER–RED scheme with conservative multi-signal confirmation and coverage gates, designed to balance lead time against the political cost of false alarms. The framework is explicitly predictive rather than causal and is tailored to data-poor environments, offering a practical blueprint for demand-side macroeconomic early warning in Romania and, by extension, other small open economies.

In the present research paper, we argue that digital transformation and students’ behavioural trends are intertwined through the increasing integration of digital technology tools into both academic and personal communication contexts. We construct our argument through a rigorous methodological approach employing quantitative and qualitative analyses, including ANOVA, Kruskal–Wallis, chi-squared, and multiple regression models, to assess the main predictors of digital communication satisfaction and engagement. Hence, we emphasise distinctly the diverse roles of digital communication platforms as relating to the expression of students’ engagement and adaptability, beyond mere technological adoption. While investigating the role of behavioural economics in modelling students’ engagement with digital technologies, we examine the impact of digitalisation on students’ communication patterns, both in terms of personal and academic purposes. To measure student engagement, we employ a mixed-methods approach by carrying out a pilot study (N = 167). The findings underline the role of digital transformation in enhancing students’ access to learning, communication, and collaboration tools, while they also align with Sustainable Development Goal 4 (Quality Education). Our intention is to develop a more comprehensive model that integrates behavioural insights with technology acceptance theories, while another further direction could be exploring longitudinal data to assess the long-term impacts of digital tools on student engagement and learning outcomes.