Corporate governance is gaining interest not only from investors but companies that want to operate in international markets, prompting a more thorough analysis of the field to prioritise stakeholder interests alongside shareholder value. By adopting a holistic approach that considers stakeholders’ diverse needs and expectations, companies can build resilience, foster trust, and create sustainable value for all stakeholders, ensuring long-term success and societal impact. This paper analyses corporate governance principles applied at the international, European, and national levels, emphasising the importance of the field for the stakeholders. The practical approach of the paper analyses the application and compliance of the corporate governance code of 18 companies in the field of financial intermediation and insurance, which are listed on the Bucharest Stock Exchange, underlining the crucial role of transparency of operations in instilling confidence and reassurance in stakeholders. The conclusions present proposals for measures to improve corporate governance practices at the level of companies.

The increasing pressure for transparency in corporate sustainability reporting, especially under frameworks such as the Corporate Sustainability Reporting Directive and the European Sustainability Reporting Standards, has raised the need for sector-specific models to integrate financial, social, and environmental indicators coherently and measurably. This study proposes a composite econometric model to assess the sustainability performance of companies in the construction sector in a digital context, a domain that remains underexplored despite its substantial economic and environmental impact. Drawing on a sample of 1600 Romanian construction companies over ten years (2013–2023), this study develops a multidimensional sustainability score and tests its financial drivers using ordinary least squares regression models. The model incorporates nine financial structure variables as predictors of sustainability outcomes across three dimensions—financial, social, and environmental—while ensuring robustness through heteroscedasticity and multicollinearity diagnostics. The results show that indicators such as the return on assets, debt ratio, and equity structure significantly influence sustainability performance, particularly in the financial and environmental dimensions. In contrast, the social dimension exhibits lower explanatory power. The findings suggest that financial resilience plays a critical role in shaping sustainable practices in the construction industry and support the adoption of integrated models for performance benchmarking and policy alignment.

Healthcare service providers face recurrent systemic disruptions (e.g., pandemics, reimbursement delays, supply shortages, and regulatory shocks), yet firm-level resilience monitoring remains underdeveloped due to limited explainability and weak out-of-time validation in prior work. We develop an explainable machine learning pipeline to predict firm-level financial resilience (a financial health/robustness proxy) for outpatient healthcare providers. Using annual data for 2600 Romanian firms (Nomenclature of Economic Activities - NACE 8622) over 2014–2023, resilience is operationalised as an ordered three-class label derived from a Principal Component Analysis (PCA)-based composite score built from eight capital structure and asset composition ratios, with train-only frozen thresholds and a strict anti-leakage protocol. We evaluate multinomial logistic regression (baseline), Random Forest (RF), and HistGradientBoosting (HGB) (primary) on a prospective 2023 hold-out using Accuracy, Balanced Accuracy, and Macro-F1, with bootstrap uncertainty for key contrasts. The primary model achieves Balanced Accuracy = 0.943 and Macro-F1 = 0.944 in 2023, outperforming the linear baseline and RF; errors concentrated between adjacent classes. Model-faithful permutation importance on HGB highlights working-capital disciplines (receivables, cash, inventory, asset structure), while RF–SHAPley Additive Explanations (SHAP) is used only for auxiliary pattern exploration and stability checks, with Individual Conditional Expectation (ICE)/Partial Dependency Plot (PDP) confirming key nonlinear regimes on HGB. Overall, the results support governance-ready, interpretable resilience monitoring while maintaining a clear separation between predictive explanations and causal claims.

The increasing pressure for transparency in corporate sustainability reporting, especially under frameworks such as the Corporate Sustainability Reporting Directive and the European Sustainability Reporting Standards, has raised the need for sector-specific models to integrate financial, social, and environmental indicators coherently and measurably. This study proposes a composite econometric model to assess the sustainability performance of companies in the construction sector in a digital context, a domain that remains underexplored despite its substantial economic and environmental impact. Drawing on a sample of 1,600 Romanian construction companies over a ten-year period (2013–2023), the study develops a multidimensional sustainability score and tests its financial drivers using Ordinary Least Squares regression models. The model incorporates nine financial structure variables as predictors of sustainability outcomes across three dimensions - financial, social, and environmental - while ensuring robustness through heteroscedasticity and multicollinearity diagnostics. Results show that indicators such as return on assets, debt ratio, and equity structure significantly influence sustainability performance, particularly in the financial and environmental dimensions. In contrast, the social dimension exhibits lower explanatory power. The findings suggest that financial resilience plays a critical role in shaping sustainable practices in the construction industry and support the adoption of integrated models for performance benchmarking and policy alignment.

By operating with the Scale Relativity Theory by means of two scenarios (Schrӧdinger and Madelung-type scenarios) in the dynamics of complex systems, we can achieve a description of these complex systems through a holographic-type perspective. Then, a gauge invariance of the Riccati type becomes functional in complex-system dynamics, which implies several consequences: conservation laws (in particular, for dynamics, the kinetic momentum conservation law), simultaneity and synchronization among the structural units’ (belonging to a complex system) dynamics, and temporal patterns through harmonic mappings. Finally, an economic case analysis is highlighted.