Efficiency and Effectiveness of Artificial Intelligence Integration in the Business Environment Mircea-Constantin Șcheau, Liviu-Marian Matac, Paul-Tiberius Coman, Gabriel Niță, Alina-Iuliana Tăbîrcă, et al. Systems, 2026 The growing integration of AI in business systems has intensified the need for empirical evidence on how organizational capability, governance orientation, and performance-related expectations shape AI adoption. This study examines how AI integration is perceived in terms of efficiency and effectiveness in relation to governance considerations and analyses the extent to which technological competence influences implementation intention. A quantitative research design was employed based on a structured questionnaire administered online to 248 respondents from diverse organizational contexts in Romania between September and December 2025, using a non-probabilistic sampling approach. The data collection procedure followed a voluntary participation approach, and the analysis includes descriptive statistics, reliability analysis, ANOVA, correlation analysis, and multiple regression. The findings indicate that AI is primarily associated with operational performance benefits, while governance-related perceptions play a contextual rather than a direct role in shaping implementation intention. Technological competence and resource adequacy emerge as the main factors associated with AI adoption, whereas favorable attitudes toward AI do not independently predict implementation decisions. The study contributes to the literature by introducing the Capability–Governance–Performance (CGP) framework as an integrative analytical perspective that explains how internal capabilities, governance considerations, and performance expectations jointly shape AI implementation intentions. It also provides empirical evidence from a transition-to-economic context, contributing to a more integrated understanding of AI adoption.
Blockchain-Driven Supply Chain Financing for SMEs in Eastern Europe Diana-Sabina Ighian, Diana-Cezara Toader, Corina-Michaela Rădulescu, Rita Toader, Ioana-Lavinia Safta (Pleșa), et al. Electronics Switzerland, 2026 Small and medium enterprises (SMEs) represent a fundamental pillar of economic development in Eastern Europe. Yet, they frequently encounter significant obstacles in accessing financing, stemming from informational asymmetries, elevated risks, the absence of collateral, and adverse regulatory environments. This research examines the primary determinants of adopting blockchain-based supply chain financing platforms, an alternative financing solution that streamlines processes, reduces costs, and enhances transparency and security. The study develops and validates an innovative conceptual model grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to a sample of 200 respondents across seven Eastern European countries, and the model’s hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The research findings demonstrate that supply chain partner readiness constitutes the most influential factor affecting behavioral intention to use blockchain-based supply chain financing platforms. Additionally, performance expectancy, effort expectancy, and perceived trust were identified as significant positive determinants. Furthermore, the study highlights blockchain readiness as a crucial factor influencing actual usage behavior. These findings provide valuable insights and contribute to advancing knowledge through the utilization of an extended UTAUT framework and validation of obtained results through comparison with other relevant studies in the field.
Merging Deep Learning Neural Networks with the Stochastic Parameterized Expectations Algorithm for Solving Nonlinear Rational Expectations Models Alexie Alupoaiei, Leonardo Badea, Iulian Panait, Valentin Radu, Mircea Constantin Șcheau Electronics Switzerland, 2025 This paper proposes a novel framework that integrates Deep Learning Neural Networks into the Stochastic Parameterized Expectations Algorithm (DLNN-PEA) to solve nonlinear rational expectations models. This method enhances traditional PEA-based solvers by employing a deep neural expectations operator that captures complex nonlinearities and asymmetries. The DLNN-PEA is implemented in Matlab R2024b. It combines deep learning approximation with the standard expectation-iteration structure of the PEA, replacing the conventional shallow ANN-based operator with a deeper architecture that improves both accuracy and stability. The methodology is applied to the stochastic Neoclassical Growth Model, where the DLNN-PEA is trained to approximate conditional expectations and decision rules under uncertainty. The results show rapid convergence, reduced boundary-related issues, and stable performance even in high-volatility environments. Compared with ANN-PEA, deep architectures exhibit greater robustness and adaptability, making them suitable for economic models characterized by stronger nonlinearities and richer state dynamics. Beyond the benchmark model, the proposed framework is well-suited for medium-scale DSGE models, nonlinear monetary policy environments, and macro-financial simulations involving high-dimensional state spaces. These features make DLNN-PEA a practical tool for applied macroeconomic analysis and model-based policy evaluation.
AI-Driven Models for Forecasting Public Expenditures in the Digital Era Bogdan Drăgulin, Veronica Ștefan, Alina-Iuliana Tăbîrcă, Mircea-Constantin Șcheau, Valentin Radu, et al. Electronics Switzerland, 2025 This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of improving the accuracy of expenditure forecasting in the budget process and supporting financial accounting decisions in public institutions. Using historical and statistical data from a group of public institutions, the research applies both univariate and multivariate forecasting strategies, evaluated with performance metrics. The research focuses on the development of an innovative forecasting model based on AI, using historical and statistical data from public sources and case studies of local public institutions to transform them into smart cities. The selected AI algorithms include artificial neural networks, support vector machines, and deep learning models, implemented and evaluated using Python v3.14. The research results show that AI can significantly improve the accuracy of budget forecasts compared to traditional methods, such as linear regression and econometric models. The use of AI contributes to increasing transparency and accountability in the management of public funds, providing more detailed and well-founded forecasts.
Cyber insurance risk analysis framework considerations Călin Mihail Rangu, Leonardo Badea, Mircea Constantin Scheau, Larisa Găbudeanu, Iulian Panait, et al. Journal of Risk Finance, 2024 PurposeIn recent years, the frequency and severity of cybersecurity incidents have prompted customers to seek out specialized insurance products. However, this has also presented insurers with operational challenges and increased costs. The assessment of risks for health systems and cyber–physical systems (CPS) necessitates a heightened degree of attention. The significant values of potential damages and claims request a solid insurance system, part of cyber-resilience. This research paper focuses on the emerging cyber insurance market that is currently in the process of standardizing and improving its risk analysis concerning the potential insured entity.Design/methodology/approachThe authors' approach involves a quantitative analysis utilizing a Likert-style questionnaire designed to survey cyber insurance professionals. The authors' aim is to identify the current methods used in gathering information from potential clients, as well as the manner in which this information is analyzed by the insurers. Additionally, the authors gather insights on potential improvements that could be made to this process.FindingsThe study the authors elaborated it has a particularly important cyber and risk components for insurance area, because it addresses a “niche” area not yet proper addressed in specialized literature – cyber insurance. Cyber risk management approaches are not uniform at the international level, nor at the insurer level. Also, not all insurers can perform solid assessments, especially since their companies should first prove that they are fully compliant with international cyber security standards.Research limitations/implicationsThis research has concentrated on analyzing the current practices in terms of gathering information about the insured entity before issuing the cyber insurance policy, level of details concerning the cyber security posture of the insured entity and way such information should be analyzed in a standardized and useful manner. The novelty of this research resides in the analysis performed as detailed above and the proposals in terms of information gathered, depth of analysis and standardization of approach made. Future work on the topic can focus on the standardization process for analyzing cyber risk for insurance clients, to improve the proposal based also on historical elements and trends in the market. Thus, future research can further refine the standardization process to analyze in more depth the way this can be implemented and included in relevant legislation at the EU level.Practical implicationsProposed improvements include proposals in terms of the level of detail and the usefulness of an independent centralized approach for information gathering and analysis, especially given the re-insurance and brokerage activities. The authors also propose a common practical procedural approach in risk management, with the involvement of insurance companies and certification institutions of cyber security auditors.Originality/valueThe study investigates the information gathered by insurers from potential clients of cyber insurance and the way this is analyzed and updated for issuance of the insurance policy.
Proposals of Processes and Organizational Preventive Measures against Malfunctioning of Drones and User Negligence Mircea Constantin Șcheau, Monica Violeta Achim, Larisa Găbudeanu, Viorela Ligia Văidean, Alexandru Lucian Vîlcea, et al. Drones, 2023 Drones have been included in more and more activities in various domains, such as military, commercial and personal use. The existing legislative framework insufficiently addresses the responsibility and preventive measures angles in case of vulnerability exploitation and negligence in drone usage. Such aspects can be addressed by the industry in technological processes and standardization. These are especially important aspects given the high impact that misuse of drones can have on individuals, property and buildings within the flight zone when the drone is misused. The aim of this research paper is to investigate how these elements are viewed in existing legislation and by individuals, while taking into account the technical specifics and the stakeholder ecosystem of drone usage. In this respect, we use a complex questionnaire which was sent to a final number of 233 respondents pertaining to firms specialized in IT, legal and cybersecurity. The responses have been analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing standardization and find the followings: (1) stakeholders across the drone ecosystem are viewed as having a shared liability in certain use cases, (2) preventive measure implementation should be dispersed across the stakeholders of drone usage and (3) automation of prevention measures is considered more useful in case of malfunctioning or misuse of drones rather than user manual intervention. In addition, we make proposals to accommodate new policy requirements for the above use cases. The results of this research paper assist policy makers in improving existing standardization framework and technological processes concerning drone usage, but also stakeholders of the drone ecosystem in generating increased trust of the drone users. Further, this research paper can also assist drone software and hardware producers in calibrating their products to ensure trust of the users. In addition, trust in the use of drones for commercial and personal purposes is increased through standardization and proper approaches for situations that may cause damages to drones and to third parties.
Legal, Economic and Cyber Security Framework Considerations for Drone Usage Mircea Constantin Șcheau, Monica Violeta Achim, Larisa Găbudeanu, Iulia Brici, Alexandru-Lucian Vîlcea Applied Sciences Switzerland, 2022 Drones have been used in recent years more and more in various economic sectors (e.g., military, agriculture, retail, transport), but also for personal use and entertainment. The current legislative framework and cyber security standards do not fully address the identification of liable stakeholders in the drone ecosystem for cyber-incidents and the requirement to implement preventive cyber-security measures. The aim of this paper is to investigate how the usage of drones fits in the context of the digital economy. For this purpose, we use a complex questionnaire which was sent to a total of 233 respondents from May to July 2021. The responses are analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing legislation and find the following: (1) respondents are willing to pay additional direct and indirect costs related to cyber security to benefit from more secure drones, (2) the entire ecosystem involved in drone production, distribution, and usage is responsible for ensuring the prevention of security breaches, and (3) respondents perceive a shared liability of stakeholders for certain types of cyber-attacks depending on the role of the stakeholders in the drone ecosystem and the type of vulnerability exploited by the cyber-attack. The details on the specific cyber-attack use cases detail each of the above for each type of cyber-attack. Finally, we make proposals to accommodate the new types of use cases brought by the use of drones in various economic contexts. The results of this research paper assist policy makers in terms of improvement to existing legislation in terms of the drone ecosystem. In addition, they increase visibility for stakeholders in the drone ecosystem in terms of aspects to focus on in order to increase the trust of clients in drone usage.
A Cryptocurrency Spectrum Short Analysis Mircea Constantin Șcheau, Simona Liliana Crăciunescu, Iulia Brici, Monica Violeta Achim Journal of Risk and Financial Management, 2020
Methods of laundering money resulted from cyber-crime Economic Computation and Economic Cybernetics Studies and Research, 2017
