This study investigates how cloud accounting technologies contribute to the sustainable transformation of business management in Romania. It aims to assess the relationship between digitalization and sustainability by examining how cloud-based systems enhance financial transparency, operational efficiency, and the integration of Environmental, Social, and Governance (ESG) principles in accounting practices. The research applies a multi-utility global method (MUGM) framework to evaluate sustainability-oriented accounting practices across multiple economic sectors, including IT, automotive, energy, and food industries. Data were collected through structured surveys and expert validation to determine sector-specific performance scores. The analysis integrates both subjective assessments and objective indicators, such as reductions in paper usage, cost efficiency, and reporting timeliness—to evaluate sustainability outcomes and potential biases. The results reveal significant sectoral variation in the adoption and sustainability impact of cloud accounting. The IT and automotive sectors lead in digital integration and ESG-oriented financial reporting, while the energy and food industries demonstrate moderate progress constrained by regulatory and investment limitations. Cloud technologies are shown to facilitate improved ESG data management, enhance corporate accountability, and support the EU twin transition, the simultaneous pursuit of digitalization and sustainability. This research extends sustainability accounting literature by positioning cloud computing as a driver of responsible corporate governance and ESG transparency. It bridges the gap between digital transformation and sustainability by demonstrating how intelligent technologies can operationalize sustainability objectives in financial management and reporting.
This study investigates the perceptions of internal auditors regarding the effectiveness of Artificial Intelligence (AI) in detecting fraudulent activities and strengthening internal control systems within public universities in Ghana. While AI is being increasingly integrated into audit practices globally, its application and perceived impact in public sector institutions, particularly in developing countries, remain underexplored. Ghanaian public universities, facing resource constraints, bureaucratic inefficiencies, and weaknesses in audit frameworks, present a compelling context for examining AI’s role in improving governance and transparency. A mixed-methods approach was employed, combining survey data from 176 internal auditors with qualitative insights from six audit leaders. The Technology Acceptance Model (TAM) and Agency Theory were applied to analyze the perceived usefulness (PU) of AI and its potential to mitigate information asymmetry. Results reveal that internal auditors generally regard AI as highly effective in enhancing fraud detection, particularly in terms of real-time anomaly identification, increasing accuracy, and reducing false positives. AI’s contribution to strengthening internal control mechanisms was also recognized, though challenges related to limited technical training, suboptimal integration of audit and IT systems, and underutilization of advanced AI tools were identified. The study highlights the need for focused auditor training, improved inter-departmental collaboration, and institutional policies that foster AI adoption. These findings contribute to the growing body of literature on the role of AI in public sector auditing, particularly in Sub-Saharan Africa. By integrating quantitative and qualitative data, the study offers a comprehensive analysis of AI’s perceived effectiveness in addressing governance challenges in Ghana’s higher education sector, filling a significant gap in existing research.
Under the influence of technological advancement, digitalisation, and mobile networks, sharing has gained a new dimension in the contemporary era. In the context of rising consumption and economic pressures, the sharing economy has emerged as a global model to promote efficient utilization of limited resources. Recent controversies have questioned how sharing city practices are integrated into urban space and whether they enable the equitable use of underutilised areas. In this context, the “sharing city” approach has been adopted in many cities worldwide. This study explored how sharing practices shaped urban spaces and examined the role of city governments in this process. The research was designed in two stages. First, a systematic review of the Scopus database selected 499 publications from 2016 to early 2025, of which 61 met the inclusion criteria and were analysed to understand the spatial and social impacts of sharing city practices. Second, twelve global cities that adopted sharing city strategies were compared in terms of policy orientation and roles of governance. The analysis demonstrated that the sharing economy produced both enabling and constraining effects on cities, particularly in housing, mobility, and public space. City governments employing a range of regulatory, incentive-based, and partnership-oriented instruments assume different roles, depending on local urban characteristics. By combining insights from the literature and cross-case analysis, the study developed a governance framework that linked municipal roles to specific sharing domains and highlighted areas where equity and data governance remained weak. The findings provide practical guidance for municipalities seeking to balance innovation with regulation, thus offering implementable tools to integrate sharing practices into sustainable urban planning.
Despite their influence on the stability of underground excavations, mineralized veinlets, particularly those composed of pyrite and chalcopyrite, are often underestimated in traditional geomechanical models. The lack of experimental data on their tensile behavior under direct stress represents a critical gap in rock mass characterization. This study experimentally evaluated the direct tensile strength of pyrite and chalcopyrite veinlets from the El Teniente mine, in order to enhance the accuracy of geotechnical models for complex geological contexts. Following the Organization for Economic Cooperation and Development (OECD) 203 (2019) guidelines, a fully randomized experimental design was employed to conduct direct tensile testing of 19 veinlet samples. The results showed that chalcopyrite veinlets exhibited greater internal cohesion with significantly higher tensile strength, reaching up to 3.17 MPa, compared to pyrite veinlets of lower values. Furthermore, chalcopyrite veinlets demonstrated a more homogeneous and cohesive failure behavior compared to pyrite, which displayed greater surface roughness and interfacial failure. This study highlights the importance of incorporating veinlet mineralogy into geotechnical models to improve underground design and safety.
The effectiveness of risk management within the Jordanian banks’ internal control systems and internal auditing is the focus of the study considering the moderate impact of AI in the form of expert systems and neural networks. This study aims to examine the impact of integrating AI in auditing and corporate governance in order to improve the organization’s ability to withstand adversity and endure over time. The study obtained data from 350 internal auditors from Jordanian conventional and Islamic banks through a structured survey. Using partial least square structural equation modeling (PLS-SEM), the study established a positive correlation between the effectiveness of internal auditing and the control of internal systems with risk management. Moreover, while neural networks have a weaker moderating impact, expert systems have a moderating impact on the relationship between the control internal systems and risk management. The study concludes that AI in the form of expert systems enhances the ability to recognize and eliminate risks through the development of internal control and audit functions. It also proposes that the study enhances the understanding of agency theory and the theory of technological superiority by demonstrating the role of AI in aiding human auditors to improve the governance systems in an organization. Moreover, the results assist bank managers, policymakers, and regulators to inform the integration of AI systems and tools to improve risk management practices.
This study examines the impact of corporate social responsibility (CSR) and internal control (IC) on corporate reputation (CR) in the context of digital transformation (DT), which serves as a moderating variable. Furthermore, a sample of 324 individuals was selected from the research community of accountants, auditors, and other specialists working in the Iraqi environment, and data were collected using a list of questionnaires analyzed based on a Likert scale. The stability and reliability of the scale were verified using Cronbach’s alpha and the split-half method according to Spearman-Brown’s value, and several statistical tests were used to verify data distribution and potential bias. In addition, regression models were designed to test four main hypotheses. The results showed a positive impact of both corporate social responsibility and internal control on corporate reputation. Digital transformation technologies (DTT) strengthens the relationship between CSR, IC, and CR. This study offers academic value as it empirically analyzes the impact of those variables in the light of DT as a moderating variable. It also provides management, regulators, and stakeholders with possible strategies in accordance with the business environment, and provides policy implications for promoting institutional development and ESG requirements.
Aceh Province is a critical case for freight and infrastructure studies due to its geographic isolation, post-disaster recovery context, and heavy dependence on roads for over 95% of commodity transport. Despite its rich agricultural output, limited multimodal infrastructure hampers efficient distribution. This study aims to (1) analyze the effect of road network connectivity on commodity transportation and regional development, and (2) develop a forecasting model to predict future commodity transportation needs in Aceh Province. The Structural Equation Modeling (SEM) was applied to analyze the relationships among Road Network Connectivity (RNC), Freight Transport (FT), and Regional Development (RD), using data from 400 respondents across 23 districts. The SEM results show all latent variables are interconnected. FT plays a strong mediating role, linking connectivity improvements to development benefits. The study also develops forecasting models for commodity generation and attraction based on population, expressed as $Y$ = 2.209 $X_1$ and $Y$ = 2.807 $X_1$. These models highlight population as a reliable predictor of freight demand and can be generalized to other regions with similar geographic and infrastructure constraints. This research introduces a novel SEM-based framework for freight analysis in Indonesia and offers policy insights for integrating road infrastructure planning with regional development strategies.
Pavement distress is a critical factor in road maintenance planning, directly influencing transportation safety, serviceability, and infrastructure costs. While traditional mechanistic and statistical models provide limited accuracy, they often fail to capture the nonlinear and multi-factorial nature of pavement deterioration. This study addresses this gap by proposing an integrated machine learning (ML) framework that incorporates real-time traffic and climatic variables for predicting pavement roughness. The framework draws on multiple open-source datasets, Long-Term Pavement Performance (LTPP), Federal Highway Administration (FHWA) traffic volumes, and National Oceanic and Atmospheric Administration (NOAA) climate records, to construct a multidimensional feature space. Four predictive algorithms were benchmarked: Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Ensemble-based models achieved superior predictive accuracy, with Random Forest attaining R$^2 \approx$ 0.89 and Root Mean Square Error (RMSE) $\approx$ 0.61, outperforming traditional regression baselines. The findings highlight that ensemble learning can more effectively capture non-linear dependencies between structural, traffic, and climatic factors than alternative approaches. Beyond technical performance, the study illustrates the potential of integrating continuously updated environmental and traffic data into pavement management systems, offering a pathway to more cost-efficient, reliable, and sustainable maintenance planning.
The key target of developing renewable energy systems is critical for countries to combat the impact of climate change and bolster energy security. Among the many available green powers, solar energy generation has been developed worldwide. The exponential acceleration of this technology has stimulated household customers in particular, to switch from the role of consumers to suppliers by selling electricity generated from their home panels. It is anticipated that this change would form a new business model for electricity sales and promote a sustainable energy supply chain, yet the change is still confined to a certain extent in developing markets. In this light, this study identified and evaluated the impact of seven barriers on the household intention to adopt photovoltaic (PV) solar systems. The results of the structural equation modeling (SEM) analysis, based on the data from 288 households in Vietnam, revealed that six barriers, namely uncertain government policies, financial barriers, brand trust barriers, product knowledge barriers, location-based barriers and technical barriers had significant negative impacts on PV adoption intention, while the hypothesized influence of environmental knowledge barriers on this intention was insignificant. Among the validated barriers, uncertain government policies and financial barriers were the most critical factors hindering the household intention to adopt PV solar systems. Notably, while rural surveyed households had the higher means in adoption intentions, technical barriers and financial barriers, their results in location-based barriers and brand trust barriers were lower than the urban ones. Theoretically, this study contributed to expansion of pro-environmental behavior theory and barriers to adoption intention of household consumers. Besides, the findings of this study suggested policy makers, enterprises and technology providers how to promote household adoption thanks to the raised awareness of which barriers are concerned in Vietnam market.
The optimization of tunnel blasting parameters and support designs is critical for enhancing both structural stability and engineering efficiency. This study employs the Holmquist-Johnson-Cook (HJC) numerical model to simulate the blasting process of the Xiahong Tunnel in China, with a particular focus on the vibration velocity and damage zones at various locations. A fluid-solid coupling method is applied to model the interaction between the surrounding rock and blasting forces, and the effects of different detonation sequences and radial uncoupling coefficients on the peak vibration velocities and damage domains are thoroughly examined. The results indicate that blasting from the outside to the inside results in a more cohesive damage domain compared to the traditional inside-out approach. Specifically, the peak vibration velocity of the surrounding rock during inside-out blasting reaches 161.4 cm/s, which is higher than the 82.2 cm/s observed with outside-in blasting. Therefore, the outside-in blasting sequence is identified as the more optimal strategy. Furthermore, an increase in the radial decoupling coefficient gradually reduces the damage domain, with the coefficient k = 2.0 showing no significant improvement in damage domain reduction. However, a decoupling coefficient that is too small leads to excessive over-excavation. Based on this analysis, the optimal radial decoupling coefficient is found to be k = 1.5, offering the most balanced damage domain reduction without causing over-excavation. The analysis also explores the influence of the initial lining thickness of sprayed concrete on the vibration characteristics of the surrounding rock. Both structural stability and economic considerations suggest an ideal thickness for the initial lining. The findings of this study provide valuable guidance for the subsequent implementation of tunnel blasting and support optimization in engineering practices.
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