This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers.
The transformation of public services into electronic formats (e-services) has gained significant momentum with the advancement of information and communication technologies, particularly due to the widespread use of the Internet and increasing citizen expectations. This transition has not only enhanced the efficiency of traditional public services but also facilitated new forms of e-governance that promote greater interaction, transparency, accessibility, and accountability between citizens and the state. Within this context, this study seeks to address the question: What are the key factors influencing citizens' satisfaction with e-services? The case of student satisfaction with the e-services provided by Anadolu University in Eskişehir, Turkey, serves as the focal point for the investigation. A survey conducted among 1,000 students from eight faculties and one graduate school at Anadolu University assessed their satisfaction with a variety of e-services, including Anasis, Mergen, Anadolu Mobil, E-Mail, library services, cafeteria services, and others. The collected data were analyzed using a combined methodology that integrated the E-GovQual model and the Importance-Performance Analysis (IPA) method. The E-GovQual model provided a comprehensive framework for evaluating the quality of e-services, allowing for an in-depth understanding of students' perceptions. The IPA method, on the other hand, facilitated the identification of performance gaps in e-service delivery and highlighted areas in need of improvement, based on students' expectations. The findings of the analysis were used to formulate strategic recommendations for decision-makers, students, and researchers. This research contributes to the growing body of knowledge on e-governance and user satisfaction in educational institutions, offering practical insights for optimizing online platforms to better meet student needs and expectations.
This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction.
The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts.
Manufacturing firms face increasing pressure to enhance their competitiveness, penetrate new markets, and prioritise customer satisfaction in an increasingly dynamic global business environment. To remain competitive, these firms must adopt innovative strategies that address the evolving demands of customers. In this context, a firm’s capacity to innovate is critical, as it directly influences both the development and implementation of strategic initiatives. Innovation capacity in manufacturing companies is shaped by numerous interrelated factors, each contributing to a firm's ability to respond to technological advancements, market shifts, and changing consumer expectations. This study aims to identify the key determinants of innovation capacity in manufacturing firms based in Ordu Province, Turkey, with a focus on the role of corporate identity. A multi-criteria decision-making (MCDM) approach, specifically the Criteria Importance Assessment (CIMAS) technique, is employed to determine the relative importance of these factors. The findings suggest that “clustering and international networking activities” emerge as the most significant factor influencing innovation capacity, while the “level of entrepreneurship” is found to have the least impact. These results underscore the importance of collaboration, international connections, and strategic partnerships in driving innovation, while highlighting the comparatively limited role of entrepreneurship in fostering innovation within the studied region. The findings have significant implications for manufacturing firms, particularly in terms of strategy development, resource allocation, and the identification of key areas for improvement in innovation processes. Additionally, the research provides valuable insights for policymakers seeking to enhance the innovation capacity of manufacturing sectors in emerging markets.
Evaluating renewable energy policies is crucial for fostering sustainable development, particularly within the European Union (EU), where energy management must account for economic, environmental, and social criteria. A stable framework is proposed that integrates multiple perspectives by synthesizing the rankings derived from four widely recognized Multi-Criteria Decision Analysis (MCDA) methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and Multi-Objective Optimization by Ratio Analysis (MOORA). This approach addresses the inherent variability in individual MCDA techniques by applying Copeland’s compromise method, ensuring a consensus ranking that reflects the balanced performance of renewable energy systems across 16 EU countries. To further enhance the reliability of the framework, the Stochastic Identification of Weights (SITW) approach is employed, optimizing the criteria weights and strengthening the consistency of the evaluation process. The results reveal a strong alignment between the rankings generated by individual MCDA methods and the compromise rankings, particularly among the highest-performing alternatives. This alignment highlights the stability of the framework, enabling the identification of critical drivers of renewable energy policy performance—most notably energy efficiency and environmental sustainability. The compromise approach proves effective in balancing multiple, sometimes conflicting perspectives, offering policymakers a structured tool for informed decision-making in the complex domain of energy management. The findings contribute to the development of advanced frameworks for decision-making by demonstrating that compromise rankings can offer robust solutions while maintaining methodological consistency. Furthermore, this framework provides valuable insights into the complex dynamics of renewable energy performance evaluation. Future research should explore the applicability of this methodology beyond the EU context, incorporating additional dimensions such as social, technological, and institutional factors, and addressing the dynamic evolution of energy policies. This framework offers a solid foundation for refining policy evaluation strategies, supporting sustainable energy management efforts in diverse geographic regions.
The integration of Electric Vehicles (EVs) into modern power grids presents both challenges and opportunities. This study investigates the influence of slack bus compensation on the stability of voltage levels within these grids, particularly as EV penetration increases. A comprehensive simulation framework is developed to model various grid configurations, accounting for different scenarios of EV load integration. Historical charging data is meticulously analysed to predict future load patterns, indicating that heightened levels of EV integration lead to a notable decrease in voltage stability. Specifically, voltage levels were observed to decline from 230 V to 210 V under conditions of 100% EV penetration, necessitating an increase in slack bus compensation from 0 MW to 140 MW to sustain system balance. Advanced machine learning techniques are employed to forecast real-time load demands, significantly reducing both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), thereby optimising slack bus performance. The results underscore the critical role of real-time load forecasting and automated control strategies in addressing the challenges posed by EV integration into power grids. Furthermore, the study demonstrates that intelligent systems, coupled with machine learning, can enhance power flow management and bolster grid stability, ultimately improving operational efficiency in the distribution of energy. Future research will focus on refining machine learning models through the utilisation of more granular data sets and exploring decentralized control methodologies, such as federated learning, thereby providing valuable insights for grid operators as the adoption of EVs continues to expand.
Bibliometric analysis is a quantitative research method employed to measure and assess the impact, structure, and trends within academic publications. It aims to uncover patterns, connections, and research gaps either within a specific field or across interdisciplinary domains. This study utilizes bibliometric methods to investigate research gaps within the digital business domain, focusing on qualitative insights identified in existing literature. A systematic literature review (SLR) approach is adopted to ensure a rigorous synthesis of relevant studies. The analysis follows three key phases: data collection, bibliometric evaluation, and data visualization. Through these phases, trends, thematic gaps, and areas for future exploration are identified, offering a clearer understanding of the evolution and direction of digital business research. The insights derived are intended to inform sustainable business practices, with implications for environmentally conscious business models, value-driven marketing strategies, and the integration of sustainable operations. Moreover, the findings highlight potential avenues for enhanced technological innovation and interdisciplinary collaboration in digital business. This study provides a robust framework for scholars seeking to explore uncharted areas within digital business and offers actionable guidance on key research themes requiring further investigation. The use of bibliometric tools ensures comprehensive coverage of existing literature and fosters the development of a coherent research agenda aligned with emerging trends in the field.
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