The relationship between agricultural financing and agricultural output in Nigeria was investigated to provide empirical insights into the efficacy of funding mechanisms in driving agricultural productivity. Government expenditure on agriculture (GOVXA), commercial bank loans to agriculture (CBLA), and disbursements under the Agricultural Credit Guarantee Scheme Fund (ACGSF) were employed as proxies for agricultural financing, while agricultural gross domestic product (AGDP) served as a proxy for agricultural output. Using quarterly data spanning from the first quarter of 2009 to the fourth quarter of 2023, the Autoregressive Distributed Lag (ARDL) model was estimated to capture both the short-run and long-run dynamics of the relationship. The analysis was conducted using EViews 9.0. The empirical findings revealed that among the financing instruments, only CBLA exerted a statistically significant and positive effect on agricultural output in both the short and long term. In contrast, neither GOVXA nor the ACGSF disbursements exhibited a significant impact on agricultural productivity during the study period. Furthermore, the inclusion of annual rainfall as a control variable indicated a robust positive effect on agricultural output, underscoring the sensitivity of Nigerian agriculture to climatic conditions. These findings suggest that while multiple funding mechanisms exist, the effectiveness of such instruments varies considerably. It is implied that the institutional efficiency and direct credit channeling associated with commercial bank lending may render it more impactful compared to broader fiscal allocations or credit guarantee schemes, which often suffer from bureaucratic inefficiencies and implementation gaps. Policy recommendations include the expansion of commercial bank lending to the agricultural sector, alongside strengthened regulatory oversight to ensure the proper utilisation of funds for productive agricultural activities. Furthermore, improvements in credit delivery mechanisms under government schemes are essential to enhance their effectiveness. A more climate-resilient approach to agricultural policy is also advocated, given the significant influence of rainfall variability on output levels.
This study investigates the dynamic interrelationships among credit default swap (CDS) premiums, exchange rates, and the Borsa Istanbul (BIST) Banking Index in the context of the Turkish financial market over the period 2013–2023. Monthly data have been employed, and the analysis has been conducted using the time-varying parameter vector autoregressive (TVP-VAR) model, a framework well-suited for capturing evolving interactions and volatility spillovers over time. Empirical results indicate that fluctuations in exchange rates have exerted a significant influence on the volatility of both CDS premiums and the BIST Banking Index. Furthermore, substantial volatility transmission has been observed from CDS premiums to the BIST Banking Index, highlighting the sensitivity of banking sector equity performance to sovereign credit risk perceptions. It has also been identified that CDS premiums exhibited pronounced volatility prior to 2018, remained highly volatile between 2018 and 2022, and experienced renewed volatility post-2022. Similarly, the BIST Banking Index demonstrated persistent volatility from 2014 through the end of 2022, suggesting an extended period of market instability within Turkey's banking sector. These findings contribute to the broader understanding of systemic risk and financial interconnectivity in emerging markets. They may provide valuable insights for policymakers, institutional investors, risk management professionals, and financial analysts concerned with market stability and investment strategy. Understanding these interdependencies is essential for the formulation of effective hedging strategies, the pricing of financial instruments, and the assessment of macro-financial vulnerabilities in economies subject to external shocks and credit risk fluctuations.
A comprehensive bibliometric analysis was conducted to systematically examine the development, thematic evolution, and collaborative networks of scholarly research on corporate governance within healthcare systems. Data were extracted from articles indexed in both the Scopus and Web of Science databases. Following an initial retrieval of 315 records, a rigorous screening process was implemented to identify studies with direct relevance to the research focus, yielding a refined dataset of 168 articles. The Biblioshiny interface, an advanced module of the Bibliometrix package, was employed for analytical processing. Key findings included the identification of the most influential publications, authors, and contributing countries in the field. Moreover, a country-level collaboration map was generated, revealing the geographical distribution and intensity of international research partnerships. Through a detailed analysis of author keywords, conceptual structures and prevailing research themes were visualised via word clouds and trend topic plots. Thematic mapping and evolutionary trajectories highlighted the dynamic nature of corporate governance discourse in healthcare, encompassing sub-themes such as hospital governance models, healthcare accountability, stakeholder engagement, and performance-based oversight. By elucidating the intellectual structure and collaborative landscape of this interdisciplinary domain, the study provides critical insights into its historical development and future directions. These findings are expected to inform both academic inquiry and policy implementation, offering a strategic foundation for advancing governance frameworks in health systems worldwide.
This study critically investigates the strategic transformation of South Korea’s entrepreneurial ecosystem within the broader trajectory of national economic modernization and innovation-centric development. The principal objective is to understand how coordinated governmental strategies, targeted institutional reforms, and private sector alignment have collectively redefined entrepreneurship as a structural pillar of economic advancement. Drawing upon a synthesis of longitudinal economic data, comparative policy frameworks, and a refined production function incorporating entrepreneurship as a distinct variable, the research adopts a multidisciplinary lens. It evaluates key dynamics such as venture investment flows, research and development spending, and startup proliferation between 2005 and 2024. Through the construction of a comprehensive entrepreneurship performance index and the estimation of an entrepreneurship-augmented growth model, the analysis captures both the macroeconomic contribution and the policy effectiveness behind Korea’s startup landscape. The findings underscore that entrepreneurship in Korea functions not as a peripheral activity but as an embedded mechanism for addressing core economic vulnerabilities, including demographic contraction, employment mismatches, and structural dependence on large conglomerates. The paper concludes that Korea’s model, characterized by institutional agility and strategic foresight, offers instructive insights for nations navigating post-industrial transitions. Its broader significance lies in demonstrating how entrepreneurship, when interwoven into national policy, education systems, and regional development, can serve as a lever for sustainable competitiveness. Rather than offering a universal blueprint, the Korean experience presents a flexible framework adaptable to diverse socio-economic contexts, especially in emerging and resource-transitioning economies.
Based on panel data of A-share listed companies in China from 2015 to 2023, this study empirically examines the impact of corporate digital transformation on the quality of information disclosure, focusing on the underlying mechanism of the information effect. The findings reveal that digital transformation significantly improves the quality of information disclosure, and this effect remains robust after addressing endogeneity and conducting a series of robustness checks. Further analysis suggests that digital transformation enhances the transparency and reliability of information disclosure by improving internal control quality and reducing information asymmetry, thereby exerting a significant information effect. Moreover, heterogeneity analysis indicates that the positive impact of digital transformation on disclosure quality is more pronounced among state-owned enterprises, non-high-tech firms, and large-scale enterprises. This study provides empirical evidence for policymakers and corporate managers on leveraging digital transformation to enhance information disclosure quality.
Despite increasing instances of fraudulent activities within the Nepalese insurance sector being periodically revealed by government bodies, regulatory authorities, and investigative journalists, a systematic academic inquiry into this issue has remained notably absent. To address this gap, an exploratory cross-sectional quantitative investigation was conducted to examine stakeholder perceptions regarding the effectiveness of fraud control mechanisms and the primary repercussions of insurance fraud on insurers in Nepal. Data were collected through a structured questionnaire administered to 200 respondents including insurance employees, policyholders, agents, insurance technicians, surveyors, and domain experts within the Pokhara Valley, selected via convenience sampling. Analytical procedures included descriptive statistics, Mann–Whitney U tests, and Kruskal–Wallis H tests. It was identified that robust legal enforcement, particularly the enactment and strict implementation of anti-fraud legislation, was perceived as the most effective control strategy. Institutional reforms, such as the establishment of a dedicated Fraud Investigation Bureau and a centralized Insurance Information Centre, were also emphasized as critical to improving the detection and monitoring of fraudulent activities. Although technology-enabled solutions, including AI-driven digital claim management and anomaly detection systems, were acknowledged for their importance, they were ranked marginally lower in perceived efficacy compared to legal and institutional measures. Fraud was reported to exert significant detrimental effects on insurers, most prominently through the erosion of public trust and social credibility. Additional impacts included claim settlement delays, reduced profitability, destabilization of share prices, and increased insurance premiums, collectively threatening both the short-term financial performance and long-term sustainability of the sector. To safeguard stakeholder interests and ensure sectoral stability, a multi-pronged anti-fraud framework has been recommended. Main recommendations include strengthening the legal framework with stringent penalties, developing a centralised fraud registry for inter-insurer information sharing, enhancing underwriting and claims verification procedures, and investing in intelligent fraud detection technologies. These findings offer empirical insights that can guide policy reform and institutional development in emerging insurance markets.
The Golden Triangle consisting of cost, time and quality serves as a fundamental framework for assessing the success of infrastructure projects. Effective risk management is critical for optimising these interconnected dimensions by proactively identifying potential threats implementing risk mitigation strategies and ensuring project control. This study investigates the application of the international standard ISO 31000:2018 in enhancing the Golden Triangle’s dimensions—time management, cost optimization and quality assurance—within the context of large-scale infrastructure projects. A qualitative research methodology was employed incorporating semi-structured interviews, document analysis and site observations to collect comprehensive data. Analytical techniques such as Failure Modes and Effects Analysis (FMEA), Bow-Tie analysis and Fishbone diagrams were utilised to prioritise risks, examine preventive measures and identify underlying causes. A total of forty-three (43) critical risks were identified as having significant impacts on the performance of the Algiers Metro project. The findings revealed that the implementation of a structured risk management approach improved adherence to project timelines, optimised cost control and ensured the delivery of quality outcomes. The integration of ISO 31000:2018 principles in conjunction with tailored analytical tools was found to add considerable value providing practical insights for improving infrastructure project performance. This work underscores the importance of systematic risk management and its role in enhancing the efficiency and success of large infrastructure projects.
Risk management in public-sector project portfolios within developing economies remains an understudied yet critical area, particularly in the context of resource-constrained administrative environments. This study examines the management of risk and uncertainty within the Directorate of Local Administration (DLA) of Ain-Temouchent, Algeria, employing a qualitative case study methodology. Data were collected through semi-structured interviews (n=8) and document analysis to explore the systemic barriers and inefficiencies that hinder effective portfolio-level risk management. The findings reveal that fragmented governance structures, a predominantly reactive approach to risk mitigation, and the limited integration of analytical tools contribute to project delays and subjective risk assessments. While these challenges align with broader critiques of public-sector risk management, significant divergences from Enterprise Risk Management (ERM) and adaptive governance frameworks are identified, primarily due to constraints in institutional capacity and resource availability. The necessity of addressing uncertainty at the portfolio level is emphasized, with a call for the adoption of reflective risk practices, proactive decision-making mechanisms, and the implementation of early-stage adaptive strategies to enhance resilience in multi-project public-sector settings. By contextualizing ERM and adaptive governance theories within a resource-limited administrative framework, this study provides a bridge between theoretical advancements and practical applications, offering actionable insights for policymakers and public administrators seeking to improve strategic alignment and project portfolio success in developing economies.
This study examines the influence of perceived organizational justice on employees’ turnover intention, with a focus on the mediating role of organizational dissent. It aims to identify the key factors contributing to turnover intention within the technology sector and to explore the interplay between organizational justice and organizational dissent in shaping this outcome. A quantitative approach was employed, with data gathered through surveys administered to white-collar employees working in technology companies in Istanbul. The study sample comprised 402 participants. The findings reveal an inverse relationship between perceived organizational justice and turnover intention, indicating that lower perceptions of organizational justice correlate with higher turnover intention. Additionally, organizational dissent was found to significantly impact turnover intention, with perceived organizational justice acting as a mediator in this relationship. The results underscore the critical role of organizational justice in fostering job satisfaction and employee commitment, thereby reducing turnover intention in the technology sector. These findings are consistent with existing literature on the relationship between organizational justice and turnover intention, offering valuable insights into the factors influencing employee retention in high-tech industries. The implications for organizational management are discussed, particularly in terms of the importance of promoting fairness and addressing dissent in order to retain talent within technology firms.
The occurrence of market anomalies has been steadily increasing in contemporary stock markets, particularly within the context of the current economic climate. The volatility of stock markets, exacerbated by the recent inflation crisis, has heightened the need for anomaly detection and informed investment decisions. This study focuses on the BIST 100 index in Turkey, specifically examining the XU030 spot market and the XU030D1 futures market, where significant economic fluctuations are prevalent. The Three Sigma Rule was applied to establish threshold values for anomaly detection, and a directional impact analysis was conducted based on these thresholds. The findings indicate that a positive anomaly in the spot market leads to an average increase of 7.65% in the futures market, while a negative anomaly in the spot market results in an average decrease of 8.69% in the futures market. Conversely, a positive anomaly in the futures market has an average positive impact of 7.82% on the spot market, while a negative anomaly in the futures market results in an average negative impact of 3.99% on the spot market. These results underscore the interconnected nature of the spot and futures markets, particularly in times of economic volatility, and provide insights into how anomalies in one market can influence the other. The study’s findings have significant implications for investors, highlighting the need for careful monitoring of market anomalies and their potential directional effects on investment strategies.
This study investigates risk distribution models in the context of auto insurance in emerging markets, with a focus on the National Insurance Company (SAA), regional directorate of Setif, Algeria. The research applies generalized linear models (GLM) and factor analysis to model the frequency of vehicle accidents and their associated risks. A comprehensive approach is employed, beginning with a discussion of the techniques used for data collection and preliminary descriptive analysis. Following this, a theoretical framework is established for understanding the risk distribution models, highlighting the role of GLM in the modelling of accident frequencies within the insurance industry. Different types of factor analysis, including basic coefficient analysis, cross-factor analysis, generalized cross-factor analysis, and mixed factor analysis, are examined in relation to their applicability to insurance risk modelling. Subsequently, generalized linear models are implemented to derive a robust model for accident frequency, utilizing R software for analysis. The results reveal that the pricing system of the National Insurance Company is influenced by multiple, non-deterministic factors, which complicate the prediction of accident rates and insurance costs. These findings underscore the importance of incorporating various risk factors into pricing strategies, rather than relying on deterministic models. The study highlights the necessity of considering a broader range of factors in the development of pricing systems, particularly in emerging markets where data may be incomplete or subject to considerable variability. Furthermore, the use of Mixed Poisson models is suggested as an effective approach for capturing the non-linear relationship between various risk factors and accident occurrence. This research contributes to the existing body of knowledge by providing a nuanced understanding of the application of GLM and factor analysis in the auto insurance sector, particularly in emerging markets.
The global iron and steel sector is currently navigating a period marked by significant volatility, driven by rising overcapacity and stagnating demand. In this challenging environment, businesses are increasingly compelled to compete not only within their local markets but also on the international stage, as the global economy becomes ever more interconnected. This necessitates a thorough evaluation of the financial performance of major firms in the iron and steel industry, particularly those listed on the Borsa İstanbul (BIST). Such assessments are critical for informing strategic decision-making within the sector. This study aims to assess the financial performance of prominent iron and steel companies traded on BIST between 2019 and 2023, employing an advanced multi-criteria decision-making (MCDM) approach. Specifically, an Improved ENTROPY method is combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the fiscal performance of these enterprises. The findings indicate that EREGL stands out as the highest-performing company in terms of financial metrics over the specified period. The study offers valuable insights into the financial health and operational efficiency of iron and steel firms, providing key information for investors and policymakers in the sector. Additionally, the proposed methodology presents a robust framework for the evaluation of corporate performance in other industries facing similar global challenges.
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