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.
Energy dependency plays a pivotal role in shaping the performance of stock markets, particularly in energy-sensitive indices such as the BIST Industrial Index in Turkey. This study presents a comparative evaluation of traditional statistical models and machine learning (ML) techniques in capturing the complex relationship between energy variables and the BIST Industrial Index. A dataset encompassing energy imports, production levels, and energy prices is utilised to assess the effectiveness of Ordinary Least Squares (OLS) regression, Random Forest (RF), and Gradient Boosting (GB) models. The results reveal that ML models substantially outperform traditional statistical methods in their ability to capture nonlinear, intricate relationships between energy metrics and market behaviour. Among the ML models, RF demonstrates the highest predictive accuracy. Feature importance analysis identifies crude oil production as the most significant variable, underscoring the dominant influence of domestic energy dynamics in shaping the BIST Industrial Index. While ML models offer superior forecasting capabilities, they introduce challenges in terms of model interpretability. In contexts where transparency is crucial, statistical models such as OLS remain more favoured for their simplicity and explainability. The findings highlight the need for a balanced approach in model selection, with hybrid models potentially offering the best of both worlds by combining the strengths of traditional and modern methodologies. The insights derived from this study can inform policymakers and investors, particularly within emerging markets, providing a nuanced understanding of the trade-offs between predictive power and model transparency in forecasting energy-sensitive financial indices.
Strategic values play a pivotal role in the long-term success of logistics enterprises, influencing interactions with customers, employees, and stakeholders, and driving sustainable outcomes. In the context of the global logistics sector, the identification and alignment of strategic values are essential for maintaining competitive advantage and fostering resilience. This study systematically investigates the strategic values of the world’s 50 leading logistics companies, focusing on those most strongly associated with sustainable success. Using a qualitative approach, content analysis was employed to evaluate and interpret the strategic documents of these enterprises, revealing key values that contribute significantly to sustainability. Among the values identified, reliability, customer-centricity, and operational efficiency were found to be most influential in ensuring both operational and strategic sustainability. These values were consistently embedded within corporate practices, shaping decision-making processes, stakeholder engagement, and long-term growth strategies. The findings indicate that the integration of sustainability as a core strategic value is critical for enduring success in an increasingly competitive and environmentally conscious market. The results provide valuable insights for both academics and practitioners, offering a framework for logistics companies to refine their strategic management practices and align their operations with sustainable development goals. By highlighting the strategic values that underpin sustainable growth, this study contributes to the understanding of how logistics enterprises can navigate the complex challenges of the modern business environment.
The phenomenon of multiple directorships (MDs) within Boards of Directors of listed entities has garnered increasing attention due to its implications on corporate governance (CG) effectiveness. This study examines the prevalence and major implications of MDs on the governance of Maltese listed entities (MLEs), identifying key determinants and evaluating potential management strategies. A mixed-methods approach is utilized, comprising semi-structured interviews with fourteen directors and company secretaries of MLEs. The findings reveal a significant occurrence of MDs among MLE directors, with impacts that vary based on the number of directorships held, individual circumstances of the directors, and the specific corporate environments of the entities involved. Critical factors contributing to the prevalence of MDs include a limited pool of qualified candidates, directors’ aspirations to serve on multiple boards, and the corporate emphasis on the perceived reputation and quality associated with MD holders. The study highlights that director overcommitment, resulting from MDs, poses potential risks to CG effectiveness. Strategies proposed to mitigate these risks include enhanced nomination committee (NC) reviews, self-assessment mechanisms for board members, and the establishment of more comprehensive guidelines within the CG code specific to directors with MDs. The originality of this research lies in its focus on the unique context of MDs within smaller states like Malta, providing valuable insights into CG enhancement in similar environments. This study offers significant contributions to the literature on MDs and CG, particularly relevant for listed companies in smaller jurisdictions and their stakeholders, by proposing actionable strategies to improve governance practices amidst the challenges posed by MDs.
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