Journal of World Economy

Level of Self-Assessed Entrepreneurial Competence of Students from Accountancy, Business and Management Programs

2025-08-09T09:03:00+00:00

Entrepreneurial competence in students is vital today, and especially for students taking Accountancy, Business, and Management (ABM) programs. This study evaluated the level of Personal Entrepreneurial Competencies (PECs) of ABM students in Santiago City, Philippines, an opening in the empirical literature for situationally embedded competency assessment in the Philippine educational landscape. Utilizing a quantitative-descriptive approach, the researchers surveyed 300 ABM students from Northeastern College, using validated tools for self-assessing and for learning competencies. It is noteworthy that 81.34% of the respondents exhibited at most satisfactory entrepreneurial skill, of which 34.67% exhibited outstanding skill. Higher proficiency levels were observed for the categories of Risk Taking (68.66%) Demand for Quality and Efficiency (68%), and Goal Setting (70.34%); highest proficiency levels allied well with the student’s inclination to engage in entrepreneurial opportunities; meanwhile, a significant portion of students had only sufficiently moderate levels of competencies embraced in Opportunity Seeking (79.33%) and Persistence (74%); suggesting it may be prudent to introduce further contextual or experiential applications of the pedagogy. This study demonstrates the need for participatory, innovative education programs like mentoring, experiential learning, and redesigning the curriculum dedicated to taking the students from moderate to high entrepreneurial ability. In the Philippines’ socio-economic context, the study emphasizes the importance of starting and growing new businesses as a route to self-employment and for sustainability.

“From Embroidery to Empowerment”: Exploring Barriers to Shui Women’s Economic Mobility

2025-08-09T09:05:32+00:00

This study explores the socio-economic challenges faced by Shui women, mainly residing in Guizhou Province, China. Despite their rich cultural heritage and traditional skills, Shui women still encounter systemic obstacles in education, employment, and legal rights. Based on thematic analysis of field interviews, policy documents, and academic literature, this study reveals how cultural expectations, insufficient institutional support, and discriminatory labor practices work together to hinder the career development of Shui women. The findings highlight the need for intersectional feminist approaches in rural development policies, and they recommend targeted educational and legal reforms to enhance the empowerment of ethnic minority women. This study enriches the literature on gender and ethnic inequality in China, providing new theoretical insights and practical suggestions for promoting inclusive socio-economic development.

The Role of Cross-Cultural Communication in Enhancing the Global Competitiveness of American Firms: A Case Study of American Multinational Corporations

2025-09-03T08:44:42+00:00

In the era of globalization, American multinational corporations (MNCs) are confronted with escalating international market competition, where cultural differences and communication barriers significantly impact their global competitiveness. This research investigates the strategic role of cross-cultural communication in enhancing the global competitiveness of American MNCs. By integrating cross-cultural psychology, management theory, and international business theory, this study constructs a comprehensive theoretical framework and employs empirical methods to verify the effectiveness of cross-cultural communication strategies. The findings indicate that these strategies significantly enhance corporate global competitiveness by improving cross-cultural capabilities and customer satisfaction. This study provides actionable recommendations for the American government and enterprises to optimize their global operations and offers valuable insights for policy-making in related fields.

The Inside Job or Outside Rule: How Six Governance Forces Shape UK Board Independence?

2025-09-03T09:05:22+00:00

This research explores how the proportion of independent directors on UK-listed company boards relates to six key institutional dimensions—public participation, political stability, government effectiveness, regulatory quality, rule of law, and anti-corruption control—drawing on agency theory and resource dependence perspectives. Using panel data from non-financial firms listed on the London Stock Exchange (2013-2023), we extract independent director ratios from annual reports and combine them with the World Bank’s Global Governance Indicators to quantify institutional quality. Our analysis employs double fixed-effects regression models and other techniques to effectively control for time and firm-specific heterogeneity. These findings reveal that while political stability significantly encourages the appointment of independent directors, the other five institutional dimensions show strong negative substitution effects. Notably, these institutional responses show no significant variation across firms of different sizes. Based on these results, the paper proposes a novel, integrated framework for board independence within a multi-dimensional regulatory system. Importantly, feasible decision-making references for investors to optimize governance risk assessment, corporate executives to adjust board composition, and regulatory authorities and industry associations to synchronously improve governance guidelines and information disclosure. In summary, this paper contributes uniquely by constructing a comprehensive multi-dimensional governance framework, integrating six distinct forces, and demonstrating how their interplay—particularly between regulatory enforcement and investor incentives—generates non-linear impacts on board independence.

Enterprise Management Digital Transformation: A Strategic Framework for SMEs

2025-09-03T09:20:40+00:00

In the digital economy era, the digital transformation of enterprise management has become a key path to enhancing corporate competitiveness and achieving sustainable development. Small and medium-sized enterprises (SMEs), as significant contributors to economic growth and employment, face numerous challenges in the digital transformation process, such as insufficient technical capabilities, limited funding, and a shortage of talent. This paper aims to construct a strategic framework for the digital transformation of SMEs that covers dimensions such as technology, organization, talent, and management. Through systematic literature reviews, case analysis, and empirical research, this paper clarifies the principles of constructing the strategic framework, combs through the elements of digital transformation, and proposes specific implementation strategies. The study finds that digital transformation following the principles of systematicness, adaptability, and sustainability can significantly improve the operational efficiency, customer satisfaction, and market competitiveness of SMEs. The case analysis of Shenyang Hanjing Entry-Exit Service Co., Ltd. further verifies the applicability and effectiveness of the constructed framework. This paper not only enriches the theoretical research on the digital transformation of SMEs but also provides practical guidance for SMEs to smoothly advance digital transformation in a resource-limited environment.

The Digital Transformation Framework for Small and Medium-Sized Enterprises: Theoretical Foundations and Practical Applications

2025-09-03T09:25:22+00:00

This paper proposes a comprehensive digital transformation framework (DTF) for small and medium-sized enterprises (SMEs), aiming to facilitate their smooth digital transformation. The framework encompasses four key modules: strategic planning, process optimization, technology implementation, and cultural change. Through systematic theoretical analysis and case study research, the paper demonstrates the application effects of the DTF in various SMEs. It is found that SMEs applying the DTF have significantly enhanced market competitiveness and operational efficiency, with an average annual revenue increase of over 50% (Zhu, J., et al., 2023). By conducting in-depth analyses of two specific cases, the paper further validates the effectiveness and feasibility of the DTF in practical operations, providing replicable experience and methods for other SMEs. Finally, the paper offers suggestions for optimizing the DTF for SMEs in different industries and sizes, providing direction for future research and practice.

Digital Intelligence in Brand Marketing: A Research on Ecosystem Reconstruction Through Big Data and AI

2025-09-03T09:33:20+00:00

The rapid development of digital technology has positioned big data and artificial intelligence (AI) as crucial driving forces in the field of brand marketing. This study delves into the current applications of big data and AI technologies in brand marketing, analyzes their transformative impact on the marketing ecosystem, and proposes innovative strategies for brand marketing based on digital technology. The findings indicate that big data technology can achieve in-depth consumer behavior insights and precise predictions, while AI significantly enhances marketing efficiency and user experience through personalized recommendations, intelligent customer service, content generation, and other means. The synergistic effect of these two technologies is propelling the transition of brand marketing from traditional models to digital and intelligent directions.

Research on Optimizing Risk Management of Securities Institutions Under Financial Cycle Fluctuations

2025-09-03T09:43:21+00:00

Financial cycle fluctuations, as a significant feature of macroeconomic and financial market operations, exert profound impacts on the business models and risk profiles of securities institutions. During expansion phases, securities firms often benefit from rising asset prices and abundant liquidity, yet simultaneously accumulate potential risks of high leverage and concentration. In contraction phases, however, they may face multiple challenges such as asset value depreciation, frequent credit defaults, and tightened liquidity. This paper systematically analyzes the transmission mechanisms through which financial cycle fluctuations affect market risk, credit risk, and liquidity risk in securities institutions. It further proposes optimization pathways including the establishment of a dynamic risk appetite management system, the strengthening of full-cycle risk identification and measurement capabilities, the improvement of cross-cycle risk management coordination mechanisms, and the innovation of adaptive risk management tools. These strategies aim to provide both theoretical and practical guidance for enhancing securities institutions’ capacity for steady operation and risk prevention across financial cycles.

From Soft Power to Sports Economy: The Global Significance of Zheng Qinwen’s Olympic Victory in Chinese Tennis

2025-09-03T09:46:09+00:00

The rise of China as a powerhouse in the international sports arena has dramatically changed its image and cultural influence abroad. Traditionally known for its economic might and political moves, China has used sports as a strategic tool to project its cultural narrative and increase its soft power. Tennis, traditionally considered a Western sport, has become a significant factor in China’s sports diplomacy, representing modernization and cultural integration. Its outstanding achievements in tennis, such as those made by Zheng Qinwen, have challenged the Western monopoly on the sport and inspired a new generation of Chinese athletes. The national pride fostered by such achievements has drawn international recognition, making China a formidable competitor in a sport that was once considered a monopoly of Western nations. Additionally, commercialization and international positioning have turned tennis into a synonym for luxury, thus influencing the attitude of people and the level of participation in China. This paper discusses how tennis has been able to shape China’s sports image internationally, starting from its historical roots, moving on to its global positioning, and finally turning it into a symbol of wealth and prestige. It also examines how the success of China in tennis has reoriented its international narrative, increasing its cultural presence and repositioning it within the global pecking order of sport.

B2B Marketing Automation: Evaluation and Dynamic Optimization

2025-09-03T09:50:17+00:00

Amidst intensified market competition and the increasing diversification of customer demands, B2B enterprises are confronted with unprecedented challenges in their marketing activities. Traditional marketing models are insufficient to meet the complex and ever-changing market demands. The advent of marketing automation technology has provided B2B companies with an efficient and precise marketing solution. However, numerous issues still exist in the implementation of B2B marketing automation, such as difficulties in data integration, inadequate process optimization, and the lack of scientific basis for effect evaluation. These problems restrict the further development and application of marketing automation. This study aims to construct a scientific B2B marketing automation evaluation system and dynamic optimization algorithm to enhance the marketing efficiency and effectiveness of B2B enterprises.

Federated Learning: Privacy-Preserving Data Sharing and Underwriting Applications in the Insurance Industry

2025-09-03T09:53:34+00:00

As the digital transformation of the insurance industry accelerates, cross-institutional data sharing (such as between insurance companies and hospitals, credit-reporting agencies) has become a crucial means of enhancing the efficiency and accuracy of underwriting. However, data privacy protection and the problem of data silos have emerged as the main contradictions constraining its development. This paper focuses on the application of federated learning technology in cross-institutional data sharing in the insurance industry. Firstly, it provides a detailed introduction to the basic principles of federated learning, including distributed model training, data encryption techniques, and collaborative learning mechanisms, elucidating the path to achieving “data availability without visibility.” Secondly, it proposes a technical solution for federated learning in insurance underwriting, which addresses the challenges of data privacy protection and sharing through data encryption, distributed model training, and collaborative output of results. Finally, in conjunction with the actual needs of the insurance industry, it explores the extended applications of federated learning in claims, anti-fraud, and other scenarios, and designs a technical architecture diagram and compliance checklist to verify its advantages in privacy protection and data security.

Creating Smart Manufacturing Based on AGI Twins and Interacting Intelligent Platforms

2025-09-03T09:56:37+00:00

Smart manufacturing using AGI twins and cross-platform management are advanced technologies that combine artificial general intelligence (AGI), digital twins and integration of various platforms to improve the efficiency and flexibility of manufacturing processes. AGI digital twins simulate real manufacturing processes, equipment and systems, allowing to predict failures, optimize operations and conduct scenario analysis. AGI digital twins can adapt to changes in the manufacturing environment, providing high accuracy and flexibility of management. Cross-platform management, firstly, ensures the integration of various systems and devices into a single management environment, secondly, centrally controls and coordinates all stages of production regardless of the technologies and platforms used, and thirdly, ensures real-time data exchange, increasing transparency and efficiency of decision-making. The use of AGI digital twins on a cross-platform basis allows you to create a flexible, scalable and smart manufacturing ecosystem. Such a solution helps to reduce downtime, improve product quality and reduce costs and provides the ability to quickly respond to changes in the market and customer requirements. Enterprises use AGI digital twins to model production scenarios and optimize logistics. Integration with management systems allows for automated production planning and control. The use of cross-platform solutions helps to unite factories, warehouses, and logistics chains into a single network.

Analysis of the Strategic Game in Sino-US Rare Earth Trade Conflicts and Cooperation: The Complexity of Great Power Competition and Interdependence

2025-09-03T10:00:26+00:00

This study focuses on Sino-US rare earth trade, exploring its role as a core issue in the two powers’ strategic competition amid great power rivalry and global supply chain interdependence. Against the backdrop, China, with a dominant global position via its complete rare earth industrial chain (controlling 60% of mining, 88% of smelting), uses export controls as a defensive response to U.S. tech suppression; the U.S., highly dependent on Chinese processed rare earths (90% of 2022 consumption), takes measures like reviving domestic industry and diversifying supply chains, with the 2025 Trump administration’s trade coercion intensifying the game.

Machine Learning in Financial Time Series Forecasting: A Systematic Review

2025-09-12T09:09:06+00:00

Time series analysis holds significant theoretical and practical value in the financial field. Due to the complex characteristics of financial time series, such as nonlinearity, dynamics, and chaos, constructing effective prediction models remains a key research direction in both academia and industry. In recent years, with the rapid development of machine learning technology, its application in financial time series prediction achieves remarkable progress. However, most studies remain fragmented and under-reviewed. This paper systematically reviews key research on time series prediction models based on machine learning in the financial field, focusing on analyzing the theoretical modeling and application effects of different models, as well as summarizing the data resources used. It not only compares the performance differences among various models but also discusses the limitations in current prediction modeling processes and proposes possible future improvement directions, aiming to provide references for researchers and practitioners in model selection and optimization. In addition, this paper incorporates the context of computational intelligence and big data, explores the potential value of integrated research approaches, and aims to offer new ideas for advancing the field of financial time series prediction.