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This study empirically examined the impact of entrepreneurs' competencies on entrepreneurial performance and the moderating role of self-sacrificial leadership. Competencies were categorized into technological, business, and networking domains, while performance was measured by financial and non-financial outcomes. Multiple regression results showed that all three competencies significantly improved performance, with networking competency exerting the strongest effect. Moderation analysis indicated that task allocation and authority exercise, as sub-dimensions of self-sacrificial leadership, strengthened the influence of business and networking competencies on performance. These findings underscore the importance of both entrepreneurial competencies and leadership structures in enhancing outcomes, highlighting the need for an integrated approach to competency development and leadership capacity in entrepreneurship education and policy.

Despite the rapid growth of outbound tourism in China, the adoption of travel insurance remains limited. This study investigates the determinants of insurance purchase among 2,000 Chinese travelers using machine learning and deep learning techniques. The dataset includes demographic, socioeconomic, and behavioral variables such as age, income, family size, health status, and travel experience. Four models—logistic regression, Random Forest, a baseline deep neural network (DNN), and a dropout- enhanced DNN—were developed and compared. Results show that the dropout-enhanced DNN achieved the highest accuracy (about 82%), outperforming the baseline DNN (79%), Random Forest (79%), and logistic regression (75%). Feature importance analysis indicated that annual income, age, and family size are the most decisive predictors of adoption. The findings highlight the value of artificial intelligence in predicting consumer behavior in the insurance sector. For practitioners, the results suggest that insurers should target higher-income and family-oriented travelers while leveraging airlines and agencies as key distribution channels. Predictive analytics can thus support more effective segmentation, targeting, and personalized insurance design.

This study aims to analyze the impact of ESG practices on the R&D investment of Chinese non-state-owned enterprises and examine its heterogeneity across industries and regions. The study used the data of Shanghai-Shenzhen A-share listed non-state-owned enterprises from 2012 to 2023, Sourced from HuaZheng ESG assessment and CSMAR database. This study adopts a two-way fixed-effects model and uses Stata 18.0 to conduct empirical analysis. The results show that ESG practices positively impact on enterprises’ R&D investment. The comparative analysis shows that the effect of ESG on less polluting industries is stronger than highly polluting ones, and more significant in the eastern and central regions compared to the western and northeastern regions. This study provides a theoretical basis for policy formulation, particularly for governments, when formulate proper ESG policies for specific regions and industries to promote high-quality growth and green transition strategies. In addition, the result also provides empirical evidence for non-state-owned enterprises that maximize the impact of ESG and strategic adoption of ESG practices can help overcome resource constraints, foster innovation and promote high-quality development.

This study explores a novel approach to assessing cultural fit during the early stages of mergers and acquisitions (M&A) by leveraging publicly available employee review data and large language models (LLMs). Recognizing the limitations of traditional due diligence in accessing internal cultural data, the proposed framework utilizes fine-tuned models and chain-of-thought (CoT) reasoning strategies to infer corporate cultural characteristics based on the Denison and Ko [2016] framework. The model evaluates four key traits-Mission, Consistency, Involvement, and Adaptability-across twelve dimensions, using Low-Rank Adaptation (LoRA) for efficient fine-tuning. Experimental results demonstrate that LoRA-tuned models consistently outperform few-shot prompting across both proprietary (e.g., GPT-4o) and open-source (e.g., Llama 3.2-3B) models, with significant improvements in both text summarization and numerical prediction accuracy. Additionally, CoT reasoning-particularly Multi-step and Hybrid strategies-yields substantial performance gains, especially in smaller models, enabling them to approximate the results of large-scale systems at reduced cost. These findings highlight the practical utility of combining PEFT and CoT methods for scalable, objective, and early-stage cultural assessments in M&A decision-making.

This study explores a unique case of charitable giving that goes beyond conventional monetary donations, focusing on an elderly American couple who voluntarily hosted an Afghan refugee family in their home following the U.S. military withdrawal from Afghanistan. Through four in-depth interviews with the couple, the research investigates their motivations, the rules and arrangements established for cohabitation, the dynamics of intercultural relationships, and the long-term aspirations shared by both families. The findings reveal that empathy derived from personal experiences, clear agreements on daily living, and mutual respect played crucial roles in sustaining this cross-cultural household. Moreover, the couple’s support extended beyond housing to include education, financial mentoring, and emotional care, enabling the refugee family to pursue independence and social integration. The study highlights that refugee settlement is not only a policy issue but also a community and individual endeavor. Drawing implications for Korea, the case demonstrates the potential of intergenerational housing and the utilization of vacant homes in addressing structural challenges such as aging, rural depopulation, and youth housing insecurity. From a management perspective, the case illustrates how structured arrangements, resource sharing, and mutual learning can generate social value and contribute to sustainable refugee integration. Ultimately, this research argues that refugee acceptance should be understood as a reciprocal process of hospitality and growth, offering valuable lessons for policymakers, local communities, and civil society actors in Korea and beyond.

An organization with sufficient data and well-defined AI processes is said to be better positioned to seize AI opportunities than its competitors, and is referred to as an AI-matured organization. Unlike traditional software systems, AI systems depend on large data sets, raising ethical and operational risks throughout the life cycle. With increasing demands for responsible AI, standardized life cycle processes such as those by IEEE and ISO were introduced. This study investigates the potential disconnection between standardized AI life cycle models established by IEEE and ISO and real-world practice. By pinpointing where and how actual practices diverge from standardized processes, this study clarifies AI life cycle operations, which is crucial for advancing theoretical development on the connection between AI process capabilities and organizational AI maturity.

This study empirically verified the effect on loyalty through the relationship benefits of insurance companies (psychological benefits, customization benefits, social benefits, economic benefits) and trust and commitment between service providers and customers. In addition, it was analyzed whether it acts as a modulating variable of Korea‘s cheong(情) in the relationship between trust and commitment path. This study established a total of 7 research hypotheses based on the theoretical background of previous studies. To test the hypothesis, a face-to-face survey was conducted on customers and insurance providers who had transaction experience with insurance companies. Based on the collected data, the established research hypothesis was verified using multiple regression analysis. Among the sub-concepts of relationship benefits, customization benefits and economic benefits had a positive effect on trust, while psychological benefits and social benefits did not have a statistically significant effect. Trust had a positive effect on relationship commitment, and relationship commitment was found to play a mediating role between trust and customer loyalty. Korea’s cheong(情) was found to play a modulating role between trust and relationship commitment. It was confirmed that the capacity to empathize of service providers is an important factor, which is noteworthy as a result differentiated from previous studies. In particular, in an insurance market competitive environment where a lot of interest and investment in CRM is made, analyzing the moderating effect of trust and commitment between customers and insurance providers using Korea‘s cheong(情) can be seen as a work to increase academic and practical value.