High-quality development in China requires higher vocational education, scientific and technological innovation, and sustainable economic development. The spatial distribution patterns of these factors show higher levels in the east and coastal areas compared to the west and inland regions, emphasizing the need for coupling coordination with the social economy. This study examines the impact of sustainable economic development on the coupling coordination degree using the spatial Durbin model. The results show a positive promotion and spillover effect, with regional variations. The main factors affecting the difference in coupling coordination are the amount of technology market contracts, fiscal expenditure on science and technology, patent application authorizations, tertiary industry output value, and the number of R&D institutions. According to the grey prediction model, the coupling coordination degree is expected to increase from 2022 to 2025, but achieving primary coordination may still be challenging in some areas. Therefore, strategies that utilize regional characteristics for coordinated development should be developed to improve the level of coupling coordination and create a mutually beneficial environment.

In the highly competitive employment environment, most college students have left their jobs for a short time after employment, and attention should be paid to students’ career adaptation. However, the further influence of skilled goal orientation, social support and career-determined self-efficacy on college students’ career adaptation needs to be confirmed. This study analyzes the effects of these factors on college students’ career adaptation. This study aims to analyze the impact of mastery goal orientation, social support, and vocational decision self-efficacy on career adaptation among 224 university students in East China. The results indicated that university students generally exhibit positive levels of mastery goal orientation, social support, vocational decision self-efficacy, and overall career adaptation. Female students demonstrate higher levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation compared to male students. As students progress in their academic years, their levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation tend to increase. Students majoring in humanities and social sciences have higher level than students majoring in science and engineering in all factors. Students majoring in humanities and social sciences exhibit more optimism in all factors compared to students in science and technology fields. The relationships among these factors show positive correlations. Mastery goal orientation, social support, and vocational decision self-efficacy all have positive effects on career adaptation. Among these, family support stands out as the most influential subordinate factor of social support on career adaptation. The most influential subordinate factor of vocational decision self-efficacy on career adaptation is conscious decision-making. Therefore, male, lower grade, science and engineering college students are the groups that need to be paid attention to in improving career adaptation. Skilled goal orientation, family support and conscious decision making have a better effect on the improvement of career adaptation. These results can provide important reference information for universities, counselors and college students in the training of career planning, and theoretically enrich the relevant research on college students’ career adaptation, and provide certain enlightenment for future researchers.

Poverty, and especially the widening disparity between the rich and the poor, leads to social unrest that can interrupt the harmonious development of human society. Understanding the reasons for income inequality, and supporting the development of an effective strategy to reduce this inequality, have been major goals in socioeconomic research around the world. To identify the determinants of the income gap, we calculated the Gini coefficients for Chinese provinces and performed regression analysis and contribution analysis for heterogeneity, using data from 30 Chinese provinces from 2002 to 2018. We found that urbanization, higher education, and foreign direct investment in eastern China and energy in central and western China were important factors that increased the Gini coefficient (i.e., decreased equality). Therefore, paying more attention to the fair distribution of the factors that can increase the Gini coefficient and investing more in the factors that can reduce the Gini coefficient will be the keys to narrowing the income gap. Our approach revealed factors that should be targeted for solutions both in China and in other developing countries that are facing similar difficulties, although the details will vary among countries and contexts.

This article examines the overseas corporate social responsibility (CSR) patterns of Chinese international contractors (CICs). Adopting an institutional and political economy approach, a unique dataset is constructed with country-specific contents drawn from CSR-related reports and website information of 50 top CICs. This dataset provides a foundation for systematic content analysis of CICs’ overseas CSR practices, revealing that both political legitimacy-seeking and strategic competitiveness-seeking motivations drive CICs’ CSR activities abroad, characterized by the prioritization of customer and community engagement. The findings highlight the coexistence of the exogenous pressures for the national image-building purpose and the endogenous awareness of CSR strategic importance for corporate internationalization. The hybridization of political and economic rationales is presented as the defining feature of CICs’ current overseas CSR patterns, with the balance between them being determined by stakeholder type and internal business needs influenced by corporate internationalization experience.

This research explores the relationship between the independent variables (need for achievement, risk-taking, family support, economic factors, and the dependent variable of women’s enterprises’ success) and examines the moderating influence of socio-cultural factors. A survey-based methodology was adopted. One hundred sixty-nine small and medium-sized enterprises (SMEs) in the Palestinian West Bank were surveyed using structured questionnaires. Structural equation modeling (SEM) was conducted by using the Smart-PLS program. The results indicate that women entrepreneurs’ success in SMEs is positively and significantly impacted by the need for achievement as an internal factor and economic factors and family support as external factors. Furthermore, sociocultural factors did not show any significant moderating influence. By gaining knowledge about the relationship between internal and external factors and the success of women-owned SMEs, this study adds to the body of literature already in existence. These factors can be considered in the success of these enterprises, particularly in an environment full of political and economic fluctuations. Furthermore, the research is said to be the first of its type in Palestine, particularly concerning SMEs run by women. It also supports entrepreneurs by providing them with resources that might aid in the growth and success of their businesses.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.