Purpose: The purpose of this paper is to discuss the relationships between person–job fit and job satisfaction, the mediating role of career self-management and the moderating role of job demands.Design/methodology/approach: In this paper, 947 employees were surveyed using questionnaires, and hypothesis testing was conducted using structural equation modeling in Mplus.Findings: Person–job fit was found to be positively related to career self-management, and career self-management plays a mediating role in the positive relationship between person–job fit and job satisfaction. Statistical analysis also confirmed that job demands moderate both the relationship between person–job fit and career self-management, as well as the relationship between person–job fit and job satisfaction via career self-management.Research limitations/implications: The results of a single study offer limited generalizability and should thus be applied with caution. More cross-industry and cross-company studies should be conducted to verify the applicability of the findings to other industries, cultures and geographical contexts.Practical implications: Managers should try to enhance employees' person–job fit and help or encourage them to engage in career self-management to increase job satisfaction. Additionally, managers should be aware of the negative influence of job demands and try to keep them at low levels.Originality/value: This study extends and enriches the understanding of the positive influence of person–job fit on work outcomes, explores the mechanism of person–job fit on job satisfaction from a new perspective, namely career self-management, and explores the boundary condition (i.e. job demands) of the relationship between person–job fit and career self-management in the Chinese context.

Public sector employees face the profound impact of digital work demands, especially with the advancement of China’s digital government construction. This study explores the dual-edged consequence of digital job demands on the work behaviors of public sector employees in China by constructing a dual-path model. Structural equation modeling (SEM) was used to validate the data of 873 public sector employees. This study found that digital job demands increase civil servants’ thriving at work, facilitating their job-crafting behaviors and increasing their workplace anxiety, leading to their work withdrawal behavior. Furthermore, this study validates the moderating effects of promotion and preventive focus. This study provides managers in the public sector with valuable insights to develop digital job demands managing strategies and for civil servants to adapt their perceptions and behaviors in the digital context.

Despite global progress in LGBTQ+ rights, sexual minorities in China face significant societal pressures and legal ambiguities, which remain poorly understood. This study explores the potential mediating role of proactive and preventive coping strategies in the relationship between perceived stress and outness levels among Chinese LGBTQ+ individuals. Survey data from 267 Chinese LGBTQ+ individuals aged 16–42 revealed high levels of perceived stress within this community. Both proactive and preventive coping strategies significantly mediated the negative impact of perceived stress on outness levels. These findings contribute to the understanding of LGBTQ+ community’s perceived stress and outness status in China, highlighting the need for inclusive policies and support systems to improve their mental health and social well-being.

Stigmatisation directed towards LGBTQ + individuals poses a considerable threat to their psychological well-being. LGBTQ + college students frequently report exposure to perceived stress and microaggressions. Research indicates that self-regulation and proactive coping mechanisms can act as a protective buffer against the stigma and bias faced by sexual minorities. Consequently, this study investigated whether and to what extent proactive coping and perceived academic support moderate the relationship between perceived stress specific to LGBTQ + college students, their psychological well-being, and academic performance. This study surveyed 359 Chinese LGBTQ + college students with an average age of 20.65 years old through snowball sampling and online recruitment. Structural equation modeling was used to test the hypotheses. Employing structural equation modelling analysis, the study revealed that (a) proactive coping moderated the relationship between perceived stress and psychological well-being, and (b) perceived academic support moderated the relationship between perceived stress and academic performance. These findings suggest that Chinese colleges should implement adequate support and counselling services to enhance the proactive coping abilities of LGBTQ + students and create an academically supportive environment to alleviate the adverse effects of perceived stress on psychological well-being.

Within the global sustainable development agenda, Sustainable Development Goal 4 (SDG 4) highlights improving the accessibility, quality, and learning experience of technical and vocational education and training (TVET). In China, students in vocational colleges often face greater disparities in academic preparation and access to educational resources than their peers in general higher education. Although artificial intelligence (AI) can provide additional learning support and help mitigate such inequalities, there is little empirical evidence on whether and how Gen-AI usage is associated with vocational students’ learning experiences and emotional outcomes, particularly academic anxiety. This study examines how Gen-AI usage is related to academic anxiety among Chinese vocational college students and explores the roles of class engagement and teacher support in this relationship. Drawing on Conservation of Resources (COR) theory, we analyse survey data from 511 students using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results indicate that Gen-AI usage is associated with lower academic anxiety, with class engagement mediating this relationship. Teacher support for Gen-AI usage positively moderates the association between Gen-AI usage and class engagement. The fsQCA results further identify several configurations of conditions leading to low academic anxiety. These findings underscore AI’s potential to enhance learning quality and experiences in TVET and provide empirical support for advancing SDG 4 in vocational education contexts.