Background: As career maturity and self-awareness increase among new-generation employees, they seek not only material rewards but also well-being and meaningful work.Methods: Drawing on Job Demands–Resources (JD-R) theory and Social Exchange Theory (SET), this study establishes a moderated mediation model to examine how intellectual humility leadership influences thriving at work and job performance, with positive job attitudes as a mediator and core self-evaluation (CSE) as a moderator. Data from 518 manager–subordinate dyads in Chinese SMEs were analyzed using structural equation modeling (SEM).Results: The findings show that intellectual humility leadership enhances thriving and performance by fostering positive job attitudes, which mediate these effects. Moreover, CSE moderates this relationship: the positive effect of intellectual humility leadership on attitudes is stronger when CSE is high and weaker when it is low.Conclusion: The study contributes theoretically by identifying intellectual humility leadership as a critical job resource and relational signal, refining JD-R and SET, and highlighting generational variations in leadership effectiveness. Practically, it suggests cultivating intellectual humility in leadership development to strengthen engagement, performance, and well-being among Millennials and Gen Z. Limitations include the SME focus and survey design; future research should test additional mediators and adopt longitudinal or mixed-method approaches.

Research shows that strategic change can reshape a company’s competitive advantage and significantly impact organizational performance. This study examines the role of Intellectual Humility (IH) in driving strategic change, particularly in the context of external successor CEOs. The overall aim of the study is to explore how external successor CEOs’ IH influences strategic change, and to uncover the underlying mechanisms at play. Based on the Upper Echelons Theory, the paper proposes a moderated mediation model to explore how strategic change occurs during external succession. It highlights the mediating role of strategic execution between IH and strategic change, and the moderating effect of organizational learning (OL). Data from 391 valid responses to electronic questionnaires from CEOs of companies established for over two years were analyzed. The results show that external successor CEOs’ IH positively influences strategic change, with strategic execution mediating this effect. However, organizational learning was found to have no significant moderating effect on this relationship. Our study fills a gap in the literature by highlighting IH as a key factor in external CEO succession. It challenges traditional views on succession and demonstrates how IH can enhance strategic execution and innovation. The findings suggest that IH should be a critical criterion in CEO selection, especially during leadership transitions, to optimize strategic change and improve long-term organizational success.

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation.