In the context of increasing Artificial Intelligence integration in higher education, understanding the factors influencing university teachers’ adoption of AI tools is critical for effective implementation. This study adopts a perception–intention–behavior framework to explores the roles of perceived usefulness, perceived ease of use, perceived trust, perceived substitution crisis, and perceived risk in shaping teachers’ behavioral intention and actual usage of AI tools. It also investigates the moderating effects of peer influence and organizational support on these relationships. Using a comprehensive survey instrument, data was collected from 487 university teachers across four major regions in China. The results reveal that perceived usefulness and perceived ease of use are strong predictors of behavioral intention, with perceived ease of use also significantly influencing perceived usefulness. Perceived trust serves as a key mediator, enhancing the relationship between perceived usefulness, perceived ease of use, and behavioral intention. While perceived substitution crisis negatively influenced perceived trust, it showed no significant direct effect on behavioral intention, suggesting a complex relationship between job displacement concerns and AI adoption. In contrast, perceived risk was found to negatively impact behavioral intention, though it was mitigated by perceived ease of use. Peer influence significantly moderated the relationship between perceived trust and behavioral intention, highlighting the importance of peer influence in AI adoption, while organizational support amplified the effect of perceived ease of use on behavioral intention. These findings inform practical strategies such as co-developing user-centered AI tools, enhancing institutional trust through transparent governance, leveraging peer support, providing structured training and technical assistance, and advancing policy-level initiatives to guide digital transformation in universities.

Poverty alleviation is a critical concern in developing countries. China launched the Targeted Poverty Alleviation (TPA) program and implemented a series of anti‐poverty initiatives, aiming to reduce poverty and enhance the well‐being of rural households. The TPA program has been recognized as significantly promoting rural development, but whether it can benefit all poor households equally has been underexplored. This study systematically examines the impact of the TPA program on poor households with different causes of poverty and empirically analyzes income differences across household and village types under different income groups using a linear quantile mixed model (LQMM), based on survey data from a high‐altitude deep‐poverty county in southwest China. The results indicate an income imbalance among poor households with different causes of poverty, whereas households in poor and non‐poor villages exhibit equivalent development. Differences are more pronounced at higher income quantiles, suggesting that the policy provides relatively uniform benefits to poorer households. Analysis of income sources reveals disparities between wage and transfer incomes, while benefits from business income remain consistent. These findings underscore the need for sustained policy support to enhance the stability and sustainability of poverty alleviation and income transformation among low‐income households. This study contributes to a deeper understanding of the TPA program, and the recommendations derived from the results can serve as references for poverty alleviation in rural areas of developing countries.

Business administration education relies on case analysis, strategic simulation, and complex decision-making, all of which generative artificial intelligence (GenAI) can substantially support. However, limited research has examined the mechanisms driving business students’ intention to use GenAI tools to support their learning. To address this gap, this study aims to investigate business administration students’ intention to use GenAI by drawing on an integrated Technology-Organization-Environment (TOE) framework and an extended Technology Acceptance Model (TAM) incorporating perceived trust and perceived risk. Using survey data from 385 Chinese business administration students, a hybrid analytical approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) is adopted to examine both the net effects and the configurational mechanisms underlying behavioral intention. The results show that perceived ease of use and perceived trust are the two strongest direct predictors of intention to use GenAI, jointly outweighing perceived usefulness, and that perceived trust serves as the sole significant mediator linking cognitive evaluations to intention to use. Perceived risk, in contrast, exhibits neither a direct nor a mediating effect, suggesting a risk tolerance pattern among business students. Among the TOE dimensions, environmental support (peer influence) emerges as the most potent contextual driver, whereas organizational support facilitates perceived ease of use but does not directly enhance perceived usefulness. The fsQCA findings further identify five typical pathways to high intention to use, namely TAM-driven, trust-compensated, environment-driven, organization-driven risk-tolerant, and comprehensive external-support configurations. These findings provide guidance for designing targeted GenAI-supported strategies and peer-driven interventions in higher business education.

The TODIM method (an acronym in Portuguese for interactive and multiple criteria decision-making) with entropy weights is influenced by rank reversal, a phenomenon where the order of two alternatives changes following the addition of another alternative. Research on rank reversal has predominantly focused on single decision-making methods. To the best of our knowledge, the reduction of rank reversal probability in hybrid methods, such as the entropy-weight TODIM method, remains an unresolved challenge. To address this, this paper introduces the expanded evaluation matrix, which incorporates virtual alternatives, to reduce the probability of rank reversal in the entropy-weight TODIM method. A simulation study is conducted to assess the effectiveness of the expanded evaluation matrix in mitigating rank reversal. The results demonstrate that the expanded evaluation matrix significantly reduces the rank reversal probability. A case study on selecting energy storage technology showcases the potential real-world applications of the expanded evaluation matrix. The reliability of the expanded evaluation matrix is further validated through sensitivity and comparative analyses. Given the simplicity and ease of implementation of the expanded evaluation matrix, it can be readily adapted to other decision-making methods and holds substantial potential for broad application.