University of Finance and Economics

Modern decision science recognizes multi-criteria group decision-making (MCGDM) as a significant research area. When addressing real-world MCGDM problems, numerous aspects must be considered, including the complexity and multi-level nature of the issues, the limitations of single evaluation methods, and the bounded rationality behavior of decision-makers (DMs). To address these challenges comprehensively, this paper proposes an extended G-TOPSIS method for hybrid MCGDM, which integrates gray relational analysis (GRA), the technique for order preference by similarity to ideal solution (TOPSIS), and prospect theory. First, the evaluation information of DMs employs hybrid criterion values, including linguistic term sets, interval numbers, crisp values, intuitionistic fuzzy sets, or probabilistic linguistic term sets. Second, by integrating the traditional TOPSIS and GRA methods and incorporating prospect theory, we propose the procedural steps for the extended G-TOPSIS method to address hybrid MCGDM problems. Subsequently, we evaluate the implementation effectiveness of rural revitalization strategies using data from four counties, applying the proposed G-TOPSIS method. Finally, the effectiveness and superiority of the approach are systematically verified.

Teacher stress has increasingly emerged as a significant challenge affecting both the efficiency of educational systems and the psychological well-being of teachers worldwide. However, empirical studies on teacher stress in Mongolia remain scarce, particularly regarding the development of psychometrically sound measurement tools. This study aimed to transculturally validate the Teacher Stress Inventory (TSI) in the Mongolian educational context and identify the major sources of teacher stress among schoolteachers. This quantitative study involved Mongolian schoolteachers from various educational settings. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to examine the factorial validity and reliability of the Mongolian version of the TSI. The findings provided empirical evidence supporting the reliability and construct validity of the instrument. A six-factor structure was identified, accounting for 68.14% of the total variance, with acceptable to excellent reliability across the scales. Both EFA and CFA confirmed the validity and adequacy of the Mongolian TSI structure, and the resulting six-factor model demonstrated acceptable fit indices. Compared with the original ten-factor structure, the Mongolian version revealed highly interrelated stress dimensions, particularly in relation to job-related stressors, time management, discipline and motivation, and physiological and behavioral reactions to stress. In addition, the study identified the major sources of teacher stress as work-related issues and professional expectation issues. The findings contribute to the limited literature on teacher stress in Mongolia and provide a valuable foundation for designing intervention strategies aimed at improving teachers’ welfare and psychological well-being.

The core and the Shapley value are two of the most widely used solution concepts in cooperative game theory. Motivated by recent studies on the fuzzy core and the fuzzy Shapley value for cooperative games in which coalitions’ value are expressed by fuzzy numbers, this paper first introduces a new partial order endowed with desirable algebraic properties on the set of all fuzzy numbers, and provides several of its equivalent characterizations. Based on this order, we then define a fuzzy central core for fuzzy cooperative games, thereby generalizing both the fuzzy interval core of fuzzy interval cooperative games and the core of classical cooperative games. Furthermore, we establish several sufficient conditions for the non-emptiness of the fuzzy central core. Finally, the relationships between the fuzzy Shapley value and the fuzzy central core are also investigated.

In today’s globalized and competitive education market, universities must develop effective branding strategies to attract and retain students. This study examined the factors that shape brand-switching intentions in Vietnamese higher education. Based on a survey of 446 first-year students from four universities in Ho Chi Minh City, we analysed six factors: alternative attractiveness, service quality, switching costs, information availability, tuition, and switching intention. Our results revealed four factors that significantly impact students’ intention to transfer institutions: alternative attractiveness, information availability, service quality, and switching costs. This study begins by discussing the global competitive landscape of higher education, followed by an exploration of brand-switching intentions, before presenting our methodology, analysis, and concluding insights for education administrators to enhance their institutions’ brand reputations and student retention strategies.

AI hallucination is not just about a one-off technical glitch at the model level but is a systemic issue with the interplay between humans, the data, and the model itself. Most existing reviews focus on isolated parts of model development, like model architecture or data governance, and do not consider the whole model development chain from users to data to the model as an integrated whole and analyze it accordingly. To systematically review advances in research addressing hallucinatory outputs in generative Artificial Intelligence, this paper takes a tripartite approach covering users, data, and models. From the user perspective, from low prompting capabilities, cognitive biases to information discernment, which result in hallucinations, and then collectively devise prompt engineering, interactive clarification, and information literacy enhancement as three countermeasures to reduce the effects of hallucinations. From the data perspective, it explores how data quality, retrieval-augmented generation, and knowledge graphs can help strengthen the factual consistency of data and outline their intrinsic weaknesses. In terms of the model, it outline factuality-oriented decoding strategies, alignment training paradigms, and self-inspection with corrective refinement techniques. This paper clarifies the above three aspects and how they work together to form a closed-loop hallucination cycle, starting with user feedback as a departure point, moving to the next step of turning error-correction signals into training instances at the data tier, and lastly to internalising the ability of veracity discrimination in the model parameters via preference alignment and knowledge rectification at the model tier.