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Scholars have recognized the need for further research to explore the role of institutional factors in social entrepreneurship (SE) within emerging markets (Urban and Kujinga, 2017). In response, this study investigates how the institutional environment influences the antecedents of social entrepreneurial intention (SEI) in Vietnam by expanding the SEI model of Mair and Noboa (2006). Drawing data from 401 university students in Vietnam, this study employed Structural Equation Modeling (SEM) to examine the research hypotheses and model. The results reveal that social entrepreneurial self-efficacy (SES) and perceived social support (PSS) significantly and positively influence the intention to become a social entrepreneur, while empathy (EMP) and moral obligation (MO) show no effect. Furthermore, the institutional environment, comprising the regulatory dimension (RD), normative dimension (ND), and cognitive dimension (CD), plays a crucial role in boosting EMP, MO, SES, and PSS. This study contributes to SE literature by extending established intention models with the inclusion of institutional dimensions. Additionally, the findings of this study imply that policymakers should design SE-supportive policies and regulations, and create a culture that values and promotes SE in order to encourage social innovation and reduce barriers to entry for social ventures. Educators should also increase opportunities for students to experience SE and develop entrepreneurial skills in order to enhance their perception of cognitive and normative environments, making SE a more attractive career option.

Purpose: The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students' intentions and actual uses of ChatGPT for their learning. Design/methodology/approach: An advanced methodology--polynomial regression with response surface analysis--and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses. Findings: Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students' intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students' intentions to use ChatGPT for learning decreases. Practical implications: Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context. Originality/value: This study shed the new light on the educational setting by testing how higher education students' intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.