Entrepreneurial education has always played an important role in developing entrepreneurship. In recent years, China has been emphasizing the integration of innovative entrepreneurship concepts into university curricula. Entrepreneurial education can also contribute to the sustainability of business development. In the context of “mass entrepreneurship and innovation”, entrepreneurial education is important to promote the restructuring of the economic and business model, enhance the development momentum, and follow the innovation-driven development path. However, whether the promotion of entrepreneurial education will lead to entrepreneurial intentions of college students, and thus, specific proposals for the promotion of the entrepreneurial education model remains unclear. Therefore, this study adopted the theory of planned behavior perspective coupled with perceived university support to extend the theory of planned behavior framework and explain the effect of such support on student entrepreneurial intention. The study results revealed that perceived university support significantly affected student attitude toward entrepreneurship, which signaled universities’ critical role in establishing entrepreneurial spirit in students. A significant effect on behavioral control was also observed for perceived university support. Regarding the effects of perceived university support on attitude toward entrepreneurship and behavioral control, the results revealed the effect was far greater on attitude than on behavioral control. Such intention was not directly affected by subjective norms. Therefore, a scientifically rational entrepreneurship course system is critical for effective entrepreneurship education. Overall, encouraging students to engage in the continuous process of entrepreneurship through entrepreneurship courses can concurrently benefit overall economic and social development.

The prevalence of COVID-19 has changed traditional teaching modes. For many teachers, online learning effectively compensated for the absence of traditional face-to-face instruction. Online learning can support students and schools and can create unique opportunities under emergency management. Educational institutions in various countries have launched large-scale online course modes in response to the pandemic. Additionally, online learning during a pandemic differs from traditional online learning modes. Through surveying students in higher education institutions, educational reform under emergency management can be explored. Therefore, university students were surveyed to investigate their continuance intention regarding online learning during the pandemic. Expectation confirmation theory was extended using the task-technology fit model to ascertain whether the technical support of promoting online learning helped student’s complete course learning tasks during the pandemic and spawned a continuance intention to use online learning in the future. Data were collected through online questionnaires. A total of 854 valid responses were collected, and partial least squares structural equation modeling was employed to verify the research hypotheses. The results revealed that the overall research framework largely explained continuance intention. Concrete suggestions are proposed for higher education institutions to promote online learning modes and methods after the COVID-19 pandemic.

In response to the Covid-19 pandemic, online learning has been carried out in many countries with different types of online learning models being promoted and implemented. In the global pandemic continues, the education environment is forced to change from traditional classroom or blended teaching mode to online learning teaching model. With the outbreak of COVID-19, China was the first to announce that online courses are to be implemented in February 2020. In China, whether online learning can replace traditional offline teaching has become a topic worth discussing. Therefore, this study investigates university students in China by questionnaires and discussions of this topic. The study is based on the Push–Pull Mooring model. Based on 854 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model. The results show that push effects (Perceived security risk, Learning convenience, and Service quality), pull effects (Usefulness, Ease of use, Teacher's Teaching Attitude, Task-technology Fit), and mooring effects (habit) all significantly influence users' switching intentions from offline to online learning platform. Finally, this study explores whether push–pull–mooring can be a reference for promoting and implementing online learning courses in Chinese colleges and universities in the future after the pandemic.

The prediction of individual interpersonal communication skills and personality traits is a critical issue in both industrial and organizational psychology and affective computing. In this study, we invited 114 participants, including 57 interviewers and 57 interviewees, to collect the ground truth of interviewees’ communication skills and personality traits as perceived by real human interviewers in a structured behavioral interview setting. We develop an asynchronous video interview (AVI) platform with an artificial intelligence (AI) decision agent based on a TensorFlow convolutional neural network (CNN), called AVI-AI, that can be used to partially displace human raters’ work in the initial stage of employment screening and to successfully predict a job candidate’s communication skills and personality traits. The experimental results show that AVI-AI can predict not only a candidate’s interpersonal communication skills but also his or her openness, agreeableness, and neuroticism, as perceived by experienced human resource professionals. The interrater reliability values were all acceptable to support the ground truth assumption. However, our AVI-AI could not predict the conscientiousness and extraversion as perceived by the real human raters in this study.

In order to enable online learning to continue developing when the COVID-19 pandemic passes, this study aimed to identify the critical factors that affected the use of e-learning by university students during the pandemic. These critical factors will help to increase the efficiency of future development and deployment of online learning systems. Through a literature review, this study employed the technology acceptance model, social support, and task–technology fit as the theoretical basis to establish the framework of the online learning environment with regards to the technology acceptance model in the context of emergency management. A questionnaire survey was administered to students in universities that had implemented online teaching during the pandemic, and 552 valid responses were collected. The survey explored the factors affecting the willingness of higher education institution students to continue using online learning, and the following conclusions were drawn. (1) The easier an online learning platform was to navigate, the better it was perceived by the students, and thus the students were more willing to use it. (2) Ease of use and usefulness were associated with the teachers’ choice of platform and their ability to achieve a satisfactory fit between the course design and platform navigation, which thereby affected the students’ learning outcomes and attitude towards use. (3) The positive attitude of teachers towards teaching increased the students’ perceived ease of use of online learning. (4) During the pandemic, family support—a major support for teachers in online teaching—enhanced teachers’ attitudes towards, and willingness to provide, online teaching. A high level of support showed that the parents urged the students to learn and complete online learning tasks as instructed by the teachers, implying that family support could affect the students’ habits towards, adaptation to, and identification of online learning. The study results provide insights into the factors affecting the willingness of teachers and students to continue using e-learning platforms.