No abstract

Purpose -The aim of this article is to investigate the relationship between Chinese university students' higher educational background and their entrepreneurial intentions. Design/methodology/approach -The TPB model was adopted and tested for the formation of Chinese university students' entrepreneurial intentions using structural equation modeling. Data were collected from students of Tongji University in Shanghai, China. Findings -The main results of this empirical research suggest that diversity of educational background offers plausible explanations on the difference of entrepreneurial intentions of Chinese university students. Higher educational institutions should develop more flexible approaches with focus on different groups of students in accordance with their various educational backgrounds. Practical implications -In response to the change of graduate labour market and the quest for sustainable competitive advantage in China, higher educational institutions have to integrate the change of mindset, skills and abilities about entrepreneurship in their general academic education in order to nurture university students' entrepreneurial intentions in China. Originality/value -The paper provides comprehensive empirical evidence about the impact of higher education on entrepreneurial intentions of university students in mainland China and thus fills an important gap in the entrepreneurship literature.

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for “soft” social skills, like teamwork and communication, increase with greater demand for “hard” technical skills and tools.