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How Do Personal Characteristics Influence Scholarly Productivity? A Comparative Study of Chinese and American Academic Professions

  • Zeng LinEmail author
  • Hong Shen
Chapter
Part of the The Changing Academy – The Changing Academic Profession in International Comparative Perspective book series (CHAC, volume 16)

Abstract

This study uses demographic characteristics and personal achievement factors to compare scholarly productivity of Chinese and American academic professionals. Overall, Chinese academics published in larger quantities than their American counterpart, but the quality of the publications deserves attention in future research. Moreover, multiple linear regression analyses reveal that educational attainment and publishing in a foreign country have a significant impact on academic productivity in both countries. Contrary to many status attainment analyses, we find that parental educational background has no significant impact on publication in both countries. Gender, years since first academic job, income, rank, and overall job satisfaction are significant predictors in China but not in the United States. In contrast, institutional setting has a significant impact in the United States but not in China. The differences between Chinese and American academics in academic productivity raise a series of questions about global competition in relation to the changing nature of the academic profession in these two countries. At the same time, the independence of academic productivity from family background is theoretically significant, especially considering that the United States is the most advanced country whereas China is the largest developing country in the world. This suggests that the academic profession is largely a merit-based profession in both countries.

Keywords

Foreign Country Personal Achievement Tenure Status Academic Productivity Academic Profession 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Wuhan UniversityWuhanChina
  2. 2.Illinois State UniversityNormalUSA
  3. 3.Huazhong University of Science and TechnologyWuhanChina

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