Abstract
The debate on whether a team’s diversity influences innovation outcomes has gained attention in literature on innovation. Our study focuses on national origin among various teams’ diversity criteria. We use Japanese patent data between 2001 and 2015 to analyze inventors teams. Our analysis reveals that the inventors’ national origin diversity positively impacts the inventions’ quality measures. Furthermore, as the national origin diversity increases, its negative effects become dominant, eliciting an inverted-U-shaped effect. The results were consistent even after controlling for other research and development outcome determinants. Our findings have theoretical and practical implications for innovation policies.
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Notes
The database is called IIP Patent Database. It is a database developed for statistical analysis of patents based on JPO’s “standardized data.“
A Japanese translation must be included when filing a patent application to the JPO. Inventor names are recorded with three types of Japanese characters: Chinese characters (Kanji), Hiragana, and Katakana. In modern Japanese writing, Chinese character is used as a standard and Katakana is normally used for loanwords and foreign names. Chinese, Japanese, (most) Korean, and Taiwanese inventors are registered with Chinese characters whereas foreign inventors from non-North East Asian countries are registered with Katakana. A few Japanese names and Korean names, respectively. There are a few exceptions: 1) Hiragana for a few Japanese names, 2) flags for Chinese and Korean names whose Chinese characters are not used in Japan, and 3) Katakana for a few Korean names which are not of Chinese origin. This unique writing system allows us to better identify more reliable inventors’ national origins than using Roman characters only.
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The authors would like to thank two anonymous reviewers for their constructive comments. All remaining errors are the authors’ own.
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Kang, B., Nabeshima, K. National origin diversity and innovation performance: the case of Japan. Scientometrics 126, 5333–5351 (2021). https://doi.org/10.1007/s11192-021-03981-4
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DOI: https://doi.org/10.1007/s11192-021-03981-4