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Article type: Research Article
Authors: Zhang, Yang | Ye, Jianmu; *
Affiliations: School of Management, Wuhan University of Technology, Wuhan, China
Correspondence: [*] Corresponding author. Jianmu Ye, School of Management, Wuhan University of Technology, 430070, Wuhan, China. E-mail: jianmuye@126.com.
Abstract: This paper takes the listed pharmaceutical manufacturing firms in China’s A-share market from 2011 to 2017 as the research sample, from the perspective of top managers’ pay gap, employs principal component analysis and general least square method to empirically investigate the effect of risk preference of top management team(TMT) on the re-innovation behavior after the failure of innovation. The study found that the risk preference of TMT is positively correlated with the re-innovation input and brand-new innovation after the failure of innovation, but not with the supplementary innovation. Besides, the pay gap not only has a positive moderating effect on the positive correlation between the risk preference of TMT and the re-innovation input, but also on the positive correlation between the risk preference of TMT and the brand-new innovation after the failure of innovation. The findings of this study contribute to: (1) through empirical research on the impact of TMT risk preference on re-innovation behavior after innovation failure, expand the relevant research content and research methods of TMT and innovation failure to make the research results more convincing; (2) by setting a reasonable executive compensation gap, TMT can avoid blindly choosing brand-new innovation behavior after innovation failure with the increase of risk preference innovation, ignoring the potential value of innovation failure projects without supplementary innovation, improving the re-innovation behavior of TMT after innovation failure and improving the re innovation success rate after innovation failure.
Keywords: TMT risk preference, re-innovation after the failure, brand-new innovation, supplementary innovation, pay gap
DOI: 10.3233/JIFS-202186
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11051-11061, 2021
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