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Kai Zeng [3]Kaiyang Zeng [1]
  1.  14
    Role of moral judgment in peers’ vicarious learning from employees’ unethical pro-organizational behavior.Kai Zeng, Duanxu Wang, Weize Huang, Zhengwei Li & Xianwei Zheng - 2022 - Ethics and Behavior 32 (3):239-258.
    ABSTRACT By integrating theories of social learning and moral judgment, we developed a theoretical model on whether and when peers imitate employees’ unethical pro-organizational behavior in the workplace. The study, which involved 256 employees in a large manufacturing company in China, revealed that employees’ UPB positively predicted peers’ vicarious learning of UPB, with the effect strengthened by employees’ organizational tenure but weakened by peers’ deontic injustice. Moreover, the positive effect of employees’ UPB on their peers’ vicarious learning was mitigated, and (...)
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  2.  13
    Roles of Multiple Entrepreneurial Environments and Individual Risk Propensity in Shaping Employee Entrepreneurship: Empirical Investigation From China.Kai Zeng, Duanxu Wang, Zhengwei Li, Yujing Xu & Xiaofen Zheng - 2022 - Frontiers in Psychology 13.
    While prior literature has widely acknowledged that the entrepreneurial environment significantly fertilizes entrepreneurship, the impact of workplace receives limited attention, and the vital role of organizations in linking social entrepreneurial environment and employee entrepreneurship has been largely ignored. Therefore, this study aims to unfold how multiple entrepreneurial environments shape employee entrepreneurship and then further reveal how such relationships vary with employees’ risk propensity. Drawn on the theoretical lens of mindsponge process, which offers an explanation of why and how organizations and (...)
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  3.  10
    Elastic–plastic deformation of PbO3–% PbTiO3single crystals during nanoindentation.Meng Fei Wong & Kaiyang Zeng - 2010 - Philosophical Magazine 90 (13):1685-1700.
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  4.  26
    Kernel Neighborhood Rough Sets Model and Its Application.Kai Zeng & Siyuan Jing - 2018 - Complexity 2018:1-8.
    Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood (...)
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