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Chao Ma [4]Chaoshang Ma [1]
  1.  32
    Why and When Employees Like to Speak Up More Under Humble Leaders? The Roles of Personal Sense of Power and Power Distance.Xiaoshuang Lin, Zhen Xiong Chen, Herman H. M. Tse, Wu Wei & Chao Ma - 2019 - Journal of Business Ethics 158 (4):937-950.
    Research investigating the underlying mechanisms and boundary conditions under which leader humility influences employee voice remains underdeveloped. Drawing from approach–inhibition theory of power and leader humility literature, we developed a moderated-mediation model in which personal sense of power was theorized as a unique mechanism underlining why employees feel motivated to speak up under the supervision of humble leaders. Additionally, the cultural value of power distance was proposed to be a relevant boundary condition to influence such relationship. We tested the model (...)
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  2.  21
    Why and When Employees Like to Speak Up More Under Humble Leaders? The Roles of Personal Sense of Power and Power Distance.Chao Ma, Wu Wei, Herman Tse, Zhen Chen & Xiaoshuang Lin - 2019 - Journal of Business Ethics 158 (4):937-950.
    Research investigating the underlying mechanisms and boundary conditions under which leader humility influences employee voice remains underdeveloped. Drawing from approach–inhibition theory of power and leader humility literature, we developed a moderated-mediation model in which personal sense of power was theorized as a unique mechanism underlining why employees feel motivated to speak up under the supervision of humble leaders. Additionally, the cultural value of power distance was proposed to be a relevant boundary condition to influence such relationship. We tested the model (...)
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  3.  9
    Dissipativity-Based Synchronization of Mode-Dependent Complex Dynamical Networks with Semi-Markov Jump Topology.Chao Ma, Wei Wu & Yidao Ji - 2018 - Complexity 2018:1-10.
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  4.  1
    An Improved Grasshopper Optimizer for Global Tasks.Hanfeng Zhou, Zewei Ding, Hongxin Peng, Zitao Tang, Guoxi Liang, Huiling Chen, Chao Ma & Mingjing Wang - 2020 - Complexity 2020:1-23.
    The grasshopper optimization algorithm is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full (...)
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