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作 者:牛玉飞 张发明 袁胜军[2] NIU Yu-fei;ZHANG Fa-ming;YUAN Sheng-jun(School of Economics and Management,Nanchang University,Nanchang 330031,China;Business School,Guilin University of Electronic Science and Technology,Guilin 541004,China)
机构地区:[1]南昌大学经济管理学院,江西南昌330031 [2]桂林电子科技大学商学院,广西桂林541004
出 处:《中国管理科学》2023年第6期241-252,共12页Chinese Journal of Management Science
基 金:国家自然科学基金资助项目(72161006,71961018);教育部人文社会科学基金资助项目(21XJA630009);广西哲学社会科学规划项目(21FGL037);江西省主要学科学术和技术带头人项目(20194BCJ 22001);广西自然科学基金面上项目(2021JJA100878);广西新文科研研究与实践项目(XWK2022011)。
摘 要:针对动态激励评价理论性、系统性与规范性不足,且机动性与可扩展性较差等问题,引入强化理论、公平理论与权变理论,构建一种泛强化激励动态评价的方法论模型。首先,以强化理论等过程型激励理论作为内在理论支撑,并以数据分析方法作为外在方法支持,提出模型的主体功能模块“泛强化激励算子”;其次,对算子中的各参数进行具体阐释,并探讨了相关性质;最后,给出模型的具象化表示方式、应用流程及注意事项。通过参数普适性检验及算例对比分析表明,该模型是一个可行的方法论体系,且具有一定的普适性;同时,有利于决策者更为广泛且深入地挖掘特定问题背景下被评价对象的特定动态特征与发展潜力,拉开被评价对象之间的档次并实现更为全面的分类优选。In order to solve the problems in dynamic incentive evaluation,such as the lack of systematization,theory basis,normalization,maneuverability and scalability,a methodological model of dynamic incentive evaluation is constructed by introducing reinforcement theory,equity theory and contingency theory.Firstly,using process incentive theories mainly of reinforcement theory as the internal basis,and the data analysis method as the external support,the principal functional module“Generic Reinforcement Incentives”operator is proposed;secondly,each parameter of the operator is explained in detail,and the related properties are discussed;finally,the representational expression,application route and attentions are illustrated respectively.Through the parameters’universality test and comparative analysis of numerical example,it shows that a feasible methodology system is provided by this model with significant universal ability,so as to help decision-maker to explore the specific dynamic characteristics and development potential of the evaluated object in specific background broadly and thoroughly,effectively widen the grades between the evaluated objects and bring about a more comprehensive classified selection.
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