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作 者:丁战[1] 来千博 王吉发[2] DING Zhan;LAI Qian-bo;WANG Ji-fa(School of Business Administration, Northeastern University, Shenyang 110167, China;School of Management, Shenyang University of Technology, Shenyang 110870, China)
机构地区:[1]东北大学工商管理学院,沈阳110167 [2]沈阳工业大学管理学院,沈阳110870
出 处:《沈阳工业大学学报(社会科学版)》2021年第6期550-558,共9页Journal of Shenyang University of Technology(Social Sciences)
基 金:辽宁省重点研发计划指导计划项目(2017401021);2021年度服务地方经济社会发展决策专项课题(FWDFGD2021018)。
摘 要:针对企业转型路径选择问题,在基于生命周期理论的RCSP模型基础上,构建三维空间下的数学模型框架结构,并在此框架下对于不同生命周期阶段的企业,综合考虑企业生命周期、行业地位、行业生命周期三个因素,给出基于马尔科夫过程的企业转型路径选择模型方法。该模型首先将企业转型路径问题转化为序贯决策过程,分解成若干个相互联系又有一定制约性的阶段,其次,利用德尔菲技术得到通过肯德尔和谐系数检验的专家组评估分数,给出权重的计算方法,再次,引入贝叶斯准则,确定起点到终点的后验概率,得到概率矩阵,最后,通过马尔科夫链的平稳分布分析,得到平衡状态下的概率矩阵和可达概率情况,从而给出某一生命周期下企业转型的最稳定路径,充实现有的决策选择方法,扩展解决实际问题的思路。Aiming at the selection of enterprise transformation paths,based on the RCSP model on life cycle theory,the framework structure of mathematical model in three-dimensional space is constructed.The three factors of enterprise life cycle,industry status and industry life cycle for enterprises in different life cycle stages are comprehensively considered,and a Markov process is presented based on the selection model of enterprise transformation path.Firstly,the model transforms the enterprise transformation path problem into a sequential decision-making process,which is decomposed into several interrelated and restrictive stages.Secondly,the Delphi technology is used to obtain the evaluation scores of the expert group passing the Kendall harmony coefficient test,and the calculation method of the weights is given.Thirdly,the Bayesian criterion is introduced to determine the posteriori probability from the starting point to the end point.Finally,through the stationary distribution analysis of Markov chain,the probability matrix and reachable probability in the equilibrium state are obtained.The most stable path of enterprise transformation is given in a life cycle,the existing decision-making and selection methods are enriched,and the idea of solving practical problems is expanded.
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