Modeling the enablers of green supply chain management
Modeling the enablers of green supply chain management
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DOI：
10.1108/bij-08-2015-0082
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
R. Malviya;R. Kant
中科院分区：
文献类型：
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作者：
R. Malviya;R. Kant
目的识别和发展绿色供应链管理使能因子之间的关系，了解这些使能因子对绿色供应链管理实施的相互影响，找出使能因子的驱动力和依赖力。设计/方法/途径本文根据文献回顾以及学术界和工业界专家的意见，确定了35个GSCME。已经进行了一项全国性的基于调查的调查，以排名这些确定的GSCME。调查结果和解释结构模型（ISM）方法已被应用到演变的相互关系，这有助于揭示每个GSCMEs的直接和间接的影响。ISM的结果被用来作为输入的模糊矩阵的影响Croises乘法聚类分析（MICMAC），以确定驱动力和依赖力的GSCMEs。结果出35个GSCMEs 29 GSCMEs（平均3.00）已被认为是通过对印度汽车组织的全国范围内的调查分析。综合方法的开发，因为ISM模型只提供了GSCMEs之间的二元关系，而模糊MICMAC分析提供了精确的分析相关的驱动和GSCMEs的依赖电源。研究限制/影响-ISM模型的发展和模糊MICMAC的权重，通过一些行业专家的判断。这是唯一的主观判断，任何偏见的人谁是判断可能会影响最终结果。实际影响-这项研究提供了重要的指导方针，从业人员，以及学者。在实施全球供应链管理过程中，从业人员需要更加认真地关注这些全球供应链和中小企业。GSCM管理者可以战略性地规划其长期增长，以满足GSCM行动计划。虽然可以鼓励学术界对不同的问题进行分类，但这对解决全球中小企业和海洋生态系统具有重要意义。独创性/价值安排的GSCMEs的层次结构，分类到驱动程序和依赖的类别，和模糊MICMAC是一个独家的努力，在该地区的GSCM的实施。
Purpose The purpose of this paper is to identify and develop the relationships among the green supply chain management enablers (GSCMEs), to understand mutual influences of these GSCMEs on green supply chain management (GSCM) implementation, and to find out the driving and the dependence power of GSCMEs. Design/methodology/approach This paper has identified 35 GSCMEs on the basis of literature review and the opinions of experts from academia and industry. A nationwide questionnaire-based survey has been conducted to rank these identified GSCMEs. The outcomes of the survey and interpretive structural modeling (ISM) methodology have been applied to evolve mutual relationships among GSCMEs, which helps to reveal the direct and indirect effects of each GSCMEs. The results of the ISM are used as an input to the fuzzy Matriced’ Impacts Croises Multiplication Appliqueea un Classement (MICMAC) analysis, to identify the driving and the dependence power of GSCMEs. Findings Out of 35 GSCMEs 29 GSCMEs (mean⩾3.00) have been considered for analysis through a nationwide questionnaire-based survey on Indian automobile organizations. The integrated approach is developed, since the ISM model provides only binary relationship among GSCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and the dependence power of GSCMEs. Research limitations/implications The weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of few industry experts. It is the only subjective judgment and any biasing by the person who is judging might influence the final result. Practical implications The study provides important guidelines for both practitioners, as well as the academicians. The practitioners need to focus on these GSCMEs more carefully during GSCM implementation. GSCM managers may strategically plan its long-term growth to meet GSCM action plan. While the academicians may be encouraged to categorize different issues, which are significant in addressing these GSCMEs. Originality/value Arrangement of GSCMEs in a hierarchy, the categorization into the driver and dependent categories, and fuzzy MICMAC are an exclusive effort in the area of GSCM implementation.