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Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China

文献类型： 外文期刊

作者： Qian, Jianping 1 ; Li, Jiali 1 ; Geng, Bojian 2 ; Chen, Cunkun 3 ; Wu, Jianjin 4 ; Li, Haiyan 4 ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China

2.Yangtze River Delta Intelligent Agr Res Inst Chine, Suzhou 215331, Peoples R China

3.Inst Agr Prod Preservat & Proc Technol, Tianjin Acad Agr Sci, Natl Engn Technol Res Ctr Preservat Agr Prod, Key Lab Postharvest Physiol & Storage Agr Prod,Min, Tianjin 300384, Peoples R China

4.Tianjin Agr Dev Serv Ctr, Tianjin 300061, Peoples R China

关键词： traceability granularity; influence factors; rough set; empirical analysis; vegetable companies

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2023 年 12 卷 11 期

页码：

收录情况： SCI

摘要： The effectiveness evaluation of the traceability system (TS) is a tool for enterprises to achieve the required traceability level. It plays an important role not only for planning system implementation before development but also for analyzing system performance once the system is in use. In the present work, we evaluate traceability granularity using a comprehensive and quantifiable model and try to find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin, China. We collect granularity indicators mostly through the TS platform to ensure the objectivity of the data and use the TS granularity model to evaluate the granularity score. The results show that there is an obvious imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range (50,60) exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed using a rough set method based on nine factors pre-selected using a published method. The results show that the factor "number of TS operation staff" is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue > Supply chain (SC) integration degree > Cognition of TS > Certification system > Company sales > Informationization management level > System maintenance investment > Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies.

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