Qian, J.; Li, J.; Geng, B.; Chen, C.; Wu, J.; Li, H. Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China. Foods2023, 12, 2124.
Qian, J.; Li, J.; Geng, B.; Chen, C.; Wu, J.; Li, H. Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China. Foods 2023, 12, 2124.
Qian, J.; Li, J.; Geng, B.; Chen, C.; Wu, J.; Li, H. Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China. Foods2023, 12, 2124.
Qian, J.; Li, J.; Geng, B.; Chen, C.; Wu, J.; Li, H. Finding Traceability Granularity Influencing Factors Using Rough Set Method: An Empirical Analysis of Vegetable Companies in Tianjin City, China. Foods 2023, 12, 2124.
Abstract
Evaluating the efficacy of the traceability systems (TS) 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 the traceability granularity using a comprehensive and quantifiable model and try find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin city, China. Granularity indicators were collected mostly by the TS platform to ensure the objectivity of the data, and the granularity score was evaluated by using a TS granularity model. The results show a clear 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 by using a rough set method based on nine factors pre-selected by 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.
Biology and Life Sciences, Food Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
