five

Partial correlation coefficient.

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://figshare.com/articles/dataset/Partial_correlation_coefficient_/17404118
下载链接
链接失效反馈
官方服务:
资源简介:
Background Information sharing plays a key role in supply chain performance. According to previous individual studies, technology, trust, commitment, and uncertainty are four potential factors affecting information sharing. However, most studies focus on testing a positive relationship between each factor and information sharing. Therefore, it is necessary to evaluate the effect of each factor on information sharing. Objective To determine the accurate effect of factors on sharing information and propose key factors to support decision-makers in improving their information sharing. Data Correlation coefficients between factors are collected from 41 individual studies with a total of 8,983 samples on Google Scholar Methods Using the rank correlation test and Egger’s regression test to test publication bias. The meta-analysis method is used to perform analysis models, including fixed-effect, random-effect, and Hunter and Schmidt methods Results Commitment plays the most important role in information sharing when compared to technology, trust, and uncertainty. Commitment accounts for 19% in the Hunter and Schmidt method and 22% in both fixed-effect and random-effect models. In addition, the result indicates that there are around 50% of other factors that affect the efficiency of sharing information besides four factors in our studies. Conclusion Technology, trust, and commitment significantly affect information sharing, of which the impact of commitment on information sharing is strongest and should be a priority in improving the effectiveness of information sharing. Our study contributes two findings to literature in the field of supply chain information sharing: 1) certain confirming the important role of commitment on sharing information, and 2) the necessity of considering other factors besides these four elements.
创建时间:
2021-12-22
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作