基于改进DEMATEL的科研人员数据素养关键影响因素识别

Identification of Key Influences on Data Literacy of Researchers Based on Improved DEMATEL

在大数据环境下,科研人员数据素养的提升对于科学数据的高效利用具有重要的推动作用。基于此,文章尝试引入改进的DEMATEL方法来识别科研人员数据素养的关键影响因素,以此更好地发挥和实现科学数据的最大价值。首先,利用文献检索法和问卷调查法对科研人员数据素养影响因素进行系统分析,并构建一套较为合理的科研人员数据素养影响因素体系;然后,同时利用三角模糊数和DEMATEL理论的优点,并在此基础上引入一种改进的DEMATEL方法;最后,利用改进的DEMATEL方法深入分析科研人员数据素养影响因素之间的内在因果关系,并以此来识别科研人员数据素养的关键影响因素。研究表明,教育培训、技术平台和素养氛围是影响科研人员数据素养的原因因素,态度表现、惩罚制度和考核机制是影响科研人员数据素养的结果因素。

As we are in a big-data world, the data literacy of researchers plays an important role in promoting the efficient use of scientific data. Based on this, this paper attempts to introduce an improved DEMATEL method to identify the key influences on the data literacy of researchers as a way to better utilize and realize the maximum value of scientific data. First, the authors used literature search and questionnaire to systematically analyze the factors influencing data literacy among researchers. The author also provides a reasonable system as a result. Then, the authors improved the DEMATEL method by exploiting the strengths of both triangular fuzzy number and the original DEMATEL theory.Finally, the article uses the improved DEMATEL method to analyze the intrinsic causal relationships among the factors influencing data literacy among researchers and use them to identify the key influences. Research shows that education and training, technology platforms and literacy climate are the causal factors. Attitudinal performance, penalty systems,and appraisal mechanisms are outcome factors.