使用验证性因子分析(CFA)作为改进数字政府评估的工具:以墨西哥国家门户网站排名为例

G. Cid, J. Ramon Gil-Garcia, D. E. Luna-Reyes, L. Luna-Reyes, Sergio Picazo-Vela, Rodrigo Sandoval-Almazán
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摘要

文献中有不同的测量和排名来评估成功采用数字政府的关键因素。每个框架都应用了不同的变量和方法来评估数字政府的成功。然而,数字政府成功的本质是复杂的,包含多个维度,这使得它难以在实践中进行评估。有必要建立更强大的方法来改进这些评估,以探索数字政府成功的多个维度。一些作者提出因子分析技术作为这项任务的有用工具。本研究利用2009-2015年墨西哥著名的州政府门户网站排名,进行了验证性因子分析,以评估该工具的维度,其中包括132个项目。这个排名可以被认为是衡量和评估数字政府的典型工具,类似于许多国家和一些国际组织使用的排名。本研究的目的是扩展我们对数字政府成功的多个维度的理解,并为改进和完善现有的衡量和评估数字政府的技术提供指导。结果证实了大多数原始维度,但允许减少问题的数量并获得更健壮的估计。在分析的基础上,我们为改进测量方法和评估数字政府提供了一套实用的建议。
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Using Confirmatory Factor Analysis (CFA) as a Tool to Improve the Assessment of Digital Government: The Case of the Mexican State Portals Ranking
There are different measurements and rankings in the literature that evaluate critical factors for a successful adoption of digital government. Each of these frameworks applies diverse variables and approaches to assessing digital government success. However, the nature of digital government success is complex and implies multiple dimensions that make it difficult to evaluate in practice. There is the need to build more robust approaches to improve these assessments to explore the multiple dimensions of digital government success. Some authors have proposed factor analysis techniques as a useful tool for this task. Using a well-known ranking of state government portals in Mexico during the period 2009-2015, this study conducted a confirmatory factor analysis to evaluate the dimensions of this instrument, which includes 132 items. This ranking could be considered a typical tool for measuring and assessing digital government, similar to the ones used in many countries and by some international organizations. The purpose of this study is to extend our understanding of the multiple dimensions of digital government success and to provide guidance for improving and refining existing techniques for measuring and assessing digital government. The results ratify most of the original dimensions, but allows reducing the number of questions and obtain more robust estimations. Based on the analysis, we provide a set of practical recommendations for improving measurement methodologies and for the assessment of digital government.
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