Research on the Elements of E-Government Performance Evaluation

2014 ◽  
Vol 571-572 ◽  
pp. 1140-1143
Author(s):  
Ying Zhang ◽  
Li Hui Xiong

China's e-government performance evaluation is at the initial stage, there is a long way to go for its practical and theoretical study to explore, how to build a science of e-government performance evaluation system is a far-reaching a major issue. After discussing the connotation of e-government, modern government functions as well as government performance assessment, the article, based on the actual development of China's e-government, separately expounded on target, subject, object of China's e-government performance evaluation and made the idea of constructing the government performance evaluation, which is to provide reference for establishment a more scientific and accurate evaluation of government performance. This work is in order to promote the orderly development of e-government.

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4125-4134
Author(s):  
Huping Shang ◽  
Chunting Wang

Studies and practices in China unanimously ignored the additivity of government performance evaluation index. In the present evaluation systems, the total score of government performance is added by simply putting the indexes values (numbers) together. Neither the researchers nor the practitioners pay any attention to the reality that the government performance evaluation indexes belong to high attribute dimensions, and they cannot be added directly. To process these high attribute indexes of government performance evaluation, we have to follow their clustering features and reduce dimensions to convert high attribute dimensions to low attribute dimensions. In this study, binary state variable was adopted to reduce dimensions. We reduce the dimension of the performance evaluation index by 4 steps: (1) separating the hazy description of into measurable sub-indexes; (2) treating each sub-index as a binary variable by judging it false or true; true and false are respectively indicated as 1 and 0 in the statistical software or mathematical language; (3) using the methods of aggregate degree, aggregate vector, and set theory to make the sub-indexes aggregate in a same class; (4) nondimensionalising the values of sub-indexes and realizing the additivity of all the sub-indexes.


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