基于模糊归类模型的电子公共服务评价实证研究

工业工程 ›› 2013, Vol. 16 ›› Issue (2): 92-96.

基于模糊归类模型的电子公共服务评价实证研究

1.天津大学管理学院，天津 300072；2.深圳国际人才交流协会，深圳 518026；3.深圳市龙岗区人大机关深圳，518172

出版日期:2013-04-30 发布日期:2013-06-08
作者简介:张径（1961-），男，吉林省人，博士，主要研究方向为工业工程在政府公共管理中的应用。

Empirical Study on Electronic Public Service Evaluation by Using Optimal Fuzzy Cluster Models

1.School of Management, Tianjin University, Tianjin 300072, China；2. Shenzhen Association For International Personnel Exchange, Shenzhen 518000, China; 3. Department of People's Congress in Longgang District, Shenzhen 518172, China

Online:2013-04-30 Published:2013-06-08

摘要/Abstract

摘要： 在公共服务管理方面，电子公共服务的应用越来越广泛，为了提高服务型政府的公共服务能力与效率，就必须对电子公共服务的质量进行评价。本文探讨应用工业工程的系统评价理论来研究公共管理领域中电子公共服务评价的问题，在现有研究成果的基础上，构建了顾客导向的电子公共服务的测评指标，采用模糊归类模型对其服务进行评价，并对某政府部门的电子公共服务系统进行实证分析。分析结果客观地反应了该部门服务的水平。这为公共服务部门了解其电子公共服务的水平提供了分析方法，对未来电子公共服务的建设具有指导意义。

关键词: 电子公共服务, 服务评价, 模糊最优归类

Abstract: As a service-oriented government, it increasingly adopts electronic system for public service. To improve the ability and efficiency of electronic public service, it is necessary to evaluate the quality of an electronic public service system. In this paper, study on performance evaluation of public service is conducted by using the system evaluation theory originally from Industrial Engineering (IE). In reference of the existing results, a set of electronic public service evaluation indexes is built. Then, statistics data are collected trough a questionnaire survey and exclusive interviews to corresponding users of a government system. With the data collected, an empirical study on service level is done by using optimal fuzzy cluster models. Results show that, by applying the proposed method, the service level obtained matches the practical one well for the considered government system.

Key words: electronic public service, service level evaluation, optimal fuzzy cluster model

张径1,2,胡明3, 汪波1. 基于模糊归类模型的电子公共服务评价实证研究[J]. 工业工程, 2013, 16(2): 92-96.

Zhang Jing1,2, Hu Ming3, Wang Bo1. Empirical Study on Electronic Public Service Evaluation by Using Optimal Fuzzy Cluster Models[J]. Industrial Engineering Journal , 2013, 16(2): 92-96.

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