摘要
公众对城市信用治理的满意程度,是一切信用工作的出发点和落脚点。目前的研究尚未形成系统完整的测评理论体系和方法论。对网络环境下公众感知和网络情绪对城市信用治理满意度的影响进行理论探讨,并提出一种基于网络舆情大数据的满意度指数编制方法。依托百度指数大数据,对17个GDP超万亿元样本城市进行实证研究,并对政务、商务、社会、司法四大领域的信用治理满意度进行分析。研究发现:各样本城市公众对当前信用治理基本满意,但满意度仍有进一步提升空间;满意度在样本城市间差距并不明显,且与城市信用环境、城市信用体系建设水平存在正向关联。研究认为,基于舆情大数据的信用治理满意度指数测算方法,可实现跨区域、跨领域、多维度、实时动态跟踪监测,是一种操作性强且经济高效的指数解决方案。
The public’s satisfaction degree of urban credit governance is the starting point and goal of allcredit work. Previous studies have not formed systematic and complete theoretical system and methodology onthe evaluation of this satisfaction. By conducting a theoretical exploration of the impact of the public’sperception and sentiment in the cyberspace on their satisfaction of urban credit governance, this article putsforward a compiling method of satisfaction degree index based on the big data of online public opinion. Relyingon the big data of Baidu index, this article conducts empirical studies on 17 sample cities with GDP exceedingone trillion yuan and analyzes the public’s satisfaction of credit governance in the field of government affairs,commerce, society, and judiciary. Two conclusions are reached: people of these sample cities are basicallysatisfied with the current credit governance, but there is still room for the further elevation of people’ssatisfaction degree;the gap of people’s satisfaction among sample cities is not obvious and there is a positivecorrelation between people’s satisfaction degree and the credit environment and credit system construction levelof the city. The study argues that the satisfaction index measurement method based on the big data of publicopinion, which can realize cross-region, cross-field, multi-dimensional monitoring in a real-time and dynamicway, is a highly operable and cost-effective index solution option.
作者
毛通
谢朝德
Mao Tong;Xie Chaode(Zhejiang Financial College,Hangzhou 310018,Zhejiang,China)
出处
《征信》
北大核心
2020年第9期15-23,共9页
Credit Reference
基金
教育部人文社会科学研究青年基金项目(18YJC790117)
教育部人文社会科学研究规划基金项目(17YJA790059)。
关键词
信用治理
感知度
舆情效应
满意度
大数据
credit governance
perception
public opinion effect
satisfaction degree
big data
