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Research On Quality Improvement Of Credit Evaluation Information Of Construction Enterprise

Posted on:2021-01-25Degree:MasterType:Thesis
Country:ChinaCandidate:M Z YangFull Text:PDF
GTID:2492306107975449Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
With more than ten years of development,the credit evaluation of construction enterprise in China has gradually become an important supporting force for standardizing the construction market.Its information is not only an important reference for construction units to select contractors,but also the basis for effective market supervision by government departments.Improving the quality of credit evaluation information of construction enterprises could help reduce the transaction risk caused by information asymmetry,and increase the value and effectiveness of credit evaluation of construction enterprises,it would enable construction enterprises to widen the gap in overall strength and form a new market competition pattern.It has great significance to optimize the allocation of market resources and promote the construction of a credit system for the construction market.This paper analyzes the status quo,existing problems,and obstacles to the process of improving the quality of credit evaluation information for construction enterprises.Then provides some recommendations for improving the quality of credit evaluation information for construction enterprises.When studying the current status and problems of the credit evaluation information quality of construction enterprises,firstly the theories of information ecosystem are used to define the concept and characteristics of the credit evaluation information ecosystem of construction enterprises.Secondly,taking Chongqing for example,analyzing the constituent elements,operation status,information ecological chain,and information ecological status of tis information ecosystem,and sort out the main problems of the credit evaluation of construction enterprises.Then analyze the impact of the problems existing in the credit evaluation of construction enterprises on the quality of information from five dimensions: accuracy,objectivity,comprehensiveness,timeliness,and usefulness.For the analysis of the key obstacle factors to the improvement of the quality of the credit evaluation information of construction enterprises,firstly referring to the literature in the fields of "credit evaluation" and "government information",and combining the current status of Chongqing construction enterprise credit evaluation information quality research to identify 31 obstacle factors.Then,supplementing and modifying the obstacle factors through expert interviews,24 obstacle factors were obtained,and every effort was made to ensure the comprehensiveness and suitability of the obstacle factors.Then,a Likert scale was used to conduct a questionnaire survey,allowing the respondents to score the degree of obstacle effect of each obstacle factor,and use factor analysis to identify 6key obstacle factors:(1)The information producer constitutes of a single unit;(2)The information quality of information producers is not high;(3)There are systematic defects in the evaluation index system;(4)Imperfect information acquisition methods;(5)Relative defects in the government system;(6)Inadequate support and guarantee of relevant systems.Finally,combined with the above analysis,based on the information ecosystem,the paper puts forward suggestions from the three aspects: a structural perspective focusing on the function and organizational relationship of the information subject,an operational perspective focusing on the information transfer process,and an iterative perspective focusing on intertemporal development.Through this research,we hope to improve the quality of credit evaluation information of construction enterprises,thereby promoting the development of credit evaluation systems of construction enterprises.
Keywords/Search Tags:Construction Enterprises, Credit Evaluation, Information Ecology, Information Quality, Promotion Strategy
PDF Full Text Request
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