A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China
Tendering and bidding is considered the stage most vulnerable to corruption in the construction industry. The prevalence of collusive tendering and bidding induces frequent accidents and even sabotages the fairness of the construction market. Although a large number of tendering and bidding corrupti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/12/2103 |
| _version_ | 1797461125652021248 |
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| author | Bing Zhang Yu Li |
| author_facet | Bing Zhang Yu Li |
| author_sort | Bing Zhang |
| collection | DOAJ |
| description | Tendering and bidding is considered the stage most vulnerable to corruption in the construction industry. The prevalence of collusive tendering and bidding induces frequent accidents and even sabotages the fairness of the construction market. Although a large number of tendering and bidding corruption cases are investigated in China every year, this information has not been fully exploited. The profile of the different corruptors remains vague. Therefore, this study uses the user profile method to establish a corruptor characteristic model based on the human paradigm, where 1737 tendering and bidding collusion cases were collected from China to extract the features. Four types of specific corruption groups are detected based on self-organizing feature map (SOM) cluster analysis, comprising low-age corruptors, grassroots mild corruptors, middle-level collapsing corruptors, and top leader corruptors. Furthermore, the profiles of different cluster corruptors are described in detail from four dimensions. This study reveals the law of tendering and bidding corruption from the perspective of the user profile and suggests that a user profile system for corruption in bidding should be developed in the process of the precise control of corruption, which promotes the transformation from strike after corruption to prevention beforehand. It is conducive to forming the resultant force of big data for precise anti-corruption. |
| first_indexed | 2024-03-09T17:14:58Z |
| format | Article |
| id | doaj.art-118028ef705846b9afa758696fa13796 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T17:14:58Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-118028ef705846b9afa758696fa137962023-11-24T13:41:54ZengMDPI AGBuildings2075-53092022-12-011212210310.3390/buildings12122103A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of ChinaBing Zhang0Yu Li1School of Building Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaSchool of Building Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaTendering and bidding is considered the stage most vulnerable to corruption in the construction industry. The prevalence of collusive tendering and bidding induces frequent accidents and even sabotages the fairness of the construction market. Although a large number of tendering and bidding corruption cases are investigated in China every year, this information has not been fully exploited. The profile of the different corruptors remains vague. Therefore, this study uses the user profile method to establish a corruptor characteristic model based on the human paradigm, where 1737 tendering and bidding collusion cases were collected from China to extract the features. Four types of specific corruption groups are detected based on self-organizing feature map (SOM) cluster analysis, comprising low-age corruptors, grassroots mild corruptors, middle-level collapsing corruptors, and top leader corruptors. Furthermore, the profiles of different cluster corruptors are described in detail from four dimensions. This study reveals the law of tendering and bidding corruption from the perspective of the user profile and suggests that a user profile system for corruption in bidding should be developed in the process of the precise control of corruption, which promotes the transformation from strike after corruption to prevention beforehand. It is conducive to forming the resultant force of big data for precise anti-corruption.https://www.mdpi.com/2075-5309/12/12/2103tendering and bidding corruptionuser profileprecise regulationChinese construction industry |
| spellingShingle | Bing Zhang Yu Li A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China Buildings tendering and bidding corruption user profile precise regulation Chinese construction industry |
| title | A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China |
| title_full | A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China |
| title_fullStr | A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China |
| title_full_unstemmed | A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China |
| title_short | A User Profile of Tendering and Bidding Corruption in the Construction Industry Based on SOM Clustering: A Case Study of China |
| title_sort | user profile of tendering and bidding corruption in the construction industry based on som clustering a case study of china |
| topic | tendering and bidding corruption user profile precise regulation Chinese construction industry |
| url | https://www.mdpi.com/2075-5309/12/12/2103 |
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