Towards generalized process patterns for detecting corruption within the government using open data

null Darusalam; Marijn Janssen; Jolien Ubacht

doi:10.1145/3209281.3209282

Towards generalized process patterns for detecting corruption within the government using open data

Darusalam, Marijn Janssen, Jolien Ubacht

Information and Communication Technology

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Governments are seeking for new ways to fight corruption within their own administration. The opening of data has the potential to involve citizens in detecting corruption by providing them the ability to view and analyze data about what is happening within the government. However, how open data can be used to detect corruption is not clear. In this paper general patterns for detecting corruption using open data are derived. The patterns are derived by analyzing a case study of e-Procurement at the local government level in Indonesia. E-procurement activities and the corresponding audit activities were analyzed. The following patterns for detecting corruption using open data were derived; 1) storing and opening documents, 2) cross-data comparison, 3) four-eyes-principles, 4) segregation of duties, 5) authorization, and 6) publishing application controls. Data about the activities and structure of the administrative processes should be opened to allow the public to scrutinize whether the process has implemented preventive and detective controls following the process patterns derived in this research. Furthermore, data should be opened about all phases of the administrative processes to enable the involvement of the public and use their 'many eyes' for detecting corruption.

Original language	English
Title of host publication	Proceedings of the 19th Annual International Conference on Digital Government Research
Subtitle of host publication	Governance in the Data Age, DG.O 2018
Publisher	Association for Computing Machinery (ACM)
ISBN (Electronic)	9781450365260
DOIs	https://doi.org/10.1145/3209281.3209282
Publication status	Published - 2018
Event	19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018 - Delf, Netherlands Duration: 30 May 2018 → 1 Jun 2018

Conference

Conference	19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018
Country/Territory	Netherlands
City	Delf
Period	30/05/18 → 1/06/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Citizen participation
Corruption
E-Government
E-Procurement
Internal control
Open data
Patterns
Process patterns

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3209281.3209282

3209281.3209282Final published version, 495 KB

Cite this

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title = "Towards generalized process patterns for detecting corruption within the government using open data",

abstract = "Governments are seeking for new ways to fight corruption within their own administration. The opening of data has the potential to involve citizens in detecting corruption by providing them the ability to view and analyze data about what is happening within the government. However, how open data can be used to detect corruption is not clear. In this paper general patterns for detecting corruption using open data are derived. The patterns are derived by analyzing a case study of e-Procurement at the local government level in Indonesia. E-procurement activities and the corresponding audit activities were analyzed. The following patterns for detecting corruption using open data were derived; 1) storing and opening documents, 2) cross-data comparison, 3) four-eyes-principles, 4) segregation of duties, 5) authorization, and 6) publishing application controls. Data about the activities and structure of the administrative processes should be opened to allow the public to scrutinize whether the process has implemented preventive and detective controls following the process patterns derived in this research. Furthermore, data should be opened about all phases of the administrative processes to enable the involvement of the public and use their 'many eyes' for detecting corruption.",

keywords = "Citizen participation, Corruption, E-Government, E-Procurement, Internal control, Open data, Patterns, Process patterns",

author = "Darusalam and Marijn Janssen and Jolien Ubacht",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018 ; Conference date: 30-05-2018 Through 01-06-2018",

year = "2018",

doi = "10.1145/3209281.3209282",

language = "English",

booktitle = "Proceedings of the 19th Annual International Conference on Digital Government Research",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

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Darusalam, , Janssen, M & Ubacht, J 2018, Towards generalized process patterns for detecting corruption within the government using open data. in Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018., a86, Association for Computing Machinery (ACM), 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018, Delf, Netherlands, 30/05/18. https://doi.org/10.1145/3209281.3209282

Towards generalized process patterns for detecting corruption within the government using open data. / Darusalam, ; Janssen, Marijn ; Ubacht, Jolien.
Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018. Association for Computing Machinery (ACM), 2018. a86.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - Governments are seeking for new ways to fight corruption within their own administration. The opening of data has the potential to involve citizens in detecting corruption by providing them the ability to view and analyze data about what is happening within the government. However, how open data can be used to detect corruption is not clear. In this paper general patterns for detecting corruption using open data are derived. The patterns are derived by analyzing a case study of e-Procurement at the local government level in Indonesia. E-procurement activities and the corresponding audit activities were analyzed. The following patterns for detecting corruption using open data were derived; 1) storing and opening documents, 2) cross-data comparison, 3) four-eyes-principles, 4) segregation of duties, 5) authorization, and 6) publishing application controls. Data about the activities and structure of the administrative processes should be opened to allow the public to scrutinize whether the process has implemented preventive and detective controls following the process patterns derived in this research. Furthermore, data should be opened about all phases of the administrative processes to enable the involvement of the public and use their 'many eyes' for detecting corruption.

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BT - Proceedings of the 19th Annual International Conference on Digital Government Research

PB - Association for Computing Machinery (ACM)

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Y2 - 30 May 2018 through 1 June 2018

ER -

Towards generalized process patterns for detecting corruption within the government using open data

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

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