Electronic platforms and transparency in public purchase

Ferreira, Isabel; Cunha, Sandra; Camões, Pedro J.; Amaral, Luís

doi:10.18803/capsi.v16.350-360

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…As a technological resource and with the objective of discovering favouritism, Data Mining (DM) has a fundamental role to contribute with its tools and methods to find hidden information in the massive volumes of data [6]. The use of this technique in public procurement is used as a critical tool, facilitating the monitoring of information, as well as the control of contracting processes [7]. Applying DM, it was established that in Sweden 58% of time the bidder who submits the lowest bid is not the winner of the process [8]; in Paraguay [9], using data from 4 years and 47,615 procurement processes, this study estimates, through the construction of a mathematical model, the correlation between the companies and their possibility of obtaining a contract, detecting the existence of a previous relationship between the supplier and the contracting entity, which produces corruption when the procurement is made.…”

Section: Status Of the Processmentioning

confidence: 99%

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Torres-Berru

Batista

2021

Electronics

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The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters.

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Section: Status Of the Processmentioning

confidence: 99%

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Torres-Berru

Batista

2021

Electronics

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“…Furthermore, coherently with the neo-institutional approach of analysis of corruption in political-administrative systems (Vannucci, 2012(Vannucci, , 2015, sets of KPIs should also involve socio-cultural factors of corruption to capture opportunities resulting in the dynamic co-evolution with the political-economic ones. Indeed, successfully applying the integrity principle at organizational level requires purposeful and detailed technological, political and cultural provisions (Ferreira et al, 2017).…”

Section: Measuring Corruption In Public Procurement: Limits and Alternativesmentioning

confidence: 99%

A dynamic performance management approach to frame corruption in public procurement: a case study

Gnoffo

2021

JOPP

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Purpose The purpose of this paper is to frame the causal relationships between corruption in public procurement and performance of local governments. Design/methodology/approach An outcome-based dynamic performance management approach is adopted to explore a representative case study of a small Italian municipality. The model is based on three sources: qualitative primary data generated by face-to-face convergent interviews; secondary data retrieved from documents describing legal cases linked to procurement and open-access repositories; and an extensive literature review. Findings Emphasizing the role of community civic morality systemically may help to understand some counterintuitive results in the past research and support decision-makers and policymakers in setting effective strategies to curb the associated negative implications. Social implications A dynamic approach of performance management aimed at framing corruption in public procurement may guide policymakers and decision-makers in keeping their “cognitive radar” constantly active, in a way to detect emerging corruptive phenomena that could be otherwise ignored by normal diagnostical approaches. It may also help to promote organizational ethical learning and improve community outcomes. Originality/value This research provides a causal and systemic framework of relationships involving local government performance and wrongdoings in public procurement, by including performance risk indicators linked to structural and individualistic causes of corruption.

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Electronic platforms and transparency in public purchase

Cited by 2 publications

References 7 publications

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

A dynamic performance management approach to frame corruption in public procurement: a case study

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