Table 11 Objective of the articles (ordered by number of citations)
No | Author/Year | Objective |
|---|---|---|
1 | (Fazekas, Toth, & King, 2016) | Intend to measure grand corruption and state capture at the micro-level of individual contracts and tenders based solely on "objective" behavioral data |
2 | (Fazekas & Tóth 2016) | Identify clusters at high-corruption risk in public procurement in a contractual network of procuring authorities and suppliers |
3 | (Fazekas & Kocsis 2017) | Contemplate the absence of feasible corruption indicators to estimate the level of fraud in public procurement |
4 | (Chiu et al. 2011) | Proposes a model to discover fraudsters based on Internet auction transaction records using data collected from Yahoo! Auctions |
5 | (Reeves-Latour & Morselli 2017) | Outlines the appearance of bidding processes and corporate criminal networks in the building industry. It describes the development of corrupt networks around dubious and continued actions of criminality, collusion, and bribery and analyzes relations among players |
6 | (Sedita & Apa 2015) | Perform an empirical investigation into a network with relationships between companies involved in public procurement schemes in the construction sector in the Veneto region between 2008 and 2012 |
7 | (Lin et al. 2012) | Detect irregular conduct and supply a classification method to evaluate the level of risk of defrauding online auctions by collusive groups |
9 | (Almendra 2013) | Recognize fraudulent dealers in auctions and combat non-delivery of non-existent goods before the scammers can act |
9 | (Luna-Pla & Carlock 2020) | Conduct an empirical process for modeling corruption practices concerning a network of hundreds of shell firms |
10 | (Yu & Lin 2013) | Uncover fraud cases on online auction sites and detect the accounts of the most active fraudsters on the network, in addition to helping buyers identify them |
11 | (Wachs & Kertesz 2019) | Intend to detect the potential cartels involved in bidding processes |
12 | (Wachs et al. 2019) | Investigate the social factors of corruption and evaluates the risk of scams due to suppressed competition and the absence of clearness in the public contracts awarded to the deal. It compares the risk of fraud in agreements with fragmented social networks and excess compulsory social capital |
13 | (Li et al. 2013) | Seeks to measure and improve the predictive capacity of reputation systems in the fight against collusion in peer-to-peer networks |
14 | (Hosseini et al. 2019) | Determine the character of the endemic corruption threats in the Iranian construction initiative, its grade of occurrence, and the strength of its effect |
15 | (Morselli & Ouellet 2018) | Investigate whether the companies' market share can be identified through the co-bidding similarity in bidding patterns and whether this measure forecasts their market share in processes with recognized conspiracy |
16 | (Huber & Imhof 2019) | Predict collusion through bid-rigging cartels in tenders within the Swiss construction sector |
17 | (Ganguly & Sadaoui 2018) | Identify and characterize Shill Bidding in e-auctions employing real data from eBay |
18 | (Velasco et al. 2021) | Identify the principal patterns of corruption risk in public expenses, such as stunts, conflicts of interest, and the participation of shell companies in bidding processes |
19 | (Rabuzin & Modrusan 2019) | Compare prediction models of suspicious bids and develop a model to detect suspicious one-bid in public procurement processes |
20 | (Anysz et al. 2018) | Compare two machine learning techniques for detecting collusion between bidders in road construction tenders |
21 | (Almendra & Enachescu 2014) | Identify fraud in product listings on online auction sites for fair operators, customers, and vendors |
22 | (Zhu et al. 2011) | Seeks to extract characteristic traits in the behaviors of fraudsters from the feedback reputation system, who manipulate their feedback scores by engaging in various legitimate sales |
23 | (Wachs et al. 2021) | Aim to investigate the risk of fraud in public procurement agreements of the Member States of the European Union |
24 | (Fazekas & Wachs 2020) | Explore the association between corruption and market anatomy in public procurement approaches |
25 | (Sharma et al. 2019) | Identify the risks of corruption in public procurement and discover the contribution of suspected actors and their relationships in the network |
26 | (Dhurandhar et al. 2015) | It analyzes corporation loss due to fraud and proposes tools and processes to quickly and cheaply identify fraud/risks related to public and private procurement and potential wrongdoers |
27 | (Mundra & Rakesh 2014) | Investigates how a contractor's network position affects its success in winning public procurement projects through its ability to partner and influence the network |
28 | (Popa 2019) | It analyzes a set of public procurement data from European countries in order to identify the emergence of close links between bidders and public administration, defined in terms of repeated interaction and geographical dispersion |
29 | (Gallego et al. 2021) | Determine trades that can become troublesome and forecast inefficiency and fraud in public procurement by employing a dataset with about two million public procurement processes in Colombia |
30 | (Alzahrani & Sadaoui 2020) | Identify shill bidding fraud in online auctions |
31 | It intends to measure the user reputation (trust score) by considering the feedback reliability status for all transactions in the e-commerce market, such as e-auction | |
32 | (Mamavi et al. 2017) | Explore conniving networks deriving from collaborative associations among firms. It suggests a protocol to investigate networks within the public sector based on a quantitative method. It also emphasizes the interactive effects of companies that might impact the granting of contracts in decision-making, besides the influence of weak and strong bonds between firms on the contracts |
33 | (Khomnotai & Lin 2015) | Recognize scams on online auctions by exploring the concept of neighborhood diversity as an effective resource to identify competitiveness and joint participation between companies that may be acting together to defraud bids regularly |
34 | (Dadfarnia et al. 2020) | Detect fraudulent users in collusive acts in online auctions |
35 | (Zhu et al. 2020) | Design a social networking standard to detect possible bidder conspiracy in the construction sector |
36 | Detect the formation of scheming groups in auctions through collusive activities or manipulation of the average price of the bids towards a higher or even unrealistic winning price | |
37 | (Majadi et al. 2017) | Provides a brief overview of leading research on bidding patterns to detect shill bidders in online auctions, illustrates the characteristics of such bidding patterns, and presents case studies identifying shill bidding behaviors in eBay's datasets |
38 | (Carneiro et al. 2020) | Detect fraud in public tenders of Portugal, investigate the clearness of the deals between network actors and promote access to relevant data |
39 | (Lin & Khomnotai 2016) | It seeks to determine the change in the reliability perception of competitors within the bidder's reputation system on online auction sites when creating false transactions |
40 | Explore analytical structures that can help public controls recognize agents more sensitive to irregular activities in Public Procurement in Ceará (Brazil) | |
41 | (Owusu et al. 2021) | It examines the effects of corruption at the stages of procurement processes and develops a framework for modeling the impacts of corruption and improving anti-corruption measures |
42 | (Xiao et al. 2021) | Examine the features of conniving bidding networks and the types of collusive bidding groups in the construction industry |
43 | (Nicolás-Carlock & Luna-Pla 2021) | Understand the criminal collusion of companies implicated in corruption scandals in public tenders through exclusive data from reported corruption cases in Mexico, where several companies were manipulated to misappropriate billions of dollars |
44 | (Fazekas et al. 2021) | Explores through empirical data the role of governance played by criminal groups organized in corruption networks, facilitating corrupt transactions by reducing the costs of searching, negotiating, and enforcing public purchases |
45 | (Czibik et al. 2021) | It studies the corruption risks in EU defense procurement that present a significant potential for corruption and state capture. It uses a large set of contract data covering ten years of investigation |
46 | (Perevezentceva et al. 2021) | It studies fraud in public procurement and the development of the control role in the bidding system and suggests introducing an anti-corruption investigation of the records of candidates participating in competitive processes |
47 | (Abidi et al. 2021) | Detect Shill Bidding (SB), which is when the seller presents false bidders to increase the final price of a bid |
48 | (Davydenko et al. 2017) | Detect likely risks of corruption in public tenders competition using empirical data from procurement processes and labeled data encompassing the registration of suppliers with lawsuits in fraudulent cases of public acquisitions |