• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.563-574
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• 2017

The predominant concerns of the study consist of: (1) the direct effects of social work students' evaluation toward connivers of exam-cheating on their evaluation toward connivers of corruptio ns in social welfare organizations; (2) the dual mediation effects of their concerning dysfunction of whistle-blowing and needing of protection arrangement for whistle-blowers. The notable findi ngs are as follows: First, the evaluation toward a person conniving at exam-cheating significantl y has an effect on the evaluation toward connivers committing corruptions in social welfare orga nizations($.211^{***}$). Second, the more positive evaluation for connivers of exam-cheating, the more concerning dysfunction of whistle-blowing, the less needing protection arrangement for whistle-blowers($-.191^{^{\prime}***}$). The students, concerning dysfunction of whistle-blowing more, show less neg ative evaluation toward a person, conniving at corruption in social welfare organizations($.245^{***}$). The more needing protection arrangement for whistle-blowers, the less positive evaluation regar ding whistle-blowers in the organizations($-.122^{***}$). Lastly, both mediating effects of the needing protection arrangement for whistle-blowers and concerning about dysfunction of whistle-blowing are significant so dual mediator are proved. Some practical implications are discussed based on the study's findings.

• Survey Research
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• v.12 no.1
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• pp.31-63
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• 2011

This study is to make an effective forecasting model for turn-out rates of the candidates with their visibilities, which are measured in their names on the media during the election period. I make a regressive model, with the data of 2010 gubernatorial election in Korea, where turn-out rate is dependent variable and each candidate's visibility, incumbency effect, local control party effect, corruption effect, strategy voting effect, restrain effect as a mid-term evaluation, and policy effect are independent variables. I got the model, T = -4.65 + 1.02V + 16.90 I + 16.78L - 9.12 R, where T is turn-out rate, V is candidate's visibility, I is incumbent effect, L is local control party effect, and R is restrain effect. This function can be used to predict turn-out rates of the candidates in the forthcoming gubernatorial election in Korea at a small outlay.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.