• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.617-626
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• 2004

본 연구는 정보통신기술 (ICT)의 발달과 컴퓨터의 대중화 인터넷 보급의 확산 및 상거래의 온라인화 등으로 전자상거래가 확산되고 이와 더불어 급격한 규모로 성장되고 있는 전자지불 시스템을 분석하고자 한다. 과거 전자지불시스템 자체에 대한 기술적인 연구나 전자화폐 등 세부매체에 대한 연구는 상당히 많이 진행되어 왔으나 본 연구에서는 특히 소액 전자지불시스템을 세분하여 비교연구하기 위한 체계를 제시한다. 전자지불시스템을 자체의 특성에 따라 분류한 기존의 연구에 근거하여 사용자들이 각각의 지불매체에 대해 얼마나 다르게 인식하여 어떠한 태도를 가지고 있는지에 대한 연구체계를 제시한다. 특히 최근 이슈가 되고 있는 모바일 지불시스템 등 소액 중심의 결제시스템을 사용자가 선택하는 이유와 방식에 대해 결제매체의 특성을 중심으로 기존의 결제시스템과 비교하여 분석하기 위한 연구모형을 제시하였다. 본 연구에서 제시된 연구모델을 통해 향후 지불서비스 이용자들이 결제시스템을 이용하는 이유에 대해 이론적으로 체계적인 분석을 함으로써 은행 및 결제대행사 등 기존의 지불서비스 제공기관외에 이동통신사나 컨텐츠 제공업체, 포털서비스 업체 등 관련 기업과 전자지불관련 정책을 수립 집행하는 정부기관에 참고할 수 있는 연구 분석 결과 도출될 수 있을 것으로 예상한다.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.