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Analyzing the Adoption of Online Payment using System Dynamics

시스템 다이내믹스를 이용한 온라인 지불결제 서비스 수용에 관한 분석

  • Mu, Hong-Lei (Dept. of Business Administration, Dongguk University) ;
  • Lee, Young-Chan (Dept. of Business Administration, Dongguk University)
  • Received : 2017.12.20
  • Accepted : 2018.03.16
  • Published : 2018.03.31

Abstract

Online retail business has provided internet-based companies with the opportunities to be connected with online customers from all over the world. However, many online customers do not complete their transactions online even if they have already choose what they want because they perceive online payment service is risky or perceive difficulty of paying online. A large body of researchers have examined the important variables that influence online payment, however, these studies can hardly predict the future development tendency after five or ten years since the environment of online market changes so fast more than ever. Therefore, the purpose of this study is to examine the importance of factors affecting online payment and to provide long term dynamic decision making model for third-party payment companies and online service providers. To serve the purpose, this study used system dynamics approach to develop a model of online payment adoption and to simulate various development paths for ten years. The analysis results show that the number of online payment customers increase continuously in ten years, and service quality, system quality, and effort expectancy are key factors for customers to pay online.

온라인 소매업은 인터넷 기반 회사들에게 전 세계의 온라인 고객들과 연결될 수 있는 기회를 제공했다. 그러나 많은 고객들은 온라인 지불결제 서비스의 위험성이나 사용의 어려움으로 인해 원하는 상품을 선택하고도 온라인에서 거래를 완료하지 못하는 경우가 종종 있다. 그동안 많은 연구자들이 온라인 지불결제에 영향을 미치는 요인들을 조사했지만 온라인 시장 환경이 급격하게 변화되면서 향후 5년 또는 10년 후의 전개될 상황을 예측하기란 매우 어렵다. 본 연구의 목적은 온라인 지불결제에 영향을 미치는 요인들을 검토하고 제3자 결제 회사 및 온라인 서비스 제공 업체에게 장기적인 동태적 의사 결정 모형을 제공하는데 있다. 이를 위해 본 연구는 온라인 지불결제 서비스 수용 모형을 개발하고 향후 10년 동안 전개될 상황을 시뮬레이션하기 위해 시스템 다이내믹스 기법을 사용하였다. 분석결과 온라인 지불결제 고객은 10년 안에 지속적으로 증가할 것이며 핵심 영향 요인으로는 서비스 품질, 시스템 품질 및 예상 소요시간인 것으로 나타났다.

Keywords

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