| Summary: | 碩士 === 中華大學 === 科技管理學系 === 106 === Online Peer-to-Peer (P2P) lending platforms were introduced in the United Kingdom in 2015 and then in the United States. P2P lending is a method of debt financing that enables individuals to borrow and lend money without the use of an official financial institution as an intermediary and without the requirement of a mortgage guarantee. The platform makes its profit from the service fee. Both parties agree to a repayment method and conclude the transaction. The role of technology is to provide space for the media. Credit ratings, loan quotas, repayment rates, etc., can be found through various P2P Internet lending companies appropriate to borrowers and lenders. The P2P lending investment threshold is low, which raises young people's behavioral intention to invest and small investments spread the risk of using this means of funding. In August 2016, the FSC announced that P2P lending was not illegal and no separate special law was necessary to control it. Therefore, more and more companies are adopting this innovative financial application. According to the industry’s big data, young people are highly dependent on the Internet, since they spend half their day on internet-based activities; hence, they are receptive to emerging financial management methods. Since the P2P lending investment threshold is low compared to tens of thousands of traditional financial management methods, such as stocks, funds, trusts, etc., low investment is the most significant advantage of this system. Therefore, this study uses twentysomethings as the research object to understand the factors that influence them to use P2P lending. The technology acceptance model is used as a theoretical basis for the study and the independent variables used to explore the behavioral intention of twentysomethings to use P2P lending are Service charge, Service, Loan conditions, Increasing product and service to meet customers’ needs, Perceived risk, Aesthetics, Relative effectiveness, Perceived ease of use, Perceived usefulness, Attitude toward Using, Performance expectation, Trust, Safety and Privacy. Questionnaires are used to collect the data and, after discarding incomplete responses, the results obtained from 314 effective responses confirmed after the Structural Equation iii Modeling found that Service charge, Increasing product and service to meet customers’ needs, Perceived risk of loans, Relative effectiveness, Platform perception risk reduce the Perceived ease of use and also have a negative effect on behavioral intention, while Performance expectation, Perceived usefulness, Privacy and Trust have a positive impact on behavioral intention. Relevant practical recommendations are made based on the research results, which are expected to provide a reference for the future financial industry and lending platforms.
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