Exploring the critical factors influencing online lending intentions
Open accessAbstract
Background: Online lending (P2P lending) is a type of internet finance mainly used to meet the financial needs of small and medium-sized enterprises and groups of individuals. It is a supplement to the traditional financial system. Method: This paper uses PPDai.com—the most influential online lending platform in China—as a research object to study the leading factors that affect lenders’ loan trust and perception of information asymmetry. It also studies the impacts of these factors on lending intention. Results: The results of this study show that the lending intention is mainly influenced by trust; perceived information asymmetry will affect trust, but it will not have a direct impact on lending intention. Trust is significantly affected by the borrowers’ reputation and information integrity. Information asymmetry has various causes, including structural assurance and legitimacy. This perception of information asymmetry significantly prevents the further development of online P2P lending. Conclusion: The findings in this research imply that there are profound differences between online lending and online purchasing, thus the results obtained in the traditional e-commerce market cannot be applied in the field of online lending without verification.
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Information asymmetry, where a seller possesses more information, leads to lower levels of trust.
“When buyers experience information asymmetry, a situation wherein a seller possesses more information, this leads to lower levels of trust (Jones & Leonard, 2014;Wang et al., 2015).”
From Antecedents of trust in the sharing economy: A systematic review (2017) · cites this paper as Wang et al., 2015
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Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008) · Decision Support Systems · doi:10.1016/j.dss.2007.07.001 · Cited by 3,613 · see citing works
Building Effective Online Marketplaces with Institution-Based Trust
Pavlou, P. A., & Gefen, D. (2004) · Information Systems Research · doi:10.1287/isre.1040.0015 · Cited by 2,516 · see citing works
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Research works printed but not linked: 16
- Bachmann A, Becker A, Buerckner D, Hike M, Kock F, Lehmann M, Funk B (2011) Online peer-to-peer lending-a literature review. J Internet Banking Commerce 16(2):1-18
- Chen D, Han C (2012) A comparative study of online P2P lending in the USA and China. J Internet Banking Commerce 17(2):2-5
- Chen M, Wang G, Deng S (2008) Sunyuan. Compared with the mechanism of the formation of initial and sustainable online trust. Manag Sci Res 29(5):187-195
- Duarte J, Siegel S, Young L (2012) Trust and credit: the role of appearance in peer-o-peer lending. Rev Financ Stud 25(8):2455-2484
- Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39-50
- Greiner ME, Wang H (2010) Building consumer-to-consumer trust in E-finance marketplaces: an empirical analysis. Int J Electron Commer 15(2):105-136
- Herzenstein M, Dholakia UM, Andrews RL (2011) Strategic herding behavior in peer-to-peer loan auctions. J Interact Mark 25(1):27-36
- Lee E, Lee B (2012) Herding behavior in online P2P lending: an empirical investigation. Electron Commer Res Appl 11(5):495-503
- Lin MF, Prabhala NR, Viswanathan S (2013) Judging borrowers by the company they keep: friendship network and information asymmetry in online peer-to-peer lending. Manag Sci 59(1):17-35
- Ma Q, Zhaojia, Zhang Y, Hao J (2012) Study on the influence mechanism of customer initial trust in C2C environment: moderating effects of online shopping experience. Manag Rev 24(7):70-81
- Nahapiet S, Ghoshal S (1998) Social capital, intellectual capital and the organizational advantage. Acad Manag Rev 23(2):242-266
- Pavlou PA (2003) Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int J Electron Commer 7(3):101-134
- Pavlou PA, Liang HG, Xue JJ (2007) Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective. MIS Q 31(1):105-136
- Qiaopei H, Song W (2012) An empirical study on the influencing factors of rebuilding reputation based on consumer perception. Manag Rev 24(5):110-117
- Yum H, Lee B, Chae M (2012) From the wisdom of crowds to My Own judgment in microfinance through online peer-to-peer lending platforms. Electron Commer Res Appl 11(5):469-483
- Zhang JJ, Liu P (2012) Rational herding in microloan markets. Manag Sci 58(2):892-912
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Antecedents of trust in the sharing economy: A systematic review
ter Huurne, M., Ronteltap, A., Corten, R., & Buskens, V. (2017) · Journal of Consumer Behaviour · doi:10.1002/cb.1667 · Cited by 456 · see citing works
- Authors
- Wang, P., Zheng, H., Chen, D., & Ding, L.
- Affiliations
- Southwestern University of Finance and Economics (CN)
- Soochow University (CN)
- Corresponding author
- Chen, Dongyu
- Venue
- Financial Innovation
- Published
- 2015-07-22
- Volume
- 1
- Issue
- 1
- ISSN
- 2199-4730 (ISSN-L)
- OpenAlex
- W1483839226
- Cited by
- 64
- References
- 2
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 5 · 2023: 6 · 2022: 12 · 2021: 15 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 5
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Financial Innovation (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (Grant code:71302008, Grant code:71302186)
- National Social Science Foundation (Grant code:13CJY121)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Keywords
- peer-to-peer lending, online lending, trust, information asymmetry, information integrity, structural assurance, legitimacy, internet finance
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