| Title |
Accelerated growth of peer-to-peer lending and its impact on the consumer credit market: evidence from Lithuania / |
| Authors |
Taujanskaitė, Kamilė ; Milčius, Eugenijus |
| DOI |
10.3390/economies10090210 |
| Full Text |
|
| Is Part of |
Economies.. Basel : MDPI. 2022, vol. 10, iss. 9, art. no. 210, p. 1-17.. eISSN 2227-7099 |
| Keywords [eng] |
households ; finance ; consumer credit market ; regulations ; peer-to-peer platforms (P2P) ; commercial banks ; macroeconomic indicators ; modern borrowing trends |
| Abstract [eng] |
The paper analyses development and drivers of accelerated growth of peer-to-peer (P2P) lending in Lithuania and its impact on the consumer credit market with a focus on related sustainability issues. Legislative discrepancies between the P2P and banking segments are analysed and their role in predetermining the different development trends within the segments is highlighted. The research is composed of several steps, where each step analyses a certain problem with the aim to compare the processes in both segments, and is using two different approaches based on macroeconomic data and legislative environment analysis. The applied setup of the research allows for distinguishing and quantitative evaluation of the impact on the segments caused by various internal and external factors, such as macroeconomics, technological advantages of P2P platforms, and discrepancies within business regulation. The obtained results could fill in the scientific literature gaps by providing quantitative evidence of the influence the analysed internal and external drivers have on the growth rate of the consumer credit market segments in Lithuania and how this could affect the performance of the whole market, including its sustainability. Conclusions made could be of interest to researchers and practitioners in other countries too, especially those which have similar legislation and regulations within the consumer credit market. Methods used: a scientific literature analysis and generalisation, comparative analysis, statistical data analysis, correlation–regression analysis, mathematical modelling. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
