Factors Affecting BoP Producer Intention to Use P2P Lending Platforms in India

Frontiers in microfinance research for small and medium enterprises (SMEs) and microfinance institutions (MFIs): a bibliometric analysis

“…Souza et al [ 94 ] discussed the importance of understanding each microfinance program and its clients on a case-by-case basis in order to use microfinance consumer market segmentation to develop the most appropriate strategies to address clients’ needs. Similarly, Kumra et al [ 95 ] found that the faster access and ease associated with P2P lending positively influence borrowers’ intention to participate, lenders are positively influenced by the high returns and diversified risk. In addition, a study by Zhang et al [ 92 ] proposes the use of new business models to leverage more opportunities to deliver customer value.…”

Analysis of Factors Affecting Individuals’ Online Consumer Credit Behavior: Evidence From China

“…So far, there are a few attempts to examine factors affecting individuals’ use of online banking and P2P lending platforms, which are very inspiring. For example, Han et al (2019) used the Technology Acceptance Model (TAM) to analyze factors affecting Chinese consumers’ use of P2P lending platforms, proposing that financing knowledge and risk attitude are two key factors associated with P2P borrowing; Kumra et al (2021) also used the Theory of Planned Behavior (TPB) when analyzing factors affecting the willingness of Indian BOP manufacturers to use P2P lending platforms, finding that while fast and easy access to P2P lending can favorably impact borrowers’ intention to participate, lenders are positively influenced by the high returns and diversified risk; Lee (2009) used the Technology Acceptance Model (TAM) and the Theory of Planned Behavior to analyze factors affecting the adoption of online banking in Taiwan, finding that the intention to use online banking is adversely affected mainly by the security/privacy risk and financial risk, and it is positively affected mainly by perceived benefit, usefulness, as well as attitudes; Karjaluoto et al (2002) used the Theory of Reasoned Action (TRA) and the Technology Acceptance Model (TAM) in their research on factors affecting individuals’ use of online banking in Finland, concluding that prior personal banking experience, demographic factors (especially occupation and household income), as well as attitudes toward computers influence both attitudes and actual behavior toward online banking; Carranza et al (2021) used the Technology Acceptance Model (TAM) to analyze factors that influence bank customers’ attitudes toward e-banking for feedbacks on banking services and the process of value co-creation, and the result showed that when e-banking users have a positive attitude toward using e-banking, they will have a greater intention to use e-banking; Ullah et al (2022) studied the impact of Pakistani consumers’ financial skills and digital literacy on their intention to adopt m-payment/m-banking using the Technology Acceptance Model (TAM), finding that on the basis of perceived ease of use, individuals’ digital literacy can have a strong association with their intention. Amongst them, it can be observed that the TAM model is widely used in research related to the adoption of emerging technologies ( Pikkarainen et al, 2004 ; Tan et al, 2012 ; Hossain et al, 2020 ), and it is an effective approach to achieving research goals.…”

“…Furthermore, studies on online lending have also proved that convenience is the main selling point of online lending platforms. Booming online lending platforms and simple online lending operations lead to more frequent use of Fin-tech lending services, proving that objective conditions can positively affect individuals’ online lending behavior ( Chaffee and Rapp, 2012 ; Kurniawan, 2019 ; Kumra et al, 2021 ). Therefore, we propose the following hypothesis:…”

“…Furthermore, studies on online lending have also proved that convenience is the main selling point of online lending platforms. Booming online lending platforms and simple online lending operations lead to more frequent use of Fin-tech lending services, proving that objective conditions can positively affect individuals' online lending behavior (Chaffee and Rapp, 2012;Kurniawan, 2019;Kumra et al, 2021). Therefore, we propose the following hypothesis: Perceived Consequences Triandis (1980) believes that, similar to the expectancy theory of motivation (Porter and Lawler, 1968), the relationship between perceived consequences and behavior can also be understood as that individuals evaluate the consequences of their behavior according to potential rewards and choose behavior according to the desirability of the rewards.…”

Adoption Factors of P2P Lending in India

“…A Bloomberg article [61] cites analyst prediction of P2P lending to reach US$559 billion by 2025. Even though online P2P lending has been a relatively young field of research, there has been an increasing amount of scientific contributions that have been published in recent years [15][16][17].…”