Customer Technology Adoption of P2P Lending Platforms in India: A Comprehensive Study using the Extended Technology Model (E-Tam) – An Empirical Study Using Machine Learning

This study investigates the customer technology adoption of P2P lending platforms in India using the E-TAM framework, augmented with Machine Learning (ML) techniques. P2P lending has come as an innovative force in the Indian financial system, offering alternative lending options to both borrowers and lenders. Understanding the factors influencing customer adoption of these platforms is crucial for their sustainable growth and market integrity. Dataset comprising all factors has been collected through questionnaires and using SEM analysis, this empirical study investigates the critical determinants of customer technology adoption in context of P2P lending platforms. The E-TAM framework, which is an extension of conventional TAM has been added with other variables such as Perceived Risk, Trust, Perceived Benefit, Perception, Awareness, Platform Performance, Privacy and Security, Platform Reputation and some other moderating variables, are used in the analysis. Moreover, machine learning techniques are used to discover new patterns and relationships in the data. The findings give insights on the important factors influencing customer adoption behavior, which provide valuable information for P2P lending platform operators, regulators, and other stakeholders. This study adds value with the help of existing articles and has given a comprehensive understanding of customer technology adoption in the P2P lending landscape and shows efficacy of ML approaches in empirical research.