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Bibliographic Details
Main Author: Neha Kukreti
Format: Recurso digital
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Published: Zenodo 2025
Online Access:https://doi.org/10.5281/zenodo.15647104
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Table of Contents:
  • <p>ABSTRACT <br>The emergence of digital lending platforms has revolutionized loan accessibility, facilitating effortless online <br>borrowing. Comprehending the determinants of adoption and their influence on the expansion of digital lending <br>is essential for financial stakeholders. This study conducts a systematic review and bibliometric-content <br>analysis to identify key areas, research shortcomings, and future directions. This study analyses authorship <br>trends and theme shifts using data from the largest database Scopus, providing a systematic framework to <br>inform future research and improve comprehension of digital lending acceptance. <br>The study was operationalized using PRISMA framework, including defining the research topic and scope, <br>creating a search strategy, and extracting data from Scopus. After data cleaning and validation, R Studio and <br>VOSviewer were used for bibliometric analysis. The study assessed the literature through systematic review and <br>synthesis to guarantee consistency and additional value. This study covers digital lending research trends, <br>subject groupings, and important contributors. <br>Academic research highlights the growing attention on financial inclusion, behavioural factors, peer-to-peer <br>lending, and Fintech innovation in emerging nations. Numerous research gaps exist in geographic <br>representation, socio-technical approaches, and inclusive financing. <br>Theory provides a framework for future study, while practical implications aid fintech practitioners, legislators, <br>and industry leaders in streamlining digital lending networks. Future evidence collection should incorporate <br>broader datasets, longitudinal research, and new technologies to ensure digital lending sustainability. <br>Keywords: Digital Lending Platforms, Internet Credit Loan, Bibliometric Analysis, Thematic Clustering, <br>Adoption Determinants </p>