Digital Payment Adoption in Crowdfunding Platforms: Systematic Literature Review
Abstract
Digital payment adoption on crowdfunding platforms has emerged as a significant research area in the field of financial technology. This study presents a systematic literature review of 40 peer-reviewed articles published between 2020 and 2025, using the PRISMA 2020 framework to analyze digital payment adoption patterns in crowdfunding platforms. Three main research questions are addressed: (1) factors influencing user preferences between traditional digital payments and cryptocurrency, (2) mechanisms by which payment technologies enhance platform security and trust, and (3) the most effective research methodologies for analyzing digital payment adoption. From an initial pool of 847 articles across four major databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect), 40 studies met the inclusion criteria. The analysis reveals that trust is the dominant factor (80%), followed by ease of use (70%), and social influence (55%). The Technology Acceptance Model (TAM) is the most commonly used theoretical framework (47.5%), with Structural Equation Modeling as the primary analytical method (32.5%), and surveys employed in 65% of the studies. QR-based mobile payments show the highest adoption rates (78%) due to perceived ease of use and the influence of the COVID-19 pandemic. Cryptocurrency adoption varies by demographics, with Millennials and Generation Z demonstrating 40% higher acceptance compared to older generations. Blockchain-based payment systems significantly improve transaction security (up to 85%) through smart contracts and decentralized architecture, yet face barriers related to technical complexity and regulatory uncertainty. These findings offer practical implications for platform developers, policymakers, and fintech stakeholders, including trust-centered design, user-friendly blockchain integration, and digital financial literacy programs. Future research should adopt standardized methodologies and longitudinal approaches to better understand long-term adoption dynamics.
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