Author: Dr. Setiawan et al. (2026)
The rapid growth of financial technology (FinTech) has transformed the delivery of financial services and created new opportunities to expand financial inclusion, particularly in emerging economies. Among the various FinTech innovations, peer-to-peer (P2P) lending has emerged as a prominent alternative financing mechanism that connects borrowers and lenders through digital platforms, reducing reliance on traditional financial intermediaries. In Indonesia, where financial inclusion remains a strategic national priority, P2P lending has the potential to improve access to financing for underserved individuals and small businesses. At the same time, the increasing integration of artificial intelligence (AI) into digital lending platforms is reshaping how financial services are designed, assessed, and delivered. Despite the growing importance of AI-enabled FinTech, limited research has examined the combined roles of technological acceptance, AI-related competencies, sustainability-oriented values, and individual risk preferences in influencing P2P lending adoption. Existing studies largely rely on traditional technology acceptance models and often overlook emerging factors associated with digital intelligence and sustainable financial behavior.
To address these gaps, this study investigates the antecedents of FinTech P2P lending adoption in Indonesia by extending the Unified Theory of Acceptance and Use of Technology (UTAUT3) framework. Specifically, the study incorporates financial risk tolerance and artificial intelligence literacy as additional determinants of adoption behavior while examining the moderating role of green self-identity and potential gender differences. By integrating technology acceptance, digital intelligence, sustainability values, and financial behavior into a single framework, the study provides a more comprehensive understanding of user adoption decisions within AI-driven financial ecosystems.
Data were collected from 421 respondents in Indonesia using a non-probability voluntary response sampling approach. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), which enables the simultaneous assessment of direct, moderating, and group comparison effects. The analytical framework was designed to identify the key drivers of behavioral intention and actual use behavior while examining how sustainability-oriented values and demographic characteristics influence the adoption process.
The findings reveal that performance expectancy, effort expectancy, social influence, and artificial intelligence literacy exert positive and significant effects on behavioral intention toward P2P lending adoption. These results suggest that users are more likely to adopt digital lending platforms when they perceive tangible benefits, find the technology easy to use, receive social encouragement, and possess adequate knowledge of AI-enabled systems. Among these factors, AI literacy emerges as a particularly important predictor, highlighting the growing relevance of digital intelligence in shaping trust and confidence in technology-based financial services. As AI becomes increasingly embedded in credit assessment, risk evaluation, and customer service functions, users with greater AI literacy appear more willing to engage with FinTech lending platforms.
The results further demonstrate that facilitating conditions positively influence actual use behavior, indicating that access to technological infrastructure, technical support, and enabling resources remains essential for the successful adoption of P2P lending services. This finding reinforces the importance of developing supportive digital ecosystems that reduce barriers to technology use and encourage broader participation in digital finance.
A particularly noteworthy finding concerns the role of green self-identity. Contrary to expectations, green self-identity significantly weakens the relationship between behavioral intention and use behavior. This result suggests that individuals with stronger environmental identities may be more cautious in translating intentions into actual FinTech usage, potentially due to concerns regarding the social and environmental implications of digital financial activities. The finding contributes a novel perspective to the FinTech literature by demonstrating that sustainability-oriented values do not always reinforce technology adoption and may, under certain conditions, create behavioral constraints.
The multi-group analysis reveals no significant gender differences in the relationships among behavioral intention, use behavior, and financial inclusion. This finding challenges earlier assumptions that men and women respond differently to financial technologies and suggests that the increasing accessibility of digital financial services may be reducing gender-based disparities in FinTech adoption. The result also indicates that policies aimed at expanding P2P lending adoption may not require substantially different strategies across gender groups.
This study makes several important contributions to the literature. First, it extends the UTAUT3 framework by incorporating AI literacy and financial risk tolerance into the analysis of FinTech adoption. Second, it introduces green self-identity as a sustainability-related moderator within the digital finance context and reveals its unexpected constraining effect on usage behavior. Third, it provides empirical evidence that AI literacy has become a critical determinant of adoption in AI-enabled financial ecosystems. Finally, the study demonstrates how responsible FinTech innovation can support financial inclusion and equitable access to financial services. The findings offer practical implications for regulators and FinTech providers by emphasizing the need for AI literacy initiatives, user-centered platform design, and collaborative governance mechanisms that promote inclusive, trustworthy, and sustainable digital finance in Indonesia.
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