Fengya Zhu

• Zhu, Fengya & Shneor, Rotem (2026). Credit risk management in peer-to-peer lending platforms: A systematic literature review. Technological Forecasting and Social Change. ISSN 0040-1625. 232. doi: 10.1016/j.techfore.2026.124831. Full text in Research Archive Show summary

  Rapid growth in peer-to-peer lending has introduced significant default risks that are anchored in platform operations under restricted and varying supervision regimes, as well as in the growing dependence on algorithm-driven lending practices that may increase loan mispricing and underestimation of systemic risk. Hence, to better understand current practices and challenges, the paper presents a systematic literature review on credit risk management in P2P lending. The review is based on 453 articles, 428 of which were analyzed in a bibliometric analysis, and 280 analyzed in a content analysis. The analysis includes thematic mapping and identification of trends and gaps in terms of theoretical, contextual, and methodological aspects of the studies reviewed. The findings suggest that the research is dominated by two streams: one focused on predictive optimization of default risk using machine learning, and the other on identifying predictors of default likelihoods using regression analyses. Both streams often rely on public platform data, focus on consumer lending in few contexts (75% of studies in China and the USA), engage in quantitative analyses (97% of studies), and have weak connection to theory (56% of studies). Opportunities for future research relate to strengthening theoretical anchoring, using alternative data sources and study contexts, and enhancing methodological plurality. Finally, the study presents a new integrative framework summarizing the key factors found to be related to default in consumer and business lending, while aggregating them by their level of analysis. This framework can both inform and shape future research efforts.

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• Zhu, Fengya & Shneor, Rotem (2025). Consumer spending behaviour in ‘Buy Now Pay Later’ model: A comparative analysis between China and Norway. Full text in Research Archive
• Zhu, Fengya; Shneor, Rotem; Zhang, Bryan & Xie, Zhifu (2023). Drivers of peer-to-peer consumer lending amounts: evidence from the Philippines. Full text in Research Archive
• Zhu, Fengya (2026). Essays on Post-adoption Behavior in Digital Consumer Lending. Universitetet i Agder. ISSN 9788284273310. Full text in Research Archive Show summary

  The objective of this dissertation is to explore the post-adoption behavior of digital consumer lending. As digital lending platforms become increasingly prevalent and continue to reshape the evolving financial landscape, lenders and policymakers must understand how alternative finance influences individual decision-making. The primary question that this research seeks to answer is: What factors influence spending behavior and repayment patterns among borrowers in the context of digital consumer lending? Particular emphasis has been placed on such dimensions as technology acceptance, credit risk, experiential learning, and customer satisfaction. The dissertation comprises three articles: one literature review (Study 1) and two empirical studies (Studies 2 and 3). Study 1 undertakes a comprehensive review of credit risk management in peer-to-peer lending, a prevailing form of digital lending. It reveals a prominent methodological trend toward ensemble modeling techniques and advanced algorithms and it highlights the theoretical and empirical insufficiency of existing studies. Building on this, Study 2 examines post-adoption behavior based on data collected from users of a digital lending platform in the Philippines. Study 2 is concerned with the antecedents of borrowing amount by leveraging major constructs of the Technology Acceptance Model (TAM) (Davis, 1989). In addition, incorporating the credit risk (Golden & Walker, 1997; Strischek, 2017) and experiential learning (Ellis et al., 2006; Kolb et al., 2014) theories enabled the adaption of borrowers’ risk profiles and financing experiences to construct an extended TAM framework. In Study 3, the research context shifts to another form of digital finance: the buy now, pay later (BNPL) model. Complementing Study 2, Study 3 provides a comparative analysis of two-dimensional spending behavior among BNPL users. An extended TAM framework is developed by introducing an impulse buying framework (Xiao & Nicholson, 2013) and Expectation-Confirmation Theory (Oliver, 1980), with the aim of illuminating variations in spending habits across cultures, informing both lending strategies and credit risk assessment. Collectively, these studies contribute to a deeper understanding of post-adoption borrower behavior in digital lending markets, contrasting with the majority of the literature in this area, which focuses on pre-adoption engagement. Firstly, the dissertation traces consumer behavior of digital lending back to credit risk, behavioral, and affective layers beyond TAM. Secondly, it clarifies supply-side strategies across marketing, customer service, and risk management. Thirdly, the generalizability of the research findings across cultures is explored, underscoring the dissertation’s attention to distinct credit infrastructures across economies.

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