Responsible Approaches to Blockchain, Cryptocurrency, and FinTech
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Item Not Enough Data to Be Fair? Evaluating Fairness Implications of Data Scarcity Solutions(2025-01-07) Karst, Fabian; Li, Mahei; Reinhard, Philipp; Leimeister, JanThis study explores the implications of the use of data scarcity solutions on fairness in machine learning, specifically in consumer credit interest rate prediction. We develop a comprehensive taxonomy of Data Scarcity Solutions (DSS) by analyzing academic literature, data science competitions, and practical implementations. We identify six distinct DSS clusters: Data Extension, Pre-Training, Public Data Inclusion, Data Sharing, Federated Learning, and Active Learning. Our evaluation shows that most DSS enhance both performance and fairness, with minimal negative correlation between the two. Notably, approaches incorporating external or synthetic data significantly improve fairness. This research contributes to understanding DSS beyond algorithmic performance, providing a framework for evaluating their societal impact. Furthermore, it offers practitioners a taxonomy to select the right method for tackling data scarcity and addresses fairness concerns in real-world scenarios.Item Characteristics of High-Risk Defaulters: An Empirical Study on the Bondora P2P Lending Platform(2025-01-07) Liu, Yajing; Mona, KyokoAbstract This study analyzed the characteristics of high-risk defaulters in the peer-to-peer (P2P) lending market using data from a European-based P2P lending platform, Bondora, from 2009 to 2021. Borrowers often participate in the P2P lending market, because they do not have access to credit through regular commercial banks for various reasons. Lenders participate in this market to diversify their portfolios and seek higher returns as compensation for the financial risk. Despite the transparency of transactions and the information provided by borrowers, lenders or investors bear the credit risk owing to asymmetric information. This study finds that financial variables, such as loan amount, interest rate, credit score, and existing liabilities, are predictors of the probability of default. In contrast, demographic and cultural characteristics, such as age, gender, employment status, education level, and language, have varying effects on default. For instance, employment status and language do not significantly impact default.Item Does an Organization's Performance Matter in Adopting Blockchain Technology? Examining the Impact of Task-Technology Fit and Trade-Off Effect among TOE Factors on Performance Outcome(2025-01-07) Shin, Soo Il; Kim, Jin Sik; Negash, Solomon; Saeed, KhawajaBlockchain technology offers various use cases and can be appropriated in different ways. However, there is a lack of clarity regarding the performance outcomes of blockchain technology at an organizational level. We identify performance outcomes of blockchain technology and examine how task-technology fit, level of adoption, and technological, organizational, and environmental factors influence these outcomes. Prior literature, technology’s specific features, and its use cases guided the identification of relevant outcomes, including cost reduction, process transparency, and business process innovation. We tested the hypotheses using the response surface methodology (RSM) supplemented by polynomial regression analyses with survey data from 419 organizations. The results indicate that task-technology fit and adoption stages merely show the statistical significance of the outcome variables. However, the TOE factors significantly influence those outcomes independently and jointly. Detailed results and discussions follow.Item Introduction to the Minitrack on Responsible Approaches to Blockchain, Cryptocurrency, and FinTech(2025-01-07) Liu, Wanli; Zhou, Kaiguo; Li, YibaiItem Are Tokens Sufficient to Resolve Collective Action Problems in Decentralized Autonomous Organizations? A Model-Based Examination(2025-01-07) Dong, Sichen; Hu , Daning; Chau, Michael; Ma, CongOpportunism caused by conflicts of interest significantly impedes collaboration within decentralized autonomous organizations (DAOs). To address this issue, we develop a model to illustrate whether the unique aspect of tokens—the interest alignment effect—can mitigate collective action problems in DAOs. This model integrates the “rational cheater” framework with the interest alignment effects introduced by token rewards. It also examines the interaction between token rewards and members’ social motivation, particularly focusing on the dynamics of crowding-out effects. Our findings highlight that the constraints on opportunism in DAOs primarily include the interest alignment costs associated with token holdings and the psychological costs tied to members’ social motivation. These costs complement each other in reducing opportunism in DAOs. Additionally, the effect of increasing token rewards on opportunism depends on the balance between the current token value and how opportunistic actions potentially affect the organization’s value.