Experts vs. Nonexperts in Online Crowdfunding Markets

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Lin, Mingfeng; Sias, Richard; Wei, Zaiyan

Abstract: The growth of crowdfunding markets that include both expert and nonexpert inves ... Expand

Abstract: The growth of crowdfunding markets that include both expert and nonexpert investors will soon accelerate due to recent changes in Securities Exchange Commission (SEC) regulations. Prior work has suggested that nonexperts (1) may benefit from experts' participation via mimicking their trades, but (2) will also face a cost, as experts crowding nonexperts out of the best opportunities will ensure that nonexperts will suffer lower returns than experts. Traditional economic theory holds that the crowding effect means that the relative importance of nonexperts in the market will decline over time until they become unimportant. Exploiting a unique period in one crowdfunding market (Prosper.com) that allowed us to directly estimate the net cost of competing with better-informed experts, we found that the net negative effects of expert participation on nonexperts are small. We used simulations to both better understand (1) the market characteristics and crowdfunding platform choices that influence experts' and nonexperts' returns, their return gap, and the extent to which nonexperts are better or worse off relative to a market without expert participation, and (2) the factors that may contribute to the small expert/nonexpert Prosper return gap. Collapse

Topics: crowdsourcing Prosper crowdfunding extensible markup language accounting

Methods: simulation descriptive statistic logistic regression experiment computational algorithm

Theories: economic theory

Digital Multisided Platforms and Women’s Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates

2023 | Information Systems Research | Citations: 0

Authors: Ozer, Gorkem Turgut; Greenwood, Brad N.; Gopal, Anandasivam

Abstract: Digital multisided platforms and peer-to-peer marketplaces are increasingly attr ... Expand

Abstract: Digital multisided platforms and peer-to-peer marketplaces are increasingly attracting attention from scholars, with a significant amount of work examining their societal implications. Despite such focus, limited work has investigated the social implications of the democratization of capital by peer-to-peer (P2P) lending platforms, particularly within the healthcare sector, where financial constraints can be daunting. In this study, we investigate the effects of P2P lending platforms on local abortion rates, a medical procedure characterized by significant financial barriers and social stigma. Leveraging the entry of the P2P platform LendingClub into different states at different times, and a differences-in-differences approach, we find that the entry of LendingClub is associated with an increase in the rate at which women choose not to carry to term. Further, results indicate the effects vary based on the characteristics of the local area, with stronger effects manifesting in more religious areas and areas with lower levels of education, suggesting that social frictions in the form of stigma and knowledge asymmetries can influence financial need. Theoretical and practical implications are discussed within.History: Ram Gopal, Senior Editor; Yan Huang, Associate Editor.Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.1126. Collapse

Topics: crowdsourcing peer-to-peer lending Prosper LendingClub peer-to-peer model

Methods: qualitative interview longitudinal research difference in differences experimental group robustness check

What’s in a “Username”? The Effect of Perceived Anonymity on Herding in Crowdfunding

2022 | Information Systems Research | Citations: 0

Authors: Jiang, Yang; Ho, Yi-Chun (Chad); Yan, Xiangbin; Tan, Yong

Abstract: This research examines the role of perceived anonymity in shaping herding behavi ... Expand

Abstract: This research examines the role of perceived anonymity in shaping herding behavior in online crowdfunding markets. Drawing on theories from social psychology literature, we argue that a lender forms different credibility perceptions toward preceding peers based on their perceived anonymity state; the lender then uses such perceptions to adjust the lender’s herding momentum toward them. Using data collected from a leading debt-based crowdfunding platform, we classify an individual’s username as either anonymous or real-seeming, with the latter referring to as a user identification that seems to reveal one’s real name. The results show that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility derived from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. We further show that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period; nevertheless, we find no such discrepancies between listings that are assigned with high-risk and low-risk credit grades. Our study contributes to the literatures dealing with anonymity and herding in online environments. Collapse

Topics: anonymity crowdsourcing crowdfunding online review privacy

Methods: robustness check hierarchical linear modeling longitudinal research statistical hypothesis test descriptive statistic

Theories: social learning theory

Information Asymmetry Among Investors and Strategic Bidding in Peer-to-Peer Lending

2022 | Information Systems Research | Citations: 0

Authors: Lu, Kai; Wei, Zaiyan; Chan, Tat Y.

Abstract: We study how investors in peer-to-peer (P2P) lending use their information advan ... Expand

Abstract: We study how investors in peer-to-peer (P2P) lending use their information advantages in decisions on when to place bids. Literature documents that better-informed bidders may withhold bidding until the last moment (i.e., “sniping”) to avoid competition. We argue that, because a collective effort from investors is required in P2P lending, it could be optimal for informed investors to bid early in projects with a low probability of being funded with the purpose of signaling the quality of these projects. With a unique data set from Prosper.com, we use a matching analysis to show that, for projects with low credit grades, the probability of being successfully funded is positively correlated with early bids from informed investors. For funded loans, informed investors are more likely to bid in the early stage than uninformed investors, whereas uninformed investors will follow and bid at the late stage. Finally, there are more early bids from informed investors for “good” loans than for “bad” loans, indicating that the bids are informative for the quality of those projects. Our findings have important implications for managing the information asymmetry and strategic behaviors among investors on P2P lending platforms. Collapse

Topics: crowdsourcing peer-to-peer lending Prosper privacy online auction

Methods: robustness check statistical hypothesis test multivariate statistics linear regression analysis propensity score matching

Theories: lemon market theory

Where You Live Matters: Local Bank Competition, Online Marketplace Lending, and Disparity in Borrower Benefits

2021 | Information Systems Research | Citations: 0

Authors: Alyakoob, Mohammed; Rahman, Mohammad S.; Wei, Zaiyan

Abstract: In the past decade, the proliferation of online marketplace lending has been dis ... Expand

Abstract: In the past decade, the proliferation of online marketplace lending has been disrupting the consumer credit market, especially personal loans used for debt consolidation. These lenders, for example, Lending Club, transcend the geographic boundaries within which local banks operate and offer homogeneous access and terms to borrowers. However, the ultimate benefits borrowers derive from marketplace lending can differ significantly because local alternatives may be used to replace marketplace loans when they are available and favorable. Correspondingly, if local bank competition drives the substitution of an existing marketplace loan with a traditional bank loan, the promise of equal benefits to all borrowers from marketplace lending is unlikely to fully materialize. This competitive dynamic also has implications for policy making, particularly in judging the ramifications of bank mergers and acquisitions. We utilize data from Lending Club, a major peer-to-peer (P2P) lending platform, to study whether local market structure drives heterogeneous incentives of traditional banks to convert borrowers of marketplace lending to conventional bank loans. The results indicate that a borrower who resides in a more competitive market is more likely to pay off a P2P loan early by making a large, one-time payment compared with a borrower from a less competitive market. Thus, borrowers from different markets do not benefit equally from online marketplace lending disrupting the consumer credit market. Our study has implications for online marketplace lending, other FinTech-based markets, and the consumer credit market in general. Collapse

Topics: crowdsourcing peer-to-peer model electronic market Prosper peer-to-peer lending

Methods: robustness check instrumental variables estimation probit regression experiment parametric test

Crowds, Lending, Machine, and Bias

2021 | Information Systems Research | Citations: 35

Authors: Fu, Runshan; Huang, Yan; Singh, Param Vir

Abstract: Big data and machine learning (ML) algorithms are key drivers of many fintech in ... Expand

Abstract: Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. Although it may be obvious that replacing humans with machines would increase efficiency, it is not clear whether and how machines can improve human decisions. We answer this question in the context of crowd lending, in which decisions are traditionally made by a crowd of investors. Using data from Prosper.com, we show that a reasonably sophisticated ML algorithm predicts listing default probability more accurately than crowd investors. The improvement of the machine over the crowd predictions is more pronounced for highly risky listings. We then use the machine to make investment decisions and find that the machine improves upon investors’ decisions and leads to greater welfare for both investors and borrowers simultaneously. When machine prediction is used to select loans, it leads to a higher rate of return for investors and more funding opportunities for borrowers with few alternative funding options. We also find suggestive evidence that the machine is biased in gender and race even when it does not use gender and race information as input. We propose a general and effective “debiasing” method that can be applied to any prediction-focused ML applications and demonstrate its use in our context. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still improves the crowd’s investment decisions in our context. These results indicate that ML can help crowd-lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources. Collapse

Topics: crowdsourcing peer-to-peer lending Prosper decision making price management

Methods: computational algorithm machine learning XGBoost descriptive statistic experiment

Theories: portfolio theory

Emotional Labor in the Sharing Economy

2018 | HICSS | Citations: 0

Authors: Lutz, Christoph; Newlands, Gemma; Fieseler, Christian

Abstract: The peer-to-peer nature of the sharing economy encourages participants to alter ... Expand

Abstract: The peer-to-peer nature of the sharing economy encourages participants to alter their behavior in ways that resemble traditional notions of emotional labor. A key element in this shift lies in the coercive nature of feedback mechanisms which condition both providers and consumers to perform emotional labor during service encounters. Using survey data from 207 sharing economy consumers in the US, we show how different facets of the feedback mechanisms employed by sharing economy services influence consumers’ emotional labor. In addition, we show how platforms and their policies matter in encouraging emotional labor, indicating the need to analyze the topic on a fine-grained level. We conclude by deriving propositions for future research and practical recommendations. Collapse

Topics: sharing platform Uber Lyft Airbnb ride sharing platform

Methods: survey descriptive statistic regression analysis method ordinary least square principal component analysis

Cheap Talk? The Impact of Lender-Borrower Communication on Peer-to-Peer Lending Outcomes

2018 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Jennifer J.; Chau, Michael

Abstract: The limited information provided by peer-to-peer (P2P) lending platforms often i ... Expand

Abstract: The limited information provided by peer-to-peer (P2P) lending platforms often is not sufficient for lenders to determine if a borrower is trustworthy and able to repay the loan. Using a unique dataset from a P2P lending platform, which allows lenders to seek information directly from borrowers and borrowers to respond to the questions and comments, we examine the impact of lender-borrower communication on funding outcomes and loan performance. Our results show that not only the amount but also the content of such direct communication matters. Specifically, the number of lender comments is negatively associated with funding success, while the number of borrower responses is positively associated with funding success, although only comments help reduce the final interest rate. The role of the communication is even stronger for listings with poor credit grades. Moreover, lenders are influenced by other lenders’ (positive or negative) comments and the quality of the information disclosed in borrower responses can affect funding outcomes. Loan performance (e.g., default ratio), however, cannot be predicted based on the amount of lender borrower communication. This research broadens and deepens our understanding of the roles of information disclosure, social influence, information quality, and trust in economic exchanges in online settings. Collapse

Topics: crowdsourcing peer-to-peer lending data quality peer-to-peer model social influence

Methods: linear regression analysis logistic regression regression analysis method qualitative coding qualitative content analysis

Theories: lemon market theory

Information sharing and user behavior in internet-enabled peer-to-peer lending systems: an empirical study

2017 | Journal of Information Technology | Citations: 0

Authors: Feller, Joseph; Gleasure, Rob; Treacy, Stephen

Abstract: Internet-based information systems (IS) have enabled various forms of collective ... Expand

Abstract: Internet-based information systems (IS) have enabled various forms of collective intelligence, action, and resources (e.g. open source software, innovation marketplaces, crowdsourcing, and crowdfunding). Within the domain of crowdfunding, Internet-enabled Peer-to-Peer Lending Systems (IP2PLS) have emerged as a disruptive technology, with implications for the financial services sector, business capitalization strategies, and personal and community development. IS research investigating user behavior in IP2PLS has revealed the saliency of social identity and personal transparency (as expressed through information sharing) in such systems. Prior research has largely focused on a small number of IP2PLS providers, thus this study examines a very large but under-researched platform. The study tests a theoretical model based on Social Identity Theory and prior IP2PLS studies, through an analysis of 116,667 loan records, and a subsequent analysis of 1000 manually coded records, to investigate the impact of information sharing on user (lenders and borrowers) behavior. The study reveals the importance of social (vs financial) data, and further reveals relationships that frequently contradict prior findings from other IP2PLS. The study thus implies the need for a more heterogeneous view of the IP2PLS domain, and the need to more fully understand as systems that support user behavior by enabling social information exchanges. Collapse

Topics: information exchange user behavior crowdsourcing social identity Prosper

Methods: theory development logistic regression qualitative coding regression analysis method theoretical contribution

Theories: social identity theory identity theory theory of information

Deep Investment Behavior Profiling by Recurrent Neural Network in P2P Lending

2017 | International Conference on Information Systems | Citations: 0

Authors: HAN, Xiao; Wang, Leye; Huang, Hailiang

Abstract: Peer to peer marketplaces, as a type of online micro loan markets, provide a hug ... Expand

Abstract: Peer to peer marketplaces, as a type of online micro loan markets, provide a huge amount of transactional data for micro loan marketing analysis. Prior work primarily studies factors that reflect listings’ quality or affect lenders’ decision in a collective level; whereas what discriminative characters that an individual investor possesses and how individuals’ investment behaviors change over time are less studied. To investigate these issues, this article conducts a new study from the individual investor level, namely investment behavior profiling. In particular, we first design a uniform and information-comprehensive feature representation to profile an individual ’s investment behavior at a certain time slot, which includes various types of attributes from the perspectives of investor, borrower, listing, investor-borrower relationship, and exterior factors. On the basis of the profile representation, we employ the deep recurrent neural network (RNN) techniques to capture and model individual investors’ long and short term time-varying behavior characteristics. By using the trained RNN profiling model, an investor’s future investment behavior profile can be predicted based on his/her historical behaviors (i.e., investment profiles in previous time slots). Some preliminary experiments verify that the RNN based investment behavior profiling can outperform the state-of-the-art time series prediction approaches. Collapse

Topics: crowdsourcing Prosper peer-to-peer model peer-to-peer lending innovation management

Methods: experiment longitudinal research recurrent neural network time series analysis gated recurrent unit