Rational Herding in Microloan Markets

Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel data set that tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance

Reducing Uncertainty in Charitable Crowdfunding

Donors on charitable crowdfunding platforms do not directly consume the benefits of their donations, making it difficult to assess whether fundraisers follow through on their promised benefits. This difficulty in monitoring the actions of charitable fundraisers increases the importance of ex-ante assessment. This study examines whether potential donors rely on the quality signals embedded in the campaign’s page when deciding the direction of their contributions. Using data on charitable campaigns from a charity-focused crowdfunding platform, GoFundMe, this study finds that campaigns with more thorough descriptions written in a more complex writing style receive more donations from more donors. Additionally, more ambitious projects with higher funding goals also receive more donations. These patterns suggest that providing these quality signals can reduce the uncertainty faced by potential donors

Determinants and Consequences of Herding in P2P Lending Markets

In this paper, we are interested in the factors that influence herding behavior in P2P lending marketplaces. We are using data from Prosper.com to examine whether internal market specific factors and external economic factors influence the amount of herding exhibited in the market. We also investigate what consequences herding has and how marketplace participants can benefit or suffer from herding behavior in the marketplace. Based on previous models of herding in P2P lending, we calculate a herding measure over time. This herding measure is the basis for our analyses. Our preliminary analyses show support that internal factors measuring uncertainty, lenders experience, and search costs in the market influence herding. We receive inconclusive results for external factors measuring uncertainty, volatility, and bullishness in the marketplace’s economic environment. Herding has several implications for borrowers and lenders including potentially lower interest rates for borrowers but fewer completed listings

Herding in foreign direct investment

This paper, to our knowledge, is the first to examine herding in foreign direct investment (FDI). We investigate it from two perspectives, first the number of countries investing in the host country and then the dollar volumes of those investments. Our results provide strong evidence of herding in FDI. We also show herding in the divestures of these investors. We show that herding in FDI is related to host country characteristics and governance parameters

Examining the Mediating Role of Commitment on Brand Herding: An Empirical Study in a Virtual Community of Consumption

Consumer social interaction is one of the most influential factors affecting people’s consumption-related decision making (Y. Chen, Wang, & Xie, 2011). When making product choice, particularly, consumers apt to follow the action of the crowd in many circumstances (Bonabeau, 2004). Referred to as herding in literature, such behavior-based social influence among market participants has long been studied (Banerjee, 1992; Hirshleifer & Hong Teoh, 2003; Raafat, Chater, & Frith, 2009). With the rise of online social platforms, other people’s actions are getting increasingly more observable, as consumers usually share with each other their product-related use experiences, opinions, and purchase decisions (Liu, Brass, Lu, & Chen, 2015). As such, behaviorbased social influence plays a critical role in shaping and affecting consumers’ choice (Duan, Gu, & Whinston, 2009). Meanwhile, research on herd behavior has grown significantly and continues to grow. Empirical research examines herd behavior in a wide range of contexts, including online purchase (C. M. K. Cheung, Xiao, & Liu, 2014; Huang & Chen, 2006), technology adoption (Sun, 2013; Walden & Browne, 2009), online auction (Simonsohn & Ariely, 2008), contribution to open source projects (Oh & Jeon, 2007), etc. While it is generally held that no single reason can explain the behavioral convergence of consumers, literature in economics, marketing, and IS (information systems) disciplines primarily highlight two utility-based mechanisms behind herding (Bikhchandani, Hirshleifer, & Welch, 1992; Y.-F. Chen, 2008; Duan et al., 2009; Huang & Chen, 2006; Langley, Hoeve, Ortt, Pals, & van der Vecht, 2014; Zhang & Liu, 2012). These mechanisms are informational cascades (i.e., ignore one’s own information and make a choice based on other’s choice due to uncertainty when making decision, see (Bikhchandani et al., 1992) and positive network externalities (i.e., additional users of a good increase the value of that good, (Kauffman, McAndrews, & Wang, 2000). Despite a wealth of literature on herd behavior, there has been little work discusses the convergent behavior occurs among customers who are already the patrons of certain brands. This setting is unique in that, available choices in the market (i.e., current brand vs. alternatives) are not in the same position from the standpoint of consumers. Empirical studies do show that the popularity of a brand, per se, positively impacts its customers’ loyalty (Raj, 1985) and favorable cost-benefit evaluation (Deval, Mantel, Kardes, & Posavac, 2013; He & Oppewal, 2018; Li, 2004). These key components, in turn, encourage the existing customers of the brand continue their patronage (Aaker, 2009). By this process, a product’s popularity establishes a hinderance to its customers’ attrition by cementing brand-customer relationship (Aaker, 2009). However, it remains uncertain how and to what extent that customers’ continuance intention (as opposed to migrating to alternative brands) is affected by the crowd’s choice. Note: in some occasions, indeed, brand popularity is negatively associated with one’s brand choice. Need for uniqueness (Tian, Bearden, & Hunter, 2001) and negative network externalities (Hellofs & Jacobson, 1999) are two common mechanisms. The former occurs primarily in the market of self-expressive products, such as luxury goods, apparels, and the like (Steinhart, Kamins, Mazursky, & Noy, 2014); whereas a typical context of the latter is that the quality of certain services being worsen off due to high service popularity (Hellofs & Jacobson, 1999). Obviously, the understanding of brand patrons’ behavioral convergence has significant implications on both theory and managerial practice. However, none of the aforementioned utility-based mechanisms of herding (i.e., informational cascades and positive network externalities) provides a satisfactory explanation in this context. One reason for this theoretical lacuna is related to the implicit assumption in the herding literature (i.e., available choices in the market are of the same position), which is not the case in the research context of business retention/switching. A second reason deals with the overwhelming emphasis on the economic utility as the underlying mechanisms. As pointed out by Bikhchandani et al. (1992), herding could also be induced by noneconomic factors, such as the decision-maker’s conformity with others (Jones, 1984), avoiding sanctions due to disobedience (Bendor & Mookherjee, 1987), and so on. Building upon the prior research on customer retention, we introduce customer commitment — the key construct in business relationship literature, into the understanding of brand patrons’ behavioral convergence. It is widely held that commitment plays the central role in people’s persistence of behavior (Newman & Sabherwal, 1996). Particularly, customer commitment involves not only the state of mind that binds a customer with the present business relationship (Kelley & Davis, 1994), but also the structural conditions that prevent her from making a change (Becker, 1960). Therefore, we contend that the perspective of commitment offers an integrative understanding of the behavioral convergence induced by both psychological and utility-based mechanisms. This research adopts the three-component commitment model (TCM) — a widely used conceptualization of commitment (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002). According to TCM, people choose to maintain the current business relationship Tang The 18th International Conference on Electronic Business, Guilin, China, December 2-6, 2018 822 because they feel they want to (affective commitment), ought to (normative commitment), or need to (calculative commitment) (Meyer, Allen, & Smith, 1993). By introducing TCM into the study of herding phenomenon, this research takes a holistic and novel view for understanding the interplay between product popularity and consumers’ continuance. In particular, we ask, how do affective commitment, normative commitment, and calculative commitment mediate the effects of product popularity on customers intention of brand continuance? In this research, we attempt to answer the research question in the settings of virtual communities of consumption (VCC), which are online groups explicitly centered on consumption-related interests (De Valck, 2005). VCCs provide plentiful of informative cues about brands’ relative popularity and consumers’ choices (C. M. K. Cheung et al., 2014), hence constitute an ideal research environment of our study. This study potentially contributes to the literature at the following perspectives. First, despite the voluminous research on herding, it remains uncertain how group mimicking behavior affect customer retention or migration. The current research adds to the literature by expanding research on herding to a domain in which, to the best of our knowledge, very little scholarly effort has been devoted. Second and more importantly, this research theorizes and empirically tests the central role of commitment components underlying the herd behavior of brand patrons. This perspective provides insights into an alternative mechanism of how herding takes effect in the context of customer migration, thus adds to both the herding and customer retention literature. In addition, our exploration of the heterogeneous roles of various popularity cues in customer retention sheds lights on marketing practice about the most effective way to retain patrons and attract potential customers

Is the Leaderboard Information Useful to Investors? : The Leaderboard Effect in P2P Lending

P2P (Online Peer-to-Peer) lending provides an open marketplace where borrowers make requests for loans by lenders who subsequently decide whether to bid or not following an examination of the relevant information posted by borrowers. In this P2P lending context, the leaderboard, where popular loan requests are displayed at the web’s front page, provides information for lenders to use when evaluating the requests. We empirically examine the effects of leaderboard information regarding the most popular existing loan requests. Our results show that the leaderboard information works ex ante in attracting additional bids to get loan requests successfully financed. However, it does not work ex post in improving the performance so that it has less potential for default

Microfinance: Combating World Poverty One Small Business at a Time

Poverty is a major problem that reaches millions of people around the world. Although many organizations and individuals work daily to combat this, much of the work done to reduce poverty lacks sustainability and serves only to remedy to the effects of poverty, rather than create a solution to the causes of poverty. Microfinance can be very basically defined as the provision of banking to the impoverished who would not otherwise have access to these services. This purpose of this thesis is to show that microfinance is the ideal solution to the poverty problem by using research and evidence from case studies. This thesis also contains analyses of these studies with the purpose of discovering best practices in microfinance

Crowdfunding our health: economic risks and benefits

Crowdfunding is an expanding form of alternative financing that is gaining traction in the health sector. This article presents a typology for crowdfunded health projects and a review of the main economic benefits and risks of crowdfunding in the health market. We use evidence from a literature review, complimented by expert interviews, to extend the fundamental principles and established theories of crowdfunding to a health market context. Crowdfunded health projects can be classified into four types according to the venture's purpose and funding method. These are projects covering health expenses, fundraising health initiatives, supporting health research, or financing commercial health innovation. Crowdfunding could economically benefit the health sector by expanding market participation, drawing money and awareness to neglected health issues, improving access to funding, and fostering project accountability and social engagement. However, the economic risks of health-related crowdfunding include inefficient priority setting, heightened financial risk, inconsistent regulatory policies, intellectual property rights concerns, and fraud. Theorized crowdfunding behaviours such as signalling and herding can be observed in the market for health-related crowdfunding. Broader threats of market failure stemming from adverse selection and moral hazard also apply. Many of the discussed economic benefits and risks of crowdfunding health campaigns are shared more broadly with those of crowdfunding projects in other sectors. Where crowdfunding health care appears to diverge from theory is the negative externality inefficient priority setting may have towards achieving broader public health goals. Therefore, the market for crowdfunding health care must be economically stable, as well as designed to optimally and equitably improve public health

Crowdfunding our health: economic risks and benefits

Crowdfunding is an expanding form of alternative financing that is gaining traction in the health sector. This article presents a typology for crowdfunded health projects and a review of the main economic benefits and risks of crowdfunding in the health market. We use evidence from a literature review, complimented by expert interviews, to extend the fundamental principles and established theories of crowdfunding to a health market context. Crowdfunded health projects can be classified into four types according to the venture's purpose and funding method. These are projects covering health expenses, fundraising health initiatives, supporting health research, or financing commercial health innovation. Crowdfunding could economically benefit the health sector by expanding market participation, drawing money and awareness to neglected health issues, improving access to funding, and fostering project accountability and social engagement. However, the economic risks of health-related crowdfunding include inefficient priority setting, heightened financial risk, inconsistent regulatory policies, intellectual property rights concerns, and fraud. Theorized crowdfunding behaviours such as signalling and herding can be observed in the market for health-related crowdfunding. Broader threats of market failure stemming from adverse selection and moral hazard also apply. Many of the discussed economic benefits and risks of crowdfunding health campaigns are shared more broadly with those of crowdfunding projects in other sectors. Where crowdfunding health care appears to diverge from theory is the negative externality inefficient priority setting may have towards achieving broader public health goals. Therefore, the market for crowdfunding health care must be economically stable, as well as designed to optimally and equitably improve public health

Understanding (Ir)rational Herding Online

Investigations of social influence in collective decision-making have become possible due to recent technologies and platforms that record interactions in far larger groups than could be studied before. Herding and its impact on decision-making are critical areas of practical interest and research study. However, despite theoretical work suggesting that it matters whether individuals choose who to imitate based on cues such as experience or whether they herd at random, there is little empirical analysis of this distinction. To demonstrate the distinction between what the literature calls "rational" and "irrational" herding, we use data on tens of thousands of loans from a well-established online peer-to-peer (p2p) lending platform. First, we employ an empirical measure of memory in complex systems to measure herding in lending. Then, we illustrate a network-based approach to visualize herding. Finally, we model the impact of herding on collective outcomes. Our study reveals that loan performance is not solely determined by whether the lenders engage in herding or not. Instead, the interplay between herding and the imitated lenders' prior success on the platform predicts loan outcomes. In short, herds led by expert lenders tend to pick loans that do not default. We discuss the implications of this under-explored aspect of herding for platform designers, borrowers, and lenders. Our study advances collective intelligence theories based on a case of high-stakes group decision-making online