Crowdlending: mapping the core literature and research frontiers

Vol.:(0123456789) Review of Managerial Science (2022) 16:2381–2411 https://doi.org/10.1007/s11846-021-00491-8 1 3 ORIGINAL PAPER Crowdlending: mapping thecore literature andresearch frontiers SamuelRibeiro‑Navarrete1· JuanPiñeiro‑Chousa2 · M.ÁngelesLópez‑Cabarcos2· DanielPalacios‑Marqués1 Received: 9 June 2021 / Accepted: 9 August 2021 / Published online: 10 November 2021 © The Author(s) 2021 Abstract Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the future. Keywords Crowdlending· P2P lending· Bibliometrics JEL Classification G00· L26· M13 * Juan Piñeiro-Chousa [email protected] Samuel Ribeiro-Navarrete sribeironavar[email protected] M. Ángeles López-Cabarcos [email protected] Daniel Palacios-Marqués [email protected].es 1 Universitat Politècnica de València, Valencia, Spain 2 Santiago de Compostela University, SantiagodeCompostela, Spain 2382 S.Ribeiro-Navarrete et al. 1 3 1 Introduction Crowdfunding is an innovative form of finance, and analysis of the factors that influence the performance of crowdfunding projects is necessary (Chen et al. 2020). The success of crowdfunding is built on the digitalization of society and the increasing presence of the Internet (Bouncken etal. 2015). This scenario requires knowledge and collaboration at all levels (Bouncken etal. 2021). Crowdfunding has gained in popularity since the 2008 financial crisis and is of particular relevance for small and medium-sized enterprises (SMEs), especially startups. Using platform mediation, crowdfunding gathers together small contributions from a large number of investors to finance projects with varying objectives (Cholakova and Clarysse 2015). Crowdfunding covers a wide range of approaches, including reward-based crowdfunding, equity crowdfunding, donation crowdfunding, and crowdlending. Crowdlending has received considerable attention from researchers in recent years (Bruton etal. 2015). This interest is expected to continue to intensify, hence the motivation for this paper. Crowdlending transactions mainly take place in online environments, where lenders and borrowers engage in convenient, trustworthy exchanges. Peer-to-peer (P2P) lending involves platforms that join lenders and borrowers, who seek funding to carry out their projects. Accordingly, P2P lending platforms act as intermediaries between lenders and borrowers. They facilitate exchanges through credit screening services, posted interest rates (Wei and Lin 2017; Franks etal. 2021), default prediction (Franks etal. 2016), and formal and informal lending procedures (Allen etal. 2019). One of the main concerns about P2P lending platforms is the need for all parties to have robust, accurate, timely information. To meet this need, borrowers and lenders use signals to transmit their intentions and positions in the lending market. Specifically, the better informed party uses signals to send information to the other party and thus aid the exchange between the two to make up for missing information. These signals overcome problems of information asymmetry that can arise with P2P transactions. To avoid information imbalances, these signals, in addition to attracting funding, must provide consistent and accurate information (Connelly etal. 2011), communicate abilities (Connelly etal. 2011), help build reputation (Walsh et al. 2015), generate credibility (Leischnig and Enke 2011), highlight the quality of offerings (Helm and Özergin 2015), and develop co-creative offerings (Patterson 2016). P2P lending transactions still face information asymmetry problems (Dorfleitner etal. 2016). Thus, lenders often have to make decisions based only on information published by borrowers without being sure about its authenticity (Klafft 2008a, b). The communication process, choice of communication channels, and organizational social capital can significantly influence the effect of signals in reducing or eliminating information asymmetries. A growing stream of research has examined the role of social capital in facilitating (or hindering) economic exchanges (Granovetter 2005; Guiso etal. 2004). Social capital refers to features of social organizations such as networks, rules, 2383 1 3 Crowdlending: mapping thecore literature andresearch… and trust. These features develop from informal interactions or norms of universal reciprocity. They are then preserved by mutual commitment and cooperation (Putnam 1995; Adler and Kwon 2002). According to Granovetter (2005), social capital works best when created by the actions, patterns, or processes of those outside the economic setting in question. Thus, social capital can facilitate transactions with third parties outside the dyad that actually creates the social capital. Social connections can be beneficial because, in addition to conveying information flows between parties, the ties they generate offer a valuable cue for outsiders to infer the quality of the agents involved. In sum, by providing information and mutual benefits, social capital can mitigate potential inefficiencies caused by information imbalances, thus enhancing market efficiency (Durlauf and Fafchamps 2005). The negative implications of information asymmetries in the P2P lending market have led to the use of rating-based models to help evaluate and rank loans according to their risk or likelihood of default (Bastani etal. 2019). Together with risk variables, the evaluation process also considers loan returns. Ultimately, lenders face a multi-objective problem corresponding to traditional portfolio optimization (Deb etal. 2011). What is not so traditional is the context in which these financial transactions occur. Technology is revolutionizing P2P lending for two reasons. First, technology is increasingly present in the models (e.g., machine learning and neural networks) used to evaluate loans and thus mitigate information asymmetries in lending exchanges. Second, technology is also present in the process of P2P lending, namely in the way that P2P platforms operate (e.g., through blockchain or artificial intelligence). This paper analyzes the P2P lending literature. Specifically, it examines why and how the concept of P2P lending has evolved from crowdsourcing and crowdfunding and explores which concepts or research lines are evolving and what direction the research will take in the future. To answer these questions, a bibliometric study was conducted. Bibliometric studies use statistical analyses of scientific publications (Pritchard 1969) to provide objective, impartial information on a specific field of research (Zupic and Čater 2015). To date, no bibliometric study has been conducted on this topic. Therefore, it is important to analyze the degree of research progress of P2P lending studies. This analysis can reveal the most important authors, references, journals, and keywords, together with the most relevant connections between them. As in recent previous research (Dana etal. 2021; Mas-Tur etal. 2020), bibliographic coupling, word co-occurrence, and co-citation analysis were used. Bibliometric methods were used to analyze the latest research on P2P lending and understand how this research topic has evolved and how it will continue to evolve in the future. Specifically, the study aims to describe the current research on P2P lending, identify the most relevant authors, publications, and journals, detect the most recurrent keywords from 2003 to February 2021, map the relationships between the key elements of this research field, and identify the fundamental topics at the P2P lending research frontier. In-depth analysis of previous findings in a particular field is necessary for that field to advance (Zupic and Čater 2015). Therefore, this bibliometric study contributes to building a complete picture of P2P lending research by considering all publications on the topic since its inception. The conclusions of the study can provide valuable insight for researchers to understand the current status 2384 S.Ribeiro-Navarrete et al. 1 3 and future of the field, for managers to search for new developments to improve performance and compete, and for policymakers to design strategies to promote growth and development. The rest of the paper is organized as follows. Section2 develops the conceptual framework. Section3 describes the method. Section4 presents and discusses the results. Section5 indicates the limitations and practical implications. And finally, Sect.6 shows the main conclusions as well as the future lines of research. 2 Theoretical framework 2.1 Crowdfunding The phenomenon of the crowd (Franzoni and Sauermann 2014) has given rise to advances such as citizen science. This area considers the active participation of different individuals in scientific projects (Cappa etal. 2016, 2018). There is thus a need to assess the openness–performance relationship (Moretti and Biancardi 2020). The intersection between citizen science and the phenomenon of the crowd forms the basis of crowdfunding, which has become a prominent research topic. Crowdfunding is a type of outsourcing where funds are raised from a crowd of individuals. More specifically, crowdfunding refers to the use of a large number of individuals or groups to finance projects through small contributions pledged online without the need for standard financial intermediaries (Mollick 2014). Crowdsourcing allows organizations to externalize problem-solving tasks to obtain solutions from the crowd (Garcia Martinez 2015), whereas crowdfunding allows the crowd to play a complementary role, not only in the solution of tasks but also in the mobilization of capital (Ordanini etal. 2011). Pre-COVID-19, these transactions were already becoming increasingly important. However, the pandemic has forced organizations that used the Internet as a secondary business channel to prioritize innovative solutions using online platforms (Al-Omoush etal. 2020). Crowdfunding has driven the democratization of the financial sector. It has helped promote entrepreneurial finance (Assenova etal. 2016; Block etal. 2018) and has connected lenders and entrepreneurs. The strength of crowdfunding lies in the widespread adoption and social acceptance of the Internet. This adoption and acceptance has created the framework to attract a multitude of online supporters and investors (Agrawal etal. 2015; Short etal. 2017). Using platform-mediated approaches, a large number of small investors can finance different types of projects—from nonprofit to innovative new ventures—that would otherwise not have had access to traditional financing. To initiate the process, entrepreneurs provide information to potential funders about the project through crowdfunding platforms. These platforms offer a marketplace where fund seekers can interact with crowds (Bruton etal. 2015). This information, which must capture funders’ attention, ranges from income figures or business plans to the aspirations and promises of entrepreneurs. Crowdfunding projects differ depending on the potential reward or the motivations of entrepreneurs and funders. There are two main types of crowdfunding: profit-based crowdfunding and donation- or reward-based crowdfunding. 2385 1 3 Crowdlending: mapping thecore literature andresearch… In donation-based crowdfunding, contributors do not receive rewards, or if they do, these rewards are merely symbolic. The motivation of funders in this case is intrinsic (Gerber and Hui 2013). In reward-based crowdfunding, the motivation of funders may be both extrinsic, when they receive some reward, and intrinsic, when they receive nonpecuniary tangible (prototypes) or intangible (experiences) rewards in exchange for their support (Cholakova and Clarysse 2015). Within reward-based crowdfunding, Coakley and Lazos (2020) differentiated between equity crowdfunding and debt crowdfunding. In equity crowdfunding, funders receive shares or enter a revenue-sharing scheme in return for their contribution. The motivation is primarily extrinsic (Cholakova and Clarysse 2015; Colombo etal. 2015; Vismara 2018). In debt crowdfunding, also known as peer-to-peer (P2P) or marketplace lending, supporters receive interest as compensation for the risk and duration of their lending (Allison etal. 2013; Bruton etal. 2015). The motivation ranges from intrinsic to extrinsic depending on the financial returns (Ordanini etal. 2011). The complexity of the process and the need for investors to become involved are also criteria for classifying crowdfunding. Equity crowdfunding, lending crowdfunding, and rewardbased crowdfunding are, in that order, the most complex forms of crowdfunding. As such, they require greater involvement. Conversely, donation crowdfunding involves less complexity and supporter involvement (Hornuf and Schwienbacher 2018). Scholars have studied crowdfunding from different points of view, including crowdfunder motivation (Ordanini etal. 2011), crowdfunder types and definitions (Mollick 2014; Schwienbacher and Larralde 2012), signaling (Burtch etal. 2013), success factors and dynamic aspects (Kuppuswamy and Bayus 2017), the geographic distribution of investments (Agrawal etal. 2011), social capital (Lin etal. 2013; Mollick, 2014), local altruism and social capital (Giudici etal. 2018), communication (Courtney etal. 2017), narratives (Parhankangas and Renko 2017), fund seekers’ education, gender, and professional background (Barbi and Mattioli 2019), fund seekers’ social ties (Simon et al. 2019), professional funding (Roma et al. 2017), consumer perceptions (Wehnert et al. 2019), and science and technology (Colombo etal. 2015; Sauermann etal. 2019). 2.2 P2P lending The concept of P2P lending, similar to that of matchmaking (Evans and Schmalansee 2016), refers to operations through multi-sided platforms and virtual marketplaces to facilitate transactions between agents. The main objective of P2P markets is to engage buyers and sellers in convenient, trustworthy transactions. Although P2P lending is linked to finance, its increasing importance is due to the rise of matchmakers such as Amazon and eBay and the appearance of fintech startups such as Monzo and Funding Circle. This novel form of financing fits with the concept of ‘ingenious’ or creative solutions’ that combine novelty and value (Lampel etal. 2014). P2P lending involves a two-sided platform that brings together a crowd of individuals and borrowers (predominantly SMEs in need of credit). In exchange for a commission for hosting the lending campaign, crowdlending platforms offer SMEs the chance to obtain financing from investors who pay no commission and expect 2386 S.Ribeiro-Navarrete et al. 1 3 some kind of financial rewad. The reputation of P2P platforms in relation to credit risk assessment, together with the informal network of relationships among companies, can determine the ability of these companies to obtain P2P loans from a wide range of investors. Thus, active P2P platforms in the SME credit market act as intermediaries between investors (small investors and financial institutions) and firms, providing a wide variety of services such as credit screening (Wei and Lin 2017; Franks etal. 2021). There are indirect network externalities between small investors and financial institutions. Investments by institutions, which perform their own monitoring, provide a guarantee for the crowd. This guarantee helps reduce information asymmetries (Cumming and Hornuf 2020). Some authors claim that small investors tend to herd after institutional investors in equity-based crowdfunding. Similar strategies occur in P2P business lending (Asterbo etal. 2018). The crowd’s endorsement for campaigns, the so-called “wisdom of the crowd” (Mollick and Nanda 2016), can likewise act as an indirect network externality for institutions. P2P marketplaces provide different sources of external debt finance. These sources range from P2P business loans to invoice finance. This form of financing habitually involves three agents: the funding platform, the borrowing firm (usually SMEs), and the crowd of investors. Although P2P lending is considered similar to bank lending, there are major differences regarding regulatory arbitrage and disintermediation (Coakley and Huang 2020). For instance, P2P loans are not subject to Basel III capital requirements. Therefore, P2P lending platforms have a relative advantage in terms of their lending rate when compared with commercial banks. In addition, for tax purposes, investors can offset bad loan losses with other crowdfunding income. Although some authors argue that P2P loans complement rather than compete with conventional banking (Milne and Parboteeah 2016), others argue that P2P lending has major advantages by combining the information advantages of informal lending with the pooling and risk-sharing benefits of financial intermediation (Allen etal. 2019). The main disadvantage of P2P loans versus bank deposits lies in their lack of protection. 2.3 Information asymmetry According to Spence (1973), signaling theory explains how signals help fill gaps in information between different parties by sending signals to make up for that missing information. The most informed party sends signals (observed variables) to the less informed party, disclosing the necessary information to make the exchange possible. Initially, this theory was applied to situations where there was little information on the credibility or quality of a product, service, or supplier (Kirmani and Rao 2000). It can now be applied to any situation where there is an information imbalance or information asymmetry between parties. Information asymmetry can lead parties to suffer adverse selection (Akerlof 1970). This adverse selection occurs when decision makers cannot observe or judge a situation based on the information provided (Pouryousefi and Frooman 2019). Ultimately, it can lead to financial losses (Petersen and Rajan 2002). Signals provide consistent information to all parties. These parties use this shared information 2387 1 3 Crowdlending: mapping thecore literature andresearch… (Connelly etal. 2011) to make transactions easier. Signals are useful for a wide variety of activities, most notably attracting financing (Ribeiro-Soriano et al. 2020). Recently, the analysis of signaling and information asymmetries with respect to crowdfunding has captured the attention of researchers (Burtch etal. 2013; Courtney etal. 2017). This topic is highly relevant to crowdfunding, especially crowdlending (Ahlers etal. 2015; Courtney etal. 2017). P2P lending has a crucial difference with respect to the traditional financial credit market in that it involves no financial intermediaries (Lee and Lee 2012). These intermediaries are seen as repositories of soft information about credit quality (Petersen and Rajan 2002). For this reason, in P2P lending transactions, information is more difficult to verify, lenders are less sophisticated, the institutional framework is less developed, and information asymmetries frequently arise. In this context, signals are crucial for investors to reduce investment risk (Ahlers etal. 2015; Courtney etal. 2017). Borrowers signal and transmit information about themselves and the characteristics of the investment project, while lenders search for credit information and screen loan applicants (Yan etal. 2015). In addition to signaling, there is still no clear evidence as to what other variables can improve overall trust in P2P lending projects. Trust management, which can significantly promote fundraising performance (Zheng etal. 2016), is crucial in financial contexts where the risk and complexity derived from economic transactions are important variables (McKnight etal. 1998). P2P lending involves financial transactions that are extremely trust intensive (Guiso etal. 2008). In many cases, investment opportunities are analyzed based only on the project information that is available online. Therefore, it is difficult for lenders to ensure the authenticity and integrity of borrowers’ information (Klafft 2008a, b). It is also difficult for them to know whether they are dealing with a legitimate fund seeker. Although lawmakers are developing regulations in many countries, the overall outcome of these efforts is not yet clear. The P2P literature states that P2P investors can infer the creditworthiness of borrowers by observing the lending decisions taken by other P2P investors (Zhang and Liu 2012). Assuming acquainted investors have an information advantage in a system with posted prices, the decision to invest first could signal the quality of the project to unacquainted investors, who thus develop feelings of trust toward the project. In turn, unacquainted investors trust and expect to benefit from the monitoring capabilities and reciprocal insurance created by acquainted lenders who finance a larger share (Lee and Persson 2016). In addition, more financing from acquainted investors can offer a proxy of social network strength, which can enhance firm performance (Gronum etal. 2012). Borrowers’ reputation is another important factor influencing trust in P2P lending projects. Developing a reputation based on telling the truth and using transparent disclosure can benefit borrowers now and in the future (Michels 2012). Borrowers with a better performance history are more likely to obtain loans and to do so at a lower cost. Thus, lenders use the reputation of borrowers as a signal in their lending decisions. In P2P loans, an effective reputation mechanism can discipline borrowers’ behavior, reducing the probability of default (Ding etal. 2019). 2388 S.Ribeiro-Navarrete et al. 1 3 2.4 Social capital Social capital plays a vital role in mitigating information asymmetries because it can avoid potential inefficiencies triggered by information imbalances, thereby improving market efficiency (Durlauf and Fafchamps 2005; Cassar etal. 2007). According to Putnam (1995, p. 67), social capital relates to “features of social organization such as network, norm, and social trust that facilitate coordination and cooperation for mutual benefit.” Nahapiet and Ghoshal (1998, p. 243) defined social capital as “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit.” Under this definition, social capital has three dimensions: cognitive (shared language), relational (social trust), and structural (the presence or absence of social interaction ties between individuals). In P2P platforms, borrowers and lenders rarely know each other in person, so the third dimension is less applicable than the first two. The characterization of group-level social capital has major implications for understanding the role of social capital in online P2P lending. The concept of grouplevel social capital varies depending on the study perspective. From the insider’s perspective, social capital refers to a common asset accessible to all members (Coleman 1990), whereas from the outsider’s perspective, it refers to the process by which individuals within the group use mutual recognition and support to emulate a privileged group where different kinds of capital are included (Bourdieu 1984). Authors have analyzed the factors that influence lending outcomes by studying how the borrower’s group reliability and verifiability can improve (or fail to improve) funding performance. The role of contextual features such as the institutional environment has also been considered. In this sense, the right institutional environment can lead social capital to enhance online P2P lending performance by improving community solidarity (Chen etal. 2016). In addition to improve lending performance (Cassar etal. 2007), social capital has several benefits such as complementing credit information for specific borrowers (Lin etal. 2013), increasing the knowledge-sharing behavior of participants in a virtual context (Chang and Chuang 2011a, 2011b), and facilitating participants’ access to valid information (Birley 1985). Although the benefits seem to be clear, authors have explored both the pro and the con arguments regarding social capital, concluding that both points of views require analysis (Light and Dana 2013). From a conventional point of view, social capital entails trusting reciprocal relationships (Mustafa and Chen 2010). However, too much social capital can lead to protecting mediocrity (Light 2010) and imposing mental conformity to entire groups (Aldrich and Kim 2007), among other detrimental outcomes. Despite the increasing dependence on group social capital to reduce the uncertainty and risks derived from the fast-paced evolution of online platforms, the P2P lending marketplace is characterized by inaccurate and uncertain information due to anonymity and ubiquity. This social capital may deceive potential lenders and lead them into the wrong lending choices, which can harm their economic performance. Therefore, it is important to find a suitable signaling feature to help prospective lenders. When there is uncertainty surrounding a project, social capital, others’ early contributions, and narratives can help crowdfunders’ make decisions by reducing 2389 1 3 Crowdlending: mapping thecore literature andresearch… information asymmetries (Herzenstein etal. 2011; Lin etal. 2013; Moss etal. 2015). These factors represent signals of trustworthiness that trigger herding behaviors (Skirnevskiy etal. 2017; Zhang and Liu 2012). One important question is how social capital influences the formation of the behavioral biases that affect both individuals’ decisions and P2P lending market performance. These biases include local bias, which represents a deviation from rational benchmarks and occurs when investors’ decisions are biased toward local assets such as local firms or borrowers (Ofir and Wiener 2016; Hirshleifer 2001). This behavior is justified by the idea that greater geographic proximity means a lower risk of default probability (Karlan 2007) due to more active group monitoring (Hung 2006). For external lenders, the support of members of the nearby geographic group is interpreted as a powerful and encouraging signal. Recently, authors have found that local biases are commonly present in the P2P lending market (Jiang etal. 2020). They have tried to determine whether loans attracting local lenders perform better or worse than others. Decisions based on local biases have been found to lead to higher default risk, lower recovery rates, and lower realized return. These findings reflect worse market performance. Based on social capital theories, social capital seems to play an important role in forming local bias because it seeks to facilitate coordination, collaboration, and cooperation, providing mutual confidence among individuals. 2.4.1 Social networks Social capital emerges when individuals are connected to each other. These connections have positive advantages for the individuals and their communities (Portes 1998). Greater complexity and uncertainty in the business environment has led to a focus on social networks (Mohrman etal. 1995). The literature generally focuses on analyzing the optimal network configuration instead of the embedded context of the social network, which significantly affects the role of social capital (Leyden 2003). In an online P2P lending market, there are two main types of social networks: friendship and group networks. There are important differences between the two. Whereas friendship networks mostly represent strongly embedded relationships created outside the objective online economic context (Durlauf and Fafchamps 2005; Lin etal. 2013), group networks are created in online environments with anonymous members between whom there is no interaction. In online P2P lending markets, the main differences relate to the absence or presence of physical connections between individuals and the motivation to create such networks (Putnam 1995). Several authors have studied which type of social network has the best economic performance. Lin etal. (2013) argued that friendship networks can provide better economic performance due to the social stigma cost of default. Other authors have concluded that a lack of connection, recurrent interaction, and closed structures may complicate the development and preservation of social capital by group networks (Nahapiet and Ghoshal 1998; Wasko and Faraj 2005). This process can in turn hinder the achievement of positive lending outcomes. Thus, an effective way to enhance social capital is through the establishment of obligations, norms, and sanctions (Knowles 2006). One problem could be that the constraints that shape human interactions in the P2P 2396 S.Ribeiro-Navarrete et al. 1 3 Table 2 Reference co-citation and author co-citation results Source: Authors based on VOSviewer results Reference co-citations Author co-citations Title Author(s) Citations Link strength Author(s) Citations Link strength Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending Lin etal. (2013) 144 136 Lin, M 177 169.2 Trust and credit: The role of appearance in peer-to-peer lending Duarte etal. (2012) 107 102 Herzenstein, M 165 156.17 What’s in a picture? Evidence of discrimination from prosper.com Pope and Sydnor (2011) 99 98 Duarte, J 113 112.69 Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending Emekter etal. (2015) 86 77 Freedman, S 106 98.75 Herding behavior in online P2P lending: An empirical investigation Lee and Lee (2012) 80 74 Pope, D.G 103 102.6 Rational herding in microloan markets Zhang and Liu (2012) 63 60 Iyer, R 98 96.21 Tell me a good story and I may lend you money: The role of narratives in peer-to-peer lending decisions Herzenstein etal. (2011) 62 62 Lee, E 87 85.33 Instance-based credit risk assessment for investment decisions in P2P lending Guo etal. (2016) 61 47 Emekter, R 86 85.00 Strategic herding behavior in peer-to-peer loan auctions Herzenstein etal. (2011) 61 58 Serrano-Cinca, C 75 71.04 Emergence of financial intermediaries in electronic markets: The case of online P2P lending Berger and Gleisner (2009) 52 48 Zhang, J.J 65 64.00 2397 1 3 Crowdlending: mapping thecore literature andresearch… adopt a behavioral finance perspective. The link strength measures the intensity of the linkages between references or authors. Based on this link strength, the results are consistent with those for the number of citations. The key references are Lin etal. (2013) and Duarte etal. (2012). The most important authors are M. Lin and M. Herzenstein. Figure2 presents the top 10 references resulting from the reference co-citation analysis. The minimum number of citations for a reference was set at 50. Of the 10,884 references considered in this study, 11 exceeded this threshold. The number of links was 55 (100% density), the total link strength was 406.5, and the degree was 10. To simplify the analysis of the references network, the references with the highest link strength were selected. A label represents each reference, and the font size denotes the number of times the reference was cited in the database. As mentioned earlier, a larger font size indicates that the reference has been cited more often. The distance between two references represents the probability that these references are cited together. Consequently, shorter distances indicate a higher probability of being cited together. The colors indicate whether there are different clusters of cited references. References in a cluster are more likely to be cited with other references in the same cluster. The graph contains two clusters. The first (in red) comprises seven papers, primarily oriented to analyzing P2P lending from a behavioral point of view. Specifically, this cluster contains papers on trust or herd behavior (Duarte etal. 2012; Zhang and Liu 2012; Herzenstein etal. 2011). In addition, this cluster contains the most important paper in terms of number of citations and link strength. This paper is located in the center of the network (Lin etal. 2013). The second cluster (in green) comprises four papers that analyze P2P lending from a financial perspective. Lee Fig. 2 Reference co-citation analysis 2398 S.Ribeiro-Navarrete et al. 1 3 and Lee (2012) authored the most notable paper in this cluster. Despite forming part of this cluster, it addresses the topic from a financial as well as a behavioral perspective. Moreover, this paper is located very close to the center of the graph. Therefore, it is highly relevant and can act as a nexus between the two clusters. In conclusion, the papers by Lin etal. (2013) and Lee and Lee (2012) are at the core of this field and provide a basis for research in this area. The author co-citation analysis reveals the most cited authors. These papers are generally among the top 10 most cited references. When considering the link strength of author co-citations, there are some slight differences with respect to the reference co-citation analysis. In this case, although the difference is very small, the author D. G. Pope is slightly more important than S. Freedman. There seems to be no divergence between the results of the author co-citation analysis and the reference co-citation analysis. These results indicate that M. Lin is the most cited author and that a paper by the same author has received the most citations. The other most cited authors are M. Herzenstein and J. Duarte. Figure3 displays the density map of the co-citation analysis of authors. The minimum number of citations of an author was set at 30, with 39 (out of the 7,773) authors exceeding this threshold. There were 729 links (98% density), a total link strength of 1,124.04, and a degree of 37.4. The possibility that there were different authors with identical names or the same author with different names was checked. Such duplicate entries can greatly affect the results. No discrepancies were found. The most cited authors appear in red. In contrast, authors in green have the lowest number of citations. The position on the map denotes the proximity between authors. The closer they are, the greater the chance is that they are cited together. The map shows three groups of authors. The central group includes the most relevant authors (M. Lin, M. Herzenstein, and J. Fig. 3 Density map of author co-citation analysis 2399 1 3 Crowdlending: mapping thecore literature andresearch… Duarte). The group located at the top of the map but very near the center is led by S. Freedman and D. G. Pope. These two groups include authors that study P2P lending from a behavioral as opposed to a financial point of view. This finding is consistent with those for Cluster 1 of the co-citation analysis of references. The group on the right includes Y. H. Guo, who analyzes P2P lending from a financial point of view. This finding is consistent with those regarding Cluster 2 of the co-citation analysis of references. 4.2 Bibliographic coupling ofsources To map the journals that publish research on P2P lending, the minimum number of documents of a source was set at five. Of the 283 sources, 11 exceeded this threshold. There were 55 links (100% density), a total link strength of 503.82, and a degree of 10. The minimum number of citations was also set to zero so as not to penalize more recent publications. Table3 shows the ranking of journals with the most published papers on P2P lending. The journal with the most publications (15) is Electronic Commerce Research and Application. It has the highest link strength (242.19), although it does not have the highest number of citations (390). The journal with the highest number of citations (785) is Management Science. It has a moderate link strength (75.78), implying that this journal includes highly cited publications in this field but that the publications are not closely related to the publications in other journals. Different results were observed for Electronic Commerce Research and Journal of Management Information Systems. They have published few papers on this topic (six and five, respectively, with 42 and 153 citations, respectively). However, their link strengths are quite high (115.23 and 110.15, respectively). Thus, their publications, despite being scarce, are relevant in this field. Figure4 shows that the journals that published the most papers on P2P lending during the second half of 2019 and 2020 were Finance Research Letters, Emerging Markets Finance and Trade, European Journal of Finance, and IEEE Access. Table 3 Bibliographic coupling of sources Source: Authors based on VOSviewer results Source Documents Citations Link strength Electronic commerce research and applications 15 390 242.19 IEEE access 10 42 97.73 Finance research letters 8 31 71.15 Emerging markets finance and trade 7 14 91.57 Financial innovation 7 44 64.97 Electronic commerce research 6 42 115.23 European journal of operational research 6 275 92.74 Management science 6 785 75.78 European journal of finance 5 3 52.27 Journal of management information systems 5 153 110.15 2400 S.Ribeiro-Navarrete et al. 1 3 Finance Research Letters has published the most papers (8) and has the most citations (31). Emerging Markets Finance and Trade has the highest link strength (91.57). 4.3 Author keyword co‑occurrence Figure5 shows the average year of publication of the documents with an author keyword. A threshold of five occurrences was used. Of the 1,131 considered, 43 Fig. 4 Bibliographic coupling of sources by average year of publication Fig. 5 Author keyword co-occurrence by average year of publication 2401 1 3 Crowdlending: mapping thecore literature andresearch… keywords exceeded this threshold. The network is quite scattered, with 199 links (density of 22%) and an average number of links per keyword (degree) of 9.3. The results indicate that the most common keyword (107 occurrences) is “peer- to-peer lending”. This keyword has the largest circle in the graph (link strength 60.00). This keyword appears in green at the center of the graph, which means that the documents containing this keyword were mostly published between 2017 and 2018. The next most important keyword is “P2P lending” (with 103 occurrences and a link strength of 59), followed by “crowdfunding” (with 36 occurrences and a link strength of 30), “fintech” (with 35 occurrences and a link strength of 30), “information asymmetry” (with 18 occurrences and a link strength of 15), “P2P” (with 18 occurrences and a link strength of 10), and “online P2P lending” (with 17 occurrences and a link strength of 4). These results appear in Table4. This analysis reveals the recent appearance of the keywords “fintech”, “machine learning”, “deep learning”, and “soft information. These keywords reveal the trend toward innovative finance based on new technologies such as blockchain, big data, and artificial intelligence. This conclusion is consistent with the results of the bibliographic coupling analysis with sources, which include technology-oriented journals such as Electronic Commerce Research and Applications, IEEE Access, Financial Innovation, and Journal of Management Information Systems. This result is also consistent with the WoS categories considered in the analysis, where the categories Computer science information systems, Computer science interdisciplinary applications, and Computer science theory methods are found to be relevant in this area. 5 Limitations andpractical implications Bibliometric analyses are subject to several limitations. Some limitations relate to the own application of the bibliometric techniques. For example, not always exists conceptual or methodological ‘proximity’ between publications that are jointly cited, as co-citation analysis proposes; or not always the number of citations means importance or relevance of a work, since citations can be the result of many factors influencing Table 4 Author keyword co-occurrence Source: Authors based on VOSviewer results Keyword Occurrences Link strength Peer-to-peer lending 107 60.00 P2P lending 103 59.00 Crowdfunding 36 30.00 Fintech 35 30.00 Information asymmetry 18 15.00 P2P 18 10.00 Online P2P lending 17 4.00 China 16 14.00 Credit scoring 16 14.00 P2P lending platform 11 4.00 2402 S.Ribeiro-Navarrete et al. 1 3 researchers when writing their papers. A deep review of the publications considered in the study in the first case, and the use of citations patterns considering both total citations and citations excluding authors’s self-citations in the second case, can help solve these limitations. Other common limitations are related to the method and choice of data. For example, although the search terms in this study were carefully selected, a broader or more restricted set of publications would have been obtained if other search terms had been considered. These differences would have influenced the study results. Moreover, some documents may have been overlooked because a single citation database was used. For example, the most up-to-date references that have not yet been assigned to a specific topic are not included in the WoS. In future studies, other databases (e.g., Scopus and Google Scholar) could be used to compare the findings with those from this study or simply to include a greater number of publications. All the analyses were performed using the most objective criteria possible. However, a certain degree of subjectivity is inevitable. For example, decisions must be taken when deciding on the number of authors or references to include in the analyses. Moreover, the author co-citation analysis only considered the first author of each document. Therefore, information on collaborating researchers was lost (Córdoba-Cely et al. 2012). However, this procedure is common in bibliometric studies. Finally, the analysis of authors considers their affiliations at the time of publication, which can lead to discrepancies. So, to avoid discrepancies and ensure exact correspondence between the authors’ names in the database and those in the publications, a thorough search and correction process was performed. Despite these limitations, this paper still offers valid analysis of the most relevant research on P2P lending over the study period. This analysis in turn provides a structural and dynamic overview of the research field. A comprehensive set of the most relevant publications on P2P lending from 2003 to February 2021 was analyzed. By examining the research frontiers in the field of P2P lending and assessing the extent to which this field can form a new and independent area of research, this study has important implications for researchers, firms, and policymakers. Researchers can build a picture of this novel research field. This picture can help them identify its core theoretical framework as well as the key topics that show the directions that research should take in the future. For example, innovative finance based on new technologies such as blockchain, big data, machine learning, and artificial intelligence is the most novel trend in this field. Moreover, managers, entrepreneurs, and all kinds of companies, especially those involved in P2P lending projects, can use this research to find evidence of the factors, advantages, weaknesses, and challenges affecting them. Finally, finding and applying theoretical and practical knowledge of P2P lending transactions can help policymakers target their efforts and design policies and programs that contribute to the effective and ethical functioning of the P2P lending market. 6 Conclusions andfuture research Three bibliometric techniques were applied to WoS-indexed publications from 2003 to February 2021. The aim was to describe the knowledge frontier in P2P lending research. The analyses reveal the following: (1) the main P2P lending research 2403 1 3 Crowdlending: mapping thecore literature andresearch… topics, (2) the consolidation of P2P lending as an independent research field, (3) the strong interconnectedness between the most common P2P lending research topics, and (4) the early development of the P2P lending research field. An even greater technological revolution will surely take place in the financial sector. Therefore, alternative financial markets and new ways of understanding finance will continue to appear and to expand in the near future. In this revolution, the role of each agent must be clearly established. The changes that take place will reshape and influence the investment philosophy, behavior, and decision-making processes of investors and the expectations and motivations of borrowers. In the P2P lending market, decisions must be made considering information related to market trading volume, posted interest rates, monetary returns or default probability, and individual behavior associated with investor decisions. The motivations, expectations, and specific circumstances surrounding people are vital to determine the outcome of P2P lending transactions. The study results suggest that future research on P2P lending should focus on the behavioral component of these transactions. For example, research should answer the question of how individual-related and context-related issues affect P2P lending transactions, considering the perspectives of both borrowers and lenders. Future research should also examine how to reduce information imbalances by avoiding the noise it generates, which can prevent these transactions from functioning correctly. It would also be valuable to analyze the moderating role of some variables (innovation orientation, entrepreneurial orientation of borrowers and lenders, and cultural values) in this causal relationship. Researchers together with public institutions and organization should analyze the way to develop a framework for the risk management and profitability in the P2P lending market with the aim of generating a greater use and confidence around P2P financial products. Undoubtedly, this synergy could enhance the sustainability and development of the P2P lending market. Furthermore, most of the P2P lending studies are based on qualitative research that seeks to identify the shortcomings and opportunities of this alternative investment and financing formula, so there is a great potential for new quantitative research. High-tech and decentralized financial environments are destined to prevail over more traditional areas. This scenario requires analysis of the variables that can affect these new financial contexts and decrease risks and the probability of failure. Aspects such as loan maturity, interest rates, platform size, participants’ geographic location, and guarantees to reduce the likelihood of default must be analyzed in depth. One aspect that deserves special attention is the situation of information asymmetry that arises in P2P lending transactions. Information imbalances can greatly influence P2P lending outcomes, so mechanisms should be used to avoid or at least minimize such imbalances. Aspects derived from the concept of social capital or the disclosure of accurate information to the P2P lending market are two effective mechanisms to avoid both information asymmetries and the probability of default. As the fourth industrial revolution takes hold, some questions must be answered. One key question is how to benefit from this revolution in new financial environments. Many countries and firms around the world are designing policies and 2404 S.Ribeiro-Navarrete et al. 1 3 making strategic decisions to promote technology-based financial environments, which seem to be an essential part of the current and future economic context. The regulations established by governments and other authorities must keep up with the appearance of new financial products, services, and markets. This need to keep pace does not mean that regulations should hinder technological advances or innovation. However, authorities must use these regulations effectively to develop reasonable rules to guarantee a healthy future of the financial industry and protect the rights of all participants. Thus, there is an urgent need to educate individual investors (considering variables such as gender, age, and previous education) and design a valuable financial ethos capable of promoting a values-based foundation that benefits all areas of society. Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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