Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms

The crowd-based acquisition of funding consists of asking a large crowd of people for their financial support and collecting their relatively small monetary contributions for accomplishing a certain financial goal. On online crowdfunding platforms, money-seeking project initiators (“project creators” or “project founders”) meet potential funders (“backers”) that are searching for interesting projects and promising investment opportunities. In order to convince funders to support their aims, project initiators present and describe their ideas using textual information, pictures, or videos. Typically, project initiators have to determine the funding goal (e.g., USD 20,000) and the length of the funding period (e.g., 30 days). If a project succeeds in reaching the funding goal by the end of the funding period, it is successfully funded. While some platforms pay out the money independently from reaching this goal (keep-it-all model, e.g., on giveforward.com), other platforms pay out the money only if the collected funding meets or exceeds the funding goal (all-or-nothing model, e.g., on kickstarter.com). Especially in the case of all-or-nothing models, which are applied on many platforms, high sums of collected funding have to be given back to the funders because the funding goal was not reached. According to which type of crowdfunding is applied, funders receive different types of compensation for their monetary support. While in reward-based crowdfunding the funders prepurchase material or immaterial (non-financial) compensations, e.g., products or services, in lending-based crowdfunding (P2P lending), funders receive repayments and interest payments. In equity-based crowdfunding (crowdinvesting), funders receive shares of a venture or some other form of participation. On the contrary, in donation-based crowdfunding, funders do not receive any compensation. However, the act of funding means psychological satisfaction through having helped other people altruistically (Gerber et al. 2012). Thus, funders derive a certain form of utility from supporting the projects even in the case of donations. The considerations concerning expected utilities are a key driver of the resulting funding decisions (Koch 2017).

Research has already explicitly dealt with certain phenomena concerning individuals’ funding decision behavior. It was found that especially friends and family members are among the first to support a project (Agrawal et al. 2015). Crowdfunding campaigns that have collected higher amounts of funding profit from a herding effect that drives people to further support the project because they trust the decisions of previous funders (Zhang and Liu 2012). However, if a project is close to reaching the designated funding goal, some kind of bystander effect leads to the effect that funders “do not contribute to a project that has already received a lot of support because they assume that someone else will provide the remaining financing” (Kuppuswamy and Bayus 2013). Investigations also show that funders “prefer to contribute funds locally and to culturally similar others” (Burtch et al. 2014). As a consequence of such funding decisions, some projects will reach their funding goal while others fail. Funding success can, thus, be interpreted as the aggregate result of the various funding decisions.

Research has also engaged in analyzing the drivers of crowdfunding success and found several interesting factors. Most basically, it was confirmed that lower funding goals are easier to reach than higher goals (Mollick 2014). Less intuitively, it was revealed that longer funding periods of projects lead to a lower probability of funding success. It seems as if longer periods are interpreted as “a sign of lack of confidence” among project initiators (Mollick 2014). Furthermore, evidence has been found for the importance of information provision. The more textual information is given, the more likely is funding success (Barbi and Bigelli 2015; Koch and Siering 2015; Pitschner and Pitschner-Finn 2014). Moreover, pictures and graphical descriptions (Koch and Siering 2015) as well as videos (Koch and Siering 2015; Mollick 2014) support funding success. These and other insights have been used, e.g., to predict campaigns’ funding success (Koch and Cheng 2016).

While such specific phenomena of crowdfunding have already gained attention in research, the superordinate question of how overall utility, i.e., the sum of all material and immaterial benefits, derived through crowdfunding could be optimized is still underresearched. From the perspective of practitioners, more specific propositions of how crowdfunding models could be enhanced are needed. Following the literature on market engineering, it is important to design electronic market places carefully while considering individuals’ behavior (e.g., Neumann et al. 2005; Weinhardt et al. 2003). This strand of literature deals with the analysis, development, and optimization of electronic markets (Weinhardt et al. 2003). For reaching a certain outcome, electronic markets need conscious design and rules. An interesting starting point for improving market outcomes on crowdfunding platforms is the great difference in funding amounts that projects receive. While some projects achieve very low levels of funding, other projects reveal massive overfunding (Koch 2016). So far, the phenomenon of overfunding has been rarely addressed in research—however, if it was addressed, it was discussed controversially (e.g., Kim et al. 2016; Liu et al. 2015; Malave 2012). A statement that is regularly made in the context of overfunding is that it can cause externalities that have an impact on other projects on the platform. Here, both positive and negative external effects of overfunding are found and discussed (Doshi 2014; Kim et al. 2016; Liu et al. 2015). More specifically, in this paper, external effects on crowdfunding platforms are understood as effects that are caused by one project (or by the processes connected to this project) and have a negative effect on the funding collection of one or more other projects on the platform. One obvious reason for such effects is that crowdfunding projects on a platform compete for funding (Burtch 2011). Funds that are assigned to one project cannot be received by another project. First of all, it has been confirmed that overfunded blockbuster projects have significant external effects on other projects (e.g., Kim et al. 2016; Liu et al. 2015). Here, it was shown that massively overfunded projects can have a positive effect on “related projects”. In other words, these projects profit from the existence of overfunded blockbuster projects (Kim et al. 2016; Liu et al. 2015). Hereby, the term “related” means that campaigns are in the same project category and reveal a high textual similarity regarding their project description. However, while some projects seem to benefit, it has been revealed that overfunded projects “hurt the performance of less-related projects” (Liu et al. 2015). As a consequence, a great number of projects is adversely influenced concerning the funding performance, since it can be assumed that there are typically more unrelated than related projects on a platform. Kim et al. (2016) confirm that there is a significant “cannibalization effect of blockbuster projects” on projects of other topics. Doshi (2014) even suggests that project initiators choose the “option of not entering” the platform with a new project because they fear to be outshined by the blockbuster projects. Kim et al. (2016) also find indication that some project initiators refuse to start new projects on the platform if there are massively overfunded blockbuster projects. Such empirical findings make us believe that overfunding cannot just be seen as a positive phenomenon in crowdfunding. This opinion finds support by a range of further facts. Mollick (2014), for example, concludes that overfunded projects are “particularly vulnerable to delay” concerning the delivery of rewards. This constitutes a negative effect for funders who have to wait much longer for their funding compensations as promised. Moreover, there are well-known overfunded projects that have struggled and have left behind a big crowd of disappointed funders (like, e.g., the Coolest Cooler, or the video game console OUYA). Here, we argue that a concentration of funders on highly overfunded projects leads to a concentration of risk. If an overfunded project fails or cannot fulfill its promises concerning the rewards, it is more likely to trigger negative publicity for the platform than a number of less funded projects. Such examples of negative external effects can be seen as possible symptoms of market failure (Williamson 1971). As a result, the funding outcomes may be biased rather than reflecting optimal funding results (Koch 2016).

From a theoretical perspective, external effects or externalities are central to the “critique of market organization” (Buchanan and Stubblebine 1962). Externalities are present when there is a “divergence between private and social costs” (Dahlman 1979). If so, the utility of an individual is not only dependent on his/her own activities, but also on the activities of other individuals. Externalities can be positive or negative and they have a considerable impact on the transactions that are made on markets. Without intervention (i.e., internalizing the externalities), some potential beneficial transactions are not realized—which harms the performance of markets and leads to market failure (Dahlman 1979). In economic theory, the internalization of externalities is a widely discussed approach to prevent market failure or to reduce its consequences for individuals and the society. For the elimination or reduction of external effects, elements of the Coasian (Coase 1960) and the Pigouvian (Pigou 1920) approaches are considered (Aidt 1998). Both approaches have the goal to reallocate resources in order to eliminate or prevent market failure caused by external effects. While the Coasian approach encourages an efficient resource allocation through negotiation of the involved parties (Coase 1960), the goal of the Pigouvian approach is to impose a tax—the so-called Pigouvian tax—on negative externalities, so that emerging external costs are carried by the perpetrator. Due to the tax, the economic acting and the behavior of the perpetrator is to be redirected in a desirable direction (Pigou 1920).

As a practical proceeding for internalizing externalities, Gupta and Prakash (1993) propose four steps: (1) the (negative) externalities need to be recognized, (2) the perpetrator and the potential victim must be identified, (3) for each party, costs and benefits of internalization need to be evaluated, and (4) the costs and benefits of internalization need to be assigned. For the assignment of costs and benefits, the policy maker needs to decide which mechanism to use. Since the mechanism depends on the purpose, different approaches are discussed in academic literature, e.g., for environmental policy (Baumol and Oates 1971) or regulating systemic risk in the financial sector (Acharya et al. 2010). We argue that the internalization of external effects is also valuable to be discussed in the context of crowdfunding.

Against the backdrop of having some crowdfunding projects worth to be funded, which fail to reach their funding goal, we decide to contribute to the discussion by addressing the negative externalities of overfunded projects. We aim at clarifying whether a funding redistribution mechanism is able to improve funding outcomes on a platform. Inspired by the Pigouvian tax, we develop a funding redistribution approach that imposes a tax on funding that goes beyond a project’s funding goal. However, according to the Pigouvian approach (Pigou 1920), the tax needs to reflect the negative consequences that all other individuals experience. Instead, our approach primarily aims at achieving the best funding results and is not directly based on individuals’ costs. This has the advantage that costs do not need to be expressed in monetary units. Moreover, the tax yield is not used to compensate all individuals’ costs, but is distributed to selected valuable projects that have closely missed their funding goal. Thus, we do not claim to fulfill the requirements of the Pigouvian tax and just call it “tax” instead. Further, we are aware of that a tax usually is imposed by governments and that our approach represents a compensation payment used as market engineering instrument. Still, the functionality is inspired by a taxation approach. As actual platform data is not able to analyze the effect of such a tax, we choose an approach based on an agent-based model simulating the processes of a crowdfunding platform.

The complexity of a crowdfunding platform mainly arises out of the big number of agents and objects involved and their multi-faceted interactions and behaviors. In crowdfunding, we can identify numerous stakeholders concerned (Koch 2017). The main agents involved are platform operators, project initiators, and funders. The operators determine platform parameters and the funding mechanism. While initiators decide on how to design the crowdfunding campaigns, funders screen these campaigns and decide for funding certain projects or not. Moreover, social connections play an important role. The first funding contributions are usually made by family members and friends who often act because of “social obligation” (Agrawal et al. 2015). All other funders concentrate on information used for the presentation of projects and to the amount of funding that has already been reached (Agrawal et al. 2015; Koch and Siering 2015; Kuppuswamy and Bayus 2013; Mollick 2014; Zhang and Liu 2012). We emphasize that further effects, like the visibility of project presentations on the platform, advertising in social media, convincing comments, and update posts also play certain roles (Li and Duan 2014). For details, we refer to the literature on crowdfunding in general (e.g., Beaulieu et al. 2015) and on specific effects, such as the herding effect (Zhang and Liu 2012) or the bystander effect (Kuppuswamy and Bayus 2013). When developing an ABM, simplifications are needed to handle the complexity. The decision of which effects are explicitly regarded in the model is described in the following section, in which we describe our whole model according to the ODD protocol (Grimm et al. 2010).

The development of ABMs is no end in itself but is supposed to provide the basis for promising investigation opportunities. Therefore, an ABM needs to be evaluated so that possible flaws or limitations can be discovered and resolved. An important step in the development of ABMs is the verification and validation of the model. The verification addresses the question of whether the model does what it is intended to do (Nikolic and Ghorbani 2011). Next, in the validation, it is checked whether “the modeled outcomes correspond with observed reality” (Nikolic and Ghorbani 2011). The fact that ABMs are simplified models of real-world processes demands for certain simplifications. These simplifications, however, must not lead to distortions of models’ outcomes. In order to calibrate our model, we choose a basic scenario that allows for comparing funding outcomes to real-world data. If the ABM is able to produce funding outcomes that are similar to actual funding outcomes of existing platforms, the processes implemented in the ABMs are able to imitate the relevant processes. The main advantage of agent-based models is the opportunity to model and analyze complex systems which could not or not easily be captured by pure mathematical calculation, e.g., by applying stochastic equations. Applying simulated scenarios, an ABM can be used for experiments and fulfills two important tasks. First, an ABM can confirm or reject a conjecture. Therefore, some kind of input is given into the system and, finally, a specific output is received that allows for confirming or rejecting the conjecture. In a simple case, this may not bring forth an amazing miracle as the basic coherences are often clear and allow for an educated guess. However, second, the ABM can be used for a sensitivity analysis and for optimization problems. By (systematically) changing the input parameters, the characteristic responses from the system can be analyzed and transformed to new insights about the system. Such inferences are important and of high value for decision situations. These insights can be used for developing rules of thumb or as a basis for managerial decisions. Consequently, the ABM becomes a powerful decision support tool of market engineering that aids, e.g., model design and price determination.