As explained in a previous post, crowdfunding platforms (CFPs) facilitate the interaction between entrepreneurs trying to raise funds and contributors willing to participate in the financing of new projects. Different types of network effects are at work on CFPs. They can result from decisions to participate in the platform or to use the platform. In this post, we focus on the second type.
Network effects related to usage
Users of two-sided platforms do not only decide about their participation to the platform but also about their usage of the platform. For instance, shoppers first decide to go to a shopping mall and then, once they are there, which shops to visit and how much to buy from these shops; as for shop owners, they decide first whether to be present in the shopping mall and then, which particular pricing or promotion strategies to apply there. Similarly, gamers first buy a particular console and they choose which game to play on this console, whereas game developers choose which console to write a game for and then the price at which to sell the game.
On CFPs, entrepreneurs do not really have any usage decisions to make: they take most of their decisions before their funding campaign is launched; once the campaign is underway, there is not much that entrepreneurs can do to change it. In contrast, once contributors have decided to participate in a crowdfunding platform, they still have a range of decisions to make: they have to decide which project(s) to back, at which stage(s) of the campaign to make a contribution, how large a contribution to make, whether to communicate with friends about their decisions, etc. In many respects, these decisions are influenced by similar decisions taken by other contributors, which generates yet other types of network effects. These ‘usage network effects’ are dynamic by nature, as they play out for the whole duration of a crowdfunding campaign.
The literature on crowdfunding has largely studied the network effects that take place within a particular project. The aim is to describe and understand the funding dynamics for a particular project. The two main questions are: (1) Do past contributions influence current ones? (2) If so, how does the cumulative distribution of contributions evolve during the campaign?
Do past contributions influence current ones?
We can safely conjecture that the answer to the first question is yes. Because contributors have limited information about the match value of the proposed projects and the trustworthiness of the entrepreneurs, they are likely to try and infer information from the choices made by previous contributors. Hence, usage decisions are also a source of within-project network effects in the group of contributors. However, the sign of these network effects is a priori ambiguous, as it depends (i) on which information prospective contributors infer from past choices, (ii) on the fraction of the funding goal that is already collected, and (iii) on the behavioral profile of prospective contributors.
To see this, consider a project that has already received a lot of support. A first reaction of prospective contributors may be to infer that this project is of high quality and, consequently, to support it as well. A herding behavior of this sort generates positive network effects, as past contributors attract new contributors for a given project. In general, herding can be rational (that is, due to observational learning and Bayesian updating) or irrational (that is, due to passive mimicking). The decisions of investors on lending-based CFPs provide evidence of herding—both rational and irrational (see, for instance, Zhang and Liu, 2012). Second, a project with large past support is, other things being equal, more likely to get funded and, thereby, to provide contributors with some form of compensation (for example, some perk in the case of reward-based crowdfunding). Prospective contributors may then be in a position to be pivotal, that is, to provide the necessary financing for the project to reach its funding goal. Whether prospective contributors decide to be pivotal or not depends on their behavioral profile. Altruistic contributors are more eager to contribute to a project that approaches its funding goal, as they think that their impact is then the largest; this goal-gradient effect provides another source of positive network effects. Kuppuswamy and Bayus (2017) find strong evidence of this effect using panel data on 10,000 funded and unfunded Kickstarter projects. In contrast, egoistic contributors tend to rely on other contributors to complete the funding (assuming that further contributors will be attracted by this already popular project); this free-riding behavior generates then a negative network effect.
How does the cumulative distribution of contributions evolve during the campaign?
We can now turn to the second question of interest. Our previous analysis only provides us with partial answers, as the various effects that we outlined not only go in opposite directions but also suppose that the project has already received a lot of support. So, the first conundrum is how to attract the first contributions. In this respect, Mollick (2014) finds that the participation of the entrepreneur’s personal network during the first days of the campaign is crucial in generating momentum. Once an initial mass of contributions has been collected, network effects can start playing; we can conjecture that herding will drive contributions to grow steadily at first but, as the funding goal approaches, the dynamics will either accelerate or slow down according to whether it is the goal-gradient or the free-riding effect that dominates. In their study, Kuppuswamy and Bayus (2017) suggest that the former effect dominates, leading to a distribution of contributions that is inverse U-shaped, with a maximum reached at the funding goal. Hornuf and Schwienbacher (2017) show that in equity crowdfunding, the dynamics depend on how securities are allocated to contributors: a concentration of contributions at the end of a campaign is much more likely to occur when securities are allocated in form of an auction instead of on a first-come-first-served basis.
We invite you to use the two posts related to network effects in crowdfunding to complete the linkage map of a crowdfunding platform.
