As seen in the previous section, the current literature divides the set of crowdfunding determinants by the density or size of the promoters’ network, the promised rewards for the funding, and the characteristics of the promoted projects, such as the demanded amount of money, the type of promoters and the type of project. This section will test these determinants for the largest Latin American crowdfunding platform—the Brazilian Kickante.
3.1. The Kickante Platform
In Brazil (and even in Latin America), Kickante is the largest crowdfunding platform. Between 2013 and 2018, more than 72,000 campaigns have been launched, generating more than US
$15 million (
Kickante 2018). Other Brazilian platforms also stand out, such as Cartase, Kickstarter, Indiegogo, StartMeUp, Broota, Impulso, Idea.me, and Bicharia.
Kickante has adopted a reward and donation crowdfunding model, suggesting that the success of the Brazilian platform is due to the social characteristics faced by Brazil, as well as philanthropic aspects. This differs from Kickstarter, which is based in the United States, and is based on debt crowdfunding and equity crowdfunding.
One factor that may explain the success of Kickante is its multiple modes of campaigns. In addition to the traditional All-or-Nothing, or to flexible campaigns (like “keep it all” in Kickstarter), Kickante has created the Kick Outreach campaign mode. In the all-or-nothing mode, the proponent only receives the amount that was donated, if s/he reaches or exceeds the stipulated target, while in the flexible campaign, the proponent receives the value at the end of the independent campaign regardless of the campaign’s success. In Kick Solidário, the donor who engages in the “Events of the Good” option determines the value of one of the 300 NGOs previously registered in the platform. Examples of “Events of the Good” include soccer matches, marriages, birth celebrations, and birthdays (
Kickante 2018).
Based on the campaigns since 2013, Kickante has developed a preliminary analysis between the goals set by the proponent and the success of the campaigns. The analyses indicate that a direct and positive relationship exists between the goal and the necessary campaign time, and time spent in the campaign on social networks (
Kickante 2018), such as, Facebook, Whatsapp, Twitter, and Instagram, among others. In other words, the following conclusions are indicated:
- -
The higher the goal, the more time spent on the campaign;
- -
The higher the goal, the greater the need for investments in social networks, in order to establish or increase trust.
Other observations by platform managers indicate:
- (i)
The need for planning before the campaign is published, using donors (who actually contribute financially to the campaign) and supporters (those who do not contribute financially but advertise on their social networks) and
- (ii)
An understanding of a hierarchy based on proximity to the proposer and the trust assigned to the campaign.
Kickante’s managers have assigned the name Network 1 to those closest to the campaign leader, such as family, friends, and fans. Network 2 basically consists of friends of friends and acquaintances, and Network 3, is the general public. For Kickante the first to contribute to the campaign will be members of Network 1, because there is already a solid level of trust, while the people in Network 3 will need to build this relationship.
Other relevant aspects suggested by the platform are the use of media, the number of people involved in the campaign and the rewards. Media, such as videos, for example, prove that the proponent is real, making it more reliable. Regarding the formation of a group, analyses carried out by the developers of the platform affirm that teams can raise 80% more than a single person (
Kickante 2018). Finally, intangible rewards are suggested as influential.
Table 1 shows some descriptive values relating to successful projects managed by the Kickante platform.
3.2. Data, Descriptive Statistics and Results
For analyzing the determinants explaining the well-succeeded projects exhibited in the Brazilian site ‘Kickante.com’ we are going to estimate the following model (Equation (1)):
The previous Equation (1) will alternatively assume three dependent variables. These dependent variables are the total raised value per project, the percentage of target, and the raised value per investor. The independent variables follow the literature review. Therefore, we used variables that focused on the project’s characteristics (
Poetz and Schreier 2012), the number of investors (
Schenk and Guittard 2009) and the rewards available to the investors (
Kappel 2008).
The independent variables are the number of investors (“kickantes”); the type of projects (social projects/causes, cultural/creative projects, entrepreneurship); the type of promoters (individuals, groups of individuals, NGOs, firms/companies); the existence of multiple aims attributed to the raised money; the minimum invested value with reward; and the minimum invested value with the maximum reward.
We considered it important to exhibit some comments about the main descriptive statistics of these variables (
Table 2). Following
Silveira et al. (
2018), we also observed that the median project promoted by the Kickante platform received US
$5375, being excessively funded 49% above the required value, and each one of the 194 investors gave around US
$45 to this representative project. It was most likely funded in 2015 and promoted by an individual. This representative project did not involve the repairing of buildings. The minimum invested value that granted a reward to the investor was around US
$7 and the minimum invested value that granted the maximum reward to the investor had a median value of US
$950.
For studying the eventuality of problems of endogeneity, we also ran the Durbin-Hu-Hausman tests against the endogeneity of the variables most likely to introduce problems; as an ultimate observation, we were able to reject the hypotheses of exogenous regressors. Full details are available upon request. Therefore, OLS is not appropriate and we preferred to estimate our regressions using two stage least squares (
Wooldridge 2007). These results, estimated by two stage least squares (2SLS) with heteroscedasticity robust standard errors, are in
Table 3.
Results from
Table 3 show that the total raised value per project rises with the number of investors (“kickantes”) and with the minimum value guaranteeing a reward in each project. The type of promoters—especially firms or companies, influence the raised value per project. The overall R2 is highly satisfactory for the first exhibited regression (0.49) and the F-val from the first stage regression is statistically significant at a level of 10%. This considers the critical values for the 2SLS relative bias, which favors the actual option for the chosen instruments. The test on endogeneity does not allow rejection of the null hypothesis for variables that are exogenous.
Regarding the other columns (i.e., related to the achieved target and to the value per investor), additional evidence arises. First, the target increases with the number of investors. Second, NGOs and firms also increase the expected value per investor as well as projects classified in the entrepreneurship category. Finally, the value per investor also tends to increase if the minimum value with a reward is higher.
If we interpret the most relevant estimates along these tables for the first column (total raised value per project), we observe the following. The most statistically significant effect from an additional investor relates to the projects focused on entrepreneurship as shown in
Table 6. One additional investor for this investment category tends to add US
$28 to the project’s funds; conversely, the least expressive effect is associated with the type of projects regarding social projects/causes (the estimated coefficient for the number of investors is 74.6).
Regarding the expectations of rewards, our estimations identified the projects regarding social projects/causes as those in which a higher minimum value with a maximum reward leads to higher amounts of money going to the project. Actually, if a project of this type increases the minimum invested with the maximum reward by one real, the total amount of the project can be expected to increase by US
$1. Therefore, in relation to the reward from the maximum prize, our estimates suggest that the social projects (
Table 4) tend to be the most reactive. The dispersion of targets (proxied by the variable “multiple functions/ends for the invested money”) seems to be more focused on the projects centered on entrepreneurship. There are also stimulating observations in regards to the type of promoters.
Firms and companies tend to be successful promoters for projects related to entrepreneurship. The percentage of target estimates is exhibited in the second column of each table. The low values of the R2 of these estimations suggest our set of independent variables are not able to explain the variance of the percentage of target achieved for each project by a significant proportion. Even so, for these estimations, the higher values granting a maximum reward tend to be associated to lower targets observed in the category of social/causes. Also, when diminishing the achieved percentage of target of these causes, we find the effect related to promoters like informal groups or NGOs. Once again, firms and companies increase the percentage of achieved target for projects related to entrepreneurship.
Regarding the raised value per investor (the third column in
Table 4,
Table 5 and
Table 6), it is interesting to observe that investors in social causes or in entrepreneurship projects tend to invest more if there are more significant rewards or if they are not single individuals promoting the project.
Overall, the R2 is high for the estimations regarding total raised value per project, but it is low for the estimations regarding percentage of target and raised value per investor. This aligns with the study by
Mourão and Costa (
2015) and can be interpreted as evidence of the individual motivations behind each one of these investments, which further generate opportunities of research in order to explore other scientific explanations for explaining the values invested by each sponsor. The tests on endogeneity returned significant values, which prove the exogeneity of the instruments. The F-values for the first stage regressions led us to conclude the quality of the chosen instruments.
We have also estimated the effect from the different states of Brazil for the estimation of the total raised value per project considering all the projects. The following table (
Table 7) shows this.
Overall, projects from states like Espírito Santo, Maranhão, Roraima, and Sergipe tend to have smaller raised values. There are several studies highlighting the importance of the surrounding area for increasing the amounts of investments raised (
Hess 2018;
Aliprantis and Carroll 2018;
Pattillo 2008) or of charitable flows (
Clifford 2017;
Ishida and Okuyama 2015) to targets located in certain places. According to the
Atlas of Human Development (
2018), these states tend to be characterized by low values on the Human Development Index when considering all Brazilian states.