Experiencia del turismo en Ecuador 2 Experiencia en Ingapirca

The following section considers the results related to all hypotheses formulated to identify the impact of backers’ incentives on the success in Kickstarter as they were developed in section 3.3.4 .

4.2.3.1Reward levels

Increasing the number of reward levels offered to backers of the campaigns was considered to exert a positive impact on the probability of projects succeeding, as it would provide increased choice to the backers regarding how they wish to support the project, an

argument discussed when developing H3a: Increased number of reward levels within a

campaign will have a positive impact on the probability of the project success. Figure 4-8 Marginal impact of reward levels

Table 4.1 shows that the number of reward levels has a positive and significant impact on the probability of a project succeeding, thus supporting H3a, as also displayed in Figure 4-8 below on the probability of observing a success for specific reward levels. The straight

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line within the graph shows that increasing the number of reward levels has a consistent effect for the first 10 increases, with each increase leading to an increase of around 0.34 percent in the probability of observing a success.

An examination of the mean number of reward levels, from Table 3.4, shows that on average each campaign had 7.39 reward levels. Utilising the underlying data for Figure 4-8 shows that at the mean level of 7.39, the probability of observing a successful project was 31.7 percent. Table 3.4 additionally shows the range of the reward levels of projects, with the minimum number of reward levels being 1 and the maximum being 179. Thus, using the underlying data for Figure 4-8 as partially shown in Table 3.6, at the minimum level a project was predicted to have a 29.6 percent probability of succeeding, conversely at the maximum level the project was predicted to have an 80.4 percent probability of succeeding providing support for hypothesis 3a.

4.2.3.2Number of days for the rewards being delivered

The second hypothesis related to backers’ incentives considered that backers would be less likely to support projects which delivered rewards at a later time period, or that they would discount future rewards, compared to closer ones, based on a typical positive discount rate assumption, as developed in section 3.3.4.1. Stating this hypothesis, H3b: Increased expected delivery times of reward levels will have a negative impact on the probability of project success.

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Figure 4-9 Marginal impact of average wait time

The results on the average waiting time, from Table 4.1 above, provide support for the hypothesis, stating that the average wait time of the backer had a negative and significant impact on the probability of a project succeeding. The scale of the impact is shown in Figure 4-9 below, the straight line in the Figure 4-9 shows a consistent decrease in the probability of success with increased waiting times, with an increase of 10 days consistently leading to a decrease of 0.11 percent chance of observing a successful outcome, to a 95 percent

confidence level. This can be considered a relatively small decrease in the probability of observing success, suggesting a relatively small impact on the level of success by increased wait times.

Furthermore, on average, backers had to wait 130.1 days to receive their rewards, as shown in Table 3.4, utilising the underlying data for Figure 4-9 as shown partially in Table 3.6, at this level the probability of observing a success was 32.0 percent. Thus, a project delivering rewards instantaneously would only increase the probability of observing success from 32 percent to 33.4 percent. Providing evidence that people are mostly willing to wait for their reward, however, there is a still a negative impact on success by increased delivery times, thus supporting the proposed hypotheses.

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4.2.3.3Global reward levels

The final hypothesis considering the impact of backers’ incentives examines the impact of global rewards on the probability of a project succeeding. With a global reward consisting of any reward which could be physically shipped to anywhere in the world. The hypothesis stemmed from the concept that backers would prefer local and digital rewards, compared to global rewards, as discussed in section 3.3.4.2,stating H3c: A larger number of global reward levels will have a negative impact on the probability of the project success. Figure 4-10 Marginal impact of global reward levels

H3c is supported by the model as seen in the results from Table 4.1, which show that the number of global rewards has a negative and significant impact on the probability of a project succeeding. Furthermore, Figure 4-10 provides evidence that this impact is consistent as the number of global rewards increases. With a decrease of around 0.03 percent for each increase in the number of global rewards.

Table 3.4 shows that the average campaign had 3.69 global reward levels. Utilising the underlying data for Figure 4-10 at this level, the chance of observing a successful campaign was 32.4 percent. Having no global rewards would increase this chance to 33.5 percent, indicating the strength of the positive increase. This increase could be considered

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relatively small, showing a small impact on success by the number of global rewards, while still supporting the proposed hypothesis.

The support for these three hypotheses demonstrates that changes to the number of rewards does impact the probability of a project succeeding in Kickstarter. However, this impact may be relatively limited, suggesting that other factors outside of the actual rewards may also be relevant to capture success within Kickstarter.

4.2.4Social capital hypotheses

The following section considers the impact that internal and external social capital

have on the probability of a project to succeed. 4.2.4.1External social capital

The following hypothesis considers how the combination of the backers and creators’

external social capital could positively increase the probability of a project succeeding, capturing the external social capital from the number of Facebook shares of the

crowdfunding project, as discussed previously in section 3.3.5.1. Stating H4a: Increased

levels of combined creator and backer external social capital have a positive impact on the probability of the project’s success.

Figure 4-11 Marginal impact of Facebook shares

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The results displayed in Table 4.1, show clear support for the hypothesis, with the number of Facebook shares being both positive and significant in their impact on the probability of a project succeeding. Natural logarithms were utilised as marginal impacts were expected to be smaller at larger number of Facebook shares due to the network distance between the original sender of the share and the recipient to be larger and thus less likely to impact their decision to support the project. Figure 4-11 demonstrates a relatively straight line, with a slightly gentler slope at early values and a slightly steeper slope at higher values, showing support for the decreasing marginal impact at higher levels of Facebook shares.

In examining the scale of the impact of Facebook shares on probability for the project to succeed, the log of the mean number of Facebook shares was 3.07, as shown in Table 3.4 below, thus on average each project had 21.7 shares, and utilising the underlying data for Figure 4-11 as partially shown in Table 3.6, the probability of observing a success at this level was 24.75 percent, compared to having zero Facebook shares, which gave the

probability of observing a success at 9.42 percent. Furthermore, the highest observed number of Facebook shares at 331224 increased the probability of observing a success at 84.14 percent. These two points show that the number of Facebook shares had a positive impact on the likelihood of a project succeeding.

4.2.4.2Creators Internal social capital

This hypothesis considers the impact of increased internal social capital captured via the number of previously backed projects by the creator. Arguing that the internal social capital of the creator can be captured by examining the amount of previously backed projects by the creator, which is used as a proxy for reciprocity, as discussed in section 3.3.5.2.

Stating H4b: Increased amount of creator internal social capital has a positive impact on the

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Figure 4-12 Marginal impact of Reciprocity

Contrary to our expectations, the results in Table 4.1, do not support H4b: with increased Reciprocity having a negative and significant effect on the probability of

successfully funding a project. This is further seen when examining the impact of increasing levels of reciprocity demonstrated in Figure 4-12 above. The negative coefficient could suggest that utilising the number of backed projects by the creator is not a good indicator of the creator’s internal social capital. Instead, creators backing other projects could be seen as wasteful to the potential backers of the creator’s project. Why are they asking for money if they are already able to give money to other creators? Thus, leading to the negative

coefficient observed in the model.

However, it should also be noted that the negative coefficient of the impact is quite small, the average campaign creator had previously backed 3.76 projects, as shown in Table 3.4. Utilising the underlying data for Figure 4-12 as partially shown in Table 3.6, would have the probability of observing a success at 32.2 percent. In comparison projects with zero previously backed projects had a probability of observing success of 32.4 percent, only 0.2 percent less than the average project. Therefore, the scale of the negative impact on the average project was very small.

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Hence, the results regarding the different hypothesis on the impact of social capital on the probability of a project’s success are mixed, showing strong support for the impact of

external social capital, but a negative, if relatively small, impact for the internal social capital, these results are discussed in more details in the discussion of the results section.

The following section considers results for hypotheses which consider the impact of increased levels of competition internally and externally to the crowdfunding platform. The results utilise both models as some of the competition results can only be examined through the restricted model.