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This hypothesis stems from suggesting that increased levels of competition outside of the Kickstarter category will increase the likelihood of a project to succeed. As projects not in the same category are less likely to be substitutes of each other, and thus the ability of extra projects to draw in more backers will have a positive impact on the likelihood of a project to succeed, as discussed in section 3.3.6.1. Stating H5b: Increased competition on the rest of the platform has a positive impact on the probability of the project’s success.

This hypothesis was tested through the examination of two variables, the launch day competition generated by projects outside of the examined projects category and the amount of competition on the entire platform throughout the duration of the examined project, expressed in index form. The impact of competition on launch day from the rest of Kickstarter was obtained through the main model. Conversely the impact of increased competition through the rest of Kickstarter was captured on the restricted model.

The launch competition results support the hypothesis reporting a positive and significant impact on the probability of a project succeeding as shown Table 4.1. Conversely results regarding the competition index do not support this hypothesis, as shown in Table 4.6, although they also report a positive and significant impact, as higher values for the

competition index indicate lower levels of competition, a positive coefficient thus suggests decreased levels of competition will increase the likelihood of a project succeeding.

Suggesting that although H5b is supported at the launch of the project, the effect of increased competition within the rest of Kickstarter changes over a project’s full duration. This is further examined by considering the impact of different levels of the variables on the probability of observing success.

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4.2.6.1Impact of launch competition from the rest of Kickstarter

Figure 4-15 Marginal impact of launch competition

This straight line in Figure 4-15 demonstrated that there is a consistent positive effect of the increased amount of competition on launch day outside of the category of the creator. However, the precision of the model decreases as the amount of launch competition

increases, as easily seen through the increase in the confidence intervals at higher levels of competition. On average, launch competition was equal to 4904.63, as displayed in Table 3.4. This states that on each project launch day, on average, other projects outside of the category of the examined project attracted 4904.63 backers to their projects. Utilising the underlying data from Figure 4-15, partially shown in Table 3.6, suggests that, at this average level of competition, there was a 33.70 percent chance of a project succeeding. In comparison, projects with the minimum level of competition of 27, as displayed in Table 3.4, lead to a 33.28 percent chance of observing a successful project. Conversely, the maximum level of competition observed of 50761, lead to a 37.68 percent chance of observing a successful project. Demonstrating a small impact on the probability of success based upon the level of competition at launch outside of the category of the project.

.3 2 .3 4 .3 6 .3 8 .4 Pr(Su cce sso rf a ilu re ) 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 Launch_Competition Predictive Margins with 95% CIs

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4.2.6.2Impact of competition index from rest of Kickstarter

Figure 4-16 Marginal impact of Kickstarter Index

The straight line in Figure 4-16, displayed above, suggests that: as the level of competition decreases within the rest of Kickstarter, its impact on success is consistent. However, the precision of this impact declines as the index increases, as indicated by the widening of the 95 percent confidence intervals at higher levels of the Kick index. Kick index is an indexed value measuring competition between projects on Kickstarter within the same category. Index values are between 0 and 10000, with higher index values showing lower levels of competition. The mean level of the Kick index observed in the model was 671.83, as shown in Table 3.5. Utilising the underlying data presented in Figure 4-16 partially shown in Table 3.6, at the mean level of 671.83, there was a 33.69 percent chance of observing a successful campaign. In comparison, projects with the highest levels of competition, at an index value of 40, as shown in Table 3.5, had a 33.01 percent chance of observing a success. Conversely, projects with the least level of competition at an index value of 6320.38 had a 39.63 percent chance of observing a success. Thus, the largest possible decrease in the level of competition within Kickstarter outside of the category would only increase the likelihood of observing a success by 6.62 percent.

.3 .3 5 .4 .4 5 .5 Pr(Su cce sso rf a ilu re ) 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Kick Index

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Therefore, in alignment with the hypotheses considering the impact of competition within the category, competition outside the category alters its effect depending on the duration of the campaign, with the initial increased competition having a positive effect on success and conversely increased competition across the entirety of its duration having a decreased impact on success. These points are further elaborated on and critically considered within section 5.3.1.

Figure 4-17 Marginal impact of average google trend

The straight line in Figure 4-17 above shows that the impact of how well the platform is competing, has a consistent and positive effect on the likelihood of a project succeeding. However, the model is most precise in the 40 to 60 range, as, at these values, the 95 percent confidence intervals are narrower than at more extreme values. The average campaign had a trend value of 48.78, as demonstrated in Table 3.4 above. Utilising the underlying data of Figure 4-17, as partially shown in Table 3.6, at this average value there is 32.20 percent chances of observing a successful project. At the lowest value recorded of 0, this chance decreased to 30.85 percent, and at the highest level of 100, it increased to 33.62 percent, showing that the impact was small with only a 2.77% difference between the highest and lowest values. .3 .3 1 .3 2 .3 3 .3 4 Pr(Su cce sso rf a ilu re ) 0 10 20 30 40 50 60 70 80 90 100 averagetrendcat

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