Relationship Between Crowd Funding Goal, Amount Pledged And Percentage Raised

Factors Influencing Success or Failure of Crowd Funding

Crowd funding involves sourcing for funds from different a wide range of individuals (Holman, et al., 1996). These funds are collected for the purpose of financing difference projects. The essence of crowd funding is that usually, the targeted individuals collect a small amounts of money and voluntarily. These amounts are collected to form a pool of funds by the respective fields (Mahmoud, et al., 2015).

Apart from the voluntary payments, crowd funding also has an option where people can make pledges and honour later on. As a result of this, a crowd funding project has various categories of funds and projected funds. Three of this funds which will form major part of this report are; the targeted fund (goal), the actual amount raised (percentage raised) and the amount of fund pledged. The percentage raised is the amount of money that has been raised out of the goal amount.

This report is based on establishing the relationship between these three funds: the goal amount, the percentage raised and the amount pledged. Specifically, this report is aimed at finding out whether it is possible to predict the actual amount of money raised through crowd funding (percentage raised) using the goal amount (the expected amount) and the amount of money pledged. Therefore, this report has developed a linear model that can be used to predict the amount of money raised through crowd funding using the goal amount and the amount of money pledged. 

Crowd funding is aimed at pooling together small amounts of funds to finance a large project. This pooling together is usually done over the internet. The success or failure of this way of financing a project is dependent on a number of factors. Some of these factors that influence the success or failure of crowd financing include: project information, picture of the project, videos used for sourcing the funds, founder information, the goal of the project and the funding level (Bryan, 2009).

The information of the project can make a great deal on targets’ impression. People will only be willing give out their money on those projects with clear information and in most of the cases, in those projects that they have interest in (Mahmoud, et al., 2015).

The picture and/or video used in a project matters a lot. The target individuals will always be critical about them. It shows a sense of social responsibility. Similarly, founder information is very critical. Most individuals will be interested in knowing many details about the founder (Chung, et al., 2005). This help in gauging the level of seriousness of the project. 

Hypotheses Development

Hypothesis is statements about a research question or problem whose truth value is subject to testing. Hypothesis is developed in pair’s i. e the null and the alternative hypothesis. A null hypothesis is that which is stated negatively while an alternative hypothesis is stated negatively (Chung, et al., 2005). The following hypothesis has been developed in this report:

Ho: There is no association between the percentage of money raised, the goal amount and the amount pledged.

H1:    There is an association between the percentage of money raised, the goal amount and the amount pledged. 

H0:  The percentage of amount raised cannot be predicted using the goal amount and the pledged amount.

H1: The percentage of amount raised can be predicted using the goal amount and the pledged amount. 

There are two analyses to be done in order to fully test the above hypotheses. The first test is the correlation test. Correlation analysis tests for any association or relationship between variables (Mahmoud, et al., 2015).  The first table is the descriptive statistics outlining the mean, standard deviation and the frequency (sample size) of each category.

The correlation test was run in SPSS and the output is display in the following figures. The correlations coefficient between goal amount and the percentage of the amount raised is -0.03. This is a weak negative correlation coefficient (David & Stanley, 1999). The correlation coefficient between goal amount and the pledged amount is 0.044. This is a weak positive correlation coefficient 

Therefore, is clear that there is an association between the 3 variables. Likewise, this observation will help us make decision of whether to reject or accept the null hypothesis. Since the correlation coefficient is not equal to zero, meaning there is an association between the three variables, we reject the null hypothesis that there is no association between the percentage of money raised, the goal amount and the amount pledged. Statistically, there is no sufficient evidence to accept the null hypothesis. 

The second test done is the regression analysis. Regression analysis tells whether one variable called a dependent variable can be predicted using other independent variable (s). The regression out have the following three table outputs: The summary table, the ANOVA table and the coefficients table.

The summary table tells that the R square is 0.02. This is the percentage of the population that is explained by the sample data.  Similarly, the regression coefficient between the dependent and the independent variables is 0.044. 

Correlation Analysis

The ANOVA tables tell whether there is a significant difference in the average amounts of pledged amount, percentage raised and the goal. The significance is 0.00, less than 0.05. This is an implication that there is no significant difference in the average amount pledged, percentage raised and the amount of goal amount. This value will help us make a decision of whether to accept or reject the null hypothesis.

From the explanations above, it is clear that we reject the null hypothesis that the percentage of amount raised cannot be predicted using the goal amount and the pledged amount. Statistically, there is sufficient evidence to prove that the percentage of amount raised can be predicted using the goal amount and the pledged amount.

When we propose that a dependent variable can be predicted using a set of independent variables, then the dependent and independent variables can be connected using a linear equation. This linear equation is known as the regression model. Hence, we can write a regression model or a linear equation that can be used to predict the amount of money raised using the amount of money pledged and the target or the goal amount. This equation or model will take the form:

Raised = B0+ B1 (goal) + B2 (pledged)

In the model, B0, B1 and B2 are constants representing the regression coefficients. B0 is a coefficient representing the intercept of the equation, B1 is the coefficient of regression between the amount raised and the goal amount and B2 is the coefficient of regression between the amount raised and the amount pledged. Similarly, Raised is the amount raised; goal is the target or the goal amount and pledged is the amount pledged. Hence, this is the universal regression model. 

The table below is the coefficients table. The coefficients B0= 113.104, B1= -0.00005455 and B2= 0.01. In this case, B0 is the intercept, B1 is the regression coefficient between the amount raised and the goal amount and finally B2 is the regression coefficient between the amount raised and amount pledged. 

Discussion and Conclusion

The correlation coefficient analysis reveals that there is an association between the dependent and the independent variables. Hence we reject the first hypothesis that there is no relationship between the e amount raised, the pledged amount and the goal amount.

The regression coefficient as well indicates that we can use the goal amount and pledged amount to predict the raised mount.  Given the regression coefficient B0, B1, and B1, we can write the regression line for prediction as follows; 

Regression Analysis

Raised amount= B0+ B1 (goals) + B2 (amntpledged)

Raised amount= 113.104 + 0.0000545(goal) + 0.01(amntpledged).

This implies that, given the pledged amount and the goal amount, we can predict the raised amount using the linear model above. 

Quality Alloys’ website has a varied number of weekly visits. From the weekly visit data, the number of visits and unique visits are relatively similar. This implies that the number of regular visits and new visits are relatively similar.

Financially, the company’s finances are given in terms of the revenue received and the profit made. From the finances, we can say the company. These finances can be used to tell whether the company is earning interest or not.

We can also use the dataset to tell whether the traditional promotions drive web traffic and increase sales. We can carry out a suitable test to find out the most suitable model for modeling the visits to the website. The site to advertise can be looked at I terms of the searches and referrals.

The number of page reviews is relatively higher than both the visits and unique visits. We can say that most of those who visit the website are satisfied and are willing to give back their views concerning their experience.  

The company is earning interest based on the financial data provided. This is because the company makes profits in all the instances. Furthermore, all the revenues are positive. There is no negative revenue meaning there is no loss hence the company is making profits.

Similarly we can say the traditional promotions drive web traffic and increase sell. This is clearly seen by looking at the number of new web visits (Dogadova & Vasiliev, 2014). Tentatively, we can say that these new visits are as a result of the web visits. This model will tell us where the QA needs to advertise as well. We may look on this based on the best referrals and top searches (Klimiuk, 2009). Based on these criteria, we can say that the best sites are Google and yahoo. 

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