Master Thesis
Your name: Pauline Dekker
Your student number: 6285724/10001974
Specialization: Organizational Economics
Field: Crowdfunding and the beauty premium
Number of credits thesis: 15 ECT
Title of your research: The possible effect of appearance on success rates in
funding a project through Kickstarter.
The thesis coordinator will assign a teacher to supervise your thesis.
Assigned supervisor (to be filled in by thesis coordinator):
Jeroen van de Ven
If a teacher has already accepted to supervise your thesis, please provide the name.
Name of supervisor:
Jeroen van de Ven
Summary
Online crowdfunding has recently become a popular way of financing your innovative ideas.
Although some articles have reviewed several aspects that might influence your chance of
getting funded, hardly any literature exists on the influence of gender and appearance on
your funding success. Therefore, surveys were used in this research to gain data on
appearance, and for this purpose 100 Kickstarter projects were used to review the effect of
gender and appearance differences on the performance of idea founders. It appeared that
men were not more likely to get their ideas funded then women; however some interesting
differences occurred between men and women. And though the appearance categories had
their predicted signs, attractiveness only had a slight negative influence if the founder was in
the average category.
Statement own work
I hereby declare, Pauline Dekker, I wrote this thesis myself and I take full responsibility for its
contents.
I confirm that the text and the work presented in this thesis are original and that I did not
make use of sources other than those mentioned in the text and in the reference.
The Faculty of Economics and Business is solely responsible for the guidance to submitting
the thesis, not the content.
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Table of contents
Summary …………………………………………………………………………………………………………………….……… 2
1. Introduction……………………………………………………………………………………………………………………. 4
2. Theoretical framework …………………………………………………………………………………………………… 5
2.1 Beauty premium ………………………………………………………………………………………………… 5
2.2 Gender differences ……………………………………………………………………………………………. 9
2.3 Crowdfunding ………………………………………………………………………………………………….. 12
3. Methodology ……………………………………………………………………………………………………………..… 15
3.1 Kickstarter …………………………………………………………………………………………………..….. 15
3.2 Hypotheses ……………………………………………………………………………………………………… 17
3.3 Project data …………………………………………………………………………………………..………… 18
3.4 Survey and treatments ……………………………………………………………………………..…..… 18
3.5 Raters ………………………………………………………………………………………………………..……. 20
4. Results …………………………………………………………………………………………………………………….….… 20
4.1 Descriptive statistics projects ………………………………………………………………………..… 21
4.2 Descriptive statistics survey ………………………………………………………………………..…… 22
4.3 Appearance …………………………………………………………………………………………………..... 24
4.4 Gender differences ……………………………………………………………………………………..….. 28
4.5 Regression analysis projects ………………………………………………………………………….… 31
4.6 Regression analysis survey …………………………………………………………………………….… 33
4.7 Possible limitations ……………………………………………………………………………………...…. 35
5. Conclusion ……………………………………………………………………………………………………………….…… 36
6. References …………………………………………………………………………………………………………….……… 37
7. Appendix ……………………………………………………………………………….……………………………………… 39
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1. Introduction & Motivation
Recently it has become increasingly popular that entrepreneurs turn to crowdfunding as a
way to finance their creative ideas. Crowdfunding involves relatively small contributions
from a relatively large number of customer-investors using the internet over a fixed time
period, usually a few weeks, and generally no longer than a month (Kuppuswamy & Bayus,
2013). Since crowdfunding is a relative new phenomenon, literature on this specific topic is
still very incomplete. Previous articles on online crowdfunding particularly focused on which
platforms are most popular and what characteristics are important to become successful
(Antonenko, Lee & Kleinheksel, 2014); what role social information plays (Kuppuswamy &
Bayus, 2013), and the underlying dynamics of success and failure regarding product features
and founders’ social network (Mollick, 2014).
Something that is quite striking is that none of these previous articles took into
consideration the physical characteristics of the founders. While in previous literature it has
been found that appearance influences earnings in the labor market (Biddle and
Hamermesh, 1994) and can benefit the company one works for (Biddle, Bosman,
Hamermesh & Pfann, 2000). It therefore seems obvious there is room for research that takes
into consideration this positive effect your appearance can have, called ‘the beautypremium’, when analyzing factors that influence online funding success.
Whereas appearance is related to gender, this general characteristic also deserves
some attention. Although there is a lot of literature on gender differences, there is only one
article (Marom, Robb & Sade, 2014) that reviews the possible effect of gender on funding a
project through online crowdfunding as far as this research is concerned. The authors
examined gender dynamics and biases in the process of raising funds for new projects via
crowdfunding on Kickstarter, and find that women enjoy higher success rates.
In this research the focus will therefore be on the effects gender and appearance can
have on others’ decision to fund your project on Kickstarter. More specifically, under some
circumstances it is conjectured that people will let their investment decision depend on
gender and appearance of the presenter, and choose the ideas of the prettier
entrepreneurs. For this purpose 100 Kickstarter projects are analyzed and rated through
surveys. Subjects were asked to rate both the project and the founder, and two different
treatments were used to explore the effect of appearance on the raters opinion; one
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showing the picture of the founder before asking any question and one treatment with
pictures after the questions. This produces a rich data set with both characteristics of the
projects together with gender and appearance ratings. Based on the above, the research
question is formulated as follows: “Does your appearance and gender significantly increase
your chance of being funded on Kickstarter?”
The possible existence of the beauty premium could be very important to aspiring
entrepreneurs for the reason that if the effect exists, one can increase their chance of
success by spending more time on their appearance rather than on their business idea .
Besides implications for the use of Kickstarter, these findings can be applied to a broader
area, your appearance might for example also influence other job related opportunities.
At first sight there also appeared to be a beauty premium in this thesis, but after
further analysis the premium was not found to exist and thus the null hypothesis that
founders in all appearance categories are as likely to get their ideas funded cannot be
rejected. Though men did perform slightly better, there was also no proof for men to be
more likely to get funded on Kickstarter than women.
The remainder of this thesis is organized as follows. Section 2 reviews the existing
related literature on gender differences, the beauty premium and crowdfunding; section 3
describes the subjects, procedure, treatments and hypotheses. In section 4 the results of the
data analysis are presented and discussed. Section 5 will conclude and in the appendix an
example survey and some tables with additional information can be found.
2. Theoretical framework
In this section all available relevant information on the beauty premium, gender differences,
and crowdfunding will be reviewed.
2.1 The beauty premium
The beauty premium has been demonstrated in many articles and is already reviewed in the
eighties by Hatfield and Sprecher (1986) in their book on physical attractiveness. Thereafter
the existence of the ‘beauty premium’ is still widely discussed in previous literature and has
proven to exist many times.
For example in their article, Biddle and Hamermesh (1994) studied the possible
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favoritism for the beautiful and the possible discrimination in the labor market against the
ugly. The authors used ratings of physical appearance in combination with three data sets.
This gives the authors the great advantage of combining three sets of data into a dataset
with sufficient sample size to make clear inferences on the effect of beauty. The findings
suggested that more attractive people are indeed paid more; however the penalty for
looking bad is larger than the premium for looking good. The bad look penalty is 9% in
earnings among men and the good look premium is estimated to be 5% in earnings. And for
women this premium was found to be 4% on average, compared to a 5% bad look penalty,
although both percentages were not significant for women. Besides these effects, there was
slight evidence that the labor market sorts the best-looking people into jobs where their
looks are most productive. Though this evidence is fairly weak, it is slightly disadvantageous
for this research that the alternative sources of earning differentials are hard to disentangle.
The authors concluded that better-looking people earn more than bad-looking people and
that these penalties and premiums reflect beauty effects in all its aspects.
Belot, Bhaskar and Ven (2012) also addressed that appearance can be in your favor.
The authors analyzed the beauty premium in their article on a TV game show with high
stakes. In the show contestants had to answer questions and after each round the best
player could eliminate another player. While the authors used a group of participants with a
variety of occupations who were not recruited by any specific criteria, the participants may
not be representative of the population, causing possible external validity issues. The
authors found that less attractive people are substantially more likely to be eliminated, even
though they do not perform any worse or do even better. Only 27% of them made it to the
final round, while the most attractive players made it to the final round in 49% of the cases.
This difference cannot be attributed to a difference in performance or cooperation. It was
also found that the elimination of less attractive players is costly, on average 25% of the
median stake, while there is no financial benefit. Compared to the less attractive people,
attractive players earned a premium because they were not eliminated by their fellow
players. The authors therefore concluded that the beauty premium is a form of taste based
discrimination.
Besides the fact that your beauty can be in your favor, it has also been proven that
your appearance can be beneficial to the company you work for. In their research among
Dutch advertising firms, Biddle et al. (2000) found that firms with better-looking executives
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have higher revenues. With this research, the authors are one of the few to focus on to what
extent the associated differences in beauty are due to differences in ability or to
discrimination. This is particularly interesting for this research because it shows company
related benefits of appearance which could be of importance for start-ups. The executives’
beauty was measured by rating black and white yearbook pictures. The use of these black
and white pictures can be seen as a disadvantage since hair color and eye color are not
visible in this case, but are proven to affect appearance ratings (Belot et al., 2012). Besides
that, the use of estimates from other studies on earnings instead of the real earnings of the
subjects is decreasing the validity of this research. The effect found exceeded the effect of
the executives’ beauty on their own possible earnings increase, 63% at most is reaped by the
executives in the form of higher wages. This implies that beauty creates firm-specific
investments where the returns are shared by the executive and the firm.
Likewise in daily life, the premium has also been found in experimental settings
several times. These laboratory experiments are particularly useful because they can be
designed to disentangle the different sources of the beauty premium. In experimental
settings one can actually rule out other sources besides your appearance in influencing your
topic of research, therefore the main experimental findings on the beauty premium are
presented hereafter.
Mulford, Orbell, Shatto and Stockard (1998) used the prisoner’s dilemma and the role
of perceived physical attractiveness to find out whether the beauty premium had an effect in
everyday exchange. This article was the first to also measure perceptions of subjects’ own
physical attractiveness besides the perception of others. One drawback of their study implies
that their results are based on only six interactions, which is not really representative for the
real world. Also, in real life the settings are much more complicated than the dilemma used
for this process. Mulford et al. (1998) results showed that subjects cooperated and played
more often if they found the other attractive. Only 28% cooperated when the other was
perceived unattractive compared to 48% when the other was perceived attractive. It also
appeared that subject’s self-assessment played a role; subjects who rated themselves low
also had a low expectation of cooperation from others in 60% of the cases, while this was
only 24% if the subject saw himself as attractive. The authors concluded by mentioning that
it is more profitable for those who are seen as attractive to involve in ‘everyday exchange’.
However, Shinada and Yamagishi (2014) found that the negative relationship
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between cooperation and physical attractiveness in a prisoners dilemma only showed up
with young men, but not once they are older or among women.
Another experimental study was presented by Mobius and Rosenblat (2006), who
used students in an experimental labor market as ‘employers’ and ‘workers’. Workers had to
perform a simple task that required true skills, which were shown to be independent of
physical attractiveness. Employers on their turn had to estimate productivity and set wages.
Even though it was about true skills, beauty raised both the workers and the employers’
productivity estimates. In their research the authors used the interview process, while the
beauty premium might have different implications and causes on the long term after
interacting repeatedly. As the authors mentioned, only the interview process was used for
this research while in the long term tasted-based discrimination might again influence the
beauty premium, as other contributors might decrease. The results indicated a beauty
premium identified through three transmission channels; prettier people were more
confident and their confidence increased wages; given the level of confidence, employers
viewed attractive workers as more able; and for a given confidence level the more beautiful
people had oral skills that increased their wages when interacting with the employer.
Solnick and Schweitzer (1999) on the other hand examined the effect of physical
attractiveness in an ultimatum game experiment but found no significant influence of
appearance and gender on the game decisions. Even though the more attractive persons
and men were offered more in the game, also more was demanded from them.
In an article by Andreoni and Petrie (2008) it was examined whether beauty and
gender mattered in a public goods experiment. Players were shown digital photos of all
other members of their group each round, one with and one without individual
contributions. Based on the game, the authors found a beauty premium; however this
premium was found to disappear once information on individual contributions was provided.
In case the individual contributions were not observable, attractive people made more
money than unattractive people, even though there was no reason for, based on their
willingness to cooperate. Therefore the authors were not completely able to draw
conclusions and the implication of their findings stays vague. Besides that, a gender effect
was observed, however not always favoring men. Women earned more money when only
group contributions were known, but in case individual contributions were known, men
earned 15% more compared to women. The differences between men and women will be
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further discussed in the next paragraph.
Though there exists substantial literature on the beauty premium, it is still a new
subject where many aspects yet have to be explored. Most previous literature is either
experimental or work atmosphere related, while your appearance might have a much
broader range in influencing your daily life.
2.2 Gender Differences
Dreber and Johannesson (2008) researched gender differences in a deception game where
subjects were either the receiver or the sender; therewith they were the first to investigate
this in an economic setting. Receivers had to choose between two actions, one that yielded
them more money and one that yielded the other party more money without knowing the
payoffs; senders knew these payoffs and had to send a true or a false message containing
which option, A or B, earned the receiver more. Women appeared to lie significantly less
than men to secure a monetary benefit, 55% of the male senders lied compared to 38% of
the female senders. The authors also tested for the extent receivers trusted the message of
the sender, perceived trustworthiness and whether it mattered if the message was sent by a
male or a female, but found no significant differences here. Though the subjects were
students and the study was experimental, the study found that men were more likely to lie,
something that might have a big effect on their performance in daily life. Men might also lie
to make their ideas on Kickstarter more appealing and thereby influence project supporters’
donations. From another study it also appeared that men are more selfish (Andreoni &
Vesterlund, 2001), and less generous than females (Eckel & Grossman, 1998) which could
also lead to performance differences between males and females.
In their experiment, Gneezy, Niederle and Rustichini (2003) examined whether
gender differences existed in several different controlled experimental settings. This allowed
them to exclude discrimination and expected discrimination. A small minus however was
that the authors were not fully able to measure possible psychological effects, as they
mentioned themselves. Subjects were given the task to resolve a maze and three payment
methods were used; piece wages, a competitive mixed tournament payment and a random
payment. There was no gender gap when participants were paid a piece wage, however the
results of Gneezy, Niederle and Rustinchini (2003) did indicate that women were less
efficient in a competitive environment than men. The increase in competitiveness increased
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performance of the male participants significantly, but not the performance of females. The
effect was found to be weaker in single-sex competition compared to the situation where
women had to compete against men. The authors concluded this meant there is a
substantial impact on performance if tournament incentives are used. These results are
particularly interesting since Kickstarter can be seen as a mixed-sex tournament competitive
environment, meaning men should also be outperforming females in this research. Though
the difference was not significant in this research, men did slightly outperform women.
Besides in strategic games, similar results were found in social dilemma games. For
example Dodge, Van de Kragt and Stockard (1988) used experimental social dilemmas to
examine sex differences in cooperation. Subjects were recruited by advertisements and
undergraduate classes and had to choose between cooperating with the group or defecting
and thus keeping all the benefits to themselves. In the first experiment participants were
allowed to discuss the decision and more cooperation was observed, once discussion was no
longer allowed the rate of defection increased. In both experiments females were slightly
more cooperative than males. However women were more likely to justify their behavior as
altruistic and more socially-oriented, whether or not they cooperated.
The results indicated that the experimental settings had much more influence on
behavior than the gender of the participant. Therefore the authors concluded that sex
differences in cooperation might be overstated and the conditions of the experiment have
far more influence on behavior than the sex of the participant. Despite the fact that the
authors used a relative old data set and only focused on cooperative behavior while the
results might differ for other kinds of behavior, the results were quite interesting. This
research is one of the few that took the effect of the experimental design into consideration
and concluded that gender effects are overstated since the experimental design had much
more influence. Likewise in this research it appeared that gender differences didn’t play as
much of a role as other influences.
The results of Dodge et al. (1988) were later confirmed by Eckel and Grossman
(2000), who reviewed the results found in literature on ultimatum, dictator and public goods
experiments. The results presented by different authors on the differences between men
and women in these games are not consistent, though some patterns emerged. In case risk
is involved there were no significant differences between men and women. However once
the participants were no longer exposed to risk, the systematic differences appeared.
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Women’s choices appeared to be more socially-oriented compared to the more individuallyoriented choices of men. All the results were conditioned by the level of risk and therefore
the authors could conclude that differences in results between men and women were
dependent on the payoff structure and the experimental procedure. With this unclear
conclusion this article shows there is still room for a clarifying article on gender differences,
especially in an environment like Kickstarter where people take the risk to set up a business
and try to find financial support for it.
An attempt to summarize all findings on experimental games considering gender
differences was presented by Croson and Gneezy (2009), who reviewed the experimental
literature on gender differences in their article. Their main insight was that men are less risk
averse than women, with exceptions for managers. Their explanations for the difference
included overconfidence, framing and emotions. Second, the authors found that most
literature agrees men and women differ in their social preferences but the literature on this
topic varies. And third, a noteworthy emergence in literature found, was that men are more
competitive in bargaining and competitive situations. The fact that men were found more
competitive than women could indicate that men are more efficient in a competitive
environment like Kickstarter.
A field experiment was conducted by Marom et al. (2014) who used Kickstarter to
investigate gender dynamics in the funding process. The authors used a very rich data set
with a total of about 25.000 projects, about the same amount of entrepreneurs, about a
million investors and over 120 million dollars of investments. The main focus was on
whether there exists a barrier for female entrepreneurs to raise capital. Results indicated
that men quested for higher amounts of capital and raise more funds compared to women.
However, even after controlling for goal amount and category, females experienced higher
rates of success in funding their projects (69.5% versus 61.4%). Most projects that were led
by females were also mainly financed by women and women make up 45% of the investors,
a larger percentage than the amount of female project leaders. To investigate the underlying
reasons why, the authors conducted a survey among Kickstarter investors and found some
evidence for taste-based behavior. The authors concluded by mentioning there is some
evidence towards increased participation of women in crowdfunding platforms but note that
further research is necessary. This article is very relevant since it uses the Kickstarter website
in combination with gender differences as one of the firsts. However the article leaves room
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for research that does not focus on the female’s participation numbers in online
crowdfunding but more on the effect gender could have on their funding success. The topic
of crowdfunding will be discussed in detail in the following paragraph.
All in all there is an incredible amount of literature on gender related issues available.
Even though most of the previous presented literature is experimental, it is relevant for this
research since it indicates gender differences in a laboratory environment where the
influences of other factors determining your outcome variable are minimized. Previous
authors used substantial data sets and gender differences are measured in several different
areas. However, not much has been published about gender in combination with
appearance yet. It may very well be that the appearance of females has different
implications than the appearance of males.
2.3 Crowdfunding
Crowdfunding is a modern way of raising capital for a new venture idea and it is usually
about small contributions by a lot of backers. As stated by Mollick (2014), considering its fast
rise in popularity, the dynamics of crowdfunding have been unstudied for a large part. There
are some articles before the year 2000 that mention the word crowdfunding but the recent
trend of online crowdfunding platforms is largely undiscovered. Since there are hardly any
articles on online crowdfunding platforms like Kickstarter in combination with the influence
of certain characteristics of the founder, first some more general articles on the dynamics of
successful crowdfunding are reviewed. This lack of literature already shows there is
considerable scope for a study like this.
One of the earlier works on using alternative sources to fund your venture idea is
presented by Macmillan, Siegel and Subba Narasimha (1985) who reviewed criteria that are
used by venture capitalists to judge a new venture idea. Experience, characteristics of the
product/service, characteristics of the market, financial consideration and personality were
measured using questionnaires filled out by venture capitalists form New York. Of course
around the time this article was published, the internet did not play a substantial role in
crowdfunding yet, so this research could complement in also involving the internet. The
authors also only focused on venture capitalists and ignore other potential investors. The
main finding from the article was that the entrepreneurs’ experience and personality are the
most important criteria. Thus, the business plan is only to indicate the entrepreneur’s ability
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to implement the idea but personality is the deciding factor for venture capitalist to fund a
project.
These findings are more or less confirmed 24 years later by Gera, Goldfarb and Kirsch
(2009) who explored venture capital opportunity screening. The authors analyzed the form
and content of business plans in their sample of 722 applications by project founders. Their
main finding stated that these documents and the content are only associated with venture
capitalists funding decisions for a small part. The most critical evaluation used for
information was found to be obtained through alternative channels by the venture
capitalists. Although this paper only focused on venture capitalists and also not involved the
internet, the authors did use a very large dataset and related actual decisions to
characteristics.
However, contradicting results were found by Kaplan, Sensoy and Stromberg (2009),
who studied firm characteristics to review ventures from their early business plan till their
initial public offering. The sample consisted of 50 firms, a rather small sample, that went
public in an IPO and had an early business plan. Despite of their small data set, the authors
believe their results support the non-human capital asset theory. The main result suggested
that the business is more important than the management team. In the business plan, IPO
and annual report the most important factor cited, by respectively 100%, 98% and 91% of
the companies, was the belief that the product/service offered is unique. The authors
argued that the core business activities, customers and competitors tend to stay the same or
broaden over time while the management team often changes in time. Even though an
initial strong business might not be sufficient, an initial strong business plan is necessary for
a company to succeed.
Although previous literature is not fully relevant on online crowdfunding, it
represents an interesting debate on whether the idea or the founder of the idea is the most
important aspect in successful crowdfunding. This is also a very important question
considering online crowdfunding on Kickstarter, since the main aim of this research is to find
out whether gender and appearance are significant determinants of your potential success.
Since this was not proven to significantly be the case, there is a little more evidence towards
the conclusion that it is the product that mainly determines the success in getting funded.
The following literature is more focused on online crowdfunding like Kickstarter.
Antonenko, Lee and Kleinheksel (2014) analyzed trends in active crowdfunding
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campaigns on educational technology startups in their article and thereby shed some light
on some possible beneficial characteristics one could use for your funding idea to succeed.
Even though the authors used educational startups, their result are still interesting since
they used Kickstarter in their analysis. The authors used content and basic frequency analysis
to find support for their hypotheses which platforms are most popular and what successful
characteristics are used to become successful. The results indicated that requesting a
reasonable amount for your project, communicating, and informing supporters, increases
the chance to become a top performing educational project and most startups used
RocktHub, Kickstarter or Indiegogo.
One of the few articles that focus mainly on Kickstarter is written by Kuppuswamy
and Bayus (2013), who analyzed two years of data on projects available at the Kickstarter
website to study the role of social information in backer funding. The authors showed that
past backer support negatively affected new backers, because potential backers suppose
that others will finance these projects since they already have a lot of support. Besides that,
reduced diffusion of responsibility and an increase in project updates towards the deadline
lead to an augmentation in backer support. Project creators thus benefit from posting public
and private updates towards the end of their funding cycle. This study used a large dataset
on Kickstarter projects and actually gave clear implications for project creators; however the
authors paid little attention to the characteristics of the founder, like gender and
appearance.
In the most recent article on Kickstarter, Mollick (2014) tried to find the underlying
dynamics of success and failure in crowdfunding. The author used the online crowdfunding
platform Kickstarter and the dataset consisted of over 48.500 projects. The results indicated
that the project quality and the personal network of the entrepreneur are associated with
success, which was also emphasized by Belleflamme et al. (2014). An interesting observation
was that geography also seemed to influence success rates but also the types of projects
proposed. Results also indicated that entrepreneurs could improve their chance of success
by showing preparedness and by using their social network. Furthermore setting a
reasonable goal prevents the founder from late delivery. Therefore, the main conclusion was
that careful planning will increase your chance of being funded and the chance of a
successful execution of the project. Although Mollick (2014) used a substantial data set and
took into account a lot of possible dynamics of success, also in this article the effect of your
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gender and appearance is omitted while previous literature has proven it to be very
influential in other job and business related subjects.
Summarizing the subject of crowdfunding it is a relatively new subject in literature,
though some articles have made fierce attempts to capture the ins and outs of
crowdfunding. Especially the effect of founder characteristics in crowdfunding through
websites like Kickstarter is not studied sufficiently yet while these results could have great
impact on the results you can achieve by using an online crowdfunding platform.
3. Methodology
In this section the data gathering process is described and explained. Some essential
background information on Kickstarter is provided, the project data is described and the
surveys used for this research are clarified. Besides that, the different treatments are
illustrated and the hypotheses are appointed.
3.1 Kickstarter
Kickstarter is a relatively new online crowdfunding platform and an alternative source in
funding project ideas. Since its launch in 2009, 8.6 million people have pledged more than
$1.7 billion, funding 84.000 projects (Kickstarter, 2015). Kickstarter divides projects in fifteen
different categories; art, comics, crafts, dance, design, fashion, film & video, food, games,
journalism, music, photography, publishing, technology and theater. In order to fully
understand a typical Kickstarter page, the definitions of the key variables are summarized in
table 3.1.
Table 3.1
Key variable
Explanation
Project goal
The amount founders aim to raise using
crowdfunding.
Funding level
The percentage of the project’s goal that has
already been raised.
Backers
The number of people supporting the
project financially.
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Pledge
The promise of backers to support the
project by a specific amount, only the total
pledge is visible on Kickstarter, no individual
pledges.
Campaign
The campaign explains the project and
optionally shows pictures, videos and
project progress.
Updates
Founders can use the update page to update
their backers on their progress and for
example new features.
Comments
Here all project backers can comment on the
project and potential backers can ask the
project creator questions.
Overfunding
If projects raise more money than their
original goal specified.
Days to go
The remaining time the project will be online
and funders still have the possibility to
support the project
From Kickstarter (2015) one can learn that anyone can launch a project, as long as the rules
are followed. Founders can place their projects online in order to raise money for their
project. Kickstarter uses an all-or-nothing system, meaning that the project will only be
funded if its project goal is reached. In that case, the founder has to implement his idea and
meet the agreements with the backers. If not, backers will get their money back from the
creator and the project will not be launched. Usually, only projects that offer their product,
service or something else in return for backer support get (over)funded. This just means that
the project creator has to keep more promises to more backers, not that there is any money
left for personal use. A 5% fee is collected by Kickstarter from the projects total funding, if
successfully funded; there are no fees if the project was unsuccessfully funded. Creators stay
the owners and Kickstarter or the backers never control the creators’ work (Kickstarter,
2015). There can be many reasons to invest in projects on Kickstarter; most people are
supporting friends or projects they already admired for a while. Other people just think the
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idea is good and support it because they want to see it exist in the real world. Another
simple motivation is the projects reward; some just want the product, an experience or
something else promised in return for their pledge. These rewards can range from a thank
you card to a private cruise party with a barbeque (Kickstarter, 2015A).
3.2 Hypotheses
Prior literature has demonstrated that men and women differ from each other in a lot of
ways. Men are for example more efficient in competitive environments (Gneezy et al., 2003).
Besides, men are more likely to lie (Dreber and Johannesson, 2008), more selfish (Andreoni
& Vesterlund, 2001) and less generous (Eckel & Grossman, 1998). In this research we expect
that men might perform better in the Kickstarter environment, since this a highly
competitive environment, and will therefore be more likely to get funded. Consequently,
based on the previous literature and to support the research question, the following
hypothesis is formulated:
Hypothesis 1: men are more likely to get their ideas funded on Kickstarter.
Besides gender, appearance has been proven to affect ones surroundings in previous
literature. For example there is evidence that attractive people get paid more (Biddle &
Hamermesh, 1994), that firms with better-looking executives have higher revenues (Biddle
et al., 2000) and that prettier people are more confident which increases their wages
(Mobius & Rosenblat, 2006). Therefore in this research prettier people are expected to be
more likely to get funded through Kickstarter than average looking people and below
average looking people. This leads to the following hypothesis:
Hypothesis 2: Prettier people have a bigger chance in getting funded through crowdfunding.
In this research, the main focus will therefore be on gender and beauty. However, if these
variables have an effect on funding this might have different causes, for example because
attractive people are seen as more creative. Hence, this research will take other possible
explanations in consideration when designing the research and analyzing the data.
17
3.3 Project data
For this thesis 100 projects were selected from the Kickstarter website and four categories
were used; technology, games, food and fashion. Two categories that might found to be
more interesting by men, and two categories that are expected to be more feminine. The
category selection was made in order to be able to make the data comparable between men
and women, if fifteen categories were to be used, the projects might differ too much to
draw conclusions. Kickstarter has a discover option on its website where all the life projects
can be viewed, either by popularity, magic, ending date, newest or most funded. Considering
the time limit to write about this topic, projects were sorted by ending date, because all
projects have a given ending date this sorting is completely random. There is no reason to
believe that certain projects would appear on top of the page for other reasons than their
upcoming ending date. Projects were recorded in excel, writing down their category, gender
of the founder, city of the founder, goal in dollars, whether the goal was reached, how many
days this took, what the final percentage funded was and whether the project was picked by
the Kickstarter staff. This last item was recorded because many of Kickstarter staff picked
projects rise in their popularity and get seen more often, which might lead to an increase in
successful funding. Due to the fact that some project goals were stated in a currency
different from the US dollar, all other curries were converted to dollars on 19-4-2015. The
exchange rates at that date are presented in table 3.3, included in the appendix.
3.4 Survey & treatments
Besides the data from Kickstarter, data on appearance was necessary for this research. Since
no such data is available yet and the purpose is to measure beauty as objective as possible,
several opinions are needed to construct data on the appearance of the project founders. In
order to obtain this data on the founder’s appearance, surveys were used to collect opinions
on appearance, creativity, productivity and the founder’s idea 1.
All surveys started with a short introduction explaining the questionnaire and why
the help of the respondents would be appreciated, after that a short summary on Kickstarter
was included to make sure everyone understood the concept of online crowdfunding.
Because possibly not everyone is familiar with the projects on Kickstarter, first an example
1
The exact questions asked can be found in the appendix.
18
project was provided. After the short introduction and explanation the survey started with
three general questions considering age, gender and whether the subject is a student or not.
The answers to these questions will be used as control variables in the analysis. Perhaps
older people are more generous in judging the project ideas or females might have different
opinions on how creative projects are. Including these variables gives the option to correct
for these differences.
After the general questions, four questions about the project followed. The first three
questions were about whether the rater thought the project was a good idea and how long
they thought it would take for the project to reach its goal. These three questions were
designed to get an opinion of the rater on the quality of the project. Without opinions about
the project one cannot find out the underlying reason why people rate some projects better
than others. To find the underlying source the fourth question asked the raters to what
extend they thought the idea was creative. This question in combination with the three
questions on the founder of the idea can give an idea of the underlying reason why the rater
did (not) like the project. The three questions about the founder measured attractiveness,
productivity and ability. This gives the opportunity to check whether judgments on project
quality were affected by its founders’ appearance. In order not to confuse the participants
and to keep the data comparable, choice options were given for each of the questions about
the projects and its founder.
For the survey, two treatments were used. In the first treatment subjects were
shown projects with the accompanying founder and questions were asked on both the
project and the founder of the project. In the second treatment subjects were first shown
the projects and were asked to fill out some questions about them, there after they were
shown the founders of the projects and were asked to answer some questions about them.
These different treatments were used to measure the underlying reasons of the possible
differences between the appearance and gender categories. If for example women are
found to receive less funding, the survey allows one to check the reason why. Perhaps
women are seen as less creative or productive or perhaps it is just discrimination. By
designing these two treatments, it becomes possible to also see whether the same projects
receive different ratings depending on whether or not the rater knows the gender of the
entrepreneur.
An example survey can be found in appendix 3.4, for the sake of overview only 1
19
survey is included from treatment 1 in the appendix, because all surveys are similar though
they are on different projects.
3.5 Raters
Every rater received a questionnaire containing some general questions and some project
and founder related questions. They were all asked to rate 10 projects each. It would be
perfect if all raters rated all the 100 projects because this would give more data points, but
also more consistent data. However this was not an option considering the length of the
questionnaire. The questionnaire already consisted of around 15 pages each when 10
projects were included. Considering that the subjects were all volunteers and had no
financial incentives the questionnaire could not be too long. Besides that it would make the
data less valuable if subjects get tired from answering questions and do not dedicate enough
attention to each project. Rating the ten projects took on average around 10 and 15
minutes. To make sure the subjects would still take the time to finish the survey, 10 projects
was perceived as the maximum amount of projects for one survey, since the only incentives
the subjects had were personal.
Raters were recruited through a personal network and Facebook where no specific
criteria were used. Raters differed in age from 16 till 55 and most raters were around the
age of 20. Most of them are college, bachelor and master students from all different
specializations and thirty percent of the raters were male. Unfortunately the response rates
were a bit low and people took long time to return the surveys, therefore eventually 6
ratings per project are available. Three of them saw the picture of the founder immediately
after the project was presented and three of them were shown the pictures of the founders
after they answered questions about the project.
4. Results
In this paragraph the data on the 100 Kickstarter projects and the answers the raters gave in
the 60 surveys will be analyzed. The results are presented and will be used to test the
hypotheses.
20
4.1 Descriptive statistics: projects
From table 4.1 it can be seen that the average goal the founders set for their project was
$17.687,95, however this average amount varies from $5.838,84 (Fashion) to $44.092,32
(Technology) in the different project categories. Within the categories the goals specified
range between $200 and $289.666. The average percentage of the goal reached was
143,20% taken over all projects, also the non-funded projects, with the highest average of
257,59% in the technology category and the lowest average of 81,33% in the fashion
category.
The average percentage of projects that reached their goal was 52%, varying per
project category. This is slightly higher than the average success rate of 44% reported on
Kickstarter (2015), a reason for this could be that projects were sorted to make sure data
was available on both successful and unsuccessful ideas. Approximately half of all the
founders were male, with higher male ratios in the technology and game categories. This
was expected, because technology and games are more popular amongst men. In the
fashion and food category, more founders were women as expected. Since Kickstarter is an
American website it seems no more than logical that the largest percentage of its users are
American citizens, as we can see from the table the average non-American users is only 17%.
Average
goal
Table 4.1
Survey statistics by project category
Average % % of projects Percent Percentage
goal
that reached -age of of team
reached
their goal
males
Percentage
of non-US
founders
Technology
$ 44.092,32
257,59%
48%
60%
20%
28%
Games
$ 10.972,20
131,91%
56%
60%
16%
24%
Fashion
$ 5.838,84
81,33%
44%
28%
16%
32%
Food
$ 9.848,84
101,63%
60%
32%
16%
12%
Total
$ 17.687,95
143,20%
52%
45%
17%
24%
In table 4.2 the summary statistics of the projects from Kickstarter are presented. From the
table it can be seen that 44 of the 83 gender observations were male and that 74 of all the
projects were American. Since most projects were created by Americans and the other
projects came from all different countries, the country variable only measured whether the
21
project was created by an American or not. As seen in table 4.2, taking all projects into
account, the average percentage of the goal reached is 143.2% ranging from 0% (the
projects that did not get any funding) to projects that raised 2386.51% of their specified
goal. The average number of days it took successful founders to reach their goal was 18.44
days, ranging from projects that took only 1 day to succeed till projects that needed 45 days.
Besides that, 12% of all the projects were selected by Kickstarter as the ‘Kickstarter Staff
pick’, which might increase their chance of being funded.
Table 4.2
Summary project statistics
Mean
Std. Dev.
0.530
0.502
Gender
Obs.
83
Min
0
Max
1
Country
100
0.740
0.441
0
1
Goal
100
17687.95
34147.39
200
289666
Reach
100
0.520
0.502
0
1
Percentage
100
1.431
2.932
0
2386.51
Days
52
18.442
12.161
1
45
Staff
100
0.120
0.327
0
1
Note: the 83 observations on gender result from 17 projects being team projects. The Days variable
only has 52 observations because 52 projects succeeded and 48 did not so there is no data on how
long it took them to succeed.
4.2 Descriptive statistics: surveys
In table 4.3 the summary statistics on the survey data are presented. As can be seen from
the table, 30% of the respondents were male; the average age of the raters was about 26
years, ranging from 16 to 55 years, 65% being students. In the survey, raters were asked
whether they thought they would invest in the project and could answer on a scale from 1 to
5, with 1 meaning totally agree and 5 totally don’t agree. On average the raters gave a 3.07,
close to the middle ‘don’t know’ option.
Raters were slightly less optimistic about whether the project would reach its goal or
not, with an average of 2.93 on a scale of 1 to 5. Survey takers estimated the average
22
number of days the project would take to reach its goal close to actual average day it took,
respectively 23.34 compared to 18.44. To the question whether the rater thought if the
founder would raise more money than their goal specified, the average answer was 0.34 on
a scale from 0 to 1. The lower amount of observations can be explained by survey takers
indicating not to have an idea about the question asked.
Creativity, productivity and attractiveness were rated on a ten point scale. The raters
were not too optimistic about the average attractiveness, which was found to be 5.17 with a
standard deviation of 2.03. The average productivity was rated more positively with a 6.35
and a standard deviation of 1.71, the average creativity was 5.50 with a standard deviation
of 2.12. The survey ended with the question whether raters considered the projects
founders as suitable to execute the project and their answers indicated a 2.37 on a 1 to 5
scale. Data on these three variables were not available for all projects because some of the
projects were created by teams and did not contain a picture to show the raters to evaluate.
Table 4.3
Summary survey statistics
Mean
Std. Dev.
0.300
0.459
Male
Obs.
600
Min
0
Max
1
Age
600
26.133
10.857
16
55
Student
600
0.650
0.477
0
1
Idea
600
3.068
1.183
1
5
Goal
600
2.925
1.129
1
5
Days
600
23.340
8.682
1
31
Money
528
0.349
0.477
0
1
Creativity
600
5.498
2.126
1
10
Productivity
498
6.349
1.713
1
10
Attractiveness
498
5.170
2.031
1
10
Suitability
498
2.376
0.880
1
5
Note: Money represents the question whether the rater thought the project would fund more
money than needed. The 528 observations for this variable are explained by some raters answering ‘I
don’t know’.
23
4.3 Appearance
In order to define the attractiveness of each project founder, the average of the 6 raters per
project were added together and the average of these six ratings determined the overall
attractiveness rating. Subsequently these ratings were divided in three categories; below,
average and above. The below category consisted of all ratings below half a standard
deviation under the mean (< 4.1545), the average category consisted of all ratings between
half a standard deviation below the mean and half a standard deviation above the mean
(4.1545 < X < 6.1855), and the above category consisted of all ratings above half a standard
deviation on top of the mean (> 6.1855). This resulted in 16 observations in the below
category, 50 observations in the average category and 17 observations in the above
category.
To analyze the effect of appearance, first the percentage of projects that reached
their goal per appearance category were calculated and presented in the graph below. From
the graph it appeared that the above category performed best with 64.17% of the project
reaching its goal. Surprisingly, people in the below attractive category reached their goal in
more cases than people who were seen as average, an appearance phenomenon that was
not found earlier in literature.
Percentage of projects that
reached their goal
100
80
60
40
20
0
1
2
3
Note: The x-axis presents the three classes of attractiveness
with 1 representing the below average looking people. The
y-axis presents the percentage of projects that reached
their goal in each appearance group .
Besides the percentage of the projects that reached their goal in each appearance category,
it would also be interesting to see the average amount of dollars funded per category.
24
However, this graph would give a distorted picture because there are more observations in
the average group compared to the below and above group, and besides that the average
goals set in each appearance category differed, ranging from 6731.88 for the above category
to 24319.84 in the average attractiveness category. Therefore the average percentage of the
goal reached was calculated and presented below to sketch a more representative picture.
This outlines an interesting picture since it appears that the below category outperforms
both the average as the above category with a 136.44% reached on average. One would
have expected the above category to outperform the other categories; especially since the
average goal set in the above category was the lowest.
Average Percentage of the goal
reached
140
120
100
80
60
40
20
0
1
2
3
Note: The x-axis presents the three classes of attractiveness
with 1 representing the below average looking people. The
y-axis presents the average percentage of the goal reached.
To further analyze the effect of the founders’ appearance, the appearance data was added
to the project data and some Kruskal-Wallis tests were performed. These tests determined
whether there is a difference between the distributions between the three appearance
categories for several other variables measured in the project data. This non-parametric test
was chosen because the data is not symmetric, more than two groups are used, and this test
does not require the data to be normal but uses the rank of the data values instead. As can
be seen from table 4.4 the gender of the founder differed significantly at the 1% level for the
different categories of attractiveness. Meaning the mean ranks were not the same in the
25
three appearance categories. The goal set and whether the project was picked by the
Kickstarter staff were the only two other variables that differed significantly for the three
categories of attractiveness of the founder, however only at the 10% level. From the table it
also becomes apparent that there is no indication that above average looking people are
more likely to reach their goal. With a p-value of 0.228, the null hypotheses, that the means
of the three appearance categories are equal, cannot be rejected. Subjects in different
appearance groups neither differed in their means for the percentage of their goal reached,
or the amount of days it took to achieve the goal.
Male
Table 4.4
Project characteristics and Attractiveness
Chi-squared P-value
Mean Below Mean Average
14.437
0.001***
0.625
0.640
Mean Above
0.118
Goal
4.839
0.089*
9003.50
24319.84
6731.88
Percentage
2.406
0.300
136.44
105.23
86.17
Reach
2.953
0.228
0.563
0.396
0.684
Days
2.160
0.340
17.78
17.14
23.82
Staff
5.773
0.056*
0.000
0.143
0.056
Note: the table presents 6 different Kruskal-Wallis tests and all tests are by attractiveness categories
and corrected for ties. Significant values are indicated by: *p<0.10,**p<0.05 and ***p<0.01.
Perhaps attractive people are seen as more creative, productive or suitable. As Mobius and
Rosenblat (2006) already found, that attractive workers are seen as more able. Therefore it
would be interesting to see whether the raters differed in their mean answers on other
survey questions for different categories of attractiveness. To see the possible effect of
appearance on the survey answers the raters gave, several Kruskal-Wallis tests were
performed on the survey data by appearance category.
In table 4.5 the means are not presented because it would make it confusing to add
these for all 10 categories of attractiveness. Hence appearance cannot be divided in three
categories in this case because all raters gave different answers on the question whether
they thought the founder was attractive, and since all projects were rated by six raters the
founders would fall in several categories at the same time. Even though there is no
26
extinction made between the levels of attractiveness, it is interesting to see that a lot of
answers differed significantly in their means for the different ratings of appearance.
From the table it appears that only for the question whether the raters thought the
project would get funded more money than needed, the mean answers of the raters per
appearance category did not significantly differ from each other. On both the questions
about whether the raters thought it was a good idea and whether they thought the project
would reach its goal, raters answered significantly different for the appearance categories.
Also the question about how many days the rater thought it would take the founder to reach
its goal was influenced by the appearance of the founder. This also held for the question on
creativity, productivity and suitability, where raters also were influenced by the appearance
of the founder while answering the questions. Therefore one can conclude that for all but
the money variable it does not hold that the mean answers of the raters were equal in the
ten appearance categories. This is a very interesting finding because it appears that your
looks influence other people’s opinion about you and your project significantly.
Table 4.5
Survey characteristics and Attractiveness
Chi-squared
P-value
Idea
22.851
0.007***
Goal
23.057
0.006***
Days
17.112
0.047**
Money
10.527
0.310
Creativity
37.620
0.000***
Productivity
77.767
0.000***
Suitability
43.694
0.000***
Note: the table presents 7 different Kruskal-Wallis
tests and all tests are by attractiveness categories
and corrected for ties. Significant values are
indicated by: *p<0.10, **p<0.05 and ***p<0.01.
27
4.4 Gender differences
From table 4.4 one could already see that the gender of the founder differed significantly at
the 1% level for the different categories of attractiveness, only not whether men and women
were equally likely to be in either of the categories. Therefore it would be interesting to see
whether these differences in mean ranks for men and women differ significantly from each
other in each of the three categories. Therefore in table 4.6 several Mann-Whitney U tests
were performed to review the gender difference per appearance category. This test was
used because two independent groups are compared that are not normally distributed and
the independent variable is ordinal. From table 4.6 one can see that men and women are not
equally likely to be found either average or above average looking, there was no difference
in means observed for the below category. It appeared that men are more often seen as
below average than women with a p-value of 0.014. However, in the above category women
were significantly seen as more attractive than men at the 1% level. This also followed from
the survey data, where 62.50% of the below average looking people were male, 64% was
male in the average category and only 11.80% was male in the above average category.
Table 4.6
Attractiveness characteristics and Gender
Z-value
P-value
Mean
male
Attractiveness Below
-0.841
0.400
0.227
Mean
female
0.154
Attractiveness Average
-2.454
0.014**
0.727
0.462
Attractiveness Above
3.798
0.000***
0.045
0.385
Note: the table presents 3 different Wilcoxon Mann-Whitney tests and all tests are by
the gender of the founder. Significant values are indicated by: *p<0.10,**p<0.05 and
***p<0.01.
Also the project characteristics might differ for the gender of the founder. From table 4.7 we
can see that the goal the founders set is significantly different for men and women since the
means cannot be assumed to be equal with a p-value of 0.04. Although the mean for men
(140.13%) was higher than the mean for women (70.35%), the percentage of the goal
reached cannot be proven to be different between men and women. Also for the reach
28
variable, which indicates whether the founder has reached its goal or not, it applies that the
means between men and women cannot be proven to be non-equal. This means that men
are thus not more likely to reach their goal then females, even though the mean was higher
for females. Herewith the null hypothesis that men and women are equally likely to get
funded through Kickstarter cannot be rejected. One of the few authors that also did not find
a significant gender influence were Solnick and Schweitzer (1999).
On the other hand, men did differ significantly in their mean from women on the
days variable, meaning men needed significantly less days to reach their goal (in case they
actually reached their goal). The staff variable, indicating whether the project was picked by
the Kickstarter staff as recommended, also differed significantly at the 5% between men and
women. From the mean values we can see that men (0.159) are more likely to get picked by
the Kickstarter staff than women (0.026).
Goal
Table 4.7
Project characteristics and Gender
Z-value
P-value
Mean males
-2.049
0.040**
19417.50
Mean females
15900.21
Percentage
-1.547
0.122
140.13
70.35
Reach
0.321
0.748
0.477
0.526
Days
2.866
0.004***
13.238
25.200
Staff
-2.044
0.041**
0.159
0.026
Note: the table presents 5 different Wilcoxon Mann-Whitney tests and all tests are by
gender of the founder. Significant values are indicated by: *p<0.10,**p<0.05 and
***p<0.01.
The gender of the founder might also have an effect on the answers the raters gave in the
survey. From table 4.8 one can see that both the idea and goal variable did not differ in their
means between men and women. Surprisingly, raters did not think males or females would
either have better ideas or be more likely to reach their goal. Though this was perhaps not
expected, the raters’ opinions did match the finding that men were indeed not more likely to
reach their goal. On a 10% level the raters did think that men would need fewer days to get
29
funded then women would. To the question whether the raters thought the project would
get funded more money than needed, the mean answers did not differ significantly for the
gender of the founder. It seemed that both attractiveness and productivity were not rated
the same for male and female founders. Men were seen as more productive whereas
women were seen as more attractive. This does not only mean that your appearance
influences a lot of the answers to the survey questions as seen before, but also that
appearance significantly differs between men and women. However, there is no significant
difference in the mean answers on how suitable or creative the raters consider the founder
based on their gender.
Idea
Table 4.8
Survey characteristics and Gender
Z-value
P-value
Mean males
0.686
0.493
3.043
Goal
0.779
0.436
2.919
2.996
Days
-1.873
0.061*
23.422
23.633
Money
-0.599
0.550
0.682
0.625
Creativity
0.181
0.856
5.601
5.233
Attractiveness
4.862
0.000***
4.775
5.604
Productivity
-2.062
0.039**
6.500
6.196
Suitability
1.557
0.119
2.314
2.442
Mean females
3.113
Note: the table presents 8 different Wilcoxon Mann-Whitney tests and all tests are by
the gender of the founder. Significant values are indicated by: *p<0.10,**p<0.05 and
***p<0.01.
30
4.5 Regression analysis on the projects
To further determine the effect of appearance and to avoid possible reversed causality, the
appearance ratings were added to the Kickstarter project data and several regressions were
conducted, presented in table 4.9. These regressions are presented because gender and
attractiveness are correlated as seen before, and adding them both to the regression gives
the opportunity to see the effect on the project features when controlling for both variables
and some control variables.
In the first column a regression with only the gender variable and some control
variables is presented to further analyze the hypothesis that men are more likely to reach
their goal than women. The coefficient shows that the gender variable is not significant,
meaning that the gender of the founder was not of significant influence on the project
reaching its goal. Also in the other specifications the gender coefficient stays non-significant.
Herewith it also seems that the null hypothesis that men and women are equally likely to get
funded through Kickstarter cannot be rejected. The country variable however is significant
and stays significant in all regressions, meaning that US citizens were significantly more likely
to reach their goal than non-US founders. This could be caused by the over representation of
US founders compared to founders from other countries.
It seems that the goal founders set barely has any influence on whether the project
reaches its goal or not, which is quite strange actually. One would expect that for example a
$50.000 goal is harder to reach than a $5.000 goal, though in all columns it appeared that
the goal set had no significant impact in determining whether the project would reach its
goal. This could perhaps be explained by people that target higher goals are more motivated
and prepared than people who set lower goals. Also the staff variable appeared not to be
significant in either of the regressions. This can be interpreted as Kickstarter staff picked
projects not having a bigger chance in reaching its goal than projects that were not picked.
In the second column the attractiveness variable was used in a regression together
with some control variables. Here appearance is not yet divided in categories to see the
overall effect of appearance on whether the project reaches its goal. As we can see from the
table, attractiveness is significant at the 10% level. With a positive coefficient, this means
that your appearance positively influences the chance of the project reaching its goal.
In the third column three category dummies for the technology, game, and fashion
31
category were added to check whether being in one of the categories made a significant
difference for reaching the projects goal. The food category was omitted to avoid perfect
multi-collinearity because all projects fell in one of these four categories. It appears tha t
being in either of the categories is not of significant impact for your performance. The
appearance coefficient stays significant at the 10% level and the control variables are, all but
the country variable, not of significant impact.
In the fourth column the appearance variable was divided in three categories: below,
average and above and added to the model. Since each project that was rated on
appearance is in one of these categories, these variables are dependent on each other and
therefore the above dummy was omitted. It can be seen that the below average category
had its predicted negative sign, however being in this category had no significant negative
impact on whether the goal was reached or not compared to being in the above category.
Remarkably, the average category did have a small significant negative effect on whether a
project would reach its goal compared to the above category. It was checked whether the
coefficients and standard errors differed once another appearance category was left out and
it appeared that the above category did have a positive coefficient when one of the other
categories was omitted, however this difference was not significant.
These category effects stayed the same once the dummy variables for the different
categories were added in the fifth column. As in the third column, being in either of the four
categories did not make a significant difference for the likeliness of reaching the goal of the
project. At last the 83 observations can be explained by the fact that 17 of the 100 projects
were performed by teams and therefore there are no gender or appearance observations for
these 17 projects.
32
Male
Country
Goal
Staff
Attractiveness
Attr Below
Attr Average
Technology
Game
Fashion
N
Table 4.9
Determinants of a project reaching its goal
(1)
(2)
(3)
(4)
0.026
0.098
0.067
0.095
(0.116)
(0.120)
(0.125)
(0.122)
0.375***
0.391***
0.380***
0.371***
(0.130)
(0.129)
(0.133)
(0.130)
-0.000
-0.000
-0.000
-0.000
(0.000)
(0.000)
(0.000)
(0.000)
0.238
0.198
0.183
0.294
(0.185)
(0.189)
(0.189)
0.746*
0.087*
(0.045)
(0.048)
-0.127
(0.175)
-0.264*
(0.148)
0.160
(0.168)
0.100
(0.161)
0.006
(0.150)
83
83
83
83
(5)
0.086
(0.130)
0.375***
(0.134)
-0.000
(0.000)
0.297
(0.195)
-0.125
(0.186)
-0.265*
(0.152)
0.107
(0.169)
-0.009
(0.161)
0.024
(0.152)
83
Note: Dependent variable: whether the founder of the project reached its goal (yes=1, no=0).
The table presents coefficients from OLS regressions. The Attr variables represent the categories
of appearance. Standard errors are in parentheses: *p<0.10,**p<0.05 and ***p<0.01.
4.6 Regression analysis on the survey
Possible effects might also occur because attractive people are seen as more creative,
productive or suitable as we saw in table 4.5. Therefore in table 4.10 several regressions are
presented on the dependent variable idea, a variable describing the question whether the
rater thought the project was a good idea, to see if these effects dominate each other.
In the first regression the attractiveness variable was regressed only with some
control variables and one can see that the overall appearance ratings had a significant
negative effect on whether the rater though the project was a good idea. In the second
column, only the control variables were used in a regression to see their influence on the
idea variable before adding attractiveness. In the third column one can see that the
significant effect of attractiveness disappears once controlled for productivity, creativity and
33
suitability. This is probably because these variables are correlated with appearance and vice
versa. Therefore in table 4.10B in the appendix, the correlations between these four
variables are presented and it appeared that all the variables are significantly correlated with
each other.
From the table we can also see that creativity is significant at the 1% level in all the
regressions, raters thus consider creativity as a crucial determinant of an idea being good,
which makes perfect sense. However against expectations, the negative sign of both the
creativity and the productivity variable indicate that these variables had a negative impact
on whether the raters thought the project was a good idea. Perhaps raters considered the
more creative ideas as more risky and therefore as ideas they would not invest in as much as
other ideas.
In the fourth column a dummy for treatment is included; this variable indicates
whether the raters saw the picture of the founder before or after having answered questions
about the project, with 1 meaning the raters saw the picture of the founder after the project
questions. This variable negatively influenced the raters’ opinion on the project, however
not significantly. This means raters were less optimistic about projects when they did not see
the picture of the founder before answering questions about the project. In the fifth column
this treatment effect was added to the attractiveness variable and some control variables
and it appeared that also here the effect of attractiveness was no longer significant, the
coefficient was even almost negligible.
In the last column, dummies for the categories were added to see if the opinions of
the raters were influenced by the categories. The food category was omitted to avoid
perfect multi-collinearity. It appeared that the technology category had a significantly worse
impact on whether the rater thought the idea was good than the food category. Both the
game and fashion category had a slightly more positive influence then the food category,
though not significant. To see the impact of the categories also the other categories were
left out and it appeared that the technology category had a significant negative effect
compared to all the other categories. The attractiveness coefficient remained practically the
same compared to the previous column and thus had no significant impact.
The same regressions as in table 4.10 were ran with goal as the dependent variable
to see if the effects were the same on whether the raters thought the project would reach
its goal, this robustness check is presented in table 4.10C, included in the appendix.
34
Table 4.10
Determinants of a rater considering the project as a good idea/investment
opportunity
(1)
(2)
(3)
(4)
(5)
Male
-0.0130
0.0389
0.0300
-0.0731
0.0431
(0.1338)
(0.1092)
(0.1104)
(0.1254)
(0.1106)
Age
0.0129**
0.0117**
0.0124**
0.0067
0.0128***
(0.0060)
(0.0048)
(0.0049)
(0.0053)
(0.0050)
Student
-0.0232
0.0691
0.0723
0.0209
0.1255
(0.1478)
(0.1230)
(0.1231)
(0.1450)
(0.1288)
Productivity
-0.0736**
-0.0687**
-0.0703**
(0.0335)
(0.0345)
(0.0345)
Creativity
-0.0296*** -0.2943***
-0.2931***
(0.0229)
(0.0231)
(0.0231)
Suitability
0.0618
0.0602
0.0617
(0.0612)
(0.0613)
(0.0612)
Attractiveness -0.1312***
-0.0141
-0.0163
(0.0270)
(0.0242)
(0.0242)
Treatment1
-0.0597
-0.1264
(0.1025)
(0.0900)
Technology
Game
Fashion
N
498
498
498
600
498
(6)
0.101
(0.111)
0.014***
(0.005)
0.196
(0.129)
-0.066*
(0.034)
-0.275***
(0.024)
0.066
(0.061)
-0.017
(0.025)
-0.143
(0.089)
-0.256**
(0.125)
0.193
(0.123)
0.097
(0.120)
498
Note: Dependent variable is whether the raters think the project is a good idea/will invest in the idea
on a scale from 1 to 5. Table presents coefficients from OLS regressions. Standard errors are in
parentheses: *p<0.10,**p<0.05 and ***p<0.01.
4.7 Possible limitations
A first possible limitation of this research is the perspective of the raters, as already
mentioned by Biddle and Hamermesh (1994), raters might have a different standard for
beauty. Who is considered as pretty, is considered as ugly by another person. Therefore
these differences could lead to measurement errors, decreasing the efficiency of the
estimates. To limit this effect each rater only rated 10 of the 100 project and besides that
the appearance per project was determined by taking the average of 6 people’s opinions.
Secondly it is hard to control for the fact that perhaps prettier people have the better
ideas. To minimize these problems, raters were asked to rate the project as well as its
founder, and data on all projects from Kickstarter were recorded. Finally, as mentioned by
35
among others, Shinada and Yamagishi (2014) mentioned that the results of this study need
to be replicated using different methodologies before one can draw final conclusions.
Conclusion
This thesis is one of the first to broach the topic of crowdfunding together with the effect of
gender and appearance, while previous literature only focused on these topics
independently of each other. Most of these papers found the existence of a beauty premium
and an advantage for males.
Even though at first sight there appeared to be a beauty premium, here, surprisingly,
only slight evidence was found that average looking people performed slightly worse
compared to the above category. There were no indications that the below category
performed worse, or the above category performed better compared to the other
categories. Therefore the hypothesis that people in different appearance categories are
equally likely to get their idea funded cannot be rejected. However further research might
be necessary before drawing conclusions. The coefficients on the above and below category
did have the predicted signs though, being in the below average category had a negative
effect and being in the above category had a positive impact.
For the second hypothesis, that stated that males would be more likely to get their ideas
funded, no support could be found. However, also here it applies that the sign did have the
predicted value.
Besides clarity on the hypotheses, some interesting findings emerged. For example it
turned out that attractiveness of the founder also had an influence on the other answers
raters gave in the survey, and that creativity and productivity were more important
determinants than appearance for raters to consider an idea as a good idea. Women were
seen as more attractive compared to men, who were seen as more productive. Also the
older the rater, the more optimistic they were about the project ideas. Among other things,
from the project data it was found that Americans were more likely to reach their goal, men
set higher goals than women, men need less days to reach their goal and they are more
likely to get selected by the Kickstarter staff.
The fact that the hypotheses could not fully be supported might lie in the number of
observations. Perhaps a larger sample might increase the effects found in this thesis. Also
36
taking the average opinions from more than 6 raters per project might increase the validity
of the research. Besides that it is hard to measure appearance objectively, if it would
somehow be possible to make this measure less influenced by subjective opinions, this
might greatly benefit future research. It would also be a nice addition if more access was
available on both the project and its founder. It might benefit the analysis if for example age
and background of the founder could be taken into account.
6. References
Andreoni, A. and Petrie, R. (2008). ‘Beauty, gender and stereotypes: Evidence from
laboratory experiments’, Journal of Economic Psychology, 29, 73-93.
Andreoni, J. And Vesterlund, L. (2001). “Which is the fair sex? Gender differences in
altruism.” The Quarterly Journal of Economics, 116(1), 293-312.
Antonenko, P.D., Lee, B.R. and Kleinheksel, A.J. (2014). “Trends in the crowdfunding of
educational technology startups.”, TechTrends, 5896), 36-41.
Belleflamme, P., Lambert, T. and Schwienbacher, A. (2014). “Crowdfunding: Tapping the
right crowd.” Journal of Business Venturing, 29, 585-609.
Belot, M., Bhaskar, V. and Ven, J van de. (2012). “Beauty and the Sources of Discrimination”,
Journal of Human Resources, 47(3), 851-872.
Biddle, J.E. and Hamermesh, D.S. (1994). “Beauty and the labor market.” The American
Economic Review, 84(50), 1174-1194.
Biddle, J.E., Bosman, C.M., Hamermesh, D.S. and Pfann, G.A. (2000). “Business success and
businesses’ beauty capital”. Economic Letters, 67, 201-207.
Croson, R. and Gneezy, U. (2009). “Gender differences in preferences.”, Journal of Economic
Literature, 47(2), 448-474.
Dodge, P.J., van de Kragt, A.J.C., and Stockard, J. (1988). “Are there sex differences in
cooperation and in its justification?” American Sociological Association, 51(2), 154-163.
Dreber, A. and Johannesson, M. (2007). “Gender differences in deception.” Economic
Letters, 99, 197-199.
Eckel, C.C. and Grossman, P.J. (1998). “Are women less selfish than men?: Evidence from
dictator experiments”. The Economic Journal, 108(448), 726-735.
37
Eckel, C.C. and Grossman, J. (2000). Differences in Economic Decisions of Men and Women:
Experimental evidence. Mimeo.
Gera, A., Goldfarb, B. and Kirsch, D. (2009). “Form or substance: The role of business plans in
venture capital decision making”. Strategic Management Journal, 30, 487-515.
Gneezy, U., Niederle, M. and Rustichini, A. (2003). “Performance in competitive
environments: gender differences.” The Quarterly Journal of Economics, 118(3), 1049-1074.
Hatfield, E. and Sprecher, S. (1986). Mirror, mirror…: The importance of looks in everyday life.
Albany, NY: State University of New York press.
Kaplan, S., Sensoy, B. and Stromberg, P. (2009), “Should Investors Bet in the Jockey or the
Horse? Evidence from the Evolution of Firms from Early Business Plan to Public Companies”,
Journal of Finance, 64(1), 75-115.
Kickstarter (2015). “Seven things to know about Kickstarter.” Retrieved May 15, 2015 from
https://www.kickstarter.com/hello?ref=footer
Kickstarter (2015A). “Frequently asked questions”. Retrieved May 15, 2015 from
https://www.kickstarter.com/help/faq/kickstarter+basics?ref=footer
Kuppuswamy, V. and Bayus, B. L. (2013). “Crowdfunding creative ideas: The dynamics of
project backers in Kickstarter.” Social Science Research Network. Retrieved May 7, 2015 from
http://funginstitute.berkeley.edu/sites/default/files/V.Kuppuswamy_Crowdfunding%20%20UCBerkeley.pdf
Macmillan, I.C., Siegel, R. and Subba Narasimha, P.N. (1985). “Criteria used by venture
capitalists to evaluate new venture proposals”, Journal of Business Venturing, 1, 119-128.
Marom, D., Robb, A. and Sade, O. (2014). “Gender Dynamics in Crowdfunding (Kickstarter):
Evidence on Entrepreneurs, Investors, Deals and Taste Based Discrimination”, working
paper.
Mobius, M.M. and Rosenblat, T.S. (2006). “Why beauty matters.” The American Economic
Review, 96(1), 222-235.
Mollick, E. (2014). “The dynamics of crowdfunding: An exploratory study.” Journal of
Business Venturing, 29, 1-16.
Mulford, M., Orbell, J., Shatto, C. and Stockard, J. (1998) “Physical attractiveness,
opportunity, and success in everyday exchange.” Journal of Sociology, 103(6), 1565-1592.
Shinada, M. and Yamagishi, T. (2014). “Physical attractiveness and cooperation in a
prisoner’s dilemma game”. Evolution and Human Behavior, 35, 451-455.
38
Solnick, S.J. and Schweitzer, M.E. (1999). “The influence of physical attractiveness and
gender on ultimatum game decisions.” Organizational Behavior and Human Decision
Processes, 79(3), 199-215.
7. Appendix
Table 3.3
CAD to US dollar
Euro to US dollar
New Zealand dollar to US dollar
Australian dollar to US dollar
Swedish crown to US dollar
Pound to US dollar
Danish crown to US dollar
0.816860
1.080550
0.767940
0.778365
0.115668
1.494500
0.144833
3.4 Survey example
Welcome,
Thank you for participating in this questionnaire about Kickstarter projects. I’m using these
questions for my master thesis at the University of Amsterdam to support my research. Your
answers will only be used for this research. The following questions are all about Kickstarter
projects. Always read the text first and answer the questions after that. Please fill out the
questions in the right order (1,2,3..) and don’t change your answers after reading new
information. The survey will take about 15 minutes to complete. After this short introduction
I will first give some general information about Kickstarter and present an example project.
Thank you for helping me complete my thesis!
About Kickstarter (from kickstarter.com):
Kickstarter is an online crowdfunding platform where you can promote your idea and try to
get it funded. Project creators can set a funding goal and deadline. If people like a project,
they can pledge money to make it happen. Usually in return for a product, thank you note or
something else. Funding is all-or-nothing; projects must reach their goal to receive any
money. To date, 44% of the projects have reached their funding goals. Success rates vary per
category between 22% and 65%. Most project raise no more than $10.000 dollar but a
growing number of projects reaches 6 to 7 figures. So far already 83.450 projects have been
funded successfully on Kickstarter. To give you an idea about successful projects I will first
use an example project:
39
Moment case, World’s best iPhone case for mobile photography:
Hi Kickstarter,
We’re back. A year ago we asked for your help. We wanted to create the best lenses in the
world for mobile photographers. Thanks to your support, we did it! Moment lenses are now
being used by thousands of customers around the world, capturing some of the most epic
adventures imaginable.
Now we want to take the next step with you in making it even easier to take better pictures
with your phone. Even with these amazing Moment Lenses we are still missing important
shots with our phones. By the time we reach into our pocket, access our phone, unlock it,
adjust the scene, and take a picture, we’ve lost valuable time. Especially with a moving
subject, by the time you tap the screen you missed the shot.
So we decided to solve this problem by making the first iPhone 6 case that truly brings the
best features of a traditional camera back to your phone. Introducing the Moment Case; the
fastest way to take better pictures with your phone. We've spent the last five months
working on this product and we think you're going to love it.
(This is what the product looks like)
This project successfully pledged 693.435(!) dollars within 1 day with 4.833 backers (people
that funded the project).
Right here the survey starts!
1.
2.
3.
Question
What is your age
Are you male/female
Are you a student? If yes,
what is your major?
Answer
40
Project 1: Now you will first get to read about ‘Shotgun dinners’ on Kickstarter:
Hello Everyone,
Over the past three years the Shotgun Dinners crew has pooled any money we actually
made doing our dinners and used it fund a pig roast the Saturday of The Twilight
Criterium. Each year its attendance has grown exponentially. This is our way of thanking
you and the awesome Athens community for supporting us for eight years.
Since we did not do any dinners last year, we do not have the funds to pay for everything
that goes into this celebration. We are hoping you guys will all pitch in so we can make it
happen this year. We will stay up all night and cook the beautiful pig we already have lined
up (anyone is welcome to join us), supply kegs of Normaltown Beer, and a great location for
the gathering. We just ask that you supply a few bucks and your appetite for fun and good
food. We also encourage anyone to bring along delicious side dishes as you have done in the
past. We would also like to add that we still fully support our local farmers and that the pig
will be coming from Benji Anderson via the Community Meat Co, our local meat co-op run by
Gus Darnell and Meg Grevemberg.
We hated to have to resort to a Kickstarter for this but we hated the thought of letting the
tradition die even more. We look forward to putting on an even better celebration for you
this year and hope you will contribute a little toward the goal!
Thank you,
The Shotgun Dinners Crew (Damien, Patrick, Noah, & Chrissy)
1.
2.
3.
4.
5.
Question
Do you think this is a good idea, in other words, would
you invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $1750 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Answer
Choose an item.
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
41
Project 2: Now you will first get to read about ‘hafa adai express’ on Kickstarter:
If you've ever craved Black Tea or are still a little bitter about having to say "flip-flops", we've
got something to make you feel right at home!
Hafa Adai Express has three introductory Guam Care Packages available for pre-order. Every
care package is filled with all your favorite snacks, drinks, and goodies from Guam! And we'll
send them anywhere in America, nationwide!
Kids go off to college, station's end, and family is all over the world. That's what happened to
me. After Sanchez, I went off to college and it was rough. I missed home! I missed the food! I
missed Black Tea! No matter what journey you or your loved ones may be on, leaving Guam
is hard. The one thing that made it easier every month was the care package my family sent
me. Let me tell you right now that after a long day, snacking on some guguria or making
some kelaguen just makes you feel better.
This is the person who came up with the idea:

1.
2.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $5000 Goal?
Answer
Choose an item.
Choose an item.
42
3.
4.
5.
6.
7.
8.
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 3: Now you will first get to read about ‘the OG Brown project’ on Kickstarter:

Hey there fellow pizza lover! Long story short I’m trying to realize the dream of seeing my
mom run a restaurant one day...Thank you!
This is the person who came up with the idea:
1.
2.
3.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $6673 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Answer
Choose an item.
Choose an item.
Click here to
enter text.
43
4.
5.
6.
7.
8.
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 4: Now you will first get to read about ‘Tipsy treats and salty sweets’ on
Kickstarter:
Every time I stayed up at night baking a double batch of cookies to bring into the office, I
would always find myself throwing back a bottle of wine, but still producing a yummy
confection. One day I came in with a batch of Salty Chocolate Chip Cookies that apparently
were especially good. I made them after a work dinner where I drank about four
Manhattans. And the idea for this book was a born. A young girl with a good taste for the
drink, and a passion for baking.
The funds for this project will be used towards supplies, ingredients, editing and design costs
to make the most beautiful book I can, to truly highlight my passion.
This is the person who came up with the idea:
44
1.
2.
3.
4.
5.
6.
7.
8.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $4000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 5: Now you will first get to read about ‘The T-Rex cookie cooper’ on Kickstarter:
From the very beginning of my career, I made it a habit of baking cookies and getting
feedback from my colleagues so I could tweak recipes for State Fair Competition. One day,
my boss asked me, "Do you want to be known for the work you do or the cookies you
bake?" Years later, I'm finally answering her question. When I told people I was leaving
corporate IT and baking cookies full time the one phrase that was repeated over and
over, "It's about time!"
This early phase of my business is establishes the T-Rex Cookie Brand. Delivering my baked
goods in my wrapped car during the week and having a presence at local Farmer's
Markets. What's the brand? High energy, Betty Draper meets Betty Crocker, retro sassy
attitude.
45
This is the person who came up with the idea:
1.
2.
3.
4.
5.
6.
7.
8.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $9000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 6: Now you will first get to read about ‘Click’ on Kickstarter:
Click is a patent-pending watch band adapter designed specifically for Apple Watch. With
Click, you can use any 22mm watch band. Yes, any!
Click comes in three materials: a durable polymer starting at just $12, anodized aluminum
starting at just $20, and stainless steel starting at just $35, each precisely machined to
perfectly blend with Apple Watch.
46
This is the person who came up with the idea:
1.
2.
3.
4.
5.
6.
7.
8.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $30000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 7: Now you will first get to read about ‘The Archaescan’ on Kickstarter:
What is Archaeoscan: Imagine yourself exploring mysterious tombs and secret places built
before the roman empire started. Picture yourself walking around, jumping or swimming
underwater in order to reach secret rooms.
47
Every single detail, every single stone is 100% authentic. And everything looks so real, so
scary; the shadows, the light reflections on the wet rock, the echo of your steps
This is the person who came up with the idea:
1.
2.
3.
4.
5.
6.
7.
8.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $10806 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 8: Now you will first get to read about ‘The sparx skate sharpner’ on Kickstarter:
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The Sparx Skate Sharpener is the first true in-home skate sharpening solution for hockey
players. Comparable to a desktop printer in size and appearance, this automated piece of
precision equipment empowers individuals to produce professional quality edges with the
push of a button.
This is the person who came up with the idea:
1.
2.
3.
4.
5.
6.
7.
8.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $60000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
On a scale from 1 to 10, how attractive do you think this
person is? With 10 being very attractive and 1 totally not
attractive.
On a scale from 1 to 10, how productive do you think this
person is? With 10 being very productive and 1 totally not
productive.
Do you think this person suitable to execute the project?
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Choose an item.
Project 9: Now you will first get to read about ‘Podo’ on Kickstarter:
49
Podo is a Bluetooth connected, stick-anywhere camera that you control with your phone.
Just stick it on a wall and get in front!
Preview the shot on your screen, take a pic, then watch as images get transferred wirelessly
to your phone. Whether it's photos, videos, or time-lapse, Podo gets 'Everybody in.'
1.
2.
3.
4.
5.
Question
Do you think this is a good idea, in other words, would you
invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $5000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Answer
Choose an item.
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Project 10: Now you will first get to read about ‘BoXYZ’ on Kickstarter:
Introducing BoXZY, the most versatile desktop fabrication device on the market. We built
BoXZY so you can do more, better. This triple-threat tool combines a 3D Printer, CNC Mill,
and Laser Engraver in one compact cube. By utilizing the quick-change heads, any maker can
shape a block of aluminum, hardwood, or plastic into intricate designs; 3D print complex
plastic shapes; or laser engrave into objects made of wood, leather or plastic.
BoXZY’s is an extremely robust maker space that rapidly becomes what you need when you
need it. Simply slide one attachment out and slide another in without any compromise in
precision. To power all these tools and create BoXZY’s 4 micron resolution, BoXZY drives all 3
axes with industrial ball screws, which are firmly nested in BoXZY’s sleek, black anodized
aluminum body.
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1.
2.
3.
4.
5.
Question
Do you think this is a good idea, in other words, would
you invest in this idea? (Assuming you can afford it)?
Do you think this project will reach its $50000 Goal?
How many days will it take you think for this project to
reach its goal? (Projects are on Kickstarter for no longer
than a month).
Do you think the project will raise more money than
needed?
On a scale from 1 to 10, how creative you think this idea
is? With 10 being very creative and 1 not creative at all.
Attractiveness
Attractiveness
1.000
Table 4.10B
Correlations
Creativity
Productivity
Creativity
0.2451***
(0.000)
1.000
Productivity
0.3693***
(0.000)
-0.2683***
(0.000)
0.4277***
(0.000)
-0.1420***
(0.002)
Suitability
Answer
Choose an item.
Choose an item.
Click here to
enter text.
Choose an item.
Choose an item.
Suitability
1.000
-0.5861***
(0.000)
1.000
Note: the table presents spearman’s rho correlations, significance levels are in
parentheses. Significant values are indicated by: *p<0.10,** p<0.05 and ***p<0.01.
51
Table 4.10C
Determinants of a rater considering the project will reach its goal
(1)
(2)
(3)
(4)
(5)
Male
-0.1756
-0.0486
-0.0519
-0.1140
-0.0437
(0.1303)
(0.1123)
(0.1136)
(0.1193) (0.1141)
Age
0.0091
0.0084*
0.0087*
0.0038
0.0089*
(0.0058)
(0.0049)
(0.0051)
(0.0050) (0.0051)
Student
-0.2465*
-0.1533
-0.1521
-0.1920
-0.1186
(0.1440)
(0.1266)
(0.1268)
(0.1380) (0.1327)
Productivity
-0.0698**
-0.0679*
-0.0690*
(0.0345)
(0.0356)
(0.0356)
Creativity
-0.2409*** -0.2403***
-0.2395***
(0.0236)
(0.0238)
(0.0238)
Suitability
0.0824
0.0818
0.0828
(0.0630)
(0.0631)
(0.0631)
Attractiveness
-0.1088***
-0.0052
-0.0066
(0.0262)
(0.0249)
(0.0249)
Treatment1
-0.0480
-0.0795
(0.0976) (0.09278)
Technology
Game
Fashion
N
498
498
498
600
498
(6)
0.101
(0.111)
0.014***
(0.005)
0.196
(0.129)
-0.066
(0.034)
-0.275***
(0.029)
0.066
(0.061)
-0.017
(0.025)
-0.143
(0.089)
-0.256
(0.125)
0.194
(0.123)
0.098
(0.120)
498
Note: Dependent variable is whether the raters think the project will reach their goal on a scale from
1 to 5. Table presents coefficients from OLS regressions. Standard errors are in parentheses:
*p<0.10,**p<0.05 and ***p<0.01.
52