Final project Kickstarter | Is there a space for an Italian version of Kickstarter

Egemen İhsan Korkusuz – 1708031
Michelangelo Matteoda – 1729227
Giulia Girardi – 1731769
CROWDFUNDING - MARKET RESEARCH

Agenda
1.  Crowdfunding global industry overview
2.  The italian market
3.  Kickstarter
4.  Business goal and objectives
5.  Methodology
6.  Data auditing
7.  Univariate and bivariate descriptive statistics
8.  Managerial implications I
9.  Factor analysis 1
10.  Cluster analysis
11.  Multiple linear regression
12.  Managerial implications II
13.  Logistic regression
14.  Discriminant analysis
15.  Managerial implications III
16.  Conjoint analysis
17.  Managerial implications IV
18.  Limitations
19.  References
Agenda

•  Crowdfunding is an innovative way to raise funds for a project.
•  Crowdfunding web sites are Internet platforms that connect backers and creators
•  Backers are the ones who invest their money and other resources
•  Creators are people or organizations who asks funds to initiate their projects
•  Through these platform creators pool small contribution of money from a large group of people.
•  Thanks to this they have the power to achieve their financial goal.
WHAT IS CROWDFUNDING?

•  The first crowdfunding website appeared in 2001 and the numbers kept on increasing dramatically.
•  In the past few years, as financial markets went through tumultuous times, the internet brought forth a new way
for individuals and companies to raise funds.
•  Crowdfunding platforms such as Kickstarter allow creative minds to introduce their ideas to the public and to
collect funds from many small contributors.
 
Crowdfunding passed a couple of significant milestones in past years.
•  In January 2012, an iPhone dock made from solid aluminium which is called as ‘’Elevation Dock’’, became the
first Kickstarter project to raise more than $1 million.
•  Four months after this huge success, Pebble, a watch that connects to smartphones through Bluetooth, became
the first crowdfunded project to pass $10 million in funding.
•  According to data published by Massolution, a research firm that specializes in crowd powered business, There
are approximately 600 crowdfunding platforms
•  From 2009 to 2012, the total volume of funds raised through crowdfunding grew 81% to reach $2.8 billion.
Moreover crowdfunding reached $5.3 billion in 2013, with a growth rate of 92% (see folowing graph)
•  To sum up crowdfunding business model is an increasing trend for creators who need monetary contribution to
realize their dreams.

A little bit of history…
WHAT IS CROWDFUNDING (I)

0.53
0.85
1.47
2.8
5.3
0
1
2
3
4
5
6
2009 2010 2011 2012 2013
Billion $
Development in worldwide crowdfunding volume between 2009 and 2013
(in billion $)

An Increasing Trend…

Model Definition
Motivations of
BACKERS
Type of BACKERS Type of CREATORS
DONATION
MODEL
To achieve a financial
goal, creators ask for
a donation with no
reciprocity.
Intrinsic and social
motivation
Philanthropists
Inventors
Avid fans
Gadget lovers
Artists
Inventors
Filmmakers
Musicians
Writers
Non-Profits
EQUITY MODEL
Backers receive an
interest for the
projects they have
funded in form of
equity.
Combination of
intrinsic,social and
financial motivation.
Investors
Stockholders
Shareholders
Entrepreneurs
Start-ups
Business Owners
REWARD MODEL
Backers make
monetary
contributions
through donations.
Rewards or also
intangible benefits
are given.
Combination of
instrinsic and social
motivation and desire
for reward.
Investors
Entrepreneurs
Avid fans
Gadget Lovers
Inventors
Start-ups
Non-profits
Entrepreneurs
Business Owners
DEBT/LENDING
MODEL
Asking monetary
contribution for
financial return and/
or interest at a future
date
Combination of
intrinsic, social and
financial motivation
Investors
Entrepreneurs
Entrepreneurs
Inventors
Start-ups
Business Owners

•  The growth in funding volumes was
primarily driven by lending- and donation-
based crowdfunding, and by small and
medium enterpries adoption of reward-
based crowdfunding.
•  Donation- and Reward-
based crowdfunding grew 85% to $1.4
billion
•  Lending-based crowdfunding grew 111%
to $1.2 billion
•  Equity-based crowdfunding grew 30%
to $116 million
•  Source: 2013CF – Crowdfunding Industry Report by MassoluNon . Source: 2013CF –
Crowdfunding Industry Report by MassoluNon .
•  Although crowdfunding offers a growing
number of countries opportunities to access
funds, North America and Europe raised much
more capital than platforms in other regions.
Both continents combined accounted for 96%
of the global crowdfunding volume, with the
remaining 4% raised in Asia and Oceania.
•  Growth rates of 2012 over 2011 :
•  North America: crowdfunding volumes grew
105% to $1.6 billion
•  Europe: crowdfunding volumes grew 65%
to $945 million
•  In total, all other markets grew close to 125%
Growth rate by Regions Growth rate by Models

1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
For Fundraising by Forbes 2014
TOP 10 CROWDFUNDING WEBSITES

RANK PROJECT CATEGORY PLATFORM
CAMPAIGN
END DATE
CAMPAIGN
TARGET
AMOUNT
RAISED
1 Star Citizen Video Game Kickstarter Ongoing $500,000 $45,762,924
2 Ubuntu Edge Smartphone Indiegogo Aug 21, 2013 $32,000,000 $12,814,196
3 Pebble Smartwatch Kickstarter May 18, 2012 $100,000 $10,266,845
4 OUYA
Video Game
Console
Kickstarter Aug 9, 2012 $950,000 $8,596,474
5 Pono Music
Digital Music
Player
Kickstarter Apr 15,2014 $800,000 $6,225,354
6 Veronica Mars Movie Kickstarter Apr 13, 2013 $2,000,000 $5,702,153
7
Elite:
Dangerous
Video Game Kickstarter Ongoing £1,250,000 £3,036,357
8
Torment: Tides
of Numenera
Video Game Kickstarter Apr 5, 2013 $900,000 $4,188,927
9 Mighty no: 9 Video Game Kickstarter Oct 1, 2013 $900,000 $4,046,579
10 Project Eternity Video Game Kickstarter Oct 16, 2012 $1,100,000 $3,986,929

•  The first platform of crowdfunding was “Produzioni dal basso” which starts operating in Italy in 2005, but
only in the last year this type of platforms has boomed .
•  From October 2013 to April 2014 the active platforms pass from 27 to 41 active (+30%) and 13 are in the
launch phase. (see following graphs).
•  Peculiarity: An increasing number of public institutions are using that to finance public activities and projects
.
•  The crowdfunding’s most successfull campaign concerns the rebuild of the City of Science, the scientific plant
of Naples destroyed in a fire accident in march 2013. Up to now one million of euro have been raised.
•  The model more spread are the reward based ones. Almost 50% of the platforms in Italy choose this type.
•  The Italy is one of the first country who regulate the equity crowdfunding, where the ones who raise a project
also get a quote of the startup. Italy was one of the first country to establish a normative in June 2013.
•  The new law of 17 november 2012, n. 221 is the first one in Europe that regulate the online funding.
•  The law is regulated by the Consob and concerns mostly start up regularly inscribed in the list of firms
managed by “Camera di commercio”.
•  The rate of failure for the project is still very high (70%).
•  In term of value of the projects the lending based model (5% of all present platforms) is the most successul
(23,5 milions €).
•  The ibride one occupy the second position with more than 4 milions € (see following graph).
Overview of Crowdfunding in Italy
(1) Source: Borsa della Ricerca, Bologna

Data updated to may 2014
HIGHEST FUNDED PROJECTS
Types of active and incoming platforms
Value of the projects per platform

“We’re an independent company of 82 people based in Greenpoint, Brooklyn.
Half of us work on the product (designing and coding), and the other half work
with the community. We love what we do, and who we do it with.”

1.  Kickstarter is a new way to fund creative projects. Since our launch in 2009, 6.2 million
people have pledged $1 billion, funding 62,000 creative projects.
2.  Each project is independently created. The creators have complete control over and
responsibility for their projects. We are not involved in the development of the projects
themselves.
3.  Kickstarter is a donation based platform for creative projects. Funding on Kickstarter is
all-or-nothing — projects must reach their funding goals to receive any money. To
date, an impressive 44% of projects have reached their funding goals.
4.  Creators keep 100% ownership of their work. Backers are supporting projects to help
them come to life, not to profit financially. Instead, project creators offer rewards to
thank backers for their support.
5.  Our mission is to help bring creative projects to life. If a project is successfully funded,
we apply a 5% fee to the funds collected.
Five things to know about Kickstarter

•  There is a steadily raising interest and a clear consistent growth of crowdfunding in EU and Italy especially.
•  In this country several corwdfunding platforms has been creating in the last year and national platform especially
are expanding significantly their customers
•  The barer of the language in fact can hinder a lot of italian investors from the multinational websites.
•  Therefore there is the necessity for Kickstarter to react against competitors.
•  Our question: is then time for Kickstarter to to invest more in the Italian market?
•  Should Kickstarter try to develop an italian version of its websites?
MAIN FOCUS OF OUR RESEARCH: AWARENESS
•  Is the awareness of crowdfunding in Italy is enough spread? Is it significant to justify a further investment of a
platform such as Kickstarter in the market?
•  How much does this awareness then drive effectively to the visit of the websites and to active usage?
•  The Italians way of investing is still very backward and this is confirmed by the fact that the lending based
platforms are still the most valuable ones. Can reward based model as Kickstarter somehow become the paradigm
in the future?
•  Which targets are the most relevant, and on which one Kickstarter should leverage on, in the future according to
the previous considerations?
KICKSTARTER: DEAL WITH THE PROBLEM

Verify the awareness of crowdfunding in the Italian market, trying to understand the
correlation between personal/social characteristic and its awareness.
Being Kickstarter a very popular crowdfunding company in other countries but still not active in the Italian market,
our research goals are:
BUSINESS GOALS AND OBJECTIVES
1
Verify how many people, among those aware of it, actually actively used this platform.
2
Investigate the reasons why people do and people don't and if there are possible
constraints imposed by the Italian culture for the future the development of this
funding method.
3
Our final aim is to decide if to invest more on this country in order to widen our business
or not.4
Discover which projects would be the most effective to be displayed in the homepage of a
potential future Italian website of Kickstarter in order to increase the number of
backers.
5

For the choice of our target, we based our decision on two demographic variables: age, gender and
nationality.
Our target is composed by Italians male and females from 20 y.o. to 40 y.o.
TARGET DEFINITION
Since our ultimate research
objective is to understand if
Kickstartert should invest in
order to enter the Italian
market, our target is
composed by respondents
with Italian nationality.
Consumers of this age
generally have more familiarity
with technology and
innovative products/services.
Moreover they navigate on
internet the most and have the
potential purchasing power to
use a service like
crowdfunding.
It wasn’t in our interest to
make any selection based on
gender. We therefore targeted
both genders, statistically
respecting their proportions
(see slide on sample error).

SAMPLE DEFINITION
SAMPLING METHOD
SAMPLE SIZE
DATA COLLECTION
Opportunity related
170 respondents
Online survey
We chose an opportunity-related type of sampling, a method not
based on likelihood, due to the time limits of this course.
We carried on our research until we reached about 100
respondents that are aware of crowdfunding.
Apart from being time and cost efficient, this methodology is in
line with our research topic and our target characteristics.
Period of collecting: April 2014
Types of variables: quantitative and qualitative

The sample size is coherent with the purposes of our university course. However, in order to be able
to apply our findings to the real business world, a more extensive sample is needed.
We did not respect the ratios of the distribution of the Italian population. Specifically, the age range
20-24 accounts for 73,53% of our total sample, compared to the ideal 19,97%. This is mainly
explained by our samplying method: the online survey was distributed leveraging on friends of
the team members at first, and then it was spread through WOM to a larger and unknown
population.
This difference in the distribution significantly affected other variables, such as education level and
income, consequently influencing our further analysis (ex. discriminant analysis, cluster analysis).
For what concerns gender, our sample is composed by 47,06% males and 52,94% females. Even
though it is not the ideal 50,25% and 49,75%, there is not a statistical evidence of diversity
(verified through a t-test, the male population in the sample is equal to the male population in
Italy with a p-value of 0,12).
Male Female Total
20-24 10,20% 9,77% 19,97%
25-29 10,59% 10,45% 21,04%
30-34 11,97% 11,99% 23,96%
35-40 17,48% 17,55% 35,03%
Total 50,25% 49,75% 100,00%
Male Female Total
20-24 35,29% 38,24% 73,53%
25-29 9,41% 10,00% 19,41%
30-34 1,18% 1,76% 2,94%
35-40 1,18% 2,94% 4,12%
Total 47,06% 52,94% 100,00%
DISTRIBUTION OF THE ITALIAN POPULATION*
SAMPLE ERROR
DISTRIBUTION OF THE OUR SAMPLE
*Source: demo.istat.it updated to 2013.

Our questionnaire is composed by 31 questions It was used to collect qualitative and quantitative data and was
specifically designed according to our research objectives. It took about 10 minutes for our respondents to
answer it.
After a introductory part, the questionnaire presents 2 filter questions and 4 major sections:
QUESTIONNAIRE SET-UP
Q1: Q3
GENERAL PROPENSITY TOWARDS NEW IDEAS/
PROJECTS
Q4
AWARENESS OF
CROWDFUNDING
Q5: Q11
FURTHER QUESTIONS ON
AWARENESS AND GENERAL ONLINE
BEHAVIOUR OF CROWDFUNDING
Q12
ACTIVE USAGE OF
CROWDFUNDING
Q13 : Q18
FURTHER QUESTIONS ON
ACTIVE USAGE
Q24 : Q31
PERSONAL DATA QUESTIONS
Q19 : Q23
ATTITUDES TOWARDS
CROWDFUNDING AND ITS FUTURE
Behavioural: 1, 2, 3
Importance/Motivation: 19, 20, 21, 22
Preference/Conjoint: 23
Demographic: 24, 25, 26, 27, 28, 29, 31
Psychographic: 30
Behavioural: 4, 5, 6, 7, 8, 9, 10, 11
Behavioural: 12. 13, 14, 15, 16, 17, 18
*the size of the white and green rectangles corresponds to number of respondents for each
category of questions to whom the question were addressed and displayed

QUESTIONNAIRE TESTING
In order to test our questionnaire we decide to make assisted survey, therefore we asked 6 people to fill in the
questionnaire in front of us.
THREATES AND PROBLEMS RAISED
•  The first questions were too long and not so clear.
•  Some adjectives and vocabularies were considered not appropriate (lessical problem)
SOLUTIONS
•  More direct form of the questions to simplify the reasoning process
•  Additional answers to give more options to our respondents
•  Additional explanations added to leave less space for the interpretation, and indication of the format for
open quantitative questions
GENERAL AIMS
•  Make the answers quick, clear and easy to understand
•  Moderate the length and fluidity of the questionnaire
•  Make the questionnaire and its answers more coherent to our objectives

VARIABLES DEFINITION
Definition of Type, Decimals, Labels, Values and Measures of our variables.
FORMAT UNIFORMIZATION
•  Uniformization of the formats for questions answered with words (“City”)
•  Thanks to the fact that we always asked to please insert a precise number in order to avoid the
possibility of our respondents to insert ranges, we just needed to uniformize the formats for open
quantitative questions (Q11, Q14, Q17, Q18, Q21, Q25).
DETECTION OF ERRORS
Respondent 118 answered yes to the question “Have you ever used crowdfunding in order to either back a
project or to get financed?” (Q12), but then he answered negatively to both subsequent questions, “Did
you use it to raise funds?” (Q13) and “Did you use it to back projects?” (Q16), therefore the answer to
Q12 was changed into “No”.
DATA CLEANING AND AUDIT

MISSING VALUES
As stated before, the questionnaire contains two main filter questions:
-  Q4 (“Do you know what crowd funding is?”) according to which, if the response is “not”, the
respondents were redirected to personal data
-  Q12 (“Have you ever actively used crowd funding to finance a project or to raise funds), according to
which, if the response is “not”, the respondents were redirected the last part of the questionnaire, just
before personal data, in order to investigate the reasons.
We furthermore had several display options, because some questions could not be answered if the previous
answers were negative.
For this reasons, our questionnaire presents a considerable number of missing values because only a
limited amount of our respondents could answer questions about crowd funding. However, we stopped
collecting answers not simply considering the number of people that responded. We specifically carried
on our research until we managed to reach 98 respondents that could overcome the first filter
question, reaching a satisfactory result for the purposes of this research/course.
However, thanks to the application of the “force response” option of Qualtrics, we made sure that our
respondents answered every question that was shown to them.
OUTLIERS DETECTION
We then used box plots in order to detect outliers to our open quantitative questions in order to ensure that
they will not influence our subsequent analysis. We didn’t decide to eliminate all outliers by definition a
priori, but we always evaluated them according to our research goals. The process is explained in detail
in the next slides.
DATA CLEANING AND AUDIT (i)

Cases 13, 62, 70, 170, with the respective values of 9,
10, 7 and 7, were considered outliers because, the
interquartile range was over the threshold, and the
results would have been biased. But most
importantly, also under a managerial point of view
users with such an active online behaviour are
extreme cases that would bias the interpretation.
DATA AUDITING OUTLIERS DETECTION
Crowdfunding websites visits per month (Q11)
Before the correction, the mean value was
1,49, with a Std. Dev. of 2,13.
After the elimination of the outliers, mean
value results in 0,9 and Std. Dev. in
0,63, allowing us to have a result 60%
more accurate.

Even after the elimination of the outliers, we can not consider this
an acceptable and reliable result under the marketing
interpretation point of view: it is highly unrealistic that
consumers would have such a high willingness to pay for a
service at such an early stage in the Italian market.
An explanation of such variability in the answers could be the
consequence of the way we formulated the question, since
we didn’t specify it was referred to crowd funding, and
respondents may have understood it related to funding
projects in a more general way, including with other
methodologies. We therefore acknowledge it as a limitation
of our work.
For these reasons, during the analysis this index will not be
considered, instead the median will be (see following slides).
Two respondents (22 and 125) answered 10000, one (7) answered 2000
and several answered 1000. Thus, the mean is 387,73 with a Std. Dev.
of 1531,787. We therefore decided that this results was not acceptable
since it was not leading to reasonable and useful insights and
eliminated the outliers, keeping only results lower than 2000.
The answers of this variable for cases 7, 22 and 125 were therefore
eliminated, corresponding to the 3,5% of the respondents of this
question.
The result a mean of 136,69 with a Std. Dev. of 288,38.
OUTLIERS DETECTION (i)
How much would you be willing to donate in order to finance a project
you are interested in? (Q21)

47.10%
52.90%
Male
Female
Our sample is composed by 47,1% males and 52,9% females. As
explained in the “Sample error” section, even though it is not the
ideal 50,25% and 49,75%, there is not a statistical evidence of
diversity (verified through a t-test, the male population in the
sample is equal to the male population in Italy with a p-value of
0,12).
Method: Frequencies
UNIVARIATE DESCRIPTIVE STATISTICS
Demographics
Gender (Q24)
Age (Q25)
73.53
19.41
2.94 4.12
20-24
25-29
30-34
35-40
Our sample is composed by 73,53% of people from 20 to 24 y.o.,
19,41% from 25 to 29 y.o., 2,94% from 30 to 34 y.o., 4,12% from
35 y.o. to 40 y.o.
As explained in the “Sample error” section, this distribution does not
correspond to the Italian population one, also because our
sampling method was a opportunity related one.
This influences other variables, such as income, level of education, etc.

1.8%
32.4%
51.7%
8.2%
5.9% Middle
School
High School
Bachelors
SpecialisNca
Master
Analyzing the level of education of our respondents could be
interesting since it could influence awareness, usage and
propensity to use crowdfunding.
Our sample is mostly composed by respondents with a level of
education higher than high school (65,8%) .
In line with the age of our respondents, most of them have
completed a Bachelor’s degree (51,8%).
Demographics (i)
Method: Frequencies
Level of education (Q26)
73.5
8.2
11.8
1.2
1.8 1.8 1.8
Student
Self-employed
Employed
Entrepreneur
Unemployed
Researcher
Altro
Occupation (Q27)
Most of our respondents (73,5%) are students, in line with the
age distribution of our sample. 11,8% of them are
employed and 8,2% are self-employed. Minor percentages
of the respondents are researchers, unemployed, and
entrepreneurs.

Demographics (ii)
Method: Frequencies
Income (Q31)
Most of our respondents (90%) have an average income per month of
less than 1500€.
Only 7,6% have an income between 1500€ and 3000€ and 2,4% higher
than 3000€.
Again, this result is influenced by the age distribution of the sample.
City of residence (Q29)
Our respondents live in 63 different cities, therefore for what concerns
this variable they can be considered an heterogeneous group.
33,5% of them come from small towns, while 66,5% of them come from
big cities (capoluoghi di provincia). 66.5
33.5
Capoluoghi
di provincia
CiYà minori
90%
7.60%
2.40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<1500 € 1501 -3000 € > 3001 €

57.6%
42.4% Yes
No
The majority of our respondents, 57,6%,
are aware of crowdfunding. Even if
the ratio is still quite proportioned, it
is an interesting result. Italy may not
be one of the countries in which this
funding method is widely know, but
first of all the result is not so
negative. Most importantly, it means
that Italy has a great potential under
this point of view that has still to be
exploited.
Method: Frequencies
Behavioural
Awareness of crowdfunding (Q4)

More than half of our respondents became aware of crowdfunding surfing the internet (53,6%). The other main ways
they became aware of it are university (38,78%) and social media (35,71%). Friends (31,63%) and newspaper
(29,59%) also cover an important part*.
The results for the university and work option are probably affected by the age distribution and therefore occupation of
our target.
The result corresponding to newspaper, articles and magazines is due the impressive growth crowdfunding had in the
last years, therefore drawing the attention of journalists, economics researchers, etc.
Behavioural (i)
Ways of getting awareness (Q5)
*The question is a multiple response question and respondents were given the opportunity to choose a maximum of 3 answers, therefore
the data represents the percentage of respondents that came in contact with crowdfunding through one of the different options.
53.06%
38.78%
35.71%
31.63%
29.59%
9.18%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
Surfing University Social Media Friends Newspapers Work
Method: MulNple response Frequencies

We then found it important to analyze the awareness not only of crowdfunding in general, but more speciﬁcally the one of the most
important companies in the Italian market and of Kickstarter. In parNcular, brand recogniNon was tested, since the respondents
were asked to Nck oﬀ from a list of companies the ones that they were aware of. InteresNng results came out:
Of the 98 repondents aware of crowdfunding:
•  30,6% was not able to recognize any brand. This may be also due to the result we collected on another quesNon (Q6), “Have you
ever seen a crowdfunding adverNsement?”, to which 62,2% of the respondents answered “No”.
•  44,9% knows Kickstarter. This means that Kickstarter is the company mostly recognized. This result is preYy interesNng, specially
if considered that it is sNll not acNve in the Italian market, on the contrary of the other company shown.
•  The second most recognized company is IndieGoGo, with 24,5%, followed by Crowdfunder (20,4%) and Crowdfunding Italia
(18,4%).
•  None of the respondents were able to idenNfy Kapipal.
Behavioural (ii)
Brand recognition (Q8)
44.9%
30.6%
24.5%
20.4% 18.4%
7.1% 6.1% 5.1% 4.1% 3.1%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%

After wiping out all the cases not aware of Kickstarter, we decided to analyze the ways our respondents got
aware of Kickstarter in particular (Q5)*.
•  Following the general trend, 61,4% of the respondents who know also Kickstarter, got in contact with
crowdfunding surfing on the internet.
•  Social media and friends still cover an important role (both 38,6%).
•  Work place increases its relevance (15,9% )
•  Newspapers/articles/journals and University decrease their relative significance in comparison with the
general trend.
Behavioural (iii)
Kickstarter ways of getting awareness (Q5)
61.40%
38.60% 38.60%
31.80%
27.30%
15.90%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Surfing Social Networks Friends University Newspapers Work

The websites’ advertisement most
seen were the ones of
Kickstarter (45,9% of the
respondents), followed by the
ones of IndieGoGo (29,7%) and
Crowdfunding Italia (27%).
Crowdfunder has still an
important double digit
percentage (18,9%).
Only 37,8% of the respondents
aware of crowdfunding has
ever seen an advertisement
about it.
Behavioural (iv)
Crowdfunding advertisement seen (Q6–Q7)
45.90%
29.70%
27.00%
18.90%
13.50%
8.10%
5.40%
5.40%
2.70%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Kickstarter
IndieGoGo
Crowdfunding Italia
Crowdfunder
Produzioni dal Basso
Ulule
Eppela
Boomstarter
Starteed
Method: Frequencies / MulNple response Frequencies

Unfortunately, awareness doesn’t imply a
significantly active online behaviour of
the respondents.
We should notice in fact that even if a good
percentage of respondents is aware of
crowdfunding and of different websites,
still a big percentage of them has never
visited one (48%). The fact that
Kickstarter is the most recognized brand
is reflected in its visits (34,7% has visited
it at least once).
Of the respondents that has at least visited
one of the proposed websites, moreover,
58,8% has visited them more than one
month ago and 19,6% some weeks ago.
Furthermore, the average number of website
visits is about 1 time per month. The
median and the mode are both 1 and the
answers vary from 0 to 2*.
Behavioural (v)
Online behaviour (Q9-Q10-Q11)
17.6%
3.9%
19.6%
58.8%
00%
10%
20%
30%
40%
50%
60%
70%
Few days
ago
One week
ago
Some
weeks ago
More than
one month
ago
Last visit
48.00%
34.70%
21.40%
11.20% 9.20%
4.10% 3.10% 3.10% 3.10% 1.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
Websites visited
In conclusion, visits of
crowdfunding websites are
not particularly frequent and
not usually repeated over
time.
*See slides about data cleaning.
Method: Frequencies / MulNple response Frequencies

Among the 98 respondents that are aware of crowdfunding
(57,6% of the total sample), only the 11,2%, e.g. 11
respondents, has ever used it in order to either back a
project or to collect funds.
This is a very low percentage, that indicates the early stage
of this financing method in Italy.
An in depth analysis will be done in the following slides in
order to understand the reasons behind this result
(see factor analysis).
Method: Frequencies
Behavioural (vi)
Active use of crowdfunding (Q12)
11.2
88.8
Yes
No

More specifically, of the 11 respondents that have actively used crowdfunding, 6 used it to post a
project in order to be financed and 9 used it in order to back a project. 4 respondents both
posted and backed, 5 only backed and 2 only posted projects.
The average of project posted is 2, while the average of the projects backed is 1,1.
The reduced number of respondents doesn’t allow us to consider this results as statistically
significant.
Method: Frequencies
Behavioural (vii)
Active use of crowdfunding to
raise funds (Q13)
Active use of crowdfunding to
back projects (Q16)
54.5
45.5 Yes
No 81.8
18.2
Yes
No

Based on our results, the most important reason
why Italians don’t use crowdfunding doesn’t
concern their interest. They are interested in
crowdfunding but there wasn’t the oppurtunity to
use it yet. As we can see also on the chart, this
option has a mode of 10 while all the others have a
mode of 1.
Other relevant reasons are uncertainty of the
success of the project, the low willingess to actively
participate in the market and the belief that the
products already on the market are more reliable
(all with median=3). This options however are still
really close to all other: their median varies from
1,5 to 3 while “Interested, but there wasn’t the
opportunity” is on the top with a median of 8.
Except from “interested, but there wasn’t the
occasion”, all the answers have a distribution
centered on low values but from the difference
between mean and median we can assume the
presence of few high values.
Why Italians don’t use crowdfunding (Q19)
Behavioural (viii)

Since we have found out that the majority of our respondents didn’t used crowdfunding, it is important to make
this analysis to forecast the possible future of crowdfunding in Italy.The mean is 5,97 and it is really close to the
median value (6), indicating that the dispersion of data is low. 78% of respondents of this question (67) choose
5 or more but only 22% choose less than 5 (19). We can say that willingness to use crowdfunding in the future
has really positive results.
Willingness to use crowdfunding in the future (Q20)
Behavioural (ix)
Method: Frequencies and descripNve

Method: Frequencies
On average, respondents are willing to donate
€136,69 for projects they are interested in. We must
however consider that the results present
significantly high variability (Std.Dev. Is 288,38).
The difference between mean and median points out
the left skewness of the distribution.
Even if the number of respondents for Q18 is
significantly small (9), we found it interesting to
compare this data with the one about the amount
actually donated by current users of crowdfunding.
Even if we cannot consider this as a properly reliable
data that can be generalized and further research
would be needed, we can observe, also considering
the confidence intervals, that the difference between
the means is statistically significant.
This can be due to both a wrong formulation of Q21
as explained in the outliers analysis, or to the fact
that non-active users are not familiar with
crowdfunding system and therefore they said higher
values.
Behavioural (x)
Money willing to donate for a project by non users (Q21)
Amount of money donated by users (Q18)

•  Most of the respondent shows
positive expectation towards the
growth of crowdfunding in the
future
•  “Yes” and “I hope so” represents
almost 70% of the responses
(69,4%)
•  30,6% instead are not confident or
uncertain about the rise of the
system (“No” or “I don’t know”).
•  Overall speaking there is a
considerable hope towards the
increase of these platforms.
Behavioural (xi)
Expectations of future growth (Q22)
21.4%
11.2%
48.0%
19.4%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Sì No Spero Non lo so
Method: Frequencies

MANAGERIAL IMPLICATIONS
One of the main observation that can be done based on our research, is that the crowdfunding
awareness is not fully spread (42% of the population does not yet know it), therefore there is
potential space for growth to be exploited.
The most common way respondents became aware of crowdfunding is on the internet. A
possible way to increase the awareness of this system could be launching a viral
campaign on the internet. In this way the popularity of this means and the subsequent
world of mouth would be exploited, since we have observed that also the social media and
friends options play an important role. This is also in line with our research results on
Kickstarter way of getting awareness.
For what concerns the competitive framework and brand recognition, first of all the most
surprising result is that Kickstarter, even if not active in the Italian market yet, is
the most recognized brand. This is probably due to its impressive success it has abroad,
thanks to its extremely successful projects. In fact, as mentioned in our introduction, 9/10 of
the most successful projects were of Kickstarter.
It is also important to point out that 30,6% of our sample does not know any
crowdfunding website. From a managerial point of view, this datum is really important
because if Kickstarter decides to enter the italian market, combining the brand reputation it
already has and an appropriate marketing investment could probably become the market
leader. This strategy would also be supported by the fact that only 37,8% of our respondents
has ever seen a crowdfunding ad. It would therefore not be a matter of enlarging the “pie” of
the market.

MANAGERIAL IMPLICATIONS (i)
One of the biggest challenges for a potential entry in the Italian market will be for Kickstarter to foster
visits from the side of the respondents. Our results have shown in fact that there is a huge gap
between respondents that are aware of crowdfunding and of different websites and the ones that
ever visited a website. Only about half of the people who actually use crowdfunding actually visit the
websites (see funnel model below).
This challenge becomes even more important because the low number of visits affects the low percentage
of active users of crowdfunding (11,2% of the respondents aware). Such a low percentage of active
users could be both a positive result, because it could indicate a huge potential to be exploited, but it
could also be due to the absence of willingness to use this methods from the side of Italian
consumers.
We therefore found it important to deeply analyse the reasons of why respondents haven’t actively used
crowdfunding yet, and we discovered that the major reason is that, even if they are interested in it,
there wasn’t the opportunity to use it. This is a significantly positive result, because it means that
there isn’t any major obstacle (such as a cultural one or a low willingness to use it, etc.) that could
limit the growth of the Italian market.
Moreover, the willingness to use crowdfunding in the future for the non-active users as well as the hope
that this system will grow in the future are also quite high, confirming the previously mentioned
positive results.
If Kickstarter manages to solve this hurdle by fostering websites visits will have a significantly bigger
probability of having actual backers and creators, and consequently higher profits.
98
51
11
72
47
40
0
50
100
150
200
Aware Visitor Active users
Funnel Model
Yes No
52%
21,6%

CROWDFUNDING AWARENESS AND INTEREST IN MOST INNOVATIVE PRODUCTS
The respondents that know crowdfunding are more interested in the most innovative products on the
market.
The significant p-value (0,004) underlines that the difference between the group of respondents that are
aware of crowdfunding and the ones that aren’t is statistically relevant.
Method: Compare means
Variables: Quant. + Qual.
General behavioural questions (Q1) and crowdfunding’s awareness (Q4)
BIVARIATE DESCRIPTIVE STATISTICS

•  The collaboration of the consumers in new ideas and projects and the perception of necessity of active
participation of consumers in the offer’s definition does not affect awareness of crowdfunding.
•  In fact, for the general behavioral questions such as Q2 and Q3 there seem to be no difference between
respondents that know crowdfunding and the ones that don’t.
Method: ANOVA
General behavioural questions (Q2/Q3) and crowdfunding awareness (Q4) (i)

There is a significant positive correlation
(p-value<5%), even if the number of
cases for some options are low, between
the study qualification (Q26) and the
crowdfunding’s awareness (Q4).
Especially for people aware of the system
this percentage grows with the level of
qualification obtained.
Method: Crosstabs
Level of education (Q26) and crowdfunding awareness (Q4)

•  Occupation and field of occupation
does not influence the awareness of
crowdfunding.
•  Both p-values are higher than 5%,
therefore we do not refuse both null
hypothesis.
Method: Crosstabs
Occupation (Q27) Field of occupation (Q28) and crowdfunding awareness (Q4)

We found it interesting to analyse if consumers who visit a
crowdfunding website (ex. Kickstarter or IndieGoGo) are more
likely to be active users compared to the ones that visit others.
It is not possible to correlate the two variables due to the fact the
one of the variables considered in the relationships is always a
constant (Q9.2-Kickstarter, Q9.1- IndieGoGo, Q9.4-
Crowdfunding Italia).
However, we can observe the different percentages among the three
different cases. People who have visited also IndieGoGo tend to
actively use crowdfunding more than the ones who have visited
also Kickstarter and Crowdfunding Italia.
85% of the respondents who also have visited Kickstarter tend to
not actively use crowdfunding. It seems therefore there is a
huge gap between the first triability of the website and the
final use, however we have not sufficient statical tools to
assess this data more precisely.
Method: Crosstabs
Active usage (Q12) and websites visited (Q9)

It was also relevant to analyse if the frequency of website visits per month is correlated to an active usage of
crowdfunding.
•  Surprisingly we notice that there is no correlation, since the p- value is too high (p-value=0,86>5%)
and we cannot reject the null hypothesis (result probably affected by the low cases of active users).
Method: ANOVA + Compare means
Active usage (Q12) and N° of website visits per month (Q11)

The analysis shows that there is no statistical correlation between the crowdfunding’s
awareness (Q4) and the age of the respondents (Q25). The p-value is 0,651 so we cannot
refuse null hypothesis. This is probably due to our sample’s age distribution (see sample
error).
Then we also analyze the correlation among awareness (Q4) and the propensity to use in the
future(Q20), the money willing to donate (Q21), (Q22)the expectation of future growth
and the age (Q25). Unfortunately most of the results were not statically significant (p-
value>5%) and not so much relevant for a further consideration.

Additional remarks: Awareness (Q4) and other variables

Since we found out that the respondents that are more interested in new products are more
likely to be aware of the crowdfunding system, an option for Kickstarter in order to increase
awareness could be to stress in their marketing campaign the most innovative
projects. In these ways, since this kind of projects are more likely compared to others to
get the attention of consumers, Kickstarter could sensitize the Italian market to innovation
and at the same time gain potential consumers.
Moreover, since we have observed that the awareness of crowdfunding increases with the level of
education, Kickstarter could lead a further research in order to understand the most
common interests of the less educated portion of the population (as could for example
be football events, etc.) in order to carry on specifically targeted marketing
campaigns.

We run our factor analysis on the variable that
assesses motivations for which crowdfunding
system is not used.
The question chosen was Q19:
FACTOR ANALYSIS
Q19 “Why don’t you use this method?”
1. Distrust in this system
2. I prefer to finance projects in other ways
3. I am not interested in the projects offered
4. I prefer to buy what the market offers without actively
participating in it
5. I prefer to buy products already in the market because
more reliable
6. I distrust online transactions
7. There isn’t any monetary reward
8. I am afraid my idea will be copied
9. I am unsure of the success of the project
10. I am interested, but there wasn’t the opportunity yet
A total of 10 variables were selected, and they are
shown below.
Before proceeding with the factor analysis we checked
the correlation, finding that many variables were
significantly correlated (see Outputs, Correlation
Factors). For this reason, reducing their number
through a factor analysis was necessary.

Before proceeding with the analysis, we ensured that all the variables included in the factor analysis
had a considerable weight, since all of them have communalities >0,400 (a satisfactory
quote of variability explained.
FACTOR ANALYSIS (i)

In order to select the appropriate number of
factors to be considered, we interpreted
the output considering together the
following values (not necessary
conditions):
1.  30% of variables selected -> 3
2.  Cumulative variance explained 60%-75%
-> 3, 4
3.  Scree plot -> 3
4.  Eigenvalue > 1 -> 2
By observing the Scree Plot, the curve is flattening after the
3.
We therefore considered 3 to be the optimal number and
we ran the factor analysis again.
FACTOR ANALYSIS (ii)

However, if we considered three factors, under
Component 3 only one variable appeared, no matter which
rotation was used, and therefore the result couldn’t be
accepted. Moreover, also considering only 2 factors could
not be accepted, because cumulative percentage would
have been of only 55%.
The next step consisted in recognizing that the same
variable was influencing negatively. Also under to a logical
point of view, we wanted to understand why respondents
didn’t use this method, and it is consistent not to consider
the option “Interested, but there wasn’t the occasion”.
In fact, unlike the other options considered, it is the only
one that doesn’t represent an obstacle to the use of
crowdfunding. Respondents that gave high rating to this
option are in fact interested in using it.
FACTOR ANALYSIS (iii)

We therefore performed factor analysis again, eliminating this variable.
The result was significantly more positive and it is shown below.
1.  30% of variables selected -> 3
2.  Cumulative variance explained 60%-75% -> 3
3.  Scree plot -> 3
4.  Eigenvalue > 1 -> 2
FACTOR ANALYSIS (iv)

The method used for rotation was
Varimax. All the methods led to
similar results.
Analysing the correlation structure
between input variables we came up
with 3 factors and appointed names
and meanings:
1. INCENTIVES factor
2. DISTRUST factor
3. ADAPTATION factor
FACTOR ANALYSIS (v)

INCENTIVES factors
DISTRUST factors
ADAPTATION factors
No monetary reward
Not interested in the projects
Uncertainty on the success of the project
Fear of being copied
Distrust in online transactions
Distrust in this system
Prefer to fund projects in other ways
I don’t want to actively participate in the market
Products already in the market are more reliable
FACTOR ANALYSIS (vi)

Following the traditional approach, we gave the 3 previous factors as input in order to perform the cluster
analysis.
Firstly, we decided to conduct a hierarchical factor analysis in order to have an idea of the number of
possible clusters.
By analysing the dendrogram and more specifically the variance covered by the different clusters
(indicated by the length of the branches) we obtained a possible number of relevant
clusters that varies from 4 to 6.
CLUSTER ANALYSIS

We therefore analyze scenarios with 4 clusters.
We therefore proceeded with a K-means cluster analysis for each of the possible number of clusters.
Even if, by analyzing the variance of the different mean values of the different clusters, the clusters means
differences were significant (p-value<0,05), we couldn’t accept 5 or 6 clusters because the cluster
division was not acceptably proportionate, with the consequent significantly low possible
interpretation. In fact, some clusters were composed only by 4 or even 2 respondents.
CLUSTER ANALYSIS (i)

•  By analysing the distribution of the cases in the different
clusters we observe that cluster n°2 is composed by 48,8%.
Moreover, the smallest cluster covers 10,5% of the
respondents. Therefore an acceptable proportionality level is
present, also under a marketing point of view.
•  By analyzing the variance of the different mean values of the
different clusters, the clusters means differences are significant
(p-value<0,05)
•  We carried out different analysis modifying the ordering of the
data in order to be sure that our potential cluster division was
be considered reliable.
For these reasons, we retain this cluster segmentation is a
good solution.
10.47%
48.84%
16.28%
24.42%

Cluster 1
Cluster 2
Cluster 3
Cluster 4
CLUSTER ANALYSIS (ii)
Analysis with 4 clusters

With the 4 cluster analysis we obtained the following final cluster centers.
•  We then used colours to better spot the features of the clusters with regards to the used variables.
According to the mean value that each factor assumes in the clusters, we highlighted the most significant
factors.
•  Secondly, we ranked each variable according to its relevance across the different clusters.
1 2 3 4
INCENTIVES ++ - + --
DISTRUST -- - ++ -
ADAPTATION + -- = ++
1)  Demotivated
2)  Active innovative
3)  Skeptical
4)  Passive
CLUSTER ANALYSIS (iii)

PASSIVE (24,42%)
Consumers who prefer to passively interact in the offer, both because they don’t feel the need
and interest in being proactive and because they believe products already present in the
market are more reliable
SKEPTICAL (16,28%)
Consumers that doubt in new technology and new systems as online transactions and
crowdfunding, and that therefore prefer to fund projects in different ways. Because of this
distrust, they also need incentives.
DEMOTIVATED (10,47%)
Consumers that don’t use this system simply because they aren’t motivated enough because of
no interest in the projects but also because there is no monetary reward. Since they are
already demotivated, risks as fear of being copied and uncertainty that the project funded
will also play a big role. They don’t have problems of distrust, but tend to be passive
consumers.
ACTIVE INNOVATIVE (48,84%)
Consumers that are more likely to become crowdfunders. In fact for them being active in the
market is not a problem and they also don’t present distrust neither they need particular
motivation.
CLUSTER ANALYSIS (iv)

INCENTIVES
DISTRUST ADAPTATION
Cluster 1
Cluster 2
Cluster 3
Cluster 4
CLUSTER ANALYSIS (v)

CLUSTER ANALYSIS (vi)
Cluster 1: DEMOTIVATED
•  10,47% of respondents – smallest segment
•  Gender: Male are prevalent (66,7%)
•  They are the segment that visit less frequently crowdfunding websites
•  One of the segments that are less willing to use crowdfunding in the future (3°)
•  Currently not particularly involved in new ideas and products (3°)
Consumers that don’t use this system simply because they aren’t motivated enough because of
no interest in the projects but also because there is no monetary reward. Since they are
already demotivated, risks as fear of being copied and uncertainty that the project funded
will also play a big role. They don’t have problems of distrust, but tend to be passive
consumers.

CLUSTER ANALYSIS (vii)
Cluster 2: ACTIVE INNOVATIVES
•  48,84% of respondents – largest segment
•  Gender: Male and female equally distributed
•  Most frequent visitors of crowdfunding websites
•  Segment most willing to use crowdfunding in the future
•  Currently most involved in new ideas and projects
Consumers that are more likely to become crowdfunders. In fact for them being active in the
market is not a problem and they also don’t present distrust neither they need particular
motivation.

CLUSTER ANALYSIS (viii)
Cluster 3: SKEPTICALS
•  16,28% of respondents
•  Gender: Male are prevalent (57,1%)
•  Second most frequent visitors of crowdfunding websites
•  Segment least willing to use crowdfunding in the future
•  Currently involved in new ideas and projects, but “old-school investors”
Consumers that doubt in new technology and new systems as online transactions and
crowdfunding, and that therefore prefer to fund projects in different ways. Because of this
distrust, they also need incentives.

CLUSTER ANALYSIS (ix)
Cluster 4: PASSIVE
•  24,42% of respondents – second largest segment
•  Gender: Male and female equally distributed
•  Second to last most frequent visitors of crowdfunding websites
•  Willing to use crowdfunding in the future (2°)
•  Currently least involved in new ideas and projects
Consumers who prefer to passively interact in the offer, both because they don’t feel the need
and interest in being proactive and because they believe products already present in the
market are more reliable
Among other variables, willingness to donate for a project, age, level of education, field of occupation, income and psychographic
characteristics and interest in new products and services present on the market were analyzed in order to discover if they could
better characterize the different clusters but the results were not statistically significant.

MULTIPLE LINEAR REGRESSION ANALYSIS

We first performed the multiple linear regression choosing a dependent
variable “How much are you willing to use it in the future?” (question 20)
and 2 independent variables.
To predict willingness to use crowdfunding in the future,
we used two independent variables which are;
Q1:How much are you interested in the most innovative
products and services that come on the market
Q31:What is your income level?
MULTIPLE LINEAR REGRESSION

However, the results were not useful to predict
‘’willingness to use crowdfunding in the future’’.
Adjusted R squareis supposed to be greater than 0,30
but in this case it is very low and also the R square is
very low to explain its variance.
Moreover, p values of
independents shouldn’t be
greater than 0,05. However,
one of them is, therefore it is
a problem.
MULTIPLE LINEAR REGRESSION (i)

To get a better result of multiple linear regression, we used stepwise analysis to
understand which independent variables are suitable for our dependent variable.
We performed the stepwise choosing a dependent variable “How much are
you willing to use it in the future?” (question 20) and all the possible
independent variables.
MULTIPLE LINEAR REGRESSION (ii)
Stepwise

The model summary shows that models have highly
significant indexes (R square > 0.30). As it is possible to
see, the overall prediction improves by adding
independent variables (R square increases) and therefore
the overall acceptability of the model improves too.
Q2:How much you are involved in cooperating for new ideas and projects?
Q3:How much do you think consumers should be involved in shaping the
offer of the market?
Q19:Why you don’t use crowdfunding?
-i don’t want to actively participate in the market (4)
-interested but there is no opportunity to use it (8)
-i will not get any monetary reward (7)
-i am afraid my idea will be imitated(11)
Q22:Do you think that crowdfunding will grow significantly in the future in
Italy?
Q30:How do you consider yourself?
-Novelty seeking (4)
-Propositive (9)
As a result of stepwise, these
are the coefficients which can
be used as an independent data.
MULTIPLE LINEAR REGRESSION (iii)
Stepwise (İ)

All the p-values are below the 0,05, which is needed for a successfull
regression model. It shows us that all these 9 variables except from the
constant that will be left in the model for the residual properties are
relevant to explain the phenomenon and we can make a multiple linear
regression with them clearly.
MULTIPLE LINEAR REGRESSION (iv)
Stepwise (İİ)

After deciding independent variables through stepwise we used the dependent variable “How much
are you willing to use it in the future?” (question 20) and 9 independent variables to do multiple
linear regression.
MULTIPLE LINEAR REGRESSION (v)
Q2:How much you are involved in cooperating for new ideas and projects?
Q3:How much do you think consumers should be involved in shaping the
offer of the market?
Q19:Why you don’t use crowdfunding?
-i don’t want to actively participate in the market (4)
-interested but there is no opportunity to use it (8)
-i will not get any monetary reward (7)
-i am afraid my idea will be imitated(11)
Q22:Do you think that crowdfunding will grow significantly in the future in
Italy?
Q30:How do you consider yourself?
-Novelty seeking (4)
-Propositive (9)

The model summary table shows that the
indicator R square is quite high. This means
that our model explains 70 % of the variability
of the data. This means that our 9
independent variables are relevant for the
explanation of the dependent variable.
The ANOVA table shows that the
combination of the predictors (F and
Significance) significantly predicts the
total evaluation. F statistic is great and
consequently p-value is smaller than
0,05 which is what is needed for a
successfull result.
MULTIPLE LINEAR REGRESSION (vi)

All the p-values are below the
0,05, which is needed for a
successfull regression model.It
shows us that these all 9
variables are relevant to explain
the phenomenon.
MULTIPLE LINEAR REGRESSION (vii)

MULTIPLE LINEAR REGRESSION (viii)
Multicollinearity Test
Variance inflation factor (VIF) supposed to be not over 5, otherwise we can say we have
a multicollinearity problem. In our chart, we can clearly see that none of our VIF over
5. Moreover they are all significant. This means that in our linear regression analysis
we have no multicollinearity problem.

First of all, the willingness to use crowdfunding in the future is significantly and positively
affected by the extent to which respondents feel they are novelty seeking and proactive.
Therefore Kickstarter in order to increase active usage could give relevance to the
most innovative and involving projects in its marketing campaign and its website.
The more respondents don’t use crowdfunding because they were interested but there wasn’t
the occasion, the more they are willing to use it in the future (in line with our previous
results).
As stated before (see managerial implications of univariates), Kickstarter should then try
to create the occasions by, for example, fostering website visits or making marketing
campaign to increase their motivation.
The more the respondents believe consumers should be active in shaping the offer of the
market, the more they are willing to use it in the future.
Also this factor should be considered in order to increase our potential demand, by
highlighting with communication the fact that consumers through this system
can decide what products will be in the market (a strength of crowdfunding).
We can deduce that fear of being copied is not a big obstacle to the use of crowdfunding, since
the more respondents don’t use this system because they fear of being copied, the more
they are willing to use it in the future (and change their behaviour).
The more the fact that through this systems you don’t get any monetary reward represents an
obstacle for the respondents, the less they are willing to use it in the future.
In order to overpass this hurdle, Kickstarter should communicate the quality and the
attractiveness of the rewards. Even if they aren’t monetary, if given the appropriate
importance they can still represent a strong motivation to back projects. As an alternative,
Kickstarter could also consider the possibility to establish an obligatory reward to every
backer.

DISCRIMINANT
/GROUPS=QCL_2(1 4)
/VARIABLES=Q1 Q2 Q3 Q20 Q21GOOD
/ANALYSIS ALL
/METHOD=DIRECT
/FUNCTIONS=2
/PRIORS EQUAL
/ROTATED STRUCTURE
/HISTORY
/STATISTICS=MEAN STDDEV UNIVF
/CLASSIFY=NONMISSING MEANSUB.
LINEAR DISCRIMINANT ANALYSIS
SYNTAX

Canonical correlation for the first function
(=eta²) is strong (greater than 0,30). Sincethe
other results are not statistically significant, we
choose the first function. Moreover the first
function explains 80% of the overall variability.
This is a fairly good representation to capture
as much information as possible.
We can only consider one
dependent variable
because all others are
greater than 0,05 and they
are not significant.
LINEAR DISCRIMINANT ANALYSIS (i)

We are aware that we need two or more independent
variables, but since we have only one variable which
can be used in discriminant analysis, we did the
analysis again only with ‘’propensity to use
crowfunding in the future’’
DISCRIMINANT
/GROUPS=QCL_2(1 4)
/VARIABLES=Q20
/ANALYSIS ALL
/METHOD=DIRECT
/FUNCTIONS=1
/PRIORS EQUAL
/ROTATED STRUCTURE
/HISTORY
/STATISTICS=MEAN STDDEV UNIVF
/CLASSIFY=NONMISSING MEANSUB.
LINEAR DISCRIMINANT ANALYSIS (ii)
Canonical correlation for the function (=eta²) is
strong (greater than 0,30), so we can use it.
Moreover Wilks’ Lambda result is also
significant which usually means whether there
are differences between the means of
identifed groups of subjects on a combination
of dependent variables. In conclusion, even if
we have one dependent varible we can say that
it is significant.

The first discriminant function identifies the
horizontal axis, and we only have one function.
Which means we only have the x axis to define the
position of the clusters.
LINEAR DISCRIMINANT ANALYSIS (iii)

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2
Cluster 2 Cluster 3 Cluster 1 Cluster 4
Propensity to use in the future
LINEAR DISCRIMINANT ANALYSIS (iv)
X axis represents the position of propensity to use in the future according to our clusters
perceptions. The more they are close to ‘1’ the more they are willing to use the crowdfunding in
the future
Since we only have one function because of our discriminant analysis results, the Y axis doesn’t
affect the position of our results.

With discriminant analysis, we were able to identify the segment which most likely to use the
crowdfunding in the future and confirm our cluster analysis results. The most attractive
segment for Kickstarter is ‘’active innovatives’’ because, compared to our other clusters, they
are far more willing to use it. So it would be better to target for Kickstarter firstly on this
segment because they are more likely to become crowdfunders and to be the early adopters.
LINEAR DISCRIMINANT ANALYSIS (v)

CONJOINT ANALYSIS
Goals and setting
Conjoint analysis was performed on Q23. The purpose of the question is to discover which would be
the characteristics of the projects shown in the homepage of the potential Kickstarter Italian website
that would lead people the most to back more projects.
In fact, the question is: “ Please evaluate how much you would be willing to back projects with the
following characteristics. The assumption for each options is that you are interested in the project in
question.”
The question was displayed only to the 98 respondents that are aware of what crowdfunding is
because in case they didn’t know how the system works they wouldn’t be able to make such a
judgement.
The purpose of the question is to discover which would be the characteristics of the projects shown in
the homepage of the potential Kickstarter Italian website that would lead people the most to back
more projects.

Based on these variables, we performed the orthogonal design, reaching a result of the 9 scenarios
that we added to the questionnaire which respondents rated from 1 to 10 (1= not willing at all, 10=
absolutely willing to).

The four variables we chose and the respecNve levels are the following:

Percentage of goal achieved Location Days to go
< 70% Urban < 5
> 70% National > 5
> 100% Worldwide
We believe that this variable is
important because the level of
achievement of the goal can stimulate
or not consumers to back them. We
decided 70% as the threshold on
purpose because it was over the half
of the achievement, in order to test if,
when the project is closer to success,
respondents are more willing to back
it. >100% was also included in order
to prove if backings went beyond the
achievement of the target.
The logic behind the
introduction of this variable is
similar to the one explained for
“percentage of goal achieved”
and can consistently affect the
propensity to back a project,
since maybe if fewer days are
left consumers have the feeling
to miss the chance to do it.
We believe that also the
location of the project
could significantly affect
backing decisions,
because it could or could
not create feelings of
involvement and interest.
ORTHOGONAL DESIGN
CONJOINT ANALYSIS (I)

The most important attribute that affects the choice of backing
a project is “Location”, followed by “Percentage of goal
achieved”.

The attribute levels with greater utility
that therefore define the best scenario
are:
• Worldwide location
• Less than 70% of goal achieved
• More than 5 days to go
CONJOINT ANALYSIS (II)

conjoint data=*/plan='/Users/giuliagirardi/Dropbox/
Marketing research/Presentation/SPSS/Newconjoint.sav'/
/score=Q23_1 to Q23_9
/FACTORS=Percentage_Goal_Achieved
'Percentage_Goal_Achieved' (1 '<70%' 2 '>70%' 3 '>100%')
Location 'Location' (1 'Urban' 2 'National' 3 'Worldwide')
Days_to_go 'Days_to_go' (1 '<5' 2 '>5')
/print summaryonly.
Syntax
CONJOINT ANALYSIS (III)

Surprisingly, the results we obtained from our conjoint analysis are different for the ones expected.
Location is the most important dimension and respondents are more interested in international
projects than local ones (urban and regional). This is maybe due to the recent trend of
attractiveness towards the foreign markets and globalization.
Furthermore, we discovered that respondents prefer to fund projects with a lower percentage of
goal achieved; probably this is due to the fact that backers feel more useful funding less
successful products. On the contrary they are less attracted by more successful projects since
they believe that these creators need less help than the others.
Moreover, the analysis also shows an interest from the side of the respondents for projects that
already achieved their target. This option is rated second and preferred over the >70% of goal
achieved probably because in this way respondents are sure that the product in question will
be launched on the market. Social factors (a lot of backers are involved and the product is
perceived as a popular) and feeling of usefulness may influence this choice.
The days to go until the end of the project covers a significantly less important role in respondents’
backing behaviour. Between the two options, more than 5 days or less than 5 days, the first
one is preferred. This is due to the fact that a project with a long term deadline is perceived as
more likely reach its goal (thanks to other backers) than a project with few days to go.
Based on this information, in order to gain more backers, crowdfunding can design its homepage in
the most effective way, with projects with the following features:
Worldwide location
Less than 70% of goal achieved
More than 5 days to go

The sample used for the research, specially the portion of it that is aware of crowdfunding, is of limited size:
further research is needed in order to have more reliable results.
In fact not only the respondents that know crowdfunding are only 98, but the ones that actively use it are
only 11. We therefore could not consider any result about active usage. However, it must be considered
that the purpose of our research was not to investigate the behaviour of active users, but the level of
awareness in the Italian market.
One of the biggest limitation of our research is not having respected the proportionality for age distribution
of the Italian population.
Moreover, being this our first experience in such type of research, the design of the questionnaire is not
optimal. A better experience in this field would have led to more appropriate questions, with more
suitable data for the analysis.
For example, some questions could have been added about the benefits active users of crowdfunding get
by it, in order to facilitate factor analysis and have more factor identified.
Our existing questions were not enough in order to create a discriminant analysis with two functions.
In some cases, questions were not precise enough, leading to not reliable results (see slide about outliers
detection of Q21).
We furthermore didn’t ask any question about the typology of the project posted (ex: technology, atrs,
etc.) such as which type was the one of the projects posted or backed by active users or preferences (that
could have led to interesting results for the conjoint analysis.
LIMITATIONS

REFERENCES
Websites
www.statista.com
www.businesslaw.co.uk
www.crowdfunding.com
www.entrepreneur.com
www.prnewswire.com
www.forbes.com
www.wikipedia.com
www.fastcoexist.com
www.italiancrowdfunding.org
Articles
Daniela Castrataro, Ivana Pais, Analysis of
Italian Crowdfunding Platforms, May 2014

Final project Kickstarter | Is there a space for an Italian version of Kickstarter

Final project Kickstarter | Is there a space for an Italian version of Kickstarter

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