Thecrowdinvestor!–!! · crowdfunding for start-ups, is still quite a recent development and the...

COPENHAGEN BUSINESS SCHOOL, 2014

M.SC. ECONOMICS & BUSINESS ADMINISTRATION

MASTER’S THESIS

The crowdinvestor –

What drives investors’ decision-‐making to participate in

crowdinvesting?

TIM EBERT & SIMONE SCHÖNDORFER

SEPTEMBER 29, 2014

SUPERVISOR: IOANNA CONSTANTIOU,

DEPARTMENT OF IT MANAGEMENT

194.462 Characters and 92 Pages

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Abstract

The phenomenon of crowdinvesting, a term used in Western Europe to describe equity-based

crowdfunding for start-ups, is still quite a recent development and the status of the

crowdinvesting industry can be described as immature and fragmented. However, it is rapidly

growing and it provides start-ups with a new way to close a fundamental funding gap that often

occurs in the seed phase.

While the motivation for start-ups to participate in crowdinvesting initiatives seems quite obvious

for practitioners and scholars, the primary reasons being money and attention, the motives for the

investors appear to be more complex and manifold. The review of relevant literature as well as

the expert interviews we conducted with researchers, crowdinvestors and platform providers lead

to the conclusion that there is a general lack of systematic comprehension of the crucial factors

attracting investors to crowdinvesting. Hence, this thesis aims to identify important drivers that

influence the decision of investors to participate in crowdinvesting.

Based on the assumption that parallels to traditional forms of investment can be drawn,

traditional investor decision literature is chosen as a point of departure for this investigation.

Furthermore, input from existing crowdfunding and crowdinvesting literature as well as expert

interviews are used to develop a conceptual model aiming at explaining the decision-making

criteria of crowdinvestors.

The factors developed in the model are investigated with an online survey among retail investors

and crowdinvestors, including 133 participants. A binary logistic regression is applied to analyse

each factor’s influence on the crowdinvestors’ decision-making process.

The results indicate that factors such as the social relevance of crowdinvesting, an early adopter

profile of the investor and the possibility to use crowdinvesting for portfolio diversification show

strong positive correlations on the investor to participate in this new type of investment

opportunity. Furthermore, the reluctance to trust online platforms, the network effect of

crowdinvesting and neutral information in the form of financial and general press coverage on

crowdinvesting influence the decision in a negative way. These results provide an interesting

starting point for further investigation about the motivation that drives crowdinvesting from an

investor’s standpoint.

Key words: decision-making, investors in crowdinvesting, crowdfunding, start-ups

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Acknowledgements

First of all, we would like to thank our thesis supervisor Prof. Ioanna Constantiou for her great

support. She provided us with valuable feedback and advice during the entire process and helped

us in critical moments.

We further acknowledge all our interview partners during the expert interviews that allowed us to

gain a deeper understanding of crowdinvesting from different perspectives. In particular, we had

the chance to learn more about crowdinvesting in academia from two researchers. Two

crowdinvestors provided us with important insights to their own motives and drivers to

participate in crowdinvesting. Furthermore, three professionals allowed us to receive helpful

knowledge regarding the topic of crowdinvesting from a platform provider’s point of view. That

also included interesting discussions about the future challenges in the market. We are thankful

for the time that these individuals dedicated to our studies during the interviews.

Furthermore, we would like to acknowledge the support from different crowdinvesting platforms

during the data collection process. Particularly, we would like to thank Innovestment, Conda and

bankless24 for distributing our survey among their crowdinvestors. We are also very grateful for

everyone who participated in our survey during the pretest or during the final investigation.

We also thank Jonathan Shortman for his linguistic review of this thesis.

Finally, we thank our friends and family for the support during the process. We thank them for

proofreading parts of the thesis, testing our survey, providing feedback and most importantly for

their mental support.

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Table of Content ABSTRACT ............................................................................................................................................................... 1

ACKNOWLEDGEMENTS ....................................................................................................................................... 2

1 INTRODUCTION ............................................................................................................................................. 6 1.1 BACKGROUND .................................................................................................................................................................. 6 1.2 MOTIVATION .................................................................................................................................................................... 7 1.3 RESEARCH QUESTION .................................................................................................................................................... 8 1.4 LIMITATIONS .................................................................................................................................................................... 9 1.5 STRUCTURE ................................................................................................................................................................... 10

2 CROWDFUNDING AND CROWDINVESTING ......................................................................................... 11 2.1 DEFINITION OF CROWDFUNDING AND CROWDINVESTING .................................................................................. 11 2.2 INDUSTRY DEVELOPMENT ......................................................................................................................................... 13 2.3 DEVELOPMENT STAGE OF CROWDFUNDING AND CROWDINVESTING ................................................................ 16

3 LITERATURE REVIEW ................................................................................................................................ 20

4 RESEARCH METHOD ................................................................................................................................... 25 4.1 QUESTIONNAIRE DESIGN ............................................................................................................................................ 27 4.1.1 Exemplary Crowdinvesting Project .............................................................................................................. 29 4.1.2 Measurement Development ............................................................................................................................. 30

4.2 SURVEY PRETEST ......................................................................................................................................................... 32 4.3 SURVEY INVESTIGATION ............................................................................................................................................. 35 4.3.1 Sample ....................................................................................................................................................................... 35

4.3.1.1 Crowdinvestors vs. Retail Investors ....................................................................................................................................... 39 4.3.1.2 Potential Response Biases .......................................................................................................................................................... 40

5 THEORETICAL FRAMEWORK AND HYPOTHESES ............................................................................. 42 5.1 LITERATURE ON INVESTOR DECISION-‐MAKING ..................................................................................................... 42 5.2 DEVELOPMENT OF THE THEORETICAL FRAMEWORK ........................................................................................... 47 5.2.1 Key Variables and Hypotheses ........................................................................................................................ 49

6 RESULTS ......................................................................................................................................................... 57 6.1 DEMOGRAPHIC CHARACTERISTICS ........................................................................................................................... 57 6.1.1 Demographic Characteristics of Crowdinvestors ................................................................................... 60

6.2 DATA ANALYSIS ........................................................................................................................................................... 61 6.3 BINARY LOGISTIC REGRESSION ................................................................................................................................. 66

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7 DISCUSSION ................................................................................................................................................... 74 7.1 INDIVIDUAL VARIABLES & THEORETICAL IMPLICATIONS .................................................................................... 74 7.2 MANAGERIAL IMPLICATIONS ..................................................................................................................................... 81 7.3 LIMITATIONS & FUTURE RESEARCH ........................................................................................................................ 84 7.3.1 Theoretical Limitations ..................................................................................................................................... 84 7.3.2 Methodological Limitations ............................................................................................................................ 86 7.3.3 Future Research .................................................................................................................................................... 88

8 CONCLUSION ................................................................................................................................................. 92

9 REFERENCES ................................................................................................................................................. 94

10 APPENDIX ................................................................................................................................................. 102 10.1 EXPERT INTERVIEWS ............................................................................................................................................. 102 10.2 PRETEST RESULTS ................................................................................................................................................. 102 10.2.1 Descriptive Statistics of the Pretest ........................................................................................................ 102 10.2.2 Factor Analysis of the Pretest ................................................................................................................... 105

10.3 SOCIO-‐DEMOGRAPHIC CHARACTERISTICS OF SURVEY PARTICIPANTS ......................................................... 108 10.3.1 Crowdinvestors ................................................................................................................................................ 108 10.3.2 Retail Investors ................................................................................................................................................ 110

10.4 DATA ANALYSIS ...................................................................................................................................................... 111 10.4.1 Descriptive Statistics ..................................................................................................................................... 111 10.4.2 Reliability and Factor Analysis ................................................................................................................. 114 10.4.3 T-‐statistics ......................................................................................................................................................... 117

10.5 SURVEY IN ENGLISH ............................................................................................................................................... 126 10.6 SURVEY IN GERMAN ............................................................................................................................................... 135

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Illustrations

ILLUSTRATION 1: CONCEPT-‐TREE OF CROWDFUNDING .......................................................................................................... 12

ILLUSTRATION 2: START-‐UP LIFECYCLE AND FUNDING REQUIREMENTS ............................................................................... 16

ILLUSTRATION 3: POSITION OF CROWDFUNDING IN HYPE CYCLE FOR THE FUTURE OF MONEY 2013. .......................... 18

ILLUSTRATION 4: PICTURE OF FICTIONAL CROWDINVESTING SCENARIO USED IN SURVEY ................................................. 29

ILLUSTRATION 5: FACTOR LABELS ACCORDING TO NAGY AND OBENBERGER (1994). ....................................................... 44

ILLUSTRATION 6: PRICE DEVELOPMENT OF THE SHARES IN A START-‐UP COMPANY. ........................................................... 46

ILLUSTRATION 7: CONCEPTUAL FRAMEWORK PRESENTING THE INDEPENDENT VARIABLES AND HYPOTHESES. ........... 56

ILLUSTRATION 8: AGE DISTRIBUTION OF SURVEY PARTICIPANTS. .......................................................................................... 57

ILLUSTRATION 9: GENDER DISTRIBUTION. ................................................................................................................................ 58

ILLUSTRATION 10: COUNTRY OF RESIDENCE. ............................................................................................................................ 58

ILLUSTRATION 11: SURVEY PARTICIPANTS’ OCCUPATION AND HIGHEST EDUCATION. ........................................................ 59

ILLUSTRATION 12: INDUSTRY DISTRIBUTION. .......................................................................................................................... 59

ILLUSTRATION 13: DISTRIBUTION OF MONTHLY INCOME AFTER TAX. .................................................................................. 60

ILLUSTRATION 14: THEORETICAL MODEL ILLUSTRATING RESULTS OF BINARY LOGISTIC REGRESSION (B-‐

COEFFICIENTS). ................................................................................................................................................................... 72

ILLUSTRATION 15: COLLECTION AND SELECTION PROCESS OF FACTORS THAT INFLUENCE DECISION-‐MAKING IN

CROWDINVESTING (OWN CREATION). .............................................................................................................................. 85

ILLUSTRATION 16: DISTRIBUTION OF NUMBER OF INVESTMENTS IN START-‐UPS (OWN CREATION). ............................... 87

Tables

TABLE 1: CRONBACH’S ALPHA VALUES OF THE PRETEST ANALYSIS. ...................................................................................... 34

TABLE 2: CHANGES IN ITEMS BASED ON THE RESULTS FROM THE SURVEY PRETEST. ......................................................... 35

TABLE 3: ADAPTED LABELS AND KEY VARIABLES FOR THIS RESEARCH PROJECT. ................................................................ 49

TABLE 4: SUMMARY OF T-‐TEST STATISTICS FOR ALL ITEMS USED IN REGRESSION ANALYSIS. ........................................... 66

TABLE 5: OUTPUT FOR BLOCK 0 OF THE BINARY LOGISTIC REGRESSION. ............................................................................. 67

TABLE 6: OUTPUT OF CHI-‐SQUARE. ............................................................................................................................................ 67

TABLE 7: OUTPUT HOSMER AND LEMESHOW TEST. ................................................................................................................ 68

TABLE 8: OUTPUT MODEL SUMMARY. ......................................................................................................................................... 69

TABLE 9: CLASSIFICATION TABLE FOR BINARY LOGISTIC REGRESSION INCLUDING ALL INDEPENDENT VARIABLES. ...... 69

TABLE 10: OUTPUT TABLE OF VARIABLES IN THE BINARY LOGISTIC REGRESSION. .............................................................. 70

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1 Introduction

1.1 Background

At its heart, crowdfunding1 is about many people, i.e. the crowd, pledging relatively small

amounts of money to a common cause. The idea of collectively funding a common goal is not

new, as illustrated by examples like the 1885 New York World funding campaign for the pedestal

of the Statue of Liberty. However, the kind of momentum modern technology enabled

crowdfunding has gained to date is impressive (tyclipso.me & startnext.de, 2012). Taking its

roots in the cultural and non-profit sector to kick-off creative and artistic projects, many types of

crowdfunding have rapidly differentiated themselves. However, divers, fragmented models and

sub-concepts are still united by the same core principles of crowdfunding (tyclipso.me &

startnext.de, 2012). The fascinating aspect of this phenomenon is the sheer volume and variety of

projects that can attract financial resources to be pooled together, e.g. cultural projects, creative

ideas, start-ups, as well as private loans or people’s education (Hemer, 2011; Kortleben &

Vollmar, 2012; Moritz & Block, 2013).

Albeit its history, when we talk about crowdfunding today, we generally mean an emerging and

immature industry which has evolved from a series of interlocking developments such as the

advancement of information and communication technology, namely web 2.0 and social media,

allowing for cost-efficient transactions, communication and coordination (Hemer, 2011; Hemer,

Schneider, Dornbusch, & Frey, 2011; Moritz & Block, 2013; Dapp & Laskawi, 2014).

Crowdfunding can involve all contribution arrangements imaginable, ranging from donations,

money for gifts (rewards), equity or loans depending on the type of project to be funded

(Crowdsourcing.org, 2012; Pierrakis & Collins, 2013; Dapp & Laskawi, 2014; Kortleben &

Vollmar, 2012). For instance, in June 2014, the start-up Protonet collected €1.5 million ($1.9

million) within only 10 hours (Die Welt, 2014, p. 1). On the one hand, proponents of

crowdinvesting, as equity based crowdfunding for start-ups is called (Kortleben & Vollmar,

2012), celebrated this event as a world record marking the beginning of a new chapter. On the

other hand, critics point out that the crowdinvesting euphoria might be dangerous as it prevents

1 The term crowdfunding and its sub-concepts are explained and defined in more detail in chapter 2.

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investors from reflecting properly on the inherent risks of start-up financing (Bradford, 2012;

Cohn, 2012; Fink, 2012; Parsont, 2014; Stemler, 2013; Weinstein, 2013; Dorff, 2013). However,

both camps seem to agree on the opportunities that crowdinvesting might entail as a new way of

investing for investors and start-ups and generally for fostering a more entrepreneurial culture as

a base for innovation and economic growth.

Despite its potential, little is known in academia about the emerging phenomenon and especially

about what drives the investors’ decision-making in a crowdinvesting context, which seems to be

a question of theoretical and practical relevance.

1.2 Motivation

Crowdfunding in its modern form, and crowdinvesting in particular, are rather new developments

and have not received a lot of attention in academia as indicated by the small body of scientific

research published on the matter. Many aspects of the topic are still not thoroughly understood

and a multi-disciplinary approach is needed to improve comprehension (Moritz & Block, 2013;

Hemer, 2011; Expert D, 2014).

Apart from regulators, there are three relevant stakeholders involved to facilitate crowdinvesting:

(1) the online platform which serves as an intermediary, (2) the start-up that wants to raise money

and (3) the investors (Kortleben & Vollmar, 2012; Klöhn & Hornuf, 2012). This master thesis

focuses on the later and investigates the forces influencing investor behaviour in a

crowdinvesting context. The investor side is especially interesting because of various reasons.

Generally, a two-sided market place like a crowdinvesting platform needs both customer groups

(Rysman, 2009). However, since the motivation of start-ups to participate in crowdinvesting

seems more obvious, the primary reasons being money and attention, the motives for the

investors appear to be more complex and manifold (Gerber, Hui, & Kuo, 2012; Lambert &

Schwienbacher, 2010; Belleflamme, Lambert & Schwienbacher, 2011). The following

investigation of the factors that influence the decision of investors to participate in

crowdinvesting does not only represent a theoretical contribution by addressing an important

research gap, it also provides practical insights for platform providers by improving their

understanding of one of their customer groups. The review of relevant literature as well as the

expert interviews we conducted with researchers, crowdinvestors and platform providers lead us

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to the conclusion that there is a general lack of systematic comprehension of what factors drive

investors to participate in crowdinvesting as an asset class. We have also learned that platform

providers are faced with the challenge to extend their investor customer base beyond innovators

and early adopters, who typically enjoy exploring new technologies and opportunities because it

satisfies their need for curiosity and promises excitement (Oren & Schwartz, 1988). However, it

seems that retail investors2, who are a potential new customer base for crowdinvesting, are not

naturally drawn to innovative forms of investments. Thus, retail investors appear to be a tough

customer base to acquire (Expert E, 2014; Expert F, 2014). Consequently, we chose to approach

the subject from a behavioural finance perspective, which offers a promising starting point for the

investigation of investor decision-making. This is why we think this project contributes to closing

a research gap and helps us to learn more about profiles and types of investors as well as the

factors influencing investor decision-making to participate in crowdinvesting. Moreover, we

expect our findings to have relevant practical implications especially for platform providers.

Another motivation for writing this thesis and why we focus on an investor’s point of view is a

rather practical one. Since June 2013 we are building a market place called talent-invest.de where

German university students can raise money from ethical investors who provide their favourite

candidates with favourable student loans and expertise. This offer shares similarities to

crowdfunding and crowdinvesting. Thus, we hope to gain more insights into the factors that drive

the investors’ decision-making since they are the ones we need to convince to invest.

1.3 Research Question

The crowdinvestor and his/her decision-making process are the central point of interest for this

research project about crowdinvesting and the corresponding online platforms. While other

studies have been focusing on the question of how an investor selects a particular project to

invest in, this research project starts on an earlier stage by investigating why an investor may

participate in crowdinvesting in general (Gerber, Hui, & Kuo, 2012; Lambert & Schwienbacher,

2010; Belleflamme, Lambert & Schwienbacher, 2011). Thus, the central research question is:

2 Retail investors – in contrast to institutional investors – are in this thesis understood as individual natural persons who buy and sell any kind of securities (Black, 2008). Chapter 4.3.1.1 will further elaborate on the differences between crowdinvestors and retail investors applied in this thesis.

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(1) Which factors drive the decision of an investor to participate in crowdinvesting?

In particular, this project aims to distinguish between several types of investors and their

corresponding motives (Expert E, 2014; Expert F, 2014). Hence, related sub-questions that are

answered during the investigation include:

(2a) Are there any differences in factors that influence decision-making between currently active

crowdinvestors and retail investors?

(2b) Are retail investors interested in crowdinvesting and how do they perceive it?

While many research papers about motivational factors of crowdinvestors focus on the question

of how investors choose a particular project, this thesis emphasises the influencing factors that

drive the decision of whether or not to participate in crowdinvesting in general. In this sense,

crowdinvesting is viewed as a separate asset class and it is being investigated why investors

decide to invest via crowdinvesting platforms instead of or in addition to other investments, such

as shares or investment funds.

Based on the understanding that crowdinvesting could be seen as an asset class, this investigation

is made from a financial decision-making perspective. The underlying conceptual framework

thus builds on traditional financial theory with particular emphasis on behavioural finance.

1.4 Limitations

This master thesis focuses on crowdinvesting which is defined for this research project as equity-

like participation in start-up companies via an online platform (Kortleben & Vollmar, 2012, p. 6).

Thus, the theoretical framework and the analysis of investor behaviour are made within this

context. Other forms of crowdfunding like donation- or reward-based crowdfunding,

crowdlending for individuals as well as for businesses or crowdinvesting for small and medium

sized enterprises are not the subject of this thesis.

Since collective investments in start-ups are theoretically possible without an online platform, as

exemplified by business angel networks and can also be organized by start-ups themselves, it has

to be stated that we only focus on the online version of crowdinvesting. Online crowdinvesting is

generally facilitated by an institutionalised and organised market place such as a crowdinvesting

platform (Kortleben & Vollmar, 2012).

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This thesis further attempts to identify and test relevant factors that influence the decision of

crowdinvestors that lead to the decision of whether or not to participate in crowdinvesting by

comparing active crowdinvestors and retail investors. The decision regarding how crowdinvestors

finally choose particular start-ups to invest in is not part of this project. Consequently, the process

on how investors navigate on crowdinvesting platforms and what information they require in

order to choose a start-up are not specifically considered in this thesis.

1.5 Structure

In order to address the research questions raised in chapter 1.3, this thesis is divided into eight

parts. After introducing the background and motivation for this project, research questions, as the

basis for this research, are elaborated on. Before starting the actual investigation and study, the

second chapter presents definitions for the most important concepts applied in this thesis and it

mainly aims to provide a clear differentiation between crowdfunding, crowdlending and

crowdinvesting, with the latter being the object of interest in this thesis. Afterwards, the current

stage of literature regarding crowdfunding and crowdinvesting is presented with the resulting

identification of a research gap that is addressed throughout this paper. Chapter four explains the

methodological approach of this thesis in detail. A rather explorative approach is selected due to

the newness of the phenomenon of crowdinvesting. In this sense, expert interviews were used as

a first step to complement existing literature, followed by an online survey that was conducted

among crowdinvestors and retail investors. In chapter five, the theoretical framework used for the

investigation survey is developed. The conceptual model is based on traditional financial theory

and behavioural finance literature, which is shortly addressed before the key variables and

hypotheses of the applied model are described in detail. In chapter six the findings of the survey

are presented and analysed. Finally, chapter seven provides a discussion of these findings,

presents some limitations of the methodology and theoretical model and gives an outlook on

potential future research topics. A conclusion attempting to answer the research questions closes

this thesis.

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2 Crowdfunding and Crowdinvesting

This chapter provides an overview of different kinds of crowdfunding to clarify and define the

most important concepts applied in this thesis. Many papers on crowdfunding offer an extensive

introduction into and overview of the history and origins of this phenomenon (see for instance,

Hemer, 2011; Hemer, Schneider, Dornbusch, & Frey, 2011; Moritz & Block, 2013 or Dapp &

Laskawi, 2014 just to name a few). That is why we want to focus on the most relevant concepts

and developments.

2.1 Definition of Crowdfunding and Crowdinvesting

Crowdfunding is an umbrella term and is used in many different contexts. The term

crowdfunding is derived from the overarching term crowdsourcing which coined Howe (2006),

this refers to the process of outsourcing a company’s tasks to a large, often anonymous number of

individuals (Howe, 2006; Kleemann, Voß, & Rieder, 2008). The general goal of crowdsourcing

is to draw on the crowd’s skills, resources, knowledge or expertise, while crowdfunding

particularly has the objective to attract money (Hemer, 2011).

Crowdfunding is a neologism combining the words „crowd” and „funding“. It refers to the idea

that small individual contributions of money are pooled to support a particular goal (Ahlers,

Cumming, Guenther, & Schweizer, 2013).

Building upon the meaning of crowdsourcing, Lambert and Schwienbacher (2010) define

crowdfunding as the following:

“[C]rowdfunding involves an open call, essentially through the Internet, for the provision of

financial resources either in form of donation or in exchange for some form of reward and/or

voting rights in order to support initiatives for specific purposes.”(p. 4).

Although this open call can be made offline, typically an online platform serves as a market place

bypassing traditional intermediaries like banks, foundations or other gatekeepers to monetary

resources. These platforms mainly act as information providers for both parties and support the

handling of the funding process (Schwienbacher & Larralde, 2010).

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As shown in illustration 1, the literature essentially distinguishes between four different types of

crowdfunding depending upon the kind of capital provided and the rewards promised: Donation-

based crowdfunding, reward-based crowdfunding, lending-based crowdfunding and equity-based

crowdfunding (Crowdsourcing.org, 2012; Pierrakis & Collins, 2013; Dapp & Laskawi, 2014;

Kortleben & Vollmar, 2012).

Illustration 1: Concept-Tree of Crowdfunding (modelled on Kortleben & Vollmar, 2012 and Dapp &

Laskawi, 2014).

While donation-based crowdfunding does generally not provide any material return, reward-

based crowdfunding offers the chance to earn a small reward. These rewards can occur in many

different forms. If the donation supports the production of an item such as a book, a film or a

music album, the promised return is often a delivery of an earlier version of the product. That is

also why this form is often called pre-ordering or pre-selling (Hemer, 2011). In other cases, the

reward may be a personal thank-you card, a signed film poster or a meet-and-greet with the

filmmaker, author or musician (Hemer, 2011). In contrast, lending-based and equity-based

crowdfunding offer a (potential) financial return. Equity-based crowdfunding provides investors

with compensation in the form of some type of equity or profit share predominantly of a start-up.

Lending-based crowdfunding mostly takes the form of peer-to-peer lending, where a private

lender provides money to a private borrower for the chance to earn a fixed income rate. More

recently there are also peer-to-business platforms where private lenders provide capital to small

and medium sized companies.

This thesis focuses particularly on the concept of equity-based crowdfunding, which is better

known as crowdinvesting in Europe. Since our research focuses on Europe and we would like to

avoid confusion about terms and meanings we use the term crowdinvesting instead of other

synonyms we have found in the subject literature like equity-based crowdfunding, equity

crowdfunding or crowdfund investing. In order to be consistent with this terminology we use the

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term crowdlending instead of lending-based crowdfunding or peer-to-peer or peer-to-business

lending.

2.2 Industry Development

The very idea of crowdfunding is probably as old as the first attempts of human kind to pool their

resources together to achieve a common goal. One of the most popular and well documented

examples is the funding of the pedestal of the Statue of Liberty in 1885s New York (Dapp &

Laskawi, 2014, p. 3). To avoid that the construction work had to stop due to empty city funds the

publisher of the New York World newspaper Joseph Pulitzer started an “open call” to the public

to donate money to the construction of the pedestal. In return he promised to publish the name of

every contributor in the New York World. Within five months 120,000 people contributed about

$100,000 of which 80% of donations were less than one US-dollar (Dapp & Laskawi, 2014, p. 3).

Though when we talk about crowdfunding today, we generally mean a quite young industry

which has emerged from a series of interlocking developments, such as the advancement of

information and communication technology, namely web 2.0 and social media, which builds the

technical foundation for cost-efficient and fast transactions, communication and coordination.

Now it has become far easier for entrepreneurs to reach a large crowd of people at a lesser cost

(Pierrakis & Collins, 2013). This new era of online crowdfunding begun in 2006 with the

creation of a website called Sellaband.com, one of the earliest successful crowdfunding

platforms. On Sellaband music fans can directly fund their favourite artists and bands and

participate from the revenue that is generated when a music album is produced (Agrawal,

Catalini, & Goldfarb, 2011). This platform shows quite beautifully that other trends have

bolstered the emergence of online crowdfunding. On the one hand artists, musicians, filmmakers

and other creatives are mostly underfunded, thus access to capital from investors, public

programs or foundations becomes difficult. On the other hand consumers, citizens and people like

to play a larger participating role using web 2.0 technologies. This environment of technology

and the will of more democratic participation as well as the need for alternative access to capital

in the creative industry has become a fertile ground for crowdfunding. Another famous example

comes from the field of microfinancing. The crowdlending platform Kiva.org connects first

world lenders with third world entrepreneurs to provide interest-free microloans since 2004

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(Burtch, Ghose, & Wattal, 2013b). Also the idea of microlending as championed by the 2006

Nobel Peace Prize laureate Muhammad Yunus needs to be mentioned in this context (Everett,

2014).

Another driver for the growth of crowdfunding, especially for crowdlending and crowdinvesting,

is the 2008 financial crisis because of the difficulties faced by entrepreneurs and early-stage

enterprises in raising funds. Due to the reduced willingness of traditional banks to lend money,

entrepreneurs and project initiators started to look elsewhere for funding (World Bank, 2013).

The trust lost in the conventional financial system has also contributed to the crowdfunding

development.

The current market leader in crowdfunding is kickstarter.com. The donation-based crowdfunding

platform has funded nearly 50,000 projects with a total volume of over $815 million until 2013,

although it was only founded in 2009 (World Bank, 2013).

According to Crowdsourcing.org’s Directory of Sites, one of the most complete databases of

crowdfunding sites, there were more than 450 crowdfunding platforms active worldwide as of

April 2012 with the majority of them being located in North America and Western Europe

(Crowdsourcing.org, 2012). In 2011, more than one million campaigns were successfully funded

globally with a total volume of almost $1.5 billion. In 2012, a transaction volume of $2.6 billion

was raised, which almost doubled in 2013 to $5.1 billion (Crowdsourcing.org, 2012; Hollas,

2014). In Europe, the focus of crowdfunding remains in the Western part, with the majority of

crowdfunding platforms located in the UK, France, the Netherlands, Germany and Spain

(Hollow, 2013).

In particular, the market for crowdinvesting raised a total of $204 million in transaction in 2013,

compared to $116 million in 2012 (Hollas, 2014). Crowdinvesting is expected to grow strongly

during the coming years. Currently, many jurisdictions are still working on corresponding laws

and regulation that allow crowdinvesting. While crowdinvesting is already legal in certain

countries, for instance in the United Kingdom, Finland and Australia, others are working on

making it easier for small companies to raise money via crowdinvesting while maintaining

protection for private investors, such as the US with the JOBS Act3 (Hollas, 2014). By 2025, the

crowdinvesting industry could reach a transaction volume of $300 billion, especially due to the

3 Abbreviation for „Jumpstart Our Business Startups Act“

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potential from developing countries. In this context, China could account for up to $50 billion of

this figure (World Bank, 2013).

Crowdinvesting for start-ups has particularly associated macro-economic relevance and the hopes

of politicians are high that it can help create jobs, produce innovation and consequently growth

for the economy (World Bank, 2013). Today, entrepreneurs generally face the challenge to attract

outside capital for their venture, especially in the very beginning. Various investors, such as

venture capital funds and banks, exist for larger amounts of capital. However, entrepreneurial

initiatives that require much smaller amounts often face difficulties to raise these funds (Cosh,

Cumming, & Hughes, 2009). Consequently, entrepreneurs have started to look for funding in a

more direct way through the Internet. Crowdinvesting is often used by start-ups for seed

financing, which is usually provided by business angels or friends and family.

Crowdinvesting allows start-ups to close a funding gap that often occurs after tests have proven a

minimum viability of an idea. In order to conduct some initial tests of the idea, relatively low

funding is sufficient, which can still be raised through donation-based or reward-based

crowdfunding. For the following step, where the main funding gap appears, crowdinvesting

seems to be an appropriate way to raise higher amounts of capital (World Bank, 2013). The

following illustration shows the funding lifecycle of a new venture and the corresponding funding

sources.

16

Illustration 2: Start-up lifecycle and funding requirements (modelled on World Bank, 2013, p.16).

There are many recent examples for highly successful crowdinvesting campaigns. In June 2014,

the start-up Protonet for instance collected €1.5 million ($1.9 million) within only 10 hours,

which is widely considered to be a crowdinvesting world record. The Hamburg based start-up

designs and produces secure cloud servers (Die Welt, 2014). However, it is still under debate

whether crowdinvesting becomes a serious alternative to angel finance and venture capital

(Schwienbacher & Larralde, 2010), but it has become an increasingly important financing

channel especially for start-ups (Ahlers et al., 2013).

2.3 Development Stage of Crowdfunding and Crowdinvesting

Although the early beginnings of crowdfunding as we know it today can be dated around the year

2006 the phenomenon and its reception by science, media and society is still in its infancy. One

interesting indicator for determining the maturity of a technology is the Gartner Hype Cycle

developed by one of the world’s leading information technology research and advisory

companies (Linden & Fenn, 2003). In contrast to other technology life cycle models the Gartner

17

Hype Cycle describes the development of a technology before it starts to be adopted by

businesses and customers. It assumes that over time an emergent technology progresses through

different stages from overenthusiasm, through to disillusionment and eventually to a stage where

the technology’s relevance and role is understood. The cycle captures five basic stages:

Technology/Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope

of Enlightenment and finally the Plateau of Productivity (Linden & Fenn, 2003).

The progression of the IT-driven phenomenon crowdfunding can also be displayed by using the

Gartner Hype Cycle. Bradley (2014) for instance analysed 50 leading crowdfunding and

crowdinvesting sites mainly from the US and Europe to determine the state of crowdfunding on

the hype cycle for social software technologies. He comes to the conclusion that the state is “very

immature” and “fragmented” (Bradley, 2014, p.1). That seems to be in line with another Gartner

Hype Cycle publication on the future of money, which constitutes that crowdfunding is currently

sliding into the Trough of Disillusionment (see illustration 3). This is the phase of consolidation,

which reveals if a technology is understood and relevant enough to be adopted further. At the

beginning of the Slope of Enlightenment the technology penetration is often significantly below

5% of the intended market growing to approximately 30% as the technology enters the Plateau of

Productivity which is considered to be the beginning of mainstream adaptation (Linden & Fenn,

2003).

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Illustration 3: Position of crowdfunding in Hype Cycle for the Future of Money 2013 (Furlonger, 2013, p. 1).

The positioning of crowdfunding by Gartner’s Hype Cycle supports the overall impression that

crowdfunding is a new phenomenon which is still in its infancy regarding media coverage,

scientific analysis and adaptation. Considering the fact that crowdinvesting is an even more

recent development, it might be placed on an earlier stage of the hype cycle.

A similar picture is drawn for the development of crowdfunding and crowdinvesting from an

industry lifecycle theory perspective. Utterback and Abernathy (1975) were one of the first

researchers who described patterns over the lifetime of innovation. According to their analysis,

the process comprises three stages, which are referred to as uncoordinated, segmental and

systemic. Similarly, Afuah and Utterback (1997) build their model on Utterback's and

Abernathy's (1975) Dynamic Model of Innovation and identify the three stages of fluid,

transitional and specific phase. Applying these two industry lifecycle frameworks to the

phenomenon of crowdinvesting, it further underlines the newness of this industry.

Considering crowdfunding and crowdinvesting in the light of these frameworks, they must be

placed in stage one. Currently, products are highly differentiated and several of them target niche

markets. There are specific crowdinvesting platforms with a pure focus on projects within

renewable energy such as crowdener.gy or greenvesting. Some focus particularly on small- and

19

medium-sized companies (e.g. bankless24 and LightFin) while others focus on start-ups in a

growth stage, such as Bergfürst and some offer only seed-financing, e.g. Companisto or

Seedmatch (Für-Gründer.de, 2014). The industry is further characterized by a high degree of

market uncertainty which is supported by the fact that it is not clear whether crowdinvesting

becomes a serious alternative to other financing opportunities for start-ups (Schwienbacher &

Larralde, 2010). Furthermore, the fluid phase or the uncoordinated process of stage one is

described by a high entry rate of new players and unstandardized processes, which also relates to

the absence of a dominant design. The current stage of missing standardisation is reflected in the

fact that most platforms use different business models and thus offer different forms of

investment in start-ups. Investors who choose to invest on Bergfürst become co-owners of the

start-ups, which means that they benefit directly from the growth of the venture. Furthermore, it

is also possible to sell and buy these stocks in a secondary market, comparable to a stock

exchange. In contrast, Companisto offers its investors a so-called sub-partnership, where

investors participate in profits, but also in asset values and exit proceeds. Additionally,

Companisto is the only crowdinvesting platform that enables crowd investing for everybody by

not requiring a minimum share amount. Thus, the company bundles the shares of all investors for

one start-up in order to reduce administrative burdens for the start-ups. Therefore, Companisto

founded its own associate company Companisto Venture Capital GmbH (Ortmann, 2012).

Analysing the current stage of crowdinvesting by means of the industry lifecycle theory by Afuah

and Utterback (1997) and Utterback and Abernathy (1975) provides indication that this industry

is still at the very beginning of stage one. Combining this result with the classification of

crowdfunding in Gartner’s Hype Cycle implies that both crowdfunding and crowdinvesting are in

a very early stage of innovation and it is not clear whether these phenomena are temporary or

whether they establish in the long run.

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3 Literature Review

This chapter gives an overview of the relevant research and literature on crowdfunding,

crowdlending and crowdinvesting that account for the many facets the phenomenon has and to

locate the research gap that this thesis then addresses. For this purpose journal articles, working

papers, industry reports and various online resources were used. The body of literature on

crowdfunding, especially crowdinvesting, that qualifies as academic is growing over the last few

years but is arguably still quite limited.

The strands of research we identified can be roughly summarised into four research streams: (1)

information asymmetry, which includes general principal-agent theory and signalling, herding

and spatial effects, (2) entrepreneurial finance, (3) regulation and (4) motivation to participate in

all kinds of crowdfunding.

(1) Information asymmetry: The first research stream is about research endeavours that focus on

situations of asymmetrical information between investors, platforms as intermediaries and

entrepreneurs. It is mainly about the general principal-agent theory and the effects that can be

described with it such as signalling effects, herding effects and effects of spatial proximity.

Kortleben and Vollmar (2012) for instance focus on agency constellations and conflicts that can

arise between platforms, investors and start-ups. Crowdfunding platforms in general play an

important role as an intermediary in the reduction of transaction costs as well as information

asymmetry. They are the prerequisite for securing market efficiency by reducing the risk of

adverse selection and moral hazard (Berger & Gleisner, 2009). Schwienbacher and Larralde

(2010) also highlight the importance of efficient communication and networking between

investors and entrepreneurs. In that sense the platform design and its features gain most of the

attention in this strand of research: Wash and Solomon (2011) discuss if the “all-or-nothing”

principle or the “keep-what-you-get” principle makes more sense for platform providers and

investors; Mäschle (2012a) investigates the “first come, first served” principle when investors bid

for a start-up during a campaign. In addition, he also creates a list of information that platforms

should disclose to reduce information asymmetries between founders and investors (Mäschle,

2012b). Hemer (2011) mentions different business models for platform providers that might have

an impact on the incentive structure for all parties, i.e. platform, investor and entrepreneur.

21

Signalling effects. Another aspect of this research stream focuses on the investor perspective.

Ahlers, Cumming, Günther and Schweizer (2012) for instance used archive data from the

Australian crowdinvesting platform ASSOB to find out what information investors use as

positive signals to make a decision. They found that the number of people in the management

team, their education and the size of their social network played an important role as well as the

planned exit strategy, the financial roadmap and the age of the company. These are some signals

venture capitalists usually also consider. Actually, Mollick (2013) found that crowdinvestors,

knowingly or not, look for similar signals of quality as venture capitalists would, but that

crowdinvestors would have a less significant geographic and gender bias. Furthermore, research

on crowdlending sheds some light on quality signals investors use that are more qualitative or

“softer” in nature, but might be transferable to crowdinvesting contexts as well. Herzenstein,

Sonenshein and Dholakia (2011) investigate identity claims borrowers construct in narratives in

their loan requests on crowdlending platforms. When the information asymmetry is high and hard

facts are more or less absent lenders use appearance signals as signals of quality. Duarte, Siegel

and Young (2012) and Pope and Sydnor (2011) show that soft facts like pictures have a

significant impact on trust building and the probability of funding success. However, if hard facts

are available, lenders predominantly use them when forming an investment decision (Iyer,

Khwaja, Luttmer, & Shue, 2009).

Peer behaviour and herding effects: When the individual has limited information and information

acquisition is costly, people tend to observe and interpret the behaviour of others to make a

decision. These peer behaviour or herding effects were also found on reward-based crowdfunding

platforms where the behaviour of others had a significant influence on the investment decision of

the individual (Ward & Ramachandran, 2010; Burtch, Ghose, & Wattal, 2013; Smith,

Windmeijer, & Wright, 2013). In a crowdlending context a similar effect was found (Berkovich,

2011; Yum, Lee, & Chae, 2012). Hildebrand, Puri and Rocholl (2013) and Kim and Viswanathan

(2013) show the positive effect of early investors on later investors on crowdlending platforms

when they are identified and recognized as experts or lead investors.

Spatial effects: Although the Internet potentially connects every citizen of the world and is

independent of geographical borders, there are still spatial effects at work. Using data from

Sellaband, Agrawal, Goldfarb and Goldfarb (2010) find that especially early investments are

driven by investors that are geographically and socially close to the entrepreneur. They categorise

these early investors as friends, family and fans that are crucial to kick-off the campaign by

22

visibly attracting money in its beginning. Although they could show that the online platform

eliminates most of the distance-related economic frictions (monitoring progress, providing input

and gathering information), it does not resolve the social-related frictions (Agrawal et al., 2011).

Furthermore, Lin and Viswanathan (2014) investigate a similar effect they call home bias. They

collect two sets of explanations for a home bias. The rational or economic perspective includes

transaction costs (shipping costs, cultural differences, cost of information acquisition,

informational advantage due to spatial proximity) and a behavioural perspective which draws on

factors like over-optimism towards home markets or homophily, that is the tendency to rather

bond with similar others.

(2) Entrepreneurial finance: The second research stream addresses the potential of

crowdinvesting to bridge the often-cited funding gap in the early stage of start-up financing.

Quite a lot of research efforts discuss the question, if crowdinvesting can close the funding gap

and whether it is a complement or an alternative to traditional start-up financing from business

angels, venture capitalists and banks. They also investigate what welfare impact this new industry

might unfold in terms of increasing the rate and direction of innovation and growth as well as job

creation (for instance: Hemer, 2011; Hemer, Schneider, Dornbusch, & Frey, 2011; Ley &

Weaven, 2011; Moritz & Block, 2013; Tomczak & Brem, 2013; Manchanda & Muralidharan,

2014 and Dapp & Laskawi, 2014). Dapp and Laskawi (2014) also mention the difficult question

of valuation methods for start-ups. They welcome the potential of crowdinvesting to bridge the

funding gap for start-up financing and its role in supporting innovation and growth for an

economy, but also draw the attention to the risks involved. It is very difficult to value a start-up

appropriately, because there is not much historical data to draw on and there are no real valuation

standards and requirements until now.

(3) Regulation: The third research stream covers crowdinvesting from a legal and regulative

perspective and takes the role of a more critical voice and view on crowdinvesting in the US

(Securities Act and JOBS Act) and Europe. Most of the literature discusses two main points that

need to be balanced: creating jobs and economic growth by making it easier for smaller

companies to attract equity from the public and at the same time making sure that investors are

protected reasonably and start-ups do not just take the money and run (Bradford, 2012; Cohn,

2012; Fink, 2012; Parsont, 2014; Stemler, 2013; Weinstein, 2013). Klöhn and Hornuf (2012) join

this discussion and give a thorough introduction of crowdinvesting in Germany and its legal

situation. The most critical voice in the crowdinvesting literature comes from Dorff (2013) who

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cautions us against the “[s]iren call of equity crowdfunding” (p.1) and stresses the risk of adverse

selection of start-ups and information disclosure requirements of platforms and start-ups in the

US. Röthler and Wenzlaff (2011) offer a more constructive tone and give recommendations for

regulation at the level of the European Union.

(4) Motivation to participate: The fourth and last research stream is about the motivation and

decision to participate in different kinds of crowdfunding. There is not much literature or research

on that topic which is why we had to go one step back to the field of crowdsourcing. Kleemann et

al. (2008) examines the motivations for contributing time, energy and skills to crowdsourcing

activities. Drawing on the work of Ryan and Deci (2000) they differentiate between intrinsic

motivations, i.e. the task itself is inherently motivating, and extrinsic motivation, i.e. some form

of external reward is the main motivator. Along similar lines Brabham (2008) investigates

motivations to participate on iStockphoto, a Canadian crowdsourcing platform and finds that

financial reward is the main driver for participation and that the possibility to learn something

new, having fun, networking and peer recognition is only secondary to this.

Few scholars have investigated the motivation to participate in crowdfunding with regards to both

the entrepreneurs and the investors. Generally, there seems to be a better understanding of the

motivation on the side of entrepreneurs. Different studies show that raising money, getting public

attention and obtaining feedback on their products or services motivate participation of

entrepreneurs in crowdfunding (Gerber et al., 2012; Lambert & Schwienbacher, 2010).

Belleflamme, Lambert and Schwienbacher (2011) for instance theorise how a rational

entrepreneur would decide to either use crowdfunding (pre-ordering) or traditional sources of

capital as venture financing. If this form of crowdfunding enables the entrepreneur to extract a

larger share of the consumer surplus from crowdfunding customers, the entrepreneur has an

incentive to use crowdfunding instead of traditional funding.

To the best of our knowledge however there is no published research, which focuses on the

motivation to participate in crowdinvesting as an investor. The closest that we could find to this is

a conference paper written by Bretschneider, Knaub and Wieck (2014) who develop 14

hypotheses and a theoretical model from the literature. The paper in which they try to validate the

model by using a survey of investor customers from the German crowdinvesting platform

Innovestment has yet to be published.

24

Summarising this research stream we can say that despite its promise, research on motivation of

crowdinvestors published in scientific journals is still quite limited, especially quantitative

research. This is way we want to contribute to close this research gap with our thesis and to learn

more about profiles and the types of investors and the factors influencing investor decision-

making to participate in crowdinvesting.

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4 Research Method

With regards to the methodology applied in this thesis, mainly a quantitative approach has been

chosen which was complemented by some qualitative research in the form of interviews. The

methodological basis is a survey that allowed collecting data that was then used to evaluate

hypotheses that have been previously developed from existing theory. This approach is applied

for this study, as it is most suitable to investigate and explain the causal relationships between the

decision to participate in crowdinvesting and several independent variables. Thus, the research

approach is explanatory and aims to explain the relationship between variables, that is the

influence of different factors on the decision whether or not to participate in crowdinvesting as an

investor (Saunders, Lewis, & Thornhill, 2012).

In order to gain insights into the decision-making process of investors in a crowdinvesting

context several methods were applied. The research questions are examined in a quantitative way

by collecting data from crowdinvestors and retail investors using an online-based standardized

questionnaire. Descriptive statistical analyses, reliability analyses, factor analyses and a logistic

regression analysis were run using IBM SPSS Statistics 21. This approach allows to measure the

concepts and the relationship between the variables quantitatively (Saunders et al., 2012).

The relationship between the dependent variable and the independent variables is investigated

based on a binary logistic regression. As in other regression analyses, the logistic regression also

aims to find the best fitting model to describe the relationship between the dependent variable,

which is dichotomous in a binary logistic regression, and a set of independent variables, which

can be of any type (Hosmer, Lemeshow, & Sturdivant, 2013). The main difference between a

linear regression model and the logistic regression is that the dependent variable is binary, which

in this investigation is the question if a respondent has previously participated in crowdinvesting

or not. A linear regression is not possible in this case since the use of a binary dependent variable

with only two values violates the assumption of normal distribution. Additionally, the

dichotomous variable would not comply with the assumption of homoscedasticity, which is also

required for a linear regression (Warner, 2012).

The quantitative approach has been complemented with some more qualitative elements in the

form of semi-structured expert interviews, which were particularly used to adapt the theoretical

model and to develop the survey. Mainly due to the fact that there is limited literature in this

26

field, qualitative research was chosen in the first phase of this investigation project. Qualitative

research is a suitable way to investigate the research field in an explorative way. The interviewee

him-/herself provides the background of the subjective meaning, which allows a reaching of an

understanding of the motivation, behaviour and emotions of the interviewee. The interviews were

made in a semi-structured way, which gives the interviewee the possibility to talk freely and play

an active role. At the same time, the interviewer has the freedom to discuss further aspects with

the interviewees based on his/her arguments since no formal questionnaire is applied (Kurz,

Stockhammer, Fuchs, & Meinhard, 2009; Mayring, 2002). The data gained from the expert

interviews were considered as a complement to literature research and have been mainly applied

for the development of the conceptual framework.

The interviews included a total of seven interviews with different stakeholders in the

crowdinvesting industry. All interviews were conducted via the telephone in German, between

the 28th of May and the 1st of July 2014. The interviews lasted approximately one hour and were

scheduled with the participants by email in advance. A list of leading questions had been

developed before the interviews and the questions had been adapted to the background and

profile of the interviewees in order to account for their specific perspectives. However, as

mentioned, the interviews were made in a semi-structured way to provide the interviewee with

the possibility to give further input in addition to the initial questions and to offer the interviewer

the chance to react to this input.

Two interviewees were current CEOs of leading crowdinvesting platforms in the German-

speaking area, with one of them also being the founder of the corresponding platform. Another

perspective of the platform providers’ point of view was reached from an interviewee who was

the marketing director of a large crowdinvesting platform in the German-speaking area. These

three interviewees have been asked several questions regarding their impression and knowledge

of their investors’ motivation to participate in crowdinvesting. The questions further focused on

topics such as how well do you know your investors, do you have statistics about your investors,

do you engage in particular market research activities to increase the knowledge of your

customers (investors), could you identify particular crowdinvesting types, how would you

describe a typical crowdinvestor and what motives do you think drive the decision of a

crowdinvestor to participate.

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Another two interviewees provided us with a more research-oriented perspective. The first of

them was en employee in the research team of a large financial institution and also published an

article about the phenomenon of crowdinvesting. The other one was a researcher at a German

university in the department of media and communication management, currently working on

topics related to crowdfunding and crowdinvesting. This interviewee has further been engaged in

establishing a crowdfunding association on both a national and European level. The questions in

these two interviews focused in particular on the current stage of crowdinvesting and the investor

motivation from a research perspective. Furthermore, as the first interviewee was employed in a

financial institution, we were especially interested in his view on crowdinvesting from a more

financial perspective and in comparison to other investment types.

Finally, two crowdinvestors have also been interviewed. One of these is called a serial

crowdinvestor as he has already invested in more than 50 start-ups. The other one has a lower

number of investments, but has been engaged in this market for about 1.5 years. Both

crowdinvestors have been asked why they started to invest in start-ups via crowdinvesting

platforms, what their motives are for such an investment and where they see the future of

crowdinvesting.

As it has been agreed with the experts during the interviews, the interviewees are kept

anonymous, which means that references and quotations are given by using Expert A-G.

Appendix 10.1 provides the full list of interviewed experts with their corresponding pseudonyms

and a short description of their background and/or position.

4.1 Questionnaire Design

Questionnaires are generally a suitable method for data collection in order to investigate the

relationship between a dependent and several independent variables, and they are particularly

recommended for explanatory research (Saunders et al., 2012). It is a form to collect data across

all subjects by using a standardised interview in the form of a self-completing questionnaire

(Brace, 2008). Brace (2008) further indicates that sometimes it is argued that questions should be

asked in different ways in order to tailor the wording to each respondent’s vocabulary and

knowledge of the topic. In order to avoid misunderstanding in our questionnaire, technical terms

were generally avoided and wording was kept as simple as possible. Additionally, the term

28

crowdinvesting was briefly defined at the beginning of the questionnaire so that all participants

had the same understanding of the concept.

The questionnaire was developed in English and later directly translated into German since most

respondents were addressed in Germany. Chapter 4.3.1 explains further details about the sample

and why mainly German-speaking countries were targeted.

The first part of the questionnaire addressed the general likelihood of the participants to

participate in crowdinvesting. Based on the novelty of crowdinvesting, the participants were

given a concrete example of a start-up looking for financing on a crowdinvesting platform. This

example is further described in chapter 4.1.1. The participants were asked to rate the likelihood to

participate in this particular example in an overall sense and in two different scenarios in

particular.

The second part consisted of several questions regarding the experience with crowdinvesting of

the participant. Therefore, a filter question was used to distinguish between respondents who

have previously participated in crowdinvesting and those who have never invested in

crowdinvesting before. Participants with crowdinvesting experience were then questioned about

further details of their investments and those without crowdinvesting experience were asked for

the reasons as to why they have not participated yet.

The next part of the questionnaire focused on the key variables of the theoretical framework.

Several items were developed for each variable and the respondents were asked to evaluate these

items by indicating their degree of agreement or disagreement on a seven point Likert scale.

The last part of the questionnaire consisted of several socio-demographic questions, including

questions about the available funds for investments per year and the investment habits of the

respondent regarding certain investment types.

The respondents required between 12 to 20 minutes to complete the survey. The participants were

assured anonymity and confidentiality.

In order to give the participants an incentive to complete the questionnaire, they were offered a

social incentive instead of a lottery with an individual prize. This was made in form of a donation

promise to the international organization Médecins Sans Frontières / Doctors Without Borders of

1 Euro per finished questionnaire. It was used in this survey to address the altruistic behaviour of

participants. This incentive is assumed to work best in this context – compared to financial

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incentives with the same quantity – due to the nature of the target respondents (Robertson &

Bellenger, 1978). Individual respondents provided positive feedback concerning the chosen

incentive structure by sending an email to the authors after completing the questionnaire.

4.1.1 Exemplary Crowdinvesting Project

For illustrative purposes and to give a concrete example of how a crowdinvesting decision might

look like, a hypothetical investment scenario was constructed. An easy to understand illustration

was considered helpful in explaining crowdinvesting especially for those inexperienced with it.

The screenshot of a real campaign from the German crowdinvesting platform Seedmatch served

as a visual frame for the fictional start-up we named CloudGuard, which was presented on the

fictional platform we named Grow-Vesting. This way the participants were provided with a short

version of all textual and visual information typically used to present a start-up on

crowdinvesting platforms and its call for funding to the crowd. Thus, the example included

elements such as the video screen where the pitch video would be, the amount asked for, the

funding progress, the amount of investors, the days left for the campaign, etc. (see illustration 6).

In the questionnaire a short explanation of what the start-up does was provided. The minimum

amount for an investment was set at €250 representing the relatively low entry amounts typical

for crowdinvesting.

Illustration 4: Picture of fictional crowdinvesting scenario used in survey (source: Seedmatch, own creation).

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In order to gain more insights into different risk-return-profiles of investors we created two

different investment and return options which were textually explained to the participants as

option A and option B. To make them as easy as possible to understand, the different options

were simplified in a way that accounts for the investment logic of crowdinvesting, but to avoid

confusion. It does not need to explain legal constructs in detail with which an investor could

acquire profit participating rights of a start-up.

We constructed the investment argument as follows: There is a chance to realize a return of x%

on an investment depending on the profit development of CloudGuard in the next five years, but

there is also a chance to lose the amount invested. Option A represented the choice with a higher

return and higher risk (900% return on investment and chance to lose all) whereas option B stood

for a choice of lower return and lower risk (250% return on investment and chance to lose not

more than 50% of amount invested). Although inspired by descriptions and forecasts from a

similar crowdinvesting campaign, the risk and return features were arbitrarily set by us to create

an easy to understand example, especially for respondents who had not heard of crowdinvesting

before.

4.1.2 Measurement Development

The questionnaire used for this research consisted mainly of opinion variables, asking for how

respondents felt about something (Saunders et al., 2012), in this particular context regarding

crowdinvesting. The questions developed were all closed-ended, which means that the respondent

had to choose an answer from a number of alternatives given. While closed-ended questions are

easier to analyse statistically, they have the disadvantage of limiting the range of participants’

responses (Jackson, 2011). Within the scope of closed-ended questions, the questionnaire

consisted primarily of rating questions, but also comprised some list, category and matrix

questions (Saunders et al., 2012).

The primarily applied measurement in this questionnaire was in the form of rating questions with

seven point Likert scales. According to Saunders et al. (2012) rating questions are especially

recommended to collect opinion data. The use of Likert scales for the rating is most suitable for

asking the respondent how strongly s/he (dis)agrees with a statement (Saunders et al., 2012). This

type of question was applied for almost all independent variables in our questionnaire. The

31

chosen seven point Likert scale contained an odd number of points and thus provided the

respondent with a neutral answer possibility. Hence, the respondent was not forced to rate every

statement positively or negatively, but had the option of a not sure / don’t know answer in the

middle of the scale (Jackson, 2011). The scale applied in this questionnaire reached from strongly

agree to strongly disagree and likely to unlikely respectively with only the two poles being

indicated. The points in between were not specifically named.

As indicated by the name, list questions offer the respondent a list of answer possibilities where

s/he can choose one or more. In addition to the given list, the respondent can be offered the

choice to add his/her own response with a catchall category of other. This possibility was chosen

in this survey for a question about the crowdinvesting platforms that have been used and the

reasons why someone has not participated in crowdinvesting so far. Furthermore, yes/no

questions belong to this category and have been applied for questions about previous experience

with crowdinvesting and involvement with the start-up industry (Saunders et al., 2012). Churchill

and Iacobucci (2005) refer to yes/no questions as dichotomous questions based on the fact that

only two alternatives are presented compared to multichotomous questions with several answer

alternatives.

Also the dependent variable was measured with a dichotomous question, providing the answer

possibility yes, with the meaning that the respondent has crowdinvesting experience, or no,

accounting for the fact that the participant does not have any crowdinvesting experience. The

investigation project aims to identify factors that influence the decision of whether or not to

participate in crowdinvesting. Instead of using a hypothetical answer to the question if someone

would participate in crowdinvesting, the participants were asked if they have actually participated

in crowdinvesting. It seems most reasonable to use the actual experience with crowdinvesting as

a way to investigate the factors that influence the decision of an investor to participate in

crowdinvesting. This question is the basis for the distinction between the two sub-samples of

crowdinvestors and retail investors.

In addition to rating and list questions, category questions were used, which are explained as

questions that “are designed [so] that each respondent’s answer can fit only one category”

(Saunders et al., 2012, p. 434). This type of questions is generally used for behavioural questions

and was mainly applied in the socio-demographic section of this questionnaire. For example

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gender, age, occupation, education and income of the participants were asked by means of

category questions.

Furthermore, one matrix question was included at the end of the questionnaire in order to identify

the investment profile of the participant. The respondent was asked for his/her familiarity with

capital markets by specifying his/her experience with various investment types. The matrix

consisted in this case of several asset classes and time frequencies (see also question 28 in

appendix 10.5). The respondent was asked to indicate his/her frequency of investing in the

corresponding investments. A matrix or grid of questions is a suitable possibility to record

responses to several similar questions at the same time (Saunders et al., 2012).

4.2 Survey Pretest

Due to the fact that most items have been newly developed for this questionnaire, a pretest was

conducted before starting the actual data collection among crowdinvestors and retail investors.

Given that questionnaires do not allow prompting and exploring issues further, the questions need

to be defined precisely prior to data collection (Saunders et al., 2012). Thus, the pretest appeared

to be a suitable method to ensure the quality of the questions and to validate and adapt measures

of the variables in the model.

Before starting the actual pretest of the questionnaire, a total of ten people tested the survey

beforehand for length, convenience, wording and understanding of the questions. Four persons

tested the German questionnaire and five the English version, while one person who is bilingual

in German and English checked both versions for consistency. The comments and suggestions for

improvement that were made by these test participants were taken into account for the pretesting

survey where reasonable.

The pretest was done with an online-based self-report survey available in English and German

and was mainly distributed among students, most of them with a background in finance,

accounting, economics or related fields, who are likely to have some knowledge about

investments. In order to motivate the participants to complete the survey, a lottery was introduced

with the possibility to win a €25 Amazon voucher. In total, 75 persons participated in the pretest

with 52 participating in the English survey and 23 in the German version. However, 12

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participants did not complete all questions of the survey and were thus not considered, which

leads to a participation of 63 people in the pretest.

The main purpose of the pretest was to assess the reliability and validity of the variables. In order

to test the variables for their internal consistency, that is their reliability, Cronbach’s Alpha was

applied as statistical measure (Cronbach, 1951). Bortz and Schuster (2010) explain Cronbach’s

Alpha as a coefficient that estimates the reliability of the scores that consist of all the test items.

In the context of this research project, Cronbach’s Alpha is considered to be a suitable tool to

measure the consistency of responses to a set of scale items that are combined as a scale to

measure a particular factor (Saunders et al., 2012). Table 2 summarizes the Cronbach’s Alpha

values for all variables included in the pretest survey. Furthermore, the validity was tested with a

factor analysis. The factor analysis is a suitable tool to test how well one item fits with another

group of items and how the items can be clustered in variables based on their correlations (Bortz

& Schuster, 2010). Individual items were excluded after the factor analysis, especially if single

items loaded on an individual factor or if the combination of different items to one factor did not

seem reasonable from a content perspective. The factor analysis showed generally that most

items loaded on the factors that they were intended to, which indicates that these factors

correspond with the variables that were developed in the conceptual framework. The results of

the factor analysis for all independent variables are included in appendix 10.2.2.

Category Factor Items Pretest Alpha

Risk-return Consider-ations

Expected return

A high financial return is important for me when considering investment alternatives. The possibility of exceptionally high returns makes crowdinvesting interesting to me. I accept the high risk of losing my investment when I have the chance of exceptionally high returns.

0,782

Diversification

In times of low interest rates, crowdinvesting seems to be an attractive alternative. Crowdinvesting is an alternative worth considering compared to other financial investments (shares, investment funds, saving accounts). I see crowdinvesting as a possibility to diversify my portfolio.

0,794

Risk The risk related to an investment on a crowdinvesting platform is too high considering the expected return. I prefer investments with a lower risk profile.

0,475

Social

Relevance Support entrepreneurship / new ventures

Crowdinvesting is a great possibility to support new ventures. I like the idea of opening possibilities for start-ups by supporting them financially via crowdinvesting. Crowdinvesting is a great opportunity to support an entrepreneurial culture.

0,862

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Close financing gap

I think it is very difficult for start-ups to get early-stage funding. I support a new venture if I like the idea even though I may not be convinced about the economic success. I like the idea of making it easier for start-ups to receive funding by supporting them with my investment.

0,658

Economic / societal impact

Crowdinvesting gives me the possibility to support innovation. I think innovation and entrepreneurship are very important for an economy. Crowdinvesting offers the opportunity to support the society, e.g. by creating new jobs.

0,729

Innovative

Invest-

ment

Early adopters

I (would) enjoy being involved in the start-up I invested in. If I invest(ed) in a new start-up, I (would) want to be one of the first investors. Among my friends, I’m often the first to try out new things.

0,567

Trust in online platforms

It feels safer to invest via my bank instead of using an online platform. I feel more comfortable investing through my online banking portal than using another online platform.

0,888

Personal

Utility

Self-representation

I like to talk about my investments. I (would) enjoy talking about the start-ups I support financially. I (would) enjoy participating in crowdinvesting because it provides an interesting conversation topic. I (would) share my investments in start-ups online (social media, blogs etc.).

0,781

Network I have a strong interest in start-ups / new ventures. An investment on a crowdinvesting platform would increase / increases my own network. I (would) enjoy interacting with the project teams (start-ups). I (would) like to interact with other crowdinvestors.

0,784

Advocate

Recom-

mendation

Advocate recommendation

I would be more likely to invest in a crowdinvesting project if it was recommended by friends / family. I generally trust investments more if they are recommended by experts. I would be more likely to invest in a crowdinvesting project if it was recommended by an expert.

0,815

Neutral

Infor-

mation

Coverage in financial press

Before making an investment decision, I get informed in the financial press. It is important for me what the financial press writes about particular investments.

0,842

Coverage in general press

It is important to me what the general press publishes about investments. If the general press published a positive article about crowdinvesting, I would be more likely to invest.

0,882

Table 1: Cronbach’s Alpha values of the pretest analysis.

Based on the factor analysis and low Alpha values, certain items were taken out in the final

survey and other items were added to the questionnaire. The following table presents an overview

of the items that have been eliminated or included respectively after the pretest.

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Factor Item Action

Expected return Crowdinvestments offer a good opportunity to earn high returns.

Excluded after pretest

Risk I consider an investment on a crowdinvesting platform to be risky.

Added after pretest

Risk Compared to other investments, I consider crowdinvesting to be too risky.

Added after pretest

Early adopters I like to explore new technologies that emerge from the internet.

Added after pretest

Early adopters When I consider making investments, I like to look for new and innovative options.

Added after pretest

Early adopters Participating in crowdinvesting would satisfy / satisfies my sense of curiosity.


Missing trust in online platforms

I am very careful with investments via online platforms, e.g. crowdinvesting platforms.


Possibility to follow the venture

I (would) like the fact that I can follow the growth of a new venture.


Interest in start-ups I have a high interest in entrepreneurship in general.


Coverage in financial press

If the financial press published a positive article about crowdinvesting, I would be more likely to invest.


Table 2: Changes in items based on the results from the survey pretest.

4.3 Survey Investigation

The data for the final study was collected during a period of 26 days from 29th of July to 23rd of

August 2014. The self-reporting questionnaire was available in English and German and the data

collection was done via the online survey tool www.soscisurvey.de. Crowdinvestors especially

were mainly targeted in German-speaking countries – Germany, Switzerland and Austria – and

thus a German version of the questionnaire was used in order to avoid comprehension issues.

Participation in the questionnaire was anonymous and voluntary.

4.3.1 Sample

Due to the impossibility of receiving responses from the entire population, in this context from all

crowdinvestors and all retail investors, it was necessary to use a sample for the investigation.

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According to Bortz and Schuster (2010) “[…] a sample represents a part of all objects under

investigation, which shall represent the relevant characteristics of the entire population as exact

as possible. Thus, a sample is a ‘thumbnail’ of the entire population” (p. 80, translated). The goal

is generally to make sure that the sample represents the entire population of the investigation.

Thus, the survey was conducted among individuals from both groups of interest to make sure that

the characteristics of the sample resemble the ones of the population as close as possible. The

data was collected by means of convenience sampling (Churchill & Iacobucci, 2005). Churchill

and Iacobucci (2005) mention that results based on convenience samples should be interpreted

with caution as the sampling process involves a certain degree of personal judgment. However, in

this investigation, convenience sample was applied due to the fact that quite a specific population

was targeted and this group, particularly crowdinvestors, was rather small and difficult to access.

Convenience sampling method is particularly helpful when cases are challenging to identify and

contact. Thus, Saunders et al. (2012) suggest to publicise a need for cases through appropriate

channels and data is then collected from those participants who responded. In the following, the

exact channels and target groups are presented in more detail.

The focus of this investigation was put on the German-speaking market, which includes

Germany, Switzerland and Austria. Several reasons explain this geographic emphasis. First, the

authors of this thesis have particular knowledge and contacts to the crowdfunding and

crowdinvesting industries in these particular countries. Considering the difficulty of getting

access to crowdinvestors, this previous contact with some important players of the industry was

seen as crucial in order to collect suitable data for the investigation. Furthermore, Germany,

Switzerland and Austria are somewhat ahead of most other countries regarding crowdinvesting.

The reason for this development is mainly based on the legal frameworks and particularly the

exemptions from prospectus requirements, if small amounts are raised (Klöhn & Hornuf, 2012).

In the US for example, corresponding legislation still needs to be adapted in order to support

crowdinvesting activities. So far, it is mainly in the European Union where important exemptions

from general securities legislation are allowed in order to promote crowdinvesting (Hornuf &

Schwienbacher, 2014). The German-speaking area is also known for its development of new and

alternative investments. Particularly Switzerland has seen a fast growing number of new financial

institutions focusing on alternative investments such as impact investment. Rodney Schwartz,

CEO of ClearlySo, mentions Swiss as well as Dutch and Scandinavian institutions to be well

ahead, when it comes to impact investment, which is due to a high demand from institutional

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investors but also from retail investors who are particularly looking for investment alternatives

that are socially responsible (UK Sustainable Investment and Finance Association, 2013). Thus,

investors from these regions may generally be more willing to try emerging investment

opportunities. Based on these reasons, it seemed reasonable to use a sample of participants in the

German-speaking region for a first investigation of influencing factors in crowdinvesting.

Furthermore, focusing on a common language area instead of one particular country is also in

line with investigations from Chen (2013) who has found language, and thereafter specifically the

encoding of time, to have an important impact on the economic behaviour of individuals.

However, generalisations of the results gained from this particular group of investors might be

somewhat limited. This issue is further addressed in chapter 7.3.2, where the results of this study

are discussed and evaluated.

The target participants for the research study were both current crowdinvestors and retail

investors. Different channels were used in order to address these two groups of participants.

Generally, the group of crowdinvestors was more difficult to access, mainly due to the fact that

they are difficult to identify and it is thus challenging to address them directly. Furthermore, there

is a rather limited number of active crowdinvestors given that the development is still quite recent

as it is also shown by the degree of innovativeness analysed in chapter 2.3. One important

channel to address crowdinvestors was the publication of the need for cases in adequate

discussion groups. Particularly the German Crowdinvesting forum proved to be an access point to

crowdinvestors. The founder and moderator of this forum also supported our request for

participants. A second key channel for distributing the hyperlink for the survey were

crowdinvesting platforms that were contacted and asked to forward the request to their investors.

Several of them shared the link of the online survey with their investors via internal newsletters

(e.g. Innovestment, Conda, investiere.ch) and/or via their social media channels (e.g. bankless24,

fundedbyme). In order to incentivise crowdinvesting platforms to distribute the survey among

their investors, they were provided with a summary of the analysis at the end of the investigation.

Both crowdinvestors and crowdinvesting platforms were also asked to share the request to

participate in the online survey within their crowdinvesting networks. Thus, the likelihood of

receiving further responses from active crowdinvestors was increased. This method appeared

most suitable in this context based on the difficulty to contact crowdinvestors directly.

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The second target group of this study, retail investors, was targeted separately via suitable

channels. Retail investors are defined and contrasted in this context as private persons who are

active investors themselves and have certain knowledge about capital markets. As this particular

group is rather difficult to target directly, a question regarding investment experience was

included in the questionnaire as control variable. Furthermore, we aimed to target in particular

private investors with some interest in new and innovative investments. Thus, one of the main

channels for addressing retail investors was the alumni association of the Foundation of German

Business. Alumni in this particular association seemed to be most suitable for this survey as they

usually have a rather high level of income and are thus more likely to have some money that they

invest themselves. Additionally, they are well known for their open-mindedness and interest in

emerging technologies. Furthermore, several contact persons within financial institutions and

financial advisors were addressed with the request to participate in the survey themselves and to

forward the hyperlink of the survey to their customers. Additionally, several discussion groups in

social media were addressed with topics in innovative investments, with a particular focus on

impact investments and a newsletter was sent out to retail investors with an interest in alternative

investments, mainly by using our own network talent-invest.de.

In total, around 900 persons were contacted directly and 527 persons opened the hyperlink of the

survey. 215 respondents participated in the survey while 149 of them completed the questionnaire

entirely. Individual questionnaires, which have not been finished, were excluded from the data

set. We are aware of the fact that the number of actual responses is not very high. There are

several possible reasons why we believe that potential participants might have opened the

hyperlink, but did not continue at all or not until the end. First, we analysed the statistics of the

questionnaire and it appears that the majority of the participants who did not complete the survey,

stopped already on page 2, which is the page after the introduction page of the study. Given that

crowdinvesting is a quite new topic, we assume that some people were just curious to see what

the study is about, but when the questions started on page 2, they did not want to actually

participate in the survey. Furthermore, there are many different studies and research projects at

the moment as we learned from our interviews (Expert B, 2014; Expert C, 2014). Some people

may just have opened the hyperlink to see our approach to the topic with no real interest for

participating in the survey. Another reason for people to just open the hyperlink, but not continue

with the survey might be the fact that a questionnaire with a length of approximately 15 minutes

appeared to be too long.

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Of the 149 questionnaires that were completed entirely, 51 responses came from people with

experience in crowdinvesting and 98 participants did not previously invest in start-ups by using

an online platform.

4.3.1.1 Crowdinvestors vs. Retail Investors

This investigation project aims to identify factors that influence the decision of an investor to

participate in crowdinvesting. For this reasons, two groups are compared with regards to their

view on different dimensions of crowdinvesting. Therefore, the group of crowdinvestors is

contrasted to retail investors. Crowdinvestors are identified in the survey by the fact that

participants have previously participated in crowdinvesting or are currently engaged in such an

investment. In contrast, the group of retail investors is not that easy to identify and a description

is given in this chapter.

First of all, it is drawn on the definition given by Black (2008), who explains a retail investor to

be “any natural person who owns stocks [or other securities] by any means” (Black, 2008, p.

303). Thus, a retail investor is understood as an individual person who is investing in securities

and has consequently some experience with capital markets. Furthermore, another important

criterion for a participant to be classified as a retail investor in this research setting is the fact that

s/he has no experience in crowdinvesting.

In order to identify retail investors in accordance to the explanation given above, several

questions have been included in the questionnaire. The first question that allowed filtering for

retail investors asked whether the participant had experience with crowdinvesting. Answering

this question with yes categorised the respondent as crowdinvestor. All participants who

answered at this point with no were considered to be potential retail investors. In the context of

this study, it is also important that the investor him-/herself trades the securities and does not only

own them as the latter could mean that the investor had a financial advisor buying and selling the

securities for him/her. In such a case, the retail investor would not have personal experience with

various investment types. Therefore, another question was constructed to account for the aspects

of being “[a] natural person” (Black, 2008, p. 303) and of having personal experience with capital

markets and different investments. This particular question asked the respondents for their

experience with different types of investments, such as sovereign bonds, stocks, investment funds

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and derivatives. The criterion for a respondent to be classified as a retail investor was to invest

personally in any of these investments at least once per year.

Among the group or retail investors, i.e. those respondents who do not have any previous

experience with crowdinvesting, 16 indicated that they also do not have any experience with

other investments. This means these respondents answered in the questionnaire that they never

invest personally in any of the investments mentioned. The data seta of these 16 respondents

were excluded from the analysis, as they are neither crowdinvestors nor financial retail investors

due to their missing experience in both areas.

4.3.1.2 Potential Response Biases

The use of self-report questionnaires may create issues about potential response biases, especially

regarding non-responses. Response biases might lead to biases in the sampling and consequently

to noises in the conclusions and in the representativeness of the results.

Non-response bias describes the bias emerging from the fact that a certain group did not

participate in the survey and can produce misleading conclusions that do not generalise to the

population (Rogelberg & Stanton, 2007). Given that a self-report survey is used for this

investigation and participation is voluntary, non-response bias might be an issue in this

investigation. The authors tried to reach as many groups of target participants as possible in order

to address a broad group of potential respondents. Furthermore, several response facilitation

approaches suggested by Rogelberg and Stanton (2007) were applied during data collection. As

recommended, potential participants have been pre-notified in the sense that crowdinvesting

platforms were contacted four weeks in advance in order to prepare them regarding the

distribution of the survey. The questionnaire was designed to be as appealing and pleasant as

possible. Items per page were kept on a low number and a graphical element was used to

illustrate the example of a crowdinvesting campaign. Also reminder notes were sent out several

times during the data collection.

However, it has to be acknowledged that the response rate was still not very high. One important

reason for a rather low response rate might be the fact that the data was collected mainly in

August, which is generally a time where many people are away on vacation. Additionally, Baruch

(1999) reported that response rates further worsened over the last years. This might be

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particularly the case in crowdinvesting since several research projects are currently conducted in

this field, many of them relying on surveys. Nevertheless, there is no indication that specific

groups have not answered the survey on purpose, which would lead to a bias in the results and

conclusions. The non-response bias appears not to be systematic and rather seems to be based on

a personal decision of the participant to participate or not according to his/her time constraints

and motivation.

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5 Theoretical Framework and Hypotheses

One of the main characteristics of crowdinvesting is that investors acquire some form of profit

participating right of a start-up company. The investor selects start-ups according to his/her own

preferences and can diversify investments by choosing several companies that are making the

public call for funding on a certain platform. Some crowdinvesting platforms, such as Bergfürst

in Germany, even offer a secondary market where shares can be traded. All these features of

crowdinvesting resemble the characteristics of traditional financial instruments, particularly

investments in the stock market. Thus, the theoretical framework applied in this thesis in order to

investigate the underlying factors of crowdinvestors’ behaviour and decision-making is based on

the research conducted on retail investors.

A process with several steps was applied in order to create a theoretical framework for the

purpose of this research project. First, a suitable model from research about retail investors and

their motivational factors was identified (Nagy & Obenberger, 1994). Second, the factors

identified in this model were adapted and complemented with research about crowdinvesting.

Third, due to the fact that research about crowdinvesting is rather limited, especially regarding

investor behaviour, further input was gained from interviews with experts in crowdinvesting.

More details about these interviews are presented in chapter 4.

5.1 Literature on Investor Decision-‐making

One of the most fundamental concepts in classic economics regarding human behaviour and

decision-making is the idea of expected utility from utility theory, which assumes that an

individual knows all the options and their outcomes in advance. The individual would pick the

option that maximises or optimises the respective expected value. Applying this idea to an

investment context we need to include the notion of inter-temporal choice, because the profits of

an investment typically lie in the future. This means that an individual investor faces a trade-off

and has to decide whether to consume now or in the future. S/he weighs the benefits of

consuming today against the possible benefits of investing unconsumed funds to gain greater

consumption benefits in the future. If s/he decides to invest unconsumed funds, s/he selects the

set of options that maximises long-term satisfaction. Utility theory assumes that investors are

43

absolutely rational, risk-averse and have limitless cognitive capabilities to deal with complex

choices. Thus, considerations of risk and return, i.e. utility from the investment, are key for

rational investors (Sultana & Pardhasaradhi, 2012). Modern portfolio theory complements this

understanding by the perspective of creating portfolios of uncorrelated asset risks, which leads to

the possibility of diversification as a suitable method to either reduce the overall risk of a

portfolio while maintaining its return level or maximising its returns at a certain risk level

(Markowitz, 1952).

However, economic utility theory and particularly the assumptions of modern portfolio theory

have been challenged by more recent fields such as behavioural finance, which aims to explain

investment decisions from a behavioural perspective. At the latest, since the publication of

prospect theory (Kahneman & Tversky, 1979), financial decision-making is not considered to be

a completely rational process. Their results show that there are many different factors that

influence an individual’s decision-making process which are also true for investors. One factor is

the presentation or framing of information, which can influence decision-making. Also the time

preference of investors can result in the fact that they value similar options in different ways.

Furthermore, investors fear losses more than they value gains, which has an impact on their

decision-making. Additionally, risk considerations differ between various decisions. Investors

tend to be risk averse in situations where they face a sure gain but are willing to take more risk if

a sure loss is at stake (Tversky & Kahneman, 1981; Kahneman, Knetsch, & Thaler, 1991).

While the research stream of behavioural finance and in particular the prospect theory might be

one of the most well-known theories that challenge the assumptions of rational investors and the

ability of making complex choices, it is certainly not the only one. There are further researchers

and studies that challenge the assumptions of economic utility theory and find other factors to be

important when making investment decisions (Barnewall, 1987; Lewellen, Lease, & Schlarbaum,

1977; Warren, Stevens, & McConkey, 1990). In contrast to behavioural finance, these studies

focus on other dimensions such as psychological (Barnewall, 1987) or demographic (Warren et

al., 1990) factors that influence investment decisions of individual investors. The common

challenge of all these investigations is to identify factors – rational as well as non-rational – that

influence investment decisions.

By now, it is quite safe to assume that the process of making an investment decision does not

only include considerations about risk and return, but manifold factors and variables that

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influence and frame an investor’s decision-making. Thus, understanding the decision-making

process of an investor is rather complex. One way through which academic studies deal with this

complexity is the use of surveys that contain different factors and are presented to investors to

provide their opinion. Nagy and Obenberger (1994) developed a list of 34 variables in order to

investigate the relative importance of various economic, behavioural and psychological factors.

These factors were ranked according to their importance by a sample of 137 experienced

shareholders. The study aimed to identify the most relevant influential factors on individual

decision-making when purchasing stocks. The results indicate that some traditional wealth-

maximization criteria, such as expected earnings of the company and diversification needs, and

some other diverse variables, such as feelings for the firm’s products and services, play an

important role in the stock purchase decision. Nagy and Obenberger (1994) further summarize

the 34 factors in seven categories: Neutral Information, Accounting Information, Self-

Image/Firm-Image Coincidence, Classic, Social Relevance, Advocate Recommendation and

Personal Financial Needs (see also illustration 4 for an overview).

Label

Neutral Information

Accounting Information

Self-Image/Firm-Image Coincidence

Classic

Social Relevance

Advocate Recommendation

Personal Financial Needs

Illustration 5: Factor labels according to Nagy and Obenberger (1994).

Different researchers used this generic framework for further studies about investors’ decision-

making. Beal, Goyen and Phillips (2005) investigate the motivation of investors who desire to

invest ethically. In particular, this study focuses upon an investment in a company with the

objective of conservation and the financial emphasis on stability rather than maximising

Factors Influencing

the Equity Selection

Process of Individual

Investors

45

shareholder returns. The results show that besides financial returns, so-called psychic returns are

an important factor for ethical investors. An example of these psychic returns can be the social

impact that is generated as an output of the activities of the firm in which the investor invests

(Beal et al., 2005).

Another example for the application of Nagy and Obenberger’s framework is a study conducted

by Clark-Murphy and Soutar (2005) who apply various factors from the original framework to

explore the share preferences of individual share investors. The authors identify four different

types of investors according to their individual preferences and investment goals: the explorers,

risk-averse investors, traders, and contrarian investors.

Furthermore, the framework developed by Nagy and Obenberger (1994) is used to analyse

decision-making of investors in various geographic regions. Bennet, Selvam, Indhumathi,

Ramkumar and Karpagam (2011) applied an adapted version of the framework to investigate the

factors that influence retail investors’ attitude and decision processes in India. Another example

particularly investigated the case study of the stock exchange in Kuala Lumpur to identify the

factors that influence the decision-making process of small investors (Baghdadabad, Tanha, &

Halid, 2011).

Based on the broad application possibilities of the framework developed by Nagy and

Obenberger (1994) to investigate different investment decisions, it is also used for this thesis as a

foundational theoretical model from which we ourselves develop to an adapted theoretical

framework which fits into the context of crowdinvesting. Although crowdinvestors may behave

quite differently during their decision-making process, it is assumed that most factors that

influence traditional financial investors also have an impact on crowdinvestors. This assumption

is made because crowdinvesting as such shares many characteristics with traditional investments.

For example, most crowdinvesting platforms offer participation rights that are similar to

securities known from other investments such as equity shares or profit participating loans.

Furthermore, crowdinvesting seems particularly similar to equity investments. Investors in

traditional equity investments buy shares that provide them with rights to participate in profits of

usually large enterprises. Equity investment thus provides the possibility to become a part of the

company’s ownership and provides regular returns in investment as dividend income or through

appreciation in share price (Sultana & Pardhasaradhi, 2012). We argue that the same is true for

crowdinvesting, where crowdinvestors purchase similar profit participating rights, benefit from

46

price appreciation and also become owners of the company4, with the main difference that the

company is not a large, established enterprise but a start-up company. Some platforms even have

a secondary market where prices of the participation rights in start-ups are listed. An example for

such a platform is Bergfürst whose secondary market resembles in many aspects those of a stock

exchange (Bergfürst, 2014). Illustration 6 shows an example of such a stock exchange on a

crowdinvesting platform.

Illustration 6: Price development of the shares in a start-up company (Bergfürst, 2014, p. 1).

There are also many small differences in the two investments such as the valuation, which is

more straightforward for multinational companies that are listed at the stock exchange compared

to new ventures. Additionally, investors in start-ups face the problem of missing track records

and limited history. Nevertheless, the mechanisms of both investments appear to be quite similar.

Hence, we assume that also the factors that influence the decision-making in crowdinvesting are

similar to other investments, particularly compared to equity investments. Consequently, we see

the factors tested by Nagy and Obenberger (1994) as a suitable starting point for the development

of our own conceptual model.

However, based on literature, mainly from crowdfunding, and expert interviews there seem to be

further factors that play an important role for crowdinvestors. Thus, the following chapter

4 Whether the crowdinvestor becomes a real owner of the start-up depends on the exact investment instruments offered by the platform. There are different possibilities with some of them providing the rights to participate in profits, but without actually participating in the ownership. Also the fact of having voting rights differs from platform to platform. Furthermore, there are legal differences between countries and the exact financial instruments offered in crowdinvesting, which are not further elaborated (see e.g. Klöhn & Hornuf, 2012 for further details).

47

presents the process of adapting the framework from Nagy and Obenberger (1994) to the context

of crowdinvesting. By adapting the original framework, a theoretical model is developed that

serves as a basis for our questionnaire to address the research questions of this thesis.

5.2 Development of the Theoretical Framework

The theoretical framework presented by Nagy and Obenberger (1994) has been adapted by

following two steps. First, crowdinvesting literature has been analysed to identify important

factors that influence crowdinvestors during their decision-making process. Second, explorative,

semi-structured interviews with crowdinvesting experts (see chapter 4 for more details) were

conducted in order to discuss their view on important factors that influence the decision-making

process of crowdinvestors.

The decision-making process in this research project is considered to be a two-step approach.

First, the investor decides whether to participate in crowdinvesting in general and second, s/he

selects the particular start-up s/he wants to invest in. Thus, we consider the decision-making

process to have a primary and secondary phase with different factors influencing the

corresponding decision. Since this research project focuses on the first phase – the question of

whether or not to participate in crowdinvesting at all – only factors that play an important role

during this phase are considered. Start-up specific variables that influence the investment

decision between different ventures presented on a platform are not considered. Given that the

framework developed by Nagy and Obenberger (1994) was applied to better understand the

decision-making process of an investor who decides between different stocks, this model had to

be adapted in order to fit the particular research question. Consequently, the first step was the

exclusion of all labels that were firm specific and focused on the question of which stock should

be chosen.

In particular, the labels Accounting Information and Self-Image/Firm-Image Coincidence were

not considered in the framework to investigate the decision-making process of crowdinvestors.

As mentioned before, the investment activity is investigated in this thesis and not the specific

investment for one start-up or the other. Both Accounting Information and Self-Image/Firm-

Image Coincidence refer to the individual investment, which is in the case of the study from

Nagy and Obenberger (1994) the individual stock. Since this thesis does not investigate the

48

decision for start-up 1 versus start-up 2, the accounting information or financial data of the

individual start-ups does not influence the decision of whether to participate or not in

crowdinvesting. The same is true for the coincidence between the self-image of the investor and

the firm-image since there is no individual firm that is of interest in this study.

Furthermore, the label Personal Financial Needs was merged with Classic since particularly

diversification considerations – one of the key variables within the label Personal Financial

Needs in the original framework – can also be regarded as a classic factor within investors’

decision-making process as it is also in accordance with Markowitz (1952). Furthermore, this

dimension was renamed to Risk-return Considerations as the label Classic appeared to be quite

generic. This new dimension includes various aspects regarding return possibilities, riskiness of

the investment and diversification aspects, which is according to Markowitz (1952) a key

instrument to reduce the overall risk of a portfolio. It thus appears reasonable to pool these three

aspects of risk, return and diversification in one category.

On the other hand, two new labels – Innovative Investment and Personal Utility – were added to

the original model, which refer mainly to the specific nature of crowdinvesting. The category

Innovative Investment is based on the fact that crowdinvesting opens a rather new option within

the investment universe and its new and innovative characteristics may influence the decision-

making process of investors. This type of investment may especially attract investors who like to

explore new possibilities, are curious and are interested in new technologies (Bretschneider et al.,

2014; Hemer, 2011; Sheth, Newman, & Gross, 1991).

Furthermore, during the expert interviews and also according to crowdfunding research, it was

pointed out that crowdinvesting seems to create not only an expected financial return, but also

emotional and personal benefits, which were assumed to play an important role (Belleflamme et

al., 2011; Expert A, 2014; Expert E, 2014). Thus, the label Personal Utility was included in the

theoretical model.

The following table presents an overview of the conceptual framework, including all categories

and variables, developed for this thesis.

49

Label Key variables

Risk-return Considerations

Expected Return Diversification Risk

Social Relevance Social Relevance

Innovative Investment Early Adopters Trust in Online Platforms

Personal Utility Self-representation Network

Advocate Recommendation Advocate Recommendation

Neutral Information Neutral Information

Table 3: Adapted labels and key variables for this research project.

The development of the key variables within each label and the corresponding hypotheses is

explained in the following section.

5.2.1 Key Variables and Hypotheses

Key variables within each label were identified from the literature regarding decision-making of

individual investors, crowdinvesting research, and expert opinion. Besides, the hypotheses are

developed, which are later analysed based on the survey among crowdinvestors and retail

investors.

Expected Return

Earning a high rate of return can be considered to be one of the traditional variables when making

a decision for an investment. Traditional schools such as economic utility theory and modern

portfolio theory understand return maximization to be one of the key considerations when making

investments (Markowitz, 1952; Sultana & Pardhasaradhi, 2012). Studies from Nagy and

50

Obenberger (1994) and Pascual-Ezama, Castellanos, De Liaño and Scandroglio (2010) support

the importance of expected returns for investment considerations. Furthermore, several studies in

crowdfunding have shown that the rate of return – or the level of reward, depending on the type

of crowdfunding – plays an important role for financial backers / investors (e.g., Gerber, Hui, &

Kuo, 2012; Hemer, 2011). Thus, it is assumed that expected returns also influence the decision-

making process in a crowdinvesting context. Bretschneider et al. (2014) found this factor to be

one of the key variables in a crowdinvesting context. This leads to the hypothesis that investors

who aim to maximise their returns are attracted by crowdinvesting.

H1: Investors who consider crowdinvesting to be a way of earning high returns are more

likely to make a decision to participate in crowdinvesting.

Diversification

Nagy and Obenberger (1994) show in their study that diversification considerations are one of the

most important factors for retail investors. Diversification does not only refer to different

investments within one asset class, but also includes various asset classes in the portfolio. This is

particularly interesting since different asset classes react differently to macroeconomic

circumstances. It is thus generally a suitable tool to control for the overall portfolio risk while still

looking for high returns (Markowitz, 1952). Crowdinvesting itself can be seen as a fairly new

asset class and it is hypothesised that investors with needs to diversify their portfolio are more

likely to consider crowdinvesting. This assumption was further supported by statements from

several investors during the interview process who mentioned diversification as one of the

primary factors that led to their decision of starting with crowdinvesting (Expert F, 2014; Expert

G, 2014).

H2: Investors who consider crowdinvesting to be a way to diversify their investments are more

likely to participate in crowdinvesting.

Risk

Together with expected return and diversification aspects, risk is considered to be one of the

classic influencing factors when it comes to investment decisions (Nagy & Obenberger, 1994). In

traditional investment theory, the objective of minimising risk of the portfolio is a key criterion

51

for the decision (Markowitz, 1952). Based on the assumption that crowdinvesting is considered to

be an alternative to other investments, risk has an important impact on investors’ considerations.

As crowdinvesting is generally seen as an investment with high risk due to its characteristics as

venture capital (Kortleben & Vollmar, 2012), it is possible that particularly risk-averse investors

do not find crowdinvesting to be attractive. Therefore, it is hypothesised that risk has a negative

influence on the decision to invest in the crowdinvesting asset class.

H3: Investors who consider crowdinvesting to be a risky investment compared to traditional

ones are less likely to participate in crowdinvesting.

Social Relevance

Besides traditional influencing factors in investor behaviour theory such as considerations of

return, risk and diversification, softer factors seem to play an important role for crowdinvestors.

One of these softer factors might be the possibility to not only create welfare on the investor’s

individual level, but to also support entrepreneurship, innovation and finally the society and

economy as a whole.

A first aspect of this dimension is the feeling of supportiveness by providing important seed

financing for start-ups. This is also a key factor that distinguishes crowdinvesting from other

investments such as stocks or investment funds. In contrast to purchasing stocks in large

enterprises, the amount raised by start-ups is much smaller and the feeling of direct support for an

individual investor might be stronger (Expert F, 2014; Expert G, 2014). This is particularly true

since there is a financing gap for start-ups between a very early stage where private and informal

money is sufficient and a later stage where a company becomes interesting to more traditional

financing entities such as financial institutions. Thus, crowdinvestors might feel compelled to

help out those start-ups, which have a quite urgent need for financing due to insufficient access to

capital. Investors may also interpret this need as more crucial since missing funding in early

stages is more likely to lead to bankruptcy for start-ups while larger companies generally have

more funding sources (Ley & Weaven, 2011).

However, social relevance does not only refer to the fact that crowdinvestors provide important

financing to close a financing gap. Some investors see crowdinvesting as an opportunity to

promote entrepreneurship and thus the innovativeness of an economy, which is at the end

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beneficial for the entire society. Research shows that certain investors also consider the wider

implications of their investments and not only their personal return. Although the financial return

of an investment is primary for the typical investor, crowdinvestors may be more altruistically

motivated and looking for some form of psychic returns when making investments in this asset

class (Beal et al., 2005). Drawing upon research from open source communities and business

angels, Bretschneider et al. (2014) mention that altruism as opposed to selfishness may also play

a role in crowdinvesting. Burtch, Ghose and Wattal (2013) have found in a study regarding

decision-making in crowdfunding, that crowdfunders are primarily motivated by altruism.

Although crowdfunding must be distinguished from crowdinvesting, it is assumed that altruism

and the mission of the investment also play a role in the decision-making process of a

crowdinvestor. According to Hemer (2011), the investor or financial backer may be intrinsically

motivated, among other things, by contributing to a “socially important mission” (Hemer, 2011,

p.18). We thus hypothesise that investors who see crowdinvesting not only as a way to maximise

their own financial return, but also as an important way to create a social impact by investing in

start-ups, are more likely to participate in crowdinvesting.

H4: Investors who consider crowdinvesting to be a suitable tool for promoting

entrepreneurship by closing a funding gap for start-ups are more likely to participate in

crowdinvesting.

Early Adopters

Crowdinvesting as it is known today and defined in this thesis is still quite a recent and new

development. Although it might be compared with other investments in certain aspects, for

example with the stock market, since most crowdinvesting platforms offer the possibility to

acquire profit participation rights such as equity, crowdinvesting has many innovative features

that are new to most investors. It is thus hypothesised that investors who are currently willing to

participate in this form of investment have a certain sense of curiosity and enjoy trying new

things, especially related to emerging possibilities on the Internet. These investor types are

usually considered to be early adopters and innovators (Oren & Schwartz, 1988). In contrast to

followers, early adopters do not wait for other investors to participate first; instead, they like to be

one of the first to try emerging investment alternatives and new opportunities (Bretschneider et

al., 2014; Hemer, 2011). During several interviews with representatives from various

53

crowdinvesting platforms it became clear that current crowdinvestors are mainly early adopters

(Expert B, 2014; Expert E, 2014). This observation is also in accordance with the epistemic value

described by Sheth et al. (1991), which is one of five key values during a consumer decision-

making process and refers to pleasure that consumers experience from trying something new and

unexpected.

H5: Investors who have a first movers profile are more likely to participate in crowdinvesting.

Trust in Online Platforms

One new aspect of crowdinvesting is the fact that investments are made via specific online

platforms. Usually, retail investors use an online banking portal to invest, which generally offers

them the possibility to choose several asset classes such as stocks and investment funds. Within

these asset classes, a large variety of investments are available to the investor. Furthermore, the

trust that an account holder has towards his/her bank is associated with the online banking portal

where s/he is registered anyway reducing perceived risk of fraud. In contrast it is necessary to

register separately on a crowdinvesting platform to be able to invest in start-ups. Also, a

particular start-up can decide which platform it uses to raise funds which means that an investor

has to register on different crowdinvesting platforms in order to finance the start-ups s/he is

interested in. This adds more complexity and effort to the investment process. Another aspect is

the fact that most crowdinvesting platforms are still rather new and investors may be cautious

towards them (Duarte et al., 2012). Hence, it is hypothesised that trust regarding online platforms

may have an impact on the decision upon whether to participate in crowdinvesting. It is assumed

that missing trust may have a negative influence on the participation in crowdinvesting and that

investors who are generally more cautious towards online platforms are reluctant to participate in

crowdinvesting.

H6: Investors who are reluctant to use online platforms are less likely to participate in

crowdinvesting.

Self-representation

Another way of benefitting personally from a crowdinvestment might be to use it as a resource

for expressing oneself. Several crowdinvestors confirmed during the interviews that they enjoy

54

talking about their investments to others and specifically about the start-ups and their products

and services (Expert F, 2014; Expert G, 2014). Thus, it is hypothesized that investors are

motivated to participate in crowdinvesting, because it offers a resource and a good opportunity to

express themselves towards others. This expression can be made in many different ways, such as

talking with peers about the topic, telling friends or colleagues about a new investment or sharing

the investment online, e.g. in social media. It might thus offer an interesting conversation topic in

different occasions. Especially at the current stage, crowdinvesting is often seen as an exciting

new alternative, it being sometimes described as a “leisure activity” (Expert G, 2014) and some

investors enjoy talking about it with colleagues and friends.

H7: Investors who enjoy sharing their experiences with new investments and start-ups are

more likely to participate in crowdinvesting.

Network

Another personal benefit may come from expanding one’s own network by investing in start-ups.

This network expansion may be the result of different aspects. Gerber et al. (2012) describe being

part of a community with a similar mind-set as a motivating factor for participating in

crowdfunding. Moritz and Block (2013) identify the interest to interact with others as an

important motive of crowdinvestors. According to Hemer (2011), investors receive certain

satisfaction and enjoyment from the possibility of “[…] being engaged in and interacting with the

project’s team” and from “[…] the chance to expand one’s own personal network” (Hemer, 2011,

p.18). It is hypothesised that the possibility to expand the investor’s owns network and thus

increasing his/her own utility from the investment increases the likelihood of participating in

crowdinvesting.

H8: Investors who are interested in expanding their network are more likely to participate in

crowdinvesting.


Given the fact that crowdinvesting is still quite a recent development, personal recommendations

– either from experts or from friends and family – are likely to have a positive influence on the

decision to start investing in start-ups. Similar to the dimension of Neutral Information, advocate

55

recommendation is a source of information from the environment of the investor. However, in

contrast to media coverage, advocate recommendation is often based on trust as other persons

from the investor’s network are providing the information and not an anonymous source. Nagy

and Obenberger (1994) found recommendations from individual stock brokers, who can be

considered to be experts in this area, and friends to have a positive impact on the investment

decision in an equity selection process.

H9: Investors are more likely to participate in crowdinvesting, if the investment is

recommended by an expert and/or friends and family.

Neutral Information

According to Nagy and Obenberger (1994), neutral information has a high influence on stock

purchasing decisions. Neutral information is considered to be “[…] an outside source of

information that is perceived to be unbiased” (Nagy & Obenberger, 1994, p. 65). The authors

mainly refer to publications in different media types. Based on the fact that crowdinvesting

represents a new form of investment, the financial press is likely to play an important role in

addition to coverage provided by the general press. It is generally assumed that investors tend to

be informed in the financial press before making an investment.

It is further assumed that positive publications about crowdinvesting in the general press, such as

daily newspapers, have a similar effect on the decision to invest in start-ups as an article in the

financial press. According to a ranking developed by Nagy and Obenberger (1994), coverage in

the financial press was indicated as slightly more important than coverage in the general press

when considering a stock picking decision. However, several experts specifically emphasised

during the interviews the importance of publications in the general press in order to increase the

attention of crowdinvesting (Expert F, 2014; Expert G, 2014). Independent of the relative

importance between coverage in the general and financial press, it is assumed that coverage in

either press, particularly if they report positively about crowdinvesting, would increase the

likelihood of investors participating in crowdinvesting.

One example of how general media can influence the decision to start crowdinvesting was given

by a crowdinvestor who explained during his/her interview how he became aware of

crowdinvesting: “The beginning was completely coincidental. I was watching a show on German

56

television that broadcasted a report about Seedmatch [Germany’s largest crowdinvesting

platform] and how it works. At that time, Seedmatch was only half a year old. I later went onto

google, searched for Seedmatch, I found it very exciting and started investing” (Expert F, 2014).

H10: Investors are more likely to participate in crowdinvesting, if the general and/or financial

press report positively about it.

The following illustration presents an overview of the key variables and the corresponding

hypotheses as conceptual framework for this research project.

Illustration 7: Conceptual framework presenting the independent variables and hypotheses.

Before analysing the relationships of the individual factors on the decision to participate in

crowdinvesting, the research method applied in this thesis is explained in greater detail in the

following chapter.

57

6 Results

This chapter presents the results of the final investigation. Firstly, the demographic characteristics

of all participants in the survey are presented. Secondly, a particular focus is put on the specific

characteristics of the crowdinvestors. Following this, the data that is obtained with the survey is

first analysed with descriptive statistics, an independent t-test, a reliability analysis and a

principle component analysis. Finally, the influence of the independent variables on the decision

to participate in crowdinvesting is investigated by using a binary logistic regression.

6.1 Demographic Characteristics

From the 133 respondents, whose answers were used for the final analysis, just over two thirds

(69%) are between 26 and 45 years old. There are no participants under 20 years, which seems

reasonable as young people are not very likely to have a high interest in the topic of financial

investments and are particularly unlikely to invest themselves. Only 8% of the participants are in

the age group of 56 and above, which might have to do with the fact that the survey was

conducted online. People in their mid-twenties to early fifties are probably used to online surveys

and are also likely to discuss topics of interest on the Internet. As this was the main channel of

distribution the hyperlink of the survey, it appears reasonable that mainly this age group was

reached. Furthermore, it might be that this group is also most active in private financial

investments and thus a high response rate in this age segment appears logical.

Illustration 8: Age distribution of survey participants.

0%

9%

42%

27%

14%

6% 2%

<20 20-‐25 26-‐35 36-‐45 46-‐55 56-‐65 >65

58

The gender distribution of participants shows a picture that one might expect in this kind of topic.

Three quarters of the respondents are male, while only one quarter is female. In particular, the

managers of crowdinvesting platforms have informed us during the interview that their clients are

pre-dominantly male. They estimated the numbers of male members on the platforms to be

around 90% (Expert B, 2014; Expert E, 2014). Furthermore, financial investment and particularly

investments with potentially high risk are generally a topic that men are more concerned about

(Dwyer, Gilkeson, & List, 2002). Thus, the distribution of 74% men and 26% women who

participated in the survey appears to be representative for the topic of investigation.

Illustration 9: Gender distribution.

As explained in chapter 4.3.1, mainly German-speaking (crowd)-investors have been targeted for

the sampling of the survey. The success of actually reaching this target group is shown by the fact

that 82% of the respondents are currently located in Germany, Switzerland or Austria. Another

4% is currently living in Scandinavia (including Finland), 7% in other countries of Europe and

7% of the participants are from outside of Europe.

Illustration 10: Country of residence.

82%

4% 2%

1% 4% 7%

Germany / Switzerland / Austria Scandinavia (Denmark, Sweden, Norway, Finland) France / Italy / Spain

U.K. / Ireland

Rest of Europe

Outside of Europe

male 74%

female 26%

59

In terms of occupation, most respondents are either employed (53%) or self-employed (26%)

with most of them holding a university degree (91%).

Illustration 11: Survey participants’ occupation and highest education.

Participants have further been asked about the industry that they are working in. The responses

do not show a clear picture of any strong predominant sectors. However, there is a slight

concentration of respondents working in finance and financial services (17%), telecommunication

and technology (17%) and consulting (15%).

Illustration 12: Industry distribution.

Employee 53% Self-‐

employed 26%

Student 11%

Other 6%

Un-‐employed

2%

Pen-‐sioner 2%

8%

19%

53%

19%

1%

Secondary school

Bachelor degree or equivalent

Master degree or equivalent

Doctoral or MBA

Other

17%

17%

15% 8%

7%

4%

4%

4%

4%

3%

1% 2% 14%

Finance & Financial Services

Telecommunications, Technology, Internet & Electronics Consulting

Education

Automotive

Utilities, Energy, and Extraction

Manufacturing

Advertising & Marketing

Government

Health Care & Pharmaceuticals

Retail & Consumer Durables

Real Estate

Other

60

We also asked the participants for their disposable income after tax per month. Illustration 13

shows that the disposable income varies quite substantially between respondents. A majority of

56% indicated that they have an income after tax per month between 1.000 and 4.000 Euros.

Around one fifth of the respondents have a disposable income above 5.000 Euros per month. Due

to the sensitivity of this information, the question was optional for the participants; nine

respondents did not provide an answer.

Illustration 13: Distribution of monthly income after tax.

6.1.1 Demographic Characteristics of Crowdinvestors

Given that the crowdinvestor and his/her motivation to participate in crowdinvesting is the centre

of this thesis, a closer look at the demographic characteristics of the crowdinvestors in this survey

shall be taken. In total, 51 participants in the survey indicated that they have previous experience

of investing in start-ups via crowdinvesting platforms and thus, these participants are classified as

crowdinvestors for this investigation. Graphical illustrations of the demographic characteristics of

crowdinvestors participating in this survey can be found in appendix 10.3.1.

Based on several interviews with crowdinvesting industry, it seems that current crowdinvestors

show quite specific characteristics and the group appears to be rather homogenous. Particularly,

CEOs and founders of crowdinvesting platforms observed their crowdinvestors to be mainly men

(above 90%) in the age around 40 and with a professional background in IT, financial services,

banking or consulting (Expert A, 2014; Expert B, 2014). Another group includes investors who

11%

20% 20% 16%

7%

17%

5% 7%

<1.000 Euro

1.000 -‐ 1.999 Euro

2.000 -‐ 2.999 Euro

3.000 -‐ 3.999 Euro

4.000 -‐ 4.999 Euro

5.000 -‐ 9.999 Euro

10.000 Euro and higher

No response

61

are slightly younger and who are so-called serial entrepreneurs, which means they have founded

several companies themselves and start investing in other start-ups via crowdinvesting (Expert E,

2014).

The picture of the demographic characteristics of the crowdinvestors participating in this study

shows a similar picture. With 88% of them being men, the gender distribution is close to what has

been observed by the crowdinvesting platforms in practice. The age structure of the participants

in this investigation is slightly different with a majority (70%) of participating crowdinvestors

being in the age group of 26-45 years. Current occupation of the crowdinvestors is with 47%

being employed and 31% being self-employed also in line with what has been observed by

industry experts in practice. Compared to the occupation of all participants, there is also a higher

percentage of self-employment when considering only the group of crowdinvestors. In terms of

industries, the picture of crowdinvestors from this study corresponds exactly with what has been

said by the experts: most crowdinvestors participating in this investigation work in the financial

sector (22%), 18% in technology, internet and electronics, and 14% in consulting. Higher

education and income per month show a similar distribution as the demographics for all

participants described in the previous chapter. Almost half of the crowdinvestors (47%) have a

master’s degree and another 40% have either a bachelor’s degree or a doctorate / MBA. Monthly

income varies between under 1.000 Euros (10%) to up to 10.000 Euros, with 20% earning a

monthly salary after tax of between 5.000 and 10.000 Euros. Hence, there is no indication that

only those with a high income decide to participate in crowdinvesting. This is generally in line

with the idea of crowdinvesting to create an investment possibility that is available for everyone

(Ortmann, 2012).

6.2 Data Analysis

Based on the final survey with a total amount of 133 responses that are used for the analysis, the

data was first examined with regards to their reliability and validity. The results of the factor

analysis, descriptive analysis and Cronbach’s Alpha for every item in the questionnaire and each

variable respectively can be found in the appendices 10.4.1 and 10.4.2. The items have also been

checked for skewness and kurtosis of the distribution, but since normal distribution is not

62

necessarily required for a logistic regression (Warner, 2012), these results have not been

considered any further.

Individual items and also one variable have been excluded after the data validity and reliability

analysis due to loadings less than .50 in the factor analysis or a decrease of Cronbach’s Alpha

below the critical value of .70. This is based on the suggestion from Nunnally (1978) who

considers .70 to be an acceptable level, particularly for early stage research.

However, an exception was made for the variables diversification and risk that have been

included in the regression analysis although their Cronbach’s Alpha values are slightly below .70.

Both risk considerations and portfolio diversification are key decision criteria in traditional

investor decision-making theory and are thus included in the final analysis. The lower values of

Alpha, which represents a lack of internal reliability of the items, have to be taken into account

when interpreting the results of the regression analysis.

Furthermore, an independent t-test for the equality of means between the two groups of interest –

crowdinvestors and retail investors – was conducted in order to get a first impression concerning

possible differences between the responses from both groups. The t-value and the corresponding

significance level are represented in table 5.

The results of the t-test give a first indication as to whether there are differences between both

groups, i.e. between crowdinvestors (N=51) and retail investors (N=82). In the first category,

which is Risk-return Considerations, it seems that there might be differences between both

groups regarding their view on crowdinvesting as a way to diversify their portfolio. Within

diversification, two out of three items show significant differences in the mean. Considering the

absolute value of the means in both groups, it can be seen that crowdinvestors agree in general

more with a diversification effect of crowdinvesting, which reflect the means between 4.9 and 6.3

whereas responses from retail investors vary around 4.5 for the corresponding items. The other

two variables within this category, which are expected return and risk, show the opposite with

two out of three items not being significantly different for the two groups. This is a first

indication that diversification might have a significant influence on the investment decision while

risk and expected return do not seem to explain the decision to participate in crowdinvesting or

not.

An interesting and promising result is indicated by the results of the t-test for the variable social

relevance. Within this dimension, all six items show a significance level of p < .05 and five of

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them even lie below p < .01. This is already an indication that social relevance might be an

explanatory variable for a decision to participate in crowdinvesting. Furthermore, all items seem

to be highly relevant to the respondents as the means of the different items lie all above 5.0.

When considering both groups of respondents individually, most items show even means of

above 6.0 for the group of crowdinvestors.

Similar to social relevance, all items within the category of Innovative Investment seem to play

an important role in explaining the decision to participate in crowdinvesting or not. They all show

a significant difference in the means between both groups with p < .01.

For self-representation, the t-test does not provide a clear picture with some items being

significant and others not. Interestingly, the level of the respondents’ agreement / disagreement

also seems to vary quite substantially for each of the four items as the means reach from a rather

low value of 2.7 to 4.7. These differences between the items also remain, if crowdinvestors and

retail investors are considered separately.

The other variable within Personal Utility, which is network, does not seem to have any

significant influence on the decision. All three items within network have a significance level of

p > .05, which means that the null hypothesis of equal means cannot be rejected. Thus, there is no

difference in the means between both groups. The same appears in the dimension of advocate

recommendation, where the t-test reports a significance level of even above .10 for all items. The

variable is still kept for further analysis in the logistic regression since recommendations

generally play an important role in the decision-making of an investor (for instance as shown by

Nagy & Obenberger, 1994; Pascual-Ezama, Castellanos, De Liaño, & Scandroglio, 2010).

With regards to neutral information, retail investors seem to agree slightly more with the

importance of press coverage, which indicates the higher mean of the items with values of above

4.0 in contrast to crowdinvestors whose agreement averages around 3.5 and 3.7. However, the

independent t-test reports a significant difference (significance level below .05) of the means

between both groups for only one item. For the other three items, the means of both groups do

not differ significantly, which gives a first indication that both groups value this dimension in a

similar way.

66

Table 4: Summary of t-test statistics for all items used in regression analysis.

6.3 Binary Logistic Regression

The binary logistic regression is applied in order to get an understanding of the relationship

between the dependent variable and a set of independent variables, which consists in this case of

ten independent variables. These ten variables have been computed by averaging the individual

items that build together the construction of the variable. The number of ten independent

variables is also in line with a guideline provided by Hosmer, Lemeshow and Sturdivant (2013)

who suggest a minimum of ten cases per independent variable. Based on a total of 133 cases, ten

variables correspond to 13.3 cases per independent variable.

The dependent variable refers to the question of whether people are crowdinvestors or not, i.e. if

the respondent has previously participated in crowdinvesting or not. It is binary and coded as 0

for no previous crowdinvesting experience and 1 for existing crowdinvesting experience, which

is equivalent to being a crowdinvestor. The logistic regression thus aims to identify the

relationship between different factors and the decision to be a crowdinvestor or not. It further

reports the direction and the strength of the relationship.

The summary for the binary logistic regression shows that all 133 cases have been included in the

analysis. Before starting the regression and without considering any independent variables, the

initial -2 Log Likelihood is 177.085 and 61.7% of the cases can be accurately classified, if it is

67

merely assumed that they all belonged to the largest group. The null model includes only the

constant.

Table 5: Output for Block 0 of the binary logistic regression.

The difference between the -2 log Likelihood in the null model at the beginning (table 5) and the

ending -2 log Likelihood of step 1 (table 8) can be used to evaluate the fit of the logistic

regression for the data set. The difference is indicated by Chi-square, which is in this case 57.644.

The Chi-square goodness-of-fit test is significant with p < .05, which means that the step of

adding the independent variables to the regression is justified. The null hypothesis that there is no

difference between the model with only a constant and the model with independent variables can

be rejected. This supports the existence of a relationship between the independent variables and

the dependent variable.

Table 6: Output of Chi-square.

68

The Hosmer and Lemeshow Goodness-of-Fit test is not significant with a value of .36. This

means that it is not possible to reject the null hypothesis that there is no difference between the

observed and predicted values of the dependent variable. This indicates that the estimates of the

regression analysis fit the data at an acceptable level.

Table 7: Output Hosmer and Lemeshow Test.

The summary for the model with all independent variables gives an overview of the amount of

variation explained by the regression. In contrast to ordinary least squares regression, R squared

does not have a clear definition in logistic regression. In ordinary least squares regression, it

measures the proportion of the variation in the dependent variable that can be explained by

independent variables in the model. Cox & Snell R square and Nagelkerke R square are an

attempt to apply a similar measure for the logistic regression. There are several other R square

measures in logistic regression and they are also referred to as pseudo R square based on the fact

that they do not have the same meaning of variance explained as the R squared known from

linear regression (Peng, Lee, & Ingersoll, 2002). Nagelkerke R square is a modification of Cox

& Snell R square with the former being easier to interpret due to the fact that the value ranges

between 0 and 1. Nagelkerke R squared shows that about 48% of the variation in the outcome

variable is explained by this logistic regression. The R squared statistics do not measure the

goodness of fit of the model but they indicate how useful the explanatory variables are in

predicting the dependent variable. However, the interpretation of this value should be made with

caution and some researchers suggest to use R squared statistics in logistic regression rather to

compare different models instead of interpreting the absolute value (e.g. Cohen, Cohen, West, &

Aiken, 2003; Peng et al., 2002).

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Table 8: Output model summary.

The classification table of step 1, which includes all independent variables and a constant, shows

that 78.9% of the cases can be predicted accurately. While classification tables are not a

goodness-of-fit measure, the result still indicates a better predictability in contrast to the null

model, which blindly estimates the most frequent category for all cases.

Table 9: Classification table for binary logistic regression including all independent variables.

The following table summarises the results of the independent variables and their level of

significance in the model. The significance is measured by a significance test of the Wald

statistic. Variables with a significant level of .05 and lower are seen as significant and are

consequently significant parameters in the model. However, based on the rather small sample

size, variables with a significance level of below .10 are also taken into considerations in the

further analysis. In total, four of the ten independent variables have a significance level of above

.10: expected return, risk, self-representation and advocate recommendation. Diversification,

social relevance, early adopters and trust in platforms are significant in the model with p < .05

and network and press coverage are significant with p < .10. These results correspond quite well

with the significance level of the items in the t-test. For example, most items within the risk

perspective do not have significantly different means between the two groups of interest and

consequently, risk does not also explain the decision of investors to participate in crowdinvesting

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or not. On the other hand, all items of the social relevance dimension show significantly different

means in the t-test and social relevance also turns out to be a significant parameter in the model.

Table 10: Output table of variables in the binary logistic regression.

The standard error of the B coefficients should be examined for possible problems of

multicollinearity. Given that all standard errors are in this case below 2.0, there is no indication of

multicollinearity.

In the following, the individual coefficients and the relationships between individual independent

variables and the dependent variable are discussed in more detail for all the independent variables

that are significant in the model. The individual coefficients, reported as B-values in the output

table for the logistic regression, represent a change in the probability of the decision to participate

in crowdinvesting. B-values are expressed in log units and are not directly interpretable.

Therefore, the last column of table 10 reports the B-coefficient as the power to which the base of

the natural logarithm, i.e. the number e, is raised. The result represents the change in the

probability of the decision to invest in start-ups via crowdinvesting with a one-unit change in the

corresponding independent variable under the assumption that all other variables are kept stable.

If the B-value is positive, the transformed log, which results in the Exp(B)-value, is greater than

one and the modelled event is more likely to occur. If the coefficient is negative, the transformed

log is less than one and the probability of the modelled event occurring decreases.

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Diversification has a significance level of .003 and a B-value of .750, which means the variable

has a positive influence on the decision to invest in start-ups. The transformed log results in

2.116, indicating that the probability of the decision to participate in crowdinvesting increases

with a positive change in diversification. This supports H2, which hypothesizes that an investor,

who understands crowdinvesting as a way to diversify and complement his/her investment

portfolio, is more likely to participate in crowdinvesting. However, one has to keep in mind that

the consistency of the scale for diversification was not very high. Cronbach’s Alpha reported a

value of only .63. The significance of this variable should thus be interpreted with caution.

Support is also found for H4, which refers to investors who see crowdinvesting as an investment

with relevance for entrepreneurship, the society and the economy are generally more willing to

support start-ups financially via crowdinvesting. The logistic regression reports social relevance

to be a significant parameter (p=.019) and to have a positive relationship with a B-value of .803,

which transforms into an Exp(B)-value of 2.232.

The dimension of being an early adopter in emerging technologies has a significant impact in the

model with a significance level of .022. As hypothesized in H5, the relationship between

investors with a high interest in innovative and emerging investment possibilities and new

technologies and the decision to actually participate in crowdinvesting is positive, which is

expressed by a B-value of .587. The transformed log value is 1.799, which implies that a one-unit

increase in being an early adopter increases the probability of participating in crowdinvesting by

around 80%.

In contrast, the missing trust in online platforms has a negative influence on the decision to

participate in crowdinvesting. As it was assumed in H6, the relationship between trust in online

platforms and the decision on crowdinvesting is negative, supported by the result of the

regression analysis with a B-value of -.515, significant at .003. A the B-value is negative, it

transforms into an Exp(B) value below 1.00, which is 0.598 in this case. This implies that a one

unit increase in missing trust in platforms leads to a decrease in the probability of participating in

crowdinvesting by around 40%.

If we consider not only the significance levels of .05 and below, but take a wider confidence

interval of 90% into account, the variables network and neutral information (press coverage)

prove to be significant as well. Considering these two variables additionally seems to be

reasonable as the Exp(B) values of both variables are less than 1. If the Exp(B) value of a

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variable is 1, it basically does not have any influence in the model. We are thus looking for

values that are higher or lower than 1, which indicates a positive or negative influence

respectively. Network is significant at p=.074 and neutral information at p=.096. Interestingly,

both variables, network and neutral information, have a negative influence on the decision to

participate in crowdinvesting with an Exp(B) value of .667 and .722 respectively, which

corresponds to B coefficients of -.405 and -.326. This means that these variables influence the

decision, but in contrast to what has been hypothesized in H8 and H10, the direction of the

influence is negative. Particularly interesting with regards to these two variables is also the fact

that the t-test indicated that there is no difference between both groups as all items except one

showed a significance of p > .05. Nevertheless, they do have a significant influence in the logistic

regression model.

The following graph illustrates the B-values as the result of the regression analysis and the

relationships between the factors and the decision to invest in crowdinvesting based on the

theoretical framework.

Illustration 14: Theoretical model illustrating results of binary logistic regression (B-coefficients).

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To summarise, based on the absence of sufficient significance of the variables in the binary

logistic regression, no support could be found for H1, H3, H7 and H9. Due to the fact that these

variables do not have any significant influence on the decision to participate in crowdinvesting, it

remains unclear whether they play a role in the decision-making process and if their impact is

positive or negative. On the other hand, H2, H4, H5 and H6 could be confirmed by the results of

the regression. The logistic regression does not only support the significance of these parameters

for the decision to invest in crowdfunding, but also the hypothesised direction of the relationship

was verified. Also H8 and H10 could be confirmed under the assumption that a significance level

of below .10 is still acceptable. However, the direction of the relationship hypothesised in chapter

5.2.1 could not be supported. The following chapter discusses the results in terms of their

meanings and practical as well as theoretical implications. Limitations of the present

investigation and future research areas are also addressed.

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7 Discussion

In the following section we discuss and interpret the results. However, when interpreting the

results one has to bear in mind that crowdinvesting in its current form is a “very immature” and

“fragmented” phenomenon as Bradley (2014, p.1) has defined it and as it is represented by its

position on the Gartner’s Hype Cycle, explained in chapter 2.3. This means that the

comprehension of this new and innovative way of technology enabled start-up funding by the

crowd, is still limited and research on that topic has to deal with the limited amount of people that

have relevant experience in the industry. The possibility of crowdinvesting as it is offered by the

first-generation of crowdinvesting platforms only started some years ago. As a consequence it is

unclear if the level of experience that crowdinvestors could gather is sufficient for them to gain

enough insight and reflection on their decision-making processes and motivations. Judging by its

early stage we can safely assume that most crowdinvestors can be considered to be early adopters

and not serial investors and therefore they have a limited base of experience. The lack of data and

the small number of crowdinvestors with a relatively low experience level make it hard to

investigate factors that influence decision-making and draw robust conclusions. Until today a

conclusive general and scientific understanding of what drives the decision to become a

crowdinvestor is absent. This research project is – to the best of our knowledge – one of the first

attempts to identify factors that influence the decision of investors to participate in

crowdinvesting, which are developed from the perspective of a theoretical finance model. The

aim of this research project is to test which of the identified ten factors in the conceptual model

influence the decision to become a crowdinvestor, i.e. investing in start-ups via crowdinvesting

platforms. The meaning of the results gained from the binary logistic regression is discussed in

the following.

7.1 Individual Variables & Theoretical Implications

According to our regression analysis we could find support for six out of ten factors whereas two

of them, i.e. network and neutral information, are only significant at p < .10. We attribute this

result to the relatively small sample size of crowdinvestors and are therefore treating them as

explanatory factors. Although the other four factors do not prove to be significant, it does not

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mean that they have no influence on the decision-making, but that they are not suitable for

explaining why one chooses to participate in crowdinvesting or not. In other words, they may

play a role, but they do not explain the differences between a crowdinvestor and a retail investor.

Although we could only find support for six factors, the findings nonetheless provide interesting

and surprising insights and implications for theory and practice.

Risk-return Considerations

The category Risk-return Considerations entails the factors of expected return, risk and

diversification.

From a classic finance perspective risk and return are the primary features when weighing

different investment opportunities against each other (Markowitz, 1952; Sultana &

Pardhasaradhi, 2012). Therefore we consider it to be interesting that according to our model and

research, expected return and risk do not seem to be a significant factor for determining whether

or not one becomes a crowdinvestor. Although investments in start-ups qualify as venture capital

with high risk and potentially high return, it seems not to impact on the decision for or against

crowdinvesting. One reason could be that the respondents in both sub-samples, i.e.

crowdinvestors and retail investors, are generally aware of the typical high risk-return profile of

start-up investments, but that this awareness does not explain the decision for participating in

crowdinvesting as an investor. Another reason might be that both groups find it hard to accurately

assess and evaluate the real risk and the return potential of a crowdinvestment. According to

Dapp and Laskawi (2014) this is one of the key issues regarding investments in start-ups, because

they often do not have a proven business model and no historic data which are a prerequisite for

conventional evaluation methods. During the interviews we conducted, some experts mentioned

that the current crowdinvestors probably have a different risk preference than other investor types

(Expert A, 2014; Expert D, 2014). That seems plausible when considering those investors

participating in crowdinvesting as early adopters which are usually characterised as being curious

and less risk adverse (Oren & Schwartz, 1988).

Furthermore, the amounts invested per start-up via crowdinvesting that we captured from the

respondents in the survey seem to paint an interesting picture. According to them 55% invested

less than 500 Euros and 73% less than 1,000 Euros per start-up and when putting it into

perspective this amount – although typical for crowdinvesting – might not be large enough to

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qualify as a serious investment. This could provide another reason why risk and expected return

do not play the role in this investigation, as one would intuitively expect. The loss of relatively

small amounts can be dealt with more easily and although the crowdinvestor might be aware of

the high risks and potentially high returns, the risk-return consideration might not be as

distinctive as it would be for larger investment amounts.

In contrast to expected return and risk we could find support for the factor diversification being a

significant predictor for explaining the decision of crowdinvesting participation. This shows that

crowdinvestors seem to perceive crowdinvesting as a real investment and alternative to other

more traditional investments, such as stocks, investment funds or corporate bonds. According to

some of the experts we interviewed, crowdinvesting was associated by most retail investors with

some kind of lottery. For that reason it would rather be considered as a gamble than a real

investment let alone an alternative to complement an existing portfolio (Expert B, 2014; Expert

F, 2014). That statement seems to agree with our findings and supports the observation that retail

investors who do not consider crowdinvesting to be an alternative or complement to traditional

investments, are less likely to participate in crowdinvesting.

Social Relevance

Social relevance is a significant factor that seems to explain the decision to participate in

crowdinvesting rather well. It appears as if crowdinvestors would place a high value on the

socially relevant components of an investment, like supporting a certain start-up as well as

entrepreneurship, innovation and the economy. They seem not only to value the opportunity to

make money, but to contribute to something that provides societal relevance. According to our

results, it is also the crowdinvestors who acknowledge this relationship between supporting a

start-up and entrepreneurship, which then leads to higher innovativeness of an economy and

finally the entire society benefits from this development. The crowdinvestors we interviewed as

experts also mentioned that they would carefully select the start-ups they invest in and would

look for business models that could achieve a certain impact (Expert F, 2014; Expert G, 2014).

This idea might also be related to the work on ethical investments of Beal, Goyen and Phillips

(2005) who conclude that psychic returns play a role when making investment decisions and that

financial considerations might not be the only motivational force. Drawing on research from open

source communities and business angels Bretschneider et al. (2014) mention that altruism as

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opposed to selfishness may also play a role in crowdinvesting. Hemer (2011) state that investors

may be may be intrinsically motivated, among other things, by contributing to a “socially

important mission” (p.18). It seems that our results support these statements.

Innovative Investment

The category of Innovative Investment comprises the factors early adopters and trust in online

platforms both of which are significant. The finding that investors who perceive themselves as

early adopters are more likely to participate in crowdinvesting, does confirm what one would

intuitively expect. As mentioned earlier, crowdinvesting is not only quite a new phenomenon, but

it is also an asset class with a high level of risk that is indicated by its classification as being

venture capital. This is a situation where the mind-set that early adopters represent is ideal. They

are known to be curious and keen to try out new things before others do which also makes them

willing to accept a certain degree of uncertainty and imperfection of the product or service (Oren

& Schwartz, 1988). That might also be why crowdinvestors seem to accept the relatively low

regulatory standards of investor protection, especially in Germany, where legal constructs for the

emission like the profit participating loan are outside the bounds of regulating authorities (for a

more detailed discussion see Klöhn & Hornuf, 2012, p. 259f.). It is not uncommon that they

actively look for new possibilities and technologies to explore. That is why they are often among

the first in their areas of interest or expertise, to test first-generation products, or in the case of

crowdinvesting, an innovative investment possibility with an uncertain prospect. Therefore, it is

not surprising that most of the crowdinvestors in our sample have a background in either the

financial sector (22%), in telecommunications, technology and internet (18%) or in consulting

(14%) (see also chapter 6.1.1 for socio-demographics).

The aspects of early adopters go hand in hand with the next factor: trust in online platforms.

Early adopters or crowdinvestors seem to be more trusting towards online investment platforms

in general. Potential security concerns like fraud or abuse of sensitive and private data might be

present, but are not as strong a reason against the participation as it is for retail investors. The

latter seem to be more conservative and careful and find it harder to trust online platforms when

handling their money. When security and certainty are primary concerns when making

investment decisions, an innovative investment like crowdinvesting does not meet the needs of

that investor type. Because not only is the investment in start-ups itself risky, but the way it is

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currently practiced as crowdinvesting is quite uncertain when it comes to investor protection and

future regulation.

Personal Utility

The factors self-representation and network build the category Personal Utility. Self-

representation is not significant in explaining the decision to become a crowdinvestor, whereas

network is based on a 90% confidence interval. The first might still be influential as a factor, but

we could not find support that it makes the difference of becoming a crowdinvestor or not.

That self-representation does not seem as important as expected is interesting. According to the

expert interviews we conducted, crowdinvestors were said to enjoy talking about their

investments to others and it can also be seen as a characteristic of early adopters to be especially

keen on sharing and talking about their experiences (Expert A, 2014; Expert E, 2014). One

reason for not being a significant factor might be due to possible similarities of the retail investor

and crowdinvestor sub-samples. As described in chapter 4.3.1 about sampling, retail investors

have been targeted that seem to have an interest in or are open towards new and alternative

investments. Thus, the difference with regards to sharing and talking about new investments

might not be very large as both groups in the sample might have a similar view on this topic.

Another reason might be that both groups do not find utility in using their investments as a means

for self-representation, which could mean they enjoy sharing their experiences with like-minded

peers, but not with others who do not have the same interest. Usually financial topics are not the

most popular in general conversation and although crowdinvesting is something new and

interesting to the crowdinvestor, s/he might not use it for self-representing for that reason.

The idea of seeing a crowdinvestment as a possibility to increase one’s network, because it offers

engagement opportunities with other investors and the start-up, was also inspired by expert

interviews (Expert B, 2014; Expert E, 2014). Moreover, Gerber et al. (2012) describe being part

of a community with a similar mind-set as a motivating factor for participating in crowdfunding,

whereas Moritz and Block (2013) identify the interest to interact with others as an important

motive of crowdinvestors. According to Hemer (2011), investors receive certain satisfaction and

enjoyment from the possibility of “[…] being engaged in and interacting with the project’s team”,

and from “[…] the chance to expand one’s own personal network” (Hemer, 2011, p.18).

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That is why it is interesting that our results indicate a negative influence of the factor network on

the decision to participate in crowdinvesting. This basically contradicts what is found in the

literature and what experts report. The variable means of the t-test statistics indicate, although the

difference is not significant, that crowdinvestors value the interaction with others, i.e. project

teams and crowdinvestors, slightly more than retail investors would. We also learned from our

expert interviews that some crowdinvestors might find it enjoyable to support certain start-ups

financially and then passively observe how they progress and grow. In this case an active

involvement and exchange might not be interesting for most investors or just not possible due to

various reasons like time constraints. This might be particularly true as one expert described one

group of their investors to consist mainly of middle managers in medium-sized or multinational

enterprises who are interested in entrepreneurship and start-ups, but who do not have the time to

actively work on their own business (Expert E, 2014). The time constraint of such crowdinvestors

might provide an explanation for the fact that they do not want to be involved in the start-up or

network personally, but they are rather interested in supporting the start-up, as they usually have

the financial capacity to do so, and in observing the development of these new ventures. This

might provide a reason for why network shows a negative influence on the decision to participate

in crowdinvesting. Although, some investors might see it as a possibility for network expansion

there are probably more efficient ways to do so when it is considered to be the primary goal.


The factor advocate recommendation is not suited to explain the reason for participation in

crowdinvesting, as it is not significant. However, Nagy and Obenberger (1994) found

recommendations from individual stock brokers, who can be considered to be experts in this area,

as well as friends and family to have a positive impact on the investment decision in an equity

selection process. Although the decision-making situation is different, it seems interesting to us

since one would expect the effect to be different for crowdinvestors and retail investors. One

would probably expect current crowdinvestors in their role as early adopters to be less dependent

on external recommendations from family, friends and experts, because they are actively looking

for themselves and are typically used to being the sender rather than the recipient of

recommendations. Similarly, one would expect retail investors to be more dependent on advocate

recommendations by peers and experts when it comes to new forms of investments, especially

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since it has been shown by financial behaviour studies that recommendations from experts,

friends and family do have an impact (Nagy & Obenberger, 1994; Pascual-Ezama et al., 2010).

Furthermore this finding seems to contradict what we learned from the expert interviews. Expert

E for example stated that quite a large proportion of their crowdinvestor customers decided to

participate in crowdinvesting due to peer recommendations. However, one also has to keep in

mind that just because the factor advocate recommendation is not significant in the logistic

regression it does not mean that it is irrelevant for the decision-making in general. It might still be

an important factor, but it does not explain the difference between current crowdinvestors and

retail investors. An indication for this might be the fact that the averages for all items within this

factor are above 4.3, which implies that people do agree with the statements that

recommendations from friends, family, and independent experts are important.

Neutral Information

Nagy and Obenberger (1994) state that neutral information is considered to be “[…] an outside

source of information that is perceived to be unbiased” (Nagy & Obenberger, 1994, p. 65) and

that this would cover all sorts of media coverage. We focused on reports from the financial and

general press since they seemed to be the most relevant to us. These two aspects of media

coverage were also investigated by Nagy and Obenberger (1994) and their study revealed both

factors to be important for an investment decision. Furthermore, experts F and G (2014)

specifically emphasized during the interviews the importance of publications in the general press

in order to increase the awareness for crowdinvesting. Interestingly enough the results seem to

contradict these statements as they indicate a negative influence of neutral information on the

decision. Additionally, the t-test statistics show a significant difference for actively informing

oneself about investments in the financial press indicating that retail investors rather tend to read

up on an investment before making a decision. The variable means for crowdinvestors and retail

investors also show that the influence of the general press does not seem to be very important to

both groups.

There might be a difference between actively using the general or financial press as a source for

neutral information and confirmation and stumbling across crowdinvesting by accident. For

instance, one expert stated that he came to become a crowdinvestor because he saw a

documentary on television and did not know before then that this form of investment even existed

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(Expert F, 2014). This could mean that investors who for some reason are receptive to innovative

investment opportunities might be inclined to become a crowdinvestor, but that the general and

financial press does not serve as a source of information or confirmation. At this point it would

be interesting to investigate further, if this is either because investors use different channels or

publications since there are not many articles on crowdinvesting anyway or if external sources

that provide information and signals are generally not relevant for the decision to participate in

crowdinvesting at this point. The latter does align with our findings regarding the influence of

advocate recommendation. What is interesting about it is that most platform providers and

investors we interviewed stated that coverage in the media would be important to expand beyond

the current customer segment of early adopters, because many potential investors, e.g. retail

investors, would not know that crowdinvesting even exists (Expert C, 2014; Expert F, 2014). It

would also be interesting to learn what effects sentiments of reports from the general or financial

press would have on the decision to become a crowdinvestor. According to the Gartner’s Hype

Cycle, the early stages are mainly influenced by a high level of hype, buzz and sentiment that the

media creates for a new technology, which are also mainly positive generating high expectations

for the market. One reason could be that participating crowdinvestors might not be receptive to

reports from the general press whereas retail investors might find them over-optimistic and rather

would like to see how the next stages play out before entering the crowdinvesting arena.

Although the results revealed that some of the identified and tested influence factors were not

significant for the explanation why an investor participates in crowdinvesting or not they still

provide some interesting points of departure for future research. This thesis was meant to shed

light into the mostly uncharted area, which is the decision-making behaviour of investors in

crowdinvesting contexts.

7.2 Managerial Implications

In addition to the theoretical implications we would like to suggest implications and policies for

the crowdinvesting practice with particular emphasis on how more investors might be convinced

to become crowdinvestors. However, we do not want to limit this discussion to crowdinvesting

platforms and thus also address other parties related to the topic of crowdinvestors. The

influencing factors that proved to be significant according to the binary logistic regression

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analysis provide useful starting points for discussing managerial implications for platforms and

policy makers, i.e. diversification, social relevance, early adopters, trust in online platforms as

well as network and press coverage.

Diversification seems to play a vital role as a factor and therefore deserves special attention when

crowdinvesting platforms are interested in expanding their customer base. Investors who perceive

crowdinvesting as a real investment and instrument to complement their portfolio are twice as

likely to participate in crowdinvesting (see table 11). That means that as long as they are not

provided with real reasons to take crowdinvesting seriously as an asset class, crowdinvesting

probably remains only interesting for early adopters. As we learned from expert interviews this

exact conundrum is one of the main challenges crowdinvesting platforms face today (Expert B,

2014). Bringing crowdinvesting from a stage where only early adopters see it as a real alternative

to other investments to the next level, where more retail investors join the movement. This goes

hand-in-hand with the aspect of early-adopters being more likely to become a crowdinvestor.

Speaking in terms of Rogers' (2003) Diffusion of Innovation Theory, crowdinvestors need to

move from the phase where innovators and early adopters join the new idea to the stage where an

early majority starts adopting the idea. In order to reach the involvement of retail investors, at the

beginning, particularly of those who are open for new investment possibilities, crowdinvesting

platforms need to work on the way crowdinvesting is perceived. It is important that investors do

not consider crowdinvesting to be some kind of a lottery or a game, but that they perceive

crowdinvesting as a real investment.

One idea to change this perception could be to include processes and product features that are

similar to what retail investors are used to from other investments. We are aware that the direct

involvement of the investor is seen as a critical ingredient for the crowdinvestment movement,

but in order to attract more retail investors, platforms could offer diversification possibilities that

enable an easy and simple distribution of larger amounts of money over a critical number of

individual start-ups on a platform or even across platforms using a fund like structure or some

kind of automated and customisable portfolio builder similar to those developed for

crowdlending platforms. For instance, the US-based crowdlending platform LendingClub offers a

feature called Automated Investing which computes efficient loan portfolios by distributing small

proportions of the intended amount over many loan requests (Paravisini, Rappoport, & Ravina,

2010). This resembles offers or product features that are more familiar to retail investors and

which might reduce the perceived lottery component that according to our expert interviews

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regular investors think of when considering crowdinvesting, due to the typical small investment

sums and high risk feature (Expert B, 2014; Expert F, 2014). Another aspect in this context could

be the establishment of a secondary market where the positions can be traded offering a

possibility for earlier liquidation. This is a feature known to most retail investors when they, for

example, invest in stocks. The German crowdinvesting platform Bergfürst has already established

a possibility for crowdinvestors to trade their securities in start-ups (Bergfürst, 2014). At the

moment, it is not clear if this market has sufficient liquidity, but it seems to be an interesting step

towards making crowdinvesting an alternative that is worthy to be considered in the context of an

overall investment strategy.

Another way of changing the perception of crowdinvesting in the eyes of retail investors in the

long run could be regulatory changes that provide enough investor protection and pay tribute to

the security needs that non-early adopters usually feel. In this context, it is more the public policy

makers who would have to take action. Currently, there are already discussions about regulating

the crowdinvesting market in Europa and in particular about enhancing investor protection by law

(see Klöhn & Hornuf (2012) and Röthler & Wenzlaff (2011) for further discussion regarding

crowdinvesting regulations in Germany and the EU).

A different instrument to increase the trust in this innovative asset class and to change the

perception of retail investors towards a more serious investment would be the use of marketing

and distribution channels that are associated with traditional financial investments. Press

coverage in the general and financial press seemingly does not play a role for current

crowdinvestors, but using channels that involve publications in specific financial journals and

online information portals, such as Bloomberg, might influence retail investors by raising the

awareness that crowdinvesting actually exists. In particular, it could be a powerful sign to offer

crowdinvestments among other investments on online portals that are usually used by retail

investors who are used to invest online. This might be online banking portals or established

market places where investors find most investment types, such as Tradegate Exchange, which is

a well-known market place in Germany for different kinds of investments. Having the

opportunity to do crowdinvestments via established financial service providers would also reduce

the problem related to the reluctance towards crowdinvesting platforms. As it has been shown by

our analysis, retail investors are less likely to participate in crowdinvesting due to missing trust in

these online platforms. Combining crowdinvesting platforms with other brokerage platforms that

are already used by retail investors might reduce several obstacles towards becoming a

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crowdinvestor. We are aware that this would be a quite drastic step and would probably also have

some drawbacks, as the direct link between the start-ups and investors might suffer, and the

involvement of investors is likely to be reduced. However, this particular clientele might also not

want to be involved too much in the interaction possibilities crowdinvesting offers, as suggested

by the negative influence the factor network showed. Thus, it might be more of a long-term

perspective and it is also dependent on the vision of the individual platforms and of the industry

as a whole, but it could provide an interesting growth potential.

Also the perception of social relevance in crowdinvesting seems to be a decisive factor as it

doubles the probability to become a crowdinvestor according to our analysis. That means, if

platforms would succeed in raising the awareness for the effects entrepreneurship, innovation and

job creation has for the economy and the society in general, investors would be more inclined to

invest in start-ups. For instance platforms as well as policy makers could influence the image of

crowdinvesting by launching public relations campaigns or write stories on their websites and

blogs portraying examples of how start-ups benefited society.

7.3 Limitations & Future Research

7.3.1 Theoretical Limitations

Since the aim of this thesis is to present a first version of a framework containing relevant factors

that affect the decision to participate in crowdinvesting from a behavioural finance perspective,

the collection and selection of factors is the corner stone of this project and the main subject of

this chapter.

Although the presented framework consists of ten influence factors there is an abundance of

factors that might play a role for crowdinvesting participation. A selection was needed to be

made as presented by the selection funnel in illustration 15.

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Illustration 15: Collection and selection process of factors that influence decision-making in crowdinvesting (own creation).

In the first step, we collected factors from behavioural finance regarding investor decision-

making behaviour as well as from relevant crowdfunding literature, as there is only a small body

of research on crowdinvesting, to account for the idiosyncrasies of this new phenomenon. Other

disciplines like (social) psychology, anthropology or research on information technologies might

have steered the collection process in a different direction according to their special emphases of

certain aspects of human behaviour, decision-making and motivation. Therefore, the collection

might be biased due to the financial and crowdfunding literature perspective. In the next step, the

collected factors were selected according to expert opinions from the interviews we conducted

and according to their fit to the framework of Nagy and Obenberger (1994). At this stage, the

selection might again be biased by a finance perspective as well as by subjective expert opinions.

Furthermore, the operationalization of the identified factors into items could have provided an

entry point for inaccuracy and subjectivity which we tried to avoid by testing the concepts in a

pretest as discussed above in chapter 4.2. According to a reliability and factor analysis we

selected and merged the respective factors in a third step. The final selection of factors that

entered the logistic regression analysis was made after the survey based on a reliability and factor

analysis. That is why the final framework and the results might be biased by the outlined

collection and selection process. Given that the investment decision-making of individuals is a

complex process, there are undoubtedly many different aspects involved. Not all of them could

be covered within the scope of this investigation project. Thus, behavioural finance and

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crowdfunding have been chosen as a first angle of this analysis, which certainly offers the

possibility to add more perspectives and factors in future research.

7.3.2 Methodological Limitations

In the following section, methodological limitations of the empirical research are discussed. The

main limitations are regarding the sampling, sample size and representativeness as well as issues

regarding generalisation.

As mentioned earlier crowdinvesting is quite a new phenomenon and this thesis is one of the first

attempts to address the research gap of decision-influencing behaviours in crowdinvesting

contexts. This initial situation puts constraints on the access possibilities of relevant target

population of crowdinvestors since there are still not many out there that can easily be identified.

Usually random sampling is recommended for gaining significant results that can be generalized

(Bortz & Schuster, 2010). The situation however, only allowed us to draw a convenient sample

based on our network that we built during our work on our own crowdfunding platform talent-

inevst.de. In addition, the rate of response to the circulated survey cannot be considered to be

high. Of the 527 individuals who started the survey by clicking on the hyperlink, 149 finished the

survey. Although we did not find that a certain group systematically did not participate in the

survey, this could lead to cases of non-response bias, which is discussed in detail in chapter

4.3.1.2.

Furthermore, the sample size of 133 for the binary logistic regression analysis did match the

recommended ratio of at least ten cases per independent variable according to Hosmer,

Lemeshow and Sturdivant (2013). Moreover, in order to achieve a high precision in a binary

logistic regression, it is recommended to have an equal distribution of participants between the

two groups under investigation to fulfil the assumptions for this particular kind of regression

analysis (Hosmer et al., 2013). Along these lines it has to be stated that the difference in size of

the crowdinvestor sub-sample (N=51) and the retail investors sub-sample (N=82) might have

influenced the precision of the outcome. According to King and Zeng (2001), however, this

situation starts to become critical when one of the groups under investigation comprises only 1%

or less of a sample. However, we could not find a consistent recommendation in the literature for

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this issue, which leads us to conclude that there is a fragmented division of opinions on this

matter.

Another methodological limitation might be the fact that we classified the sample in two groups

for a first investigation, which does not pay tribute to the variances within each sub-sample. For

instance the depth and scope of experience with start-up investments is distributed quite

differently in the crowdinvestor sample measured in the number of investments. 47% indicated

that they had made three or less investments whereas 29% already invested in ten and more start-

ups (see also illustration 16). Here the differences between more and less frequent investors

might be interesting to investigate in the future given a bigger sample size. Another implication

illustrated by the quite different distribution of experience is the fluid distinction between retail

investors who may have an affinity for innovative investments and are thinking of becoming a

crowdinvestor and less frequent crowdinvestors with three or less investments under their belt.

This simple distinction between the two groups might also be a reason for why there are not as

many significant differences for crowdinvestors and retail investors in our results as expected.

Illustration 16: Distribution of number of investments in start-ups (own creation).

It is important to notice that the survey has been conducted by means of a convenience sample

and mainly crowdinvestors and retail investors in German-speaking countries have been targeted.

Thus, the generalisation of the results has to be made with caution. This is particularly true as

investor decisions are related to the cultural context. While conclusions might be drawn for this

particular population, that is the group of crowdinvestors in the German-speaking area,

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generalisations to other countries with different cultures might be problematic. Without going

very deep in the discussion of cultural differences in an investment context, a few thoughts about

how culture might influence an investment decision in various countries are provided.

One example of cultural influence on finance is given by Stulz and Williamson (2003), who

investigated the impact of cultural proxies, such as religion and language on investor protection

in the particular country. They have found a particular strong relationship between a country’s

principal religion and the legislation regarding investor protection. As it has been discussed

before the legal system and particularly investor protection, is one of the reasons why

crowdinvesting is still at its very infancy in several countries, e.g. particularly in the US. Thus,

the results of this investigation have to be considered carefully when referring to countries with

other cultural backgrounds and specifically with other religions. While the variables discussed in

this thesis might be influential in the decision-making of a German, Austrian or Swiss investor

with regards to investments in start-ups, investors in other countries might be influenced by

completely different factors such as the mere possibility to do crowdinvesting, e.g. investor

protection might not allow such a risky investment due to a particular cultural context.

Other factors might have an impact for investors in other countries as well, but they might be

valued in distinct ways. One example that has also received some attention in academic research

is the question regarding risk perception in different cultures and countries (e.g. Bontempo,

Bottom, & Weber, 1997; Hofstede, 2001; Hsee & Weber, 1999). Weber and Hsee (1998) found

differences in risk perception between China, the US, Germany and Poland, when it comes to

buying financial options. They observed for example Chinese respondents to be significantly less

risk-averse than Americans. Considering that crowdinvesting is a form of venture capital and thus

by definition a very risky investment, the decision-making process for investors with another

degree of risk-averse behaviour might be completely different. While the risk perspective turned

out not to be not a significant factor in the decision process in this investigation, risk might be

significantly important when repeating this study in another cultural context.

7.3.3 Future Research

Although some of the identified factors did not prove to be significant in explaining the reason

why an investor would become a crowdinvestor or not, we could provide some interesting points

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of departure for future research. Crowdinvesting is still an emerging phenomenon. In the

following we present several ideas where future research might be helpful in refining the

approach of this project as well as improving the understanding of other areas from different

perspectives.

This study and the identified factors provide an interesting starting point for a more detailed

analysis in several regards. Firstly, it might be an interesting endeavour to understand the

dependence of factors that influence the decision on different cultural contexts. This study

focused on the German-speaking area in particular and it seems interesting to expand such an

investigation to other cultures and countries. As mentioned in the limitations the perception of

risk and the general willingness to take risk in an investment context seem to be an example.

Distinctions in perception might also be different for each of the diverse groups that might be

constructed for crowdinvestors and retail investors. For instance, frequent crowdinvestors might

see crowdinvesting as something different as investors who invest in one or two start-ups because

it is fun or similar to a lottery.

Secondly, crowdinvesting in this project is understood as one concept and for reasons of

simplicity generalizations had to be made. However, for future research it might be fruitful to

differentiate in more detail not only between investor types but also between crowdinvesting

platforms and the start-ups asking for funding. For instance when asking investors for self-reports

on crowdinvesting in general it might not account for the diversity and fragmentation we find in

the field. Expert D (2014), for instance, emphasised the importance of taking the different

platform models into account when theorising about crowdinvesting. Indeed, there are quite

different types of platforms that are niche-specific and specialized, open to all sorts of innovative

start-ups or only allow later stage ventures. Some even provide a secondary market where shares

can be traded which is – at least until now – atypical for crowdinvesting as it shares

characteristics with a regular stock exchange. Therefore, research that takes this diversity into

account could possibly shed some light on how it affects the motivation and decision-making

behaviour of investors. Since the form of capital (e.g. pure equity, mezzanine capital or fixed

income) and the investment object (e.g. people, start-ups, small and medium sized enterprises,

etc.) might play a decisive role for investor motivation. In this context, future research attempts

could also contribute to a better understanding by investigating on-platform behaviour and find

implications for how the interface and usability but also the business model, the presented

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information and other platform relevant features impact investor decision-making. Research

efforts that generally focus in principal agency issues might benefit from that.

Thirdly, it would be interesting to test how the factors we identified are weighed and prioritised

by different types of investors. Along these lines research endeavours might be promising that

focus on motivation and differentiate between intrinsic and extrinsic drivers that influence

investor behaviour in crowdinvesting contexts (see Bretschneider, Knaub, & Wieck, 2014).

Lastly, research on the influence of the macroeconomic environment on investor behaviour could

be a promising point of departure. For example, since the central bank interest rate in Europe is at

an all-time low, decreasing the return that can be earned from most traditional investments might

lead to retail investors moving to actively look for alternatives and to take on higher risks.

Given that crowdinvesting is rather unknown from a research perspective, there are many more

aspects that might be interesting to investigate in the future. For example, it would also be

interesting to investigate, if there is a hierarchy of private or family related financial security

needs such as retirement planning, residential house financing or education financing for one’s

children that need to be fulfilled before investors think about investing their disposable income in

innovative asset classes like crowdinvesting. Although every investor is unique in his/her needs,

wishes and goals, it would be a worthwhile endeavour to create a typology and profiles of

crowdinvestors and retail investors with an interest for innovative investments to account for their

diversity and different perceptions.

An area of research that is often overlooked, but nonetheless interesting and important, is the role

geography plays for the decision-making on the Internet, like Agrawal et al. (2011) point out for

crowdfunding. Building on their ideas the investigation of spatial effects would be a fruitful topic

for future research on the geography of crowdinvesting. For instance there might be reputation

effects stemming from certain industry clusters that are known for a certain technology like

Southern Germany for the automotive industry, or the Silicon Valley for digital technologies.

Next to effects of transaction costs, costs of monitoring or homophily as discussed in the

literature review in chapter 2 there may exist effects from stereotypes investors may have

regarding the location and origin of start-ups and their founders.

Finally, some crowdinvestors are highly involved in groups and organizations campaigning for

legal frameworks on national and supra-national levels that are more suited to the needs of the

crowdinvesting movement. The lobbying effects of organizations like the German Crowdfunding

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Network or the European Crowdfunding Network may be interesting to investigate as well as

what role leading crowdinvestors play in the market. Furthermore, it would be interesting and

probably helpful for lobbying efforts to use the anecdotal evidence, that crowdinvesting has a

positive impact on society. This is because crowdinvesting fosters an entrepreneurial culture,

creating innovation and jobs, and therefore by extension economic growth. For further insight,

more quantitative data could provide additional support for the argument that crowdinvesting

provides social benefits since it has already proved to be an important factor according to our

research.

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8 Conclusion

Crowdfunding and the sub-concept crowdinvesting are still immature and fragmented

phenomena, which are new to practitioners and scholars alike. This research project provides one

of the first scientific attempts to identify factors that affect the decision-making of investors to

participate in crowdinvesting from a finance perspective, which is supported by relevant

crowdfunding literature.

According to a binary logistic regression analysis of the sub-samples crowdinvestors and retail

investors six out of the ten factors we identified seem to drive the decision of an investor to

participate in crowdinvesting. Therefore, we can answer the research question as follows: The

factors diversification, social relevance and early adopters show a positive influence on the

decision-making, whereas trust in online platforms, network and neutral information appear to

affect the decision negatively.

A key difference between currently active crowdinvestors and retail investors is their perception

of crowdinvesting as an investment alternative to other more established investment types, which

is reflected by the dimension of diversification. At the current stage, it appears that

crowdinvestors see crowdinvesting as an investment alternative while retail investors do not take

it seriously. Furthermore, it is mainly investors with a first mover or early adopter profile who are

currently participating in crowdinvesting. This group is known to be curious and keen to try out

new things. Consequently, they are also not reluctant to use online platforms. On the other hand,

retail investors appear to be significantly more reluctant towards crowdinvesting platforms since

they find them hard to trust. Another main difference was found within the dimension of social

relevance. Although both groups of investors seem to agree that social relevance plays an

important role when making investment decisions, crowdinvestors are more perceptive towards

the link between social relevance and crowdinvesting and value it positively. Interestingly and in

contrast to what is known from literature, investment as a network and the media coverage of

crowdinvesting were observed to have a negative effect on the likelihood to participate in

crowdinvesting.

The perception of crowdinvesting among retail investors is still undetermined. According to our

online survey, there are some reasons that need to be mentioned: Many retail investors simply do

not know that crowdinvesting exists, which was also confirmed by most experts we interviewed.

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Those few who do know about it might not yet be convinced that crowdinvesting is a kind of

investment that can be taken seriously. The high risk involved cannot appropriately be evaluated

at this point due to a lack of robust information and historic data. Furthermore, possibilities to

easily reduce risk with the use of portfolios or funds are not offered at the moment. Therefore, it

might just be too early for retail investors, who might generally share an affinity for innovative

investments, to seriously consider crowdinvesting as an investment alternative.

Finally, the future of crowdinvesting is highly dependent on how restrictive coming regulations

and investor protection standards will turn out to be. Although the JOBS Act, for instance, has

made it easier for smaller companies to raise money from the public in the US, it still puts

relatively high restrictions on who is allowed to become an accredited crowdinvestor. Therewith,

it significantly reduces the crowd start-ups can tap into (Klöhn & Hornuf, 2012). However, the

regulatory situation is different in Germany. The investor protection standards are less strict for

crowdinvesting, which gives more room for experimentation. According to Expert D (2014) this

is the main reason why Germany has more experience with crowdinvesting although the

crowdfunding movement started in the US. However, since the crowdinvesting movement is

growing, policy-makers in Germany and elsewhere acknowledge the potential of crowdinvesting

for the economy, but increasingly see the need to define new investor protection standards,

especially for small investors (Federal Ministry of Finance, 2014). Therefore it is interesting to

observe how the journey of crowdinvesting continues and whether future regulation might help or

hinder the realization of its full potential.

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10 Appendix

10.1 Expert Interviews

Pseudonym Background / position Interview day

Expert A Marketing director of one of the largest crowdinvesting platforms in the German-speaking area.

20.06.2014

Expert B CEO of one of the largest crowdinvesting platforms in the German-speaking area. 28.05.2014

Expert C

Researcher at a German university in the department of media and communication management and founder of a Crowdfunding association.

20.06.2014

Expert D Researcher at a large German financial institution with focus on Crowdinvesting; publisher of an article in Crowdinvesting

16.06.2014

Expert E CEO and Co-founder of one of the largest crowdinvesting platforms in the German-speaking area.

23.06.2014

Expert F Serial crowdinvestor with more than 50 investments made. 25.06.2014

Expert G Crowdinvestor who started approx. 1.5 years ago to invest in start-ups. 01.07.2014

All interviews were conducted in German via telephone. A CD containing the interviews as audio

files is enclosed to this thesis.

10.2 Pretest Results

10.2.1 Descriptive Statistics of the Pretest

The following table presents the results of the descriptive statistics for the items in the pretest

questionnaire.

105

10.2.2 Factor Analysis of the Pretest

108

10.3 Socio-‐demographic Characteristics of Survey Participants

10.3.1 Crowdinvestors

N=51

0%

10%

35% 35%

14%

6% 0%

<20 20-‐25 26-‐35 36-‐45 46-‐55 56-‐65 >65

Age distribution

male 88%

female 12%

Gender distribution

Employee 47% Self-‐

employed 31%

Student 12%

Other 8%

Pensioner 2%

Current occupation

16% 22%

47%

14%

2%

Secondary school



Doctoral or MBA

Other

Highest education

21%

17%

14%

4%

8% 4%

4%

6% 2%

2%

2% 16%

Industry Finance & Financial Services


Education

Automotive


Manufacturing


Government


Real Estate

Other

109

10%

24% 20% 18%

4%

20%

0% 6%

<1.000 Euros

1.000 -‐ 1.999 Euros

2.000 -‐ 2.999 Euros

3.000 -‐ 3.999 Euros

4.000 -‐ 4.999 Euros

5.000 -‐ 9.999 Euros

10.000 Euros and higher

No response

Income per month after tax

27% 25% 22%

4% 6% 0%

6% 10%

<5.000 Euros

5.000 -‐ 9.999 Euros

10.000 -‐ 19.999 Euros

20.000 -‐ 29.999 Euros

30.000 -‐ 39.999 Euros

40.000 -‐ 49.999 Euros

50.000 Euros and more

No response

Available money for investments per year

19

18

11 8

5 4

4

3 3 3 2 2

Primarily used crowdinvesting platforms (Number of times mentioned; multiple answers were possible)

Seedmatch

Companisto

Conda

Innovestment

Fundsters

bankless24

Greenrocket

Bergfürst

Bettervest

Welcome Investment

investiere.ch

Fundedbyme

47%

18% 6%

29%

3 and less 4-‐6 7-‐9 10 and more

Number of start-‐up investments

18%

37%

18% 22%

6%

0 -‐ 99 Euros

100 -‐ 499 Euros

500 -‐ 999 Euros

1.000 -‐ 4.999 Euros


Average amount invested per project

110

10.3.2 Retail Investors

N=82

0% 9%

46%

22% 15%

6% 2%

<20 20-‐25 26-‐35 36-‐45 46-‐55 56-‐65 >65

Age distribution

male 66%

female 34%

Gender distribution

Employee 57%

Self-‐employed 23%

Student 11%

Other 5%

Pensioner 1%

Unem-‐ployed 3%

Current occupation

4%

17%

57%

22%

0%

Secondary school



Doctoral or MBA

Other

Highest education

13%

16%

16%

11% 6%

5% 5%

3% 5%

4%

2%

12%

Industry Finance & Financial Services


Education

Automotive


Manufacturing


Government


Real Estate

Other

111

10.4 Data Analysis

10.4.1 Descriptive Statistics

The following table presents the descriptive statistics for all items in the questionnaire.

11%

17% 20%

15%

9%

15%

7% 7%

<1.000 Euros

1.000 -‐ 1.999 Euros

2.000 -‐ 2.999 Euros

3.000 -‐ 3.999 Euros

4.000 -‐ 4.999 Euros

5.000 -‐ 9.999 Euros


No response

Income per month after tax

37%

18% 12% 13%

2% 0%

10% 7%

<5.000 Euros

5.000 -‐ 9.999 Euros

10.000 -‐ 19.999 Euros

20.000 -‐ 29.999 Euros

30.000 -‐ 39.999 Euros

40.000 -‐ 49.999 Euros

50.000 Euros and more

No response

Available money for investments per year

114

10.4.2 Reliability and Factor Analysis

The following table presents the results of the reliability analysis and the principal component

factor analysis for the items and variables that entered the logistic regression.

117

10.4.3 T-‐statistics

The following table presents the results of the independent t-test for equal means for all items in

the questionnaire.

126

10.5 Survey in English

135

10.6 Survey in German

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