Cultural entrepreneurship in the digital era : how quality signals affect success of crowdfunding projects

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UNIVERSITY OF AMSTERDAM

Thesis Work

Cultural Entrepreneurship in the Digital Era:

How quality signals affect success of

crowdfunding projects

Name: Zsófia Rita Szinetár

Student number: 11376538 Submission date: 10th August 2017

Faculty of Business Economics MSc in Business Administration

Track: Entrepreneurship and Management in the Creative Industries Supervisor: dr. Monika Kackovic

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STATEMENT OF ORIGINALITY

This document is written by Student Zsófia Rita Szinetár who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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ABSTRACT

Crowdfunding is a nascent way of fundraising, which gives the opportunity for entrepreneurs to establish and realize their projects. As a result of the information gap between the entrepreneurs and theirs potential investors, the entrepreneurs seek to provide signals of quality in order to increase the probability of an investment. Factors of a successful crowdfunding project have been already discussed however, few studies have focused on crowdfunding in the context of signaling theory. The purpose of this study is to examine whether certain quality signals (i.e. number of updates, previous success and diversity of signals) affect the success of the crowdfunding projects, and if so to what extent. To be specific, I analyze 778 projects on a Dutch reward-based crowdfunding platform to observe whether these quality signals are associated with the success of the projects. By conducting a binary logistic regression, the results show that the number of updates and the diversity of signals affect positively the project success, while in case of the previous success no significant effect was found.

Keywords: crowdfunding, signaling theory, quality signals, success factors,

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1. INTRODUCTION

As a consequence of the increasing importance of social media and the increasing access to Internet worldwide (World Bank, 2013) an alternative way of new venture- and product funding has appeared in the form of crowdfunding. Crowdfunding is a form of peer-to-peer investment (Barasinska, Schäfer, 2014) and is defined by Mollick (2013, pg. 2) as “the efforts by entrepreneurial groups or individuals – cultural, social and for-profit – to fund their ventures by drawing on relatively small contributions from a relatively large number of individuals using the internet, without standard financial intermediaries”. In other words, crowdfunding is a new way of early-stage funding (Kuppuswamy and Bayus, 2015) for projects led by individual – or a group of – entrepreneurs on online platforms, where the financial contributions emerge from a relatively large group of individuals; the crowd.

A general characteristic of new venture investments is their opaqueness (Courtney et al., 2017); meaning that the potential investors do not have – perfect – knowledge about the projects and their quality. Therefore, these investors need to rely on partial or sometimes even unreal information regarding the projects (Moss et al., 2014). On the other hand, we can assume that entrepreneurs are generally aware of their projects’ quality (Ahlers et al., 2015; Courtney et al., 2017). This opaqueness results in an information gap between the entrepreneurs and the potential investors. The academic literature (Kirmani and Rao, 2000; Spence, 2002; Moss et al., 2014) identify two possible solutions for reducing information gap; either the buyer provides incentives or the seller transmits signals.

Based on previous literature about signaling theory (Spence, 1973; Connelly et al., 2011; Kirmani and Rao, 2000; Bergh et al., 2014; Moss et al., 2014) we can assume that it is also applicable to the context of crowdfunding. The concept of signaling theory is highly

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important in this context as investors eventually make their decision based on the information provided by the project-owners (which are considered as quality signals).

Signaling theory describes the phenomenon when one party uses tangible (e.g. prototype of the final product, photo, video etc.) or intangible (e.g. skills and capabilities of the entrepreneurs) objects – i.e. signals – in order to attract potential investors (Spence, 1973; Connelly et al., 2011; Kirmani, Rao, 2000). Investors generally do not have perfect knowledge about the enterprises and their quality, therefore entrepreneurs seek to develop and communicate quality signals to increase their potential investments (Ahlers et al., 2015; Moss et al., 2014). For instance, in the context of crowdfunding, quality signals might be the use of images or videos of the process (Mollick, 2014), frequent communication with the investors (Connelly et al., 2011), or the depth of the project description (Koch and Siering, 2015).

An important distinction should be made between signals based on their origin. First, there are the so-called first-party signals, which are directly transmitted by the entrepreneurs. These signals might serve as valuable information about the projects’ quality, therefore resulting in the increase of their attractiveness. On the other hand, due to the general self-interest in presenting ourselves in a better position, these first-party signals might also be biased (Akdeniz, Calantone, and Voorhees, 2014). Second, signals transmitted by third parties are generally found to be more trustworthy (Akdeniz, Calantone, and Voorhees, 2014; Pollock and Rindova, 2003) however, these signals are rare in the context of crowdfunding. Consequently, this thesis focuses on the first-party signals, i.e. those which are communicated towards the potential investors directly from the crowdfunding entrepreneurs.

Signaling theory has been extensively researched in the academic literature however, its connection to crowdfunding is yet an under-researched field of study. Little is known so far about the success factors of a crowdfunding project and their relationship with quality signals (i.e. communicating signals about the quality in order to reduce information gap).

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Ahlers et al. (2015) did the first-ever empirical examination from this point of view, and hopefully this thesis will provide further contribution to the literature both in signaling theory and in crowdfunding. Furthermore, crowdfunding as a fundraising way is extremely popular among cultural entrepreneurs, who – thanks to these new platforms – are able to introduce themselves and show their ideas also to the general public. However, no study has researched a crowdfunding platform, which is exclusively dedicated for artistic and creative crowdfunding campaigns.

The purpose of this thesis is to fill the above described gap in the literature. In doing so I aim to investigate the success factors of the crowdfunding projects in the context of the creative industries – where crowdfunding has a major role in project funding.

The present paper aims to give an insight to my thesis project. Namely, I seek to examine how first-party signals might affect the success of a crowdfunding campaign through the example of Voordekunst, a crowdfunding company in the Netherlands. Considering the already described gap in the academic literature, the main objective of the present thesis is to answer the following research questions;

To what extent does (1) the number of updates, (2) previous success and (3) the diversity of signals entrepreneurs use to signal quality on a crowdfunding platform affect the success of the project?

The subject of crowdfunding has already been researched in the academic literature however, the context of creative industries will give new insight to the field. The re-contextualization of the existing literature will allow testing the applicability of the signaling theory in a new context; the study will allow future studies to reveal the differences and similarities between the general- and the artistic crowdfunding platforms with respect to the quality signals and their effect on the project success. The study and its results will

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hopefully provide a grounded basis for future research in the field of crowdfunding and signaling theory. Also, he research hopefully will provide clear entrepreneurial as well as managerial implications.

The empirical setting of this thesis is the cultural entrepreneurship, where artistic and cultural operators run their own projects on a crowdfunding platform. Consequently, the data used during the research was provided by a Dutch crowdfunding platform, Voordekunst, which aims to connect artistic and cultural entrepreneurs with the crowd of potential investors. The dataset from Voordekunst contains a large number of samples (779 projects), thus providing a highly appropriate basis for my study.

The thesis work is structured as follows; first, I will give an overview of the existing academic literature on signaling theory and its connection to crowdfunding (Section 2). Based on these theories I frame 3 hypotheses. Afterwards, the data and the method of data collection within the company and the measurements of the main variables are described (Section 3). Section 4 provides the analysis; the descriptive statistics, the correlation matrix and the results of the binary logistic regression analysis. Finally the results are discussed and the thesis is concluded in Section 5.

2. THEORETICAL FRAMEWORK

Some academic literature provide appropriate background and contributions to both fields of study however, only limited number of published studies exist, which connect signaling theory with the context of crowdfunding. Consequently, this Section aims to provide a review on the roots, the players and models of crowdfunding (Section 2.1) and on signaling theory (Spence, 1973) (Section 2.2) – the two main focuses of the present thesis work.

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In Section 2.1.1 the concept of crowdfunding and its origins from crowd-based power (Labrecque et al., 2013) are presented, while in Sections 2.1.2 and 2.1.3 the players of crowdfunding and the four crowdfunding models are introduced.

2.1.1. The roots of crowdfunding

Even though, crowdfunding is a clearly novel phenomenon, its roots originate back to the early history; collecting small amount of money from a relatively large group has been present throughout our history; “Mozart and Beethoven financed concerts and new music compositions with money from interested patrons, the Statue of Liberty in New York was funded by small donations from the American and French people” (Kuppuswamy and Bayus, 2015 pg. 2). Still, its main starting event was the 2008 economic and financial crisis (World Bank, 2013), which reshaped the global economy; the strategy for the economic recovery favoured the large multinational companies and the banks, thus creating a heavy burden on the small businesses and individual entrepreneurs (Buystere De et al., 2012). The rise of Internet and the increasing use of social media provided a new opportunity for those whole suffered the most - in the form of crowdfunding.

Scholars (see Labrecque et al., 2013) recorded a shift in the consumer-firm relationship due to this increased use of Internet and that of the social media; by being able to express and share their thoughts with numerous other potential and actual consumers, consumer empowerment has sharply increased recently. This empowerment let to the strengthened crowd-based power, which is identified as the major push for crowdfunding (Labreque et al., 2013), and is defined as the one that “resides in the ability to pool, mobilize, and structure resources in ways that benefit both the individuals and the groups” (pg. 264). Crowd-based power therefore gives the opportunity for individuals to start and fund their own enterprises by pooling many other individuals (i.e. the crowd) financial resources.

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There are various players involved in the process of funding a crowdfunding project. First, the entrepreneurs (groups or individuals) who own and lead the crowdfunding projects. Chen et al. (2010) distinguish between types of entrepreneurs, and define the cultural entrepreneurs as those individuals or groups who aim not only to generate profit but also social and cultural value. The entrepreneurs take financial and personal risks, while setting up their own projects and seeking the necessary contributions to fund their ideas through the crowdfunding platforms (Ordanini et al., 2011) Second, the crowd; the group of potential investors and customers, who are willing to invest in the entrepreneurs’ ideas. Third, the

company that owns and operates the crowdfunding platform. These companies often provide

consulting session for their entrepreneurs; company representatives give advice on how to launch and operate a crowdfunding campaign in order to be able to reach their goal (Ordanini et al., 2011).

2.1.3. Crowdfunding models

In return for the investments investors get certain private benefits. These benefits also represent the motivation for investing. Based on the motivation of investors we can talk about four crowdfunding models.

First, patronage model – also called donation-based model – is followed by such philanthropists, who do not expect any direct return for their donations (e.g. humanitarian projects) (Kuti, Madarász, 2014).

Second, in case of the lending model, funding can be considered to be a loan, with an expected return percentage for the backer. We can describe this model as a lender-debtor relationship, where the lenders are the backers and the debtors are the entrepreneurs, where

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“funds are offered as a loan, with the expectation of some rate of return on capital invested” (Mollick, 2014 pg. 3).

The third approach is called the reward-based model, which gives the opportunity for the backers to get certain rewards in return for their contributions to the projects, such as early access to the final product or service, getting a discount on the final product or service, being credited in the final outcome, meeting the creators etc. (World Bank, 2013). One form of the reward-based model is the so-called pre-selling, where the rewards provided for the backers is a free sample of the final product (Mollick, 2014).

Finally, in case of the equity stake model, backers are also investors, which results in the fact that funders do rely on the success of the project as well, therefore it can be a really risky choice of backing a project (Kuti, Madarász, 2014). At the same time, taking the risk of a potential failure shows the investors’ high expectations for a future success, which is a strong signal of the project quality.

From 2009 to 2012 patronage-based platforms experienced a compound annual growth rate (CAGR) of 43 percent, lending-based platforms that of 78 percent, reward-based platforms 524 percent and equity-based platform 114 percent (World Bank, 2013). Consequently, reward-based platforms are the most widespread across the globe (Mollick, 2014) as well as they are the most popular ones as well among the amateur individual funders, while only a few customers tend to donate to the project with no expect future return or benefit.

2.2. Signaling theory

Spence “illustrated how high-quality prospective employees distinguish themselves from low-quality prospects via the costly signal of rigorous higher education” (Connelly et al., 2010 pg. 40). In his seminar work he describes the job market signaling model as a

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phenomenon, where the potential employers do not have perfect knowledge about the candidates’ skills, and where employees use their education as a quality signal (Spence, 1973) thus decreasing the information gap.

Connelly et al. (2011) identify the key concepts and actors of signaling theory as the followings; signaler, signal, receiver, and feedback. First, the signaler is referred to the entity, who is aware of the quality of the subject (Spence, 1973), and who transmits the signals towards the receivers. Second, the signal is a generally positive message about the unknown attributes, and is transmitted to the receiver. Based on the transmitting individual or organization (i.e. the signalers), we can distinguish first and third party signals. In case of crowdfunding entrepreneurs communicate the signals, therefore signals are hereinafter considered to be first-party signals (Akdeniz, Calantone, and Voorhees, 2014). Third, the

receiver receives the communicated signal sent by the signaler. Fourth, feedback is the

message or reflection on the signal, which is communicated by the receiver to the signaler (Connelly et al., 2011; Bergh et al., 2014).

Looking at the key actors of the crowdfunding and those of signaling theory, we can easily recognize the similarities. First, the entrepreneurs are the signalers, who own the projects as well as transmit the signals towards crowd. Second, the project updates, photos, videos, evidence of preparedness etc. on a crowdfunding platform are considered to be the signals. Third, the crowd is the group of receivers, who receive the signals as well as invest in the entrepreneurs’ crowdfunding projects.

2.2.1. Previous research on signaling theory

Since its first mention by Spence in 1973, signaling theory has been used several times in various business contexts. In this section I provide examples of previous academic

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researches that provide a grounded basis for continuing the work with signaling theory in the context of crowdfunding.

For instance, signaling theory in context of the initial public offerings (IPO) has been extensively researched in the past due to its high importance of today’s economics (Moss et al., 2014; Payne et al., 2013). “Recent research on initial public offerings … has suggested that investors seek tangible and intangible information about the characteristics of firms in which they might invest, as this information can be used to alleviate concerns about risk and uncertainty” (Bruton, Chahine, & Filatotchev, 2009 in Moss et al., 2014 pg. 28). In such a setting firms signal in order to communicate and enhance value, quality and/or potential future success in order to reduce the information gap between them and the investors.

Furthermore, other studies were focusing on how virtuous orientation (VO) affects IPOs (Payne et al., 2013) and microfinances (Moss et al., 2014). VO is described by Moss et al. (2014 pg. 28) as the “rhetoric reflecting organizational values of integrity, empathy, warmth, courage, conscientious, and zeal”. The use of such organizational values (i.e. VOs) were found to be strong positive signals of the microenterprises’ positive characteristics (Moss et al., 2014), which enables the reduction of the investors’ uncertainty and the increase of their trust and willingness-to-invest. Investors highly rely on venture capitalists (VCs) and their opinion and assessment of new ventures. By playing an important role in the selection of new venture investment, VCs shape the business environment and the organization within.

2.2.2. Signaling theory in crowdfunding

“Information asymmetries arise between those who hold that information and those who could potentially make better decisions if they had it” (Connelly et al., 2011 pg. 42). In case of ‘traditional’ ventures investors need to rely on potentially partial and/or unreal information and face ex-ante (i.e. pre-purchase) decisions (Moss et al., 2014). Crowdfunding

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creates an opportunity for the entrepreneurs to provide information about their projects to their potential investors, thus reducing the already mentioned information gap between the entrepreneurs and the investors. By using crowdfunding platforms, entrepreneurs are able to communicate updates, create photos and videos and share them with the investors, thus letting the investors monitor the whole procedure of the projects. Therefore the communication of project-related information increases the chances of selling the projects to the investors and to the potential customers (Chen et al., 2009).

Assuming that entrepreneurs are generally aware the value and quality of their projects (Ahlers et al., 2015), crowdfunding allows investors to reduce the above mentioned information gap even from great distances through the use of internet; they are able to monitor the dynamics of the project as well as able to contact the entrepreneurs. Such a reduced information gap favours also the entrepreneurs; through communicating with their investors they are able to develop their projects, as well as due to the geographically extended customer base they might be able to collect higher amount of capital (Mendes-De-Silva et al., 2016), even though the majority of the investments still tend to be local (Mollick, 2014).

Crowdfunding is a sharply strengthening and an increasingly used market. Crowdfunding projects - especially in the artistic and cultural industries - tend to have only initial prototypes of the final products, or tend to be experience goods (for instance films, visual art performances or paintings); therefore it is hard to determine their real quality before the launch of the products. Because of the fact that signaling might be particularly effective in case of markets with new products and in case of products whose quality is unknown until the purchase (Kirmani, Rao, 2000; Pollock and Rindova, 2003), entrepreneurs might also overcome the above described pre-purchase information scarcity by sending signals to the investors.

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Investors of a crowdfunding platform are generally small investors, who lack the business sophistication and experience of for instance a venture capitalist (VC), and who tend to invest a relatively low amount of money (Ahlers et al., 2015; Mollick, 2014). The majority of the investors of crowdfunding projects individuals, who may freely register on the crowdfunding platforms and generally lack the expert knowledge and experience of the products and services (Moorthy, Ratchford, and Talukdar, 1997). Following this thought, in this thesis the investors are considered to be non-experts.

2.2.3. Number of updates as quality signal

The high number of visible updates and posts on crowdfunding contributes to the overall success of the campaign (Connelly et al., 2011), because it creates engagement and emotional investment, as well as increases credibility and trustworthiness (Labovitz, 2010).

Kirmani and Rao (2000) make a distinction between low- and high-quality firms, and argue that high-quality firms are encouraged to communicate and give signals to the customers in order to induce trial and reveal quality; i.e. they are encouraged to be more transparent. Additionally, Moss et al. 2014) investigate microenterprises and argue that those, “which communicate signals are more likely to get funded and will receive their investments more quickly than those microenterprises that do not” (pg. 28).

Furthermore, according to Connelly et al. (2011) one of the characteristics of a successful signal is its observability, which describes the extent to which – in case of crowdfunding – the potential investors and customers are able to notice the signal communicated by the entrepreneur. The more the entrepreneurs signal to their investors, the higher the level of observability will be, which leads to a higher level of potential success in the future. In other words, if entrepreneurs post updates about their crowdfunding projects often, it is more likely that their potential investors notice the campaigns and decide to

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financially contribute to the project. Furthermore, Connelly et al. (2011) in their review paper about signaling theory (Spence, 1973) identified numerous academic sources, which highlighted the importance of the “signal frequency” (pg. 53). They argue that “if signalers wish to remain differentiated, they will signal repetitively to keep reducing information gap” (Janney & Folta, 2003, 2006; Park & Mezias, 2005 in Connely et al., 2011 pg. 53). This is in line with the arguments of Koch and Siering (2015), who say that potential investors – who are waiting for evidence of progress – might be convinced that the project is worth funded, due to the frequent and regular project updates.

These arguments suggest that entrepreneurs of high-quality projects are more likely to communicate and give signals to the investors. One might argue that low-quality entrepreneurs could also communicate more signals because of the low costs of signaling. However, in that case we could also assume that these signals are also low-quality signals. Consequently, I frame hypothesis that there is a positive relationship between the quantity of signals and the success of the crowdfunding projects.

H1: There is a positive association between the number of updates entrepreneurs use

to signal quality on a crowdfunding platform and the success of the project.

2.2.4. Previous success as a quality signal

Entrepreneurs are free to launch as many projects as they wish on a crowdfunding platform. However, they often face challenges in proving quality when presenting their projects on such a platform, as they are still in the so-called start-up phase (Ahlers et al., 2015; Koch and Siering, 2015). Therefore entrepreneurs need time and practice in order to gain the adequate knowledge and other resources needed for running a crowdfunding campaign.

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Resources are tangible or intangible assets an enterprise or an entrepreneur owns. By tangible resources we understand resources that come in deployable form (e.g. canvas for a painter), whereas intangible resources develop as they are being used (e.g. knowledge and reputation). By allocating the resources, entrepreneurs are able to stabilize, develop, strengthen, but also to deteriorate their positions in various circumstances and conditions on the market. For instance, reputation is a strong intangible resource, which results from a series of previous success. Reputation has a strong positive effect on prominence (Boyd, Bergh & Ketchen, 2010); the stronger the positive reputation is, the higher the entrepreneurs’ and their project’s visibility will be, while a negative reputation might lead to declension. Positive reputation therefore increases the positive observability of the transmitted signals (Connelly et al., 2011) and therefore the visibility of the entrepreneurs themselves, which is a major characteristic of a strong and effective signal, and therefore provides a source of competitive advantage for the entrepreneurs.

Furthermore, according to Mollick (2014) Matthew effect (i.e. the rich get richer, the poor get poorer) might also be associated with the ‘previous success leading to future success’ assumption, because of the strengthening ability of quality signals. Merton’s (1968) arguments regarding Matthew effect suggest that talented entrepreneurs are more likely to be involved in multiple successful projects, and projects will have greater customer awareness when they are run by those who already had successful projects behind themselves. In other words, Matthew effect suggests that success of the past is often followed by a success in the future.

Based on the previous arguments regarding previous entrepreneurial success and crowdfunding, we can suppose that previous success is a strong indicator of the entrepreneurial professionalism and competence, and expect that previous success strengthens the possibility of a future success. Therefore I hypothesize as follows;

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H2: There is a positive association between the entrepreneurs’ previous success on a

crowdfunding platform and the success of the project.

2.2.5. Diversity as a quality signal

According to Connelly et al. (2011) one of the major characteristics of a successfully transmitted signal is its observability, which often results in the increase of the visibility of the entrepreneurs. Greater visibility might not only be reached by the increased number of first-party posts (i.e. signals), but also by the diversity of these first-party signals. By delivering different kinds of signals to their investors, entrepreneurs might be able to increase their visibility compared to the other entrepreneurs. Additionally, they might be able to broaden their audience size as well, due to the investors’ various fields of interest. For instance certain individuals are more interested in the detailed description of the crowdfunding projects, others are more concerned about the regular process updates via photos and videos, while there are people who are driven by a certain level of herding behavior (Koch and Siering, 2015) and pay more attention to the other investors, and make their final decision based on the number of backers.

Several academic sources (Ahlers et al., 2015; Mollick, 2014; Koch and Siering, 2015, etc.) examine different kinds of first-party signals in the context of crowdfunding that might increase the probability of a successful campaign. Based on their finding we can assume that the above described assumption might be true, and the increased diversity might lead to the increased probability of an investment opposed to the rival entrepreneurs. Consequently, I frame the hypothesis that there is a positive relationship between the diversity of signals and the success of the crowdfunding projects.

H3: There is a positive association between the diversity of signals entrepreneurs use

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3. DATA AND MEASURES

3.1. Data

In order to be able to investigate the research question and to demonstrate the final results, I am using the database of a Dutch crowdfunding platform dedicated for artistic- and cultural projects; the Voordekunst (https://www.voordekunst.nl/).

There are separate databases integrated into the crowdfunding platform of Voordekunst and only the employees can access that information. Based on their experiences with entrepreneurs, by this time they know which are the essential parameters – for the company – and therefore Voordekunst collects data, which they as a company can actively use (and monitor). The databases of Voordekunst include parameters about the projects, about the entrepreneurs, and about the investors. Because of the heavy costs and time consumptions, only those parameters are collected that can be directly used by Voordekunst. Certain parameters are not yet collected because of the fact that the majority of the entrepreneurs are in the start-up phase (i.e. they are beginners), as soon as they start using the platform on a regular basis, other parameters will be relevant to collect and analyze. Also, due to certain regulations and privacy issues, only limited types of information can be collected about the investors.

The final dataset used during the present thesis work was provided by Voordekunst and it consists of 779 projects for the year 2016. The whole dataset provides a highly representative sample of the dynamics of the crowdfunding industry. While cleaning and coding the data, one project could not be found, consequently I analyze 778 recently ended crowdfunding campaigns. The final dataset includes 620 successful and 158 failed campaigns from 27th September 2015 until 30th December 2016. Being numerical and having only finite

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number of values, the data is discrete data, therefore it is considered to be at the highest level of precision (Saunders, Lewis, Thorhill, 2009).

3.2. Measures

In order to be able to answer the research question, the variables are measured in the following ways during the analysis of the thesis work;

3.2.1. Dependent Variable

Success is described as a dummy variable (if 0 the project did not managed to reach

the project goal in the given timeframe, if 1 the project managed to reach 80% of the project goal) (Moss et al., 2014; Ahlers et al., 2015; Hobbs, et al., 2016). At Voordekunst projects are determined to be successful if they manage to generate at least 80% of the initially stated project goal.

3.2.2. Independent Variables

Number of updates is the total number of first-party signals (e.g. videos, pictures,

posts, preparedness & sion, previous success etc.) communicated directly by the entrepreneurs for the potential investors (Mollick, 2014; Labovitz, 2010; Hobbs, et al., 2016).

Previous success is described as a dummy variable (if 0 it signals that there was no

previous success, if 1 it signals that the producers had another successful project).

Diversity index is determined based on the number of the different types of quality

signals provided by the entrepreneurs to the investors (Mollick, 2014). For being able to operationalize diversity, I measure this variable based on the Herfindahl–Hirschman-index (hereinafter, HHI). HHI is generally used for measuring market potential of companies by using the square of the market share of the company. However, this measure is also applicable for measuring diversity. In this thesis the following quality signals are taken into account while calculating the diversity;

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21 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 𝑖𝑛𝑑𝑒𝑥 = (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑎𝑐𝑘𝑒𝑟𝑠2+ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑝𝑑𝑎𝑡𝑒𝑠2+ 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑒𝑛𝑡𝑟𝑒𝑝𝑟𝑒𝑛𝑒𝑢𝑟𝑖𝑎𝑙 𝑝𝑎𝑠𝑠𝑖𝑜𝑛2+ 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑝𝑟𝑒𝑝𝑎𝑟𝑒𝑑𝑛𝑒𝑠𝑠2+ 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑢𝑐𝑐𝑒𝑠𝑠2) / (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑎𝑐𝑘𝑒𝑟𝑠 + 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑝𝑑𝑎𝑡𝑒𝑠 + 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑒𝑛𝑡𝑟𝑒𝑝𝑟𝑒𝑛𝑒𝑢𝑟𝑖𝑎𝑙 𝑝𝑎𝑠𝑠𝑖𝑜𝑛 + 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑝𝑟𝑒𝑝𝑎𝑟𝑒𝑑𝑛𝑒𝑠𝑠 + 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑠𝑢𝑐𝑐𝑒𝑠𝑠) 3.2.3. Control Variables

Level of Entrepreneurial Passion (Chen et al., 2009; Hobbs et al., 2016; Agema, J.,

2017; Elsbach & Kramer, 2003; Cardon et al., 2009) of the project description is measured on a scale from 1 to 5 (where 1 = Lack of passion demonstrated, and 5 = The campaigner has demonstrated clear evidence of their passion for the project). While rating the projects I was looking for the words ‘passion’, ‘dream’, ‘desire’ and the synonyms, for the evidence of personal attachment (e.g. a personal story), for the emotional motivation and rated the projects according to Hobbs et al. (2016).

Table 1: Project description analysis ratings - Level of entrepreneurial passion

Rating

1 Lack of passion demonstrated. 2 Limited amount of passion evident.

3 There is evidence of passion from the project. 4 Passion for the project is demonstrated.

5 The campaigner has demonstrated clear evidence of their passion for the project. Source: Hobbs et al., 2016

Chen et al. (2009) highlight certain academic studies, which found that passion for work has strong positive effects on venture growth; passionate entrepreneurs turned out to have significantly greater motivation in attracting customers as well as in growing their ventures. Passion often serves as a strong signal of the entrepreneurial motivation, and an indicator whether they are likely to “continue pursuing goals when confronted with difficulties” (Chen et al., 2009 pg. 199). Consequently, entrepreneurial passion might

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overcome the challenge of information gap and convince the targeted investors to invest in their crowdfunding projects.

Level of Preparedness (Chen et al., 2009; Hobbs et al., 2016; Agema, J., 2017;

Elsbach & Kramer, 2003; Cardon et al., 2009) of the project description is measured on a scale from 1 to 5 (where 1 = Pitch description limited in detail, and 5 = There is a high level of detail within the pitch document giving the reader a clear and coherent understanding of the project and the campaigners). While rating the projects based on the level of preparedness of their description I was looking at the understandability, the depth of the details, the presence and the quality of the photos and videos, and at the bold letter. I was also looking at whether the entrepreneurs described their projects in great details and in more languages, and whether they described the rewards in details at the end of the pitch. Similarly to the level of entrepreneurial passion, I was also rating the projects based on the paper of Hobbs et al. (2016).

Table 2: Project description analysis ratings - Level of preparedness

Rating

1 Pitch description limited in detail.

2 Pitch description provides a good understanding of the project. 3 Pitch description goes into detail about the project.

4

Pitch description is substantial and coherent and provides the reader with an understanding of both the project and campaigners.

5

There is a high level of detail within the pitch document giving the reader a clear and coherent understanding of the project and the campaigners. Source: Hobbs et al., 2016

The research of Elsbach and Kramer (2003) suggests that a thoughtful and reasonable description indicates the efforts and time spent on the pitch, i.e. how well the entrepreneur is prepared. As preparedness represents a strong signal of quality, a thoughtful (Chen et al., 2009) and detailed (Hobbs et al., 2016) crowdfunding project description can positively impact the investment decisions of the potential investors. Jelle Agema (marketer at

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Voordekunst) also highlighted that preparedness plays an important role in the project quality; clear project incentives in the description and structured text (bold letter included) clearly contribute to the appeal of a campaign. Entrepreneurs need to be really concise and clear about their product, also, a structured and fragmented text in the project description – including bold letters – is essential because of the transparency, as well as it must catch and maintain the attention of the crowd.

Average investment is described by the amount funded – number of backers ratio.

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠 = 𝐴𝑚𝑜𝑢𝑛𝑡 𝑓𝑢𝑛𝑑𝑒𝑑 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑎𝑐𝑘𝑒𝑟𝑠

Project duration is the total number of days of the project accepts investments

(Mollick, E., 2014; Ahlers et al., 2015). At voordekunst.nl entrepreneurs are advised to run their campaigns for 30-40 days however, it is not fixed and not set by the company. Campaigns can run until 90 days.

Project goal is the amount of investment entrepreneurs seek to raise

(Mollick, E., 2014; Ahlers et al., 2015).

Discipline describes the category of the crowdfunding campaign (Mollick, E. 2014).

On the platform of Voordekunst there are ten categories; dance, design, film, heritage, media, music, photography, publications, theatre and visual arts.

4. ANALYSIS AND RESULTS

4.1. Intraclass Correlation Coefficient

As already described in Section 3.2.3 the level of entrepreneurial passion and that of preparedness were rated manually. In order to establish the reliability of the measures, an Intraclass Correlation Coefficient (ICC) analysis – using Single-Rating, Absolute-Agreement

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and Two-Way Random Model – was conducted. The analysis consists of 103 randomly selected crowdfunding projects that were measured by three human coders – including myself.

The analysis at a 95% confidence interval provided the Intraclass Correlations; for the level of entrepreneurial passion =.890 (See Appendix 1), for the level of preparedness =.831 (See Appendix 2). In both cases the Intraclass Correlations for the single measures were above .8, thus proving optimal agreement between the three coders; there is only a limited variability and a high consistency among the measures. Consequently, the measures of the two control variables are considered to be reliable.

4.2. Descriptive Statistics

Table 3: Descriptive Statistics

Min. Max. Mean S.D.

Success 0 1 0.80 0.403 Independent Variables Number of updates 0 27 3.94 4.187 Previous success 0 1 0.01 0.101 Diversity index 1 1088 57.54 67.490 Control Variables Super-success 0 1 0.20 0.402 Average investments 0 2662 94.83 145.24 Project duration 7 112 42.65 14.767 Project goal 1000 200000 6402.74 9725.257 Level of Entrepreneurial Passion 1 5 2.93 1.162 Level of Preparedness 1 5 3.42 1.094

Valid N = 778 (number of samples)

Table 3 provides the descriptive statistics of the variables (independent, dependent and control variables) of the dataset. From the sample provided by Voordekunst 620 crowdfunding projects were able to meet their targets (i.e. overall success rate = 79.69%). The

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average of the projects’ duration is 42.65 days, which means that generally around one and a half months are needed for a crowdfunding campaign at Voordekunst.

The data also indicate that the average number of post are around 4 per project (= 3.94); there are entrepreneurs who post updates 27 times during the lifetime of the project, while 20% (=156) of the projects were left without any update posted by the founder. The average investment is almost EUR 100 (=EUR 94.83), while the highest donation was more than EUR 2500 (=EUR 2662). The average project goal is around EUR 6,500 (=EUR 6,402.74) however, there are great deviations shown in the sample as the minimum value is EUR 1000, while the largest project goal was set to be EUR 200,000. Similarly large discrepancies are shown regarding the diversity index (HHI), which is due to the fact that it is highly affected by the amount of the project goal.

There are differences also in terms of the means of the level of entrepreneurial passion (=2.93) and the level of preparedness (=3.42). According to the descriptive statistics, the mean of the level of preparedness is significantly higher, which indicates that the entrepreneurs might pay more attention to the outlook and the professional content of the project pitches than to the emotional content and the presentation of the attachment.

Looking at the data I realized that there are 465 projects which managed to realize 80-110% of their goal. This being really interesting, I intended to study those projects, which were excessively successful (i.e. in this case above 110%). It is more than interesting to see than if we are looking at the so-called super-success, only 157 managed to reach at least the 110% out of the 778 (i.e. super-success rate = 20.18 %). Interestingly, this number is far below the overall success rate (=79.69%).

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Table 4: Descriptive Statistics of Disciplines

N Success Success rate

Dance 31 27 0.87 Design 25 23 0.92 Film 68 47 0.69 Heritage 20 14 0.70 Media 16 12 0.75 Music 266 219 0.82 Photography 69 58 0.84 Publication 84 66 0.79 Theatre 107 87 0.81 Visual Arts 92 67 0.73 N = 778 (number of samples)

In terms of industry variance Table 4 and Figure 1 represent the main trends and differences. First, in case of each discipline there are more successful than failed projects. Second, music (n=266) is the most popular category in our dataset, while media (n=16) and heritage (n=20) are the least popular ones. This pattern was also confirmed by Jelle Agema during our interview. In terms of success rates design (=.92) and dance (=.87) turned out to be the most successful, while the film category received the lowest success rate (=.69).

31 ₂₅ 68 20 16 266 69 84 107 92 4 2 21 6 4 47 11 18 20 25 0 50 100 150 200 250 300 Nm ber o f pro jec ts

Figure 1: Discipline distribution

Successful Failed

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27 4.3. Correla tion M at ri x Table 5 : Me an s, S tan dar d De viat ion s an d Pe arson C orrelati on s Mea n S .D. 1 2 3 4 5 6 7 8 9 10 11 1. S uc ce ss 0.80 0. 403 1 2. Numbe r of upda te s 3. 94 4. 187 .261 ** 1 3. P re vious S uc ce ss 0. 01 0. 101 -.012 -.032 1 4. Dive rsit y index 57. 54 67. 490 .337 ** .270 ** -.029 1 5. Disc ipl ine -.022 -.014 -.022 -.036 1 6. S upe r-succ ess 0. 20 0. 402 .254 ** .088 * .012 .173 ** .036 1 7. Ave ra ge investm ents 94. 83 145 .24 .041 -.027 -.030 -.070 -.117 ** -.070 1 8. P roje ct dura ti on 42. 65 14. 767 -.047 .142 ** -.080 * .059 -.009 -.078 * .173 ** 1 9. P roje ct g oa l 640 2. 74 9725. 257 -.074 * .122 ** -.044 .334 ** -.096 ** -.090 * .584 ** .285 ** 1 10. L ev el of Entr epr en eu ria l P assi on 2. 93 1. 162 .548 ** .227 ** .050 .281 ** -.034 .264 ** .065 .003 .017 1 11. L ev el of P re pa re dn es s 3. 42 1. 094 .571 ** .232 ** .054 .266 ** -.073 * .270 ** .125 ** .035 .077 * .580 ** 1 **. C or re lation i s si gnific ant at the 0.01 le ve l (2 -ta il ed) . *. Co rr elation i s si gnific ant at the 0.05 le ve l (2 -tail ed) .

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The – independent, dependent and control – variables, their means, standard deviations and Pearson correlation coefficients are provided in the Correlation Matrix (Table 5). According to Table 5, the number of updates (.261, p < .01), the diversity index (.337, p < .01), the super-success (.254, p < .01) have positive and medium effects on the success of the crowdfunding projects, while the level of entrepreneurial passion (.581, p < .01) and the level of preparedness (.622, p < .01) have positive and large effects on the success.

Table 5 further shows that there are relatively low correlations among the discussed variables, except for level of entrepreneurial passion and the level of preparedness (.558, p < .01), and understandably between the average investments and the project goal (.584, p < .01).

4.4. Hypotheses testing

For testing the three hypotheses a one-way ANOVA test (Table 6) and a hierarchical binary logistic regression (Table 7) analysis were conducted, while controlling for the discipline, the super-success, the average investment, the project duration, the project goal, the level of entrepreneurial passion, and the level of preparedness.

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Table 6: One-way ANOVA

SS DF Mean F Sig. Number of updates (H1) 928.176 1 928.176 56.740 .000 Error 12694.221 776 16.359 Total 13622.397 777 Previous success (H2) 0.001 1 0.001 0.110 .741 Error 7.917 776 0.010 Total 7.918 777 Diversity index (H3) 401610.784 1 401610.784 99.328 .000 Error 3137586.142 776 4043.281 Total 3539196.925 777 Discipline 2.171 1 2.171 0.374 .541 Error 4503.707 776 5.804 Total 4505.878 777 Super-success 8.074 1 8.074 53.439 .000 Error 117.244 776 0.151 Total 125.317 777 Average investments 28199.840 1 28199.840 1.337 .248 Error 16363239.916 776 21086.649 Total 16391439.756 777 Project duration 372.707 1 372.707 1.711 .191 Error 169065.284 776 217.868 Total 169437.991 777 Project goal 406227631.609 1 406227631.609 4.313 .038 Error 73082912311.847 776 94179010.711 Total 73489139943.455 777 Level of Entrepreneurial Passion 315.211 1 315.211 333.037 .000 Error 734.465 776 0.946 Total 1049.676 777 Level of Preparedness 303.263 1 303.263 375.751 .000 Error 626.297 776 0.807 _Total 929,.59 777

Valid N = 778 (number of samples)

First a one-way ANOVA (Table 6) was conducted, which shows that there is a statistically significant effect of number of updates (p = .000) and the diversity index (p = .000) on the success of the crowdfunding campaigns. On the other hand, previous success has no significant effect on the success (p = .741). This suggests that H1 and H3 are supported however, in order to confirm these results further research is needed.

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Second, a hierarchical logistic regression (Table 7) was made. It is necessary for the analysis because of the binary dependent variable, which means there might be only two possible outcomes (i.e. success or failure). The analysis is a highly appropriate model for predicting the outcome based on the Classification Accuracy (Overall Percentage = 98.2). The closer the Classification Accuracy is to 100, the more appropriate the model is for testing the hypotheses.

Table 7 provides the results of the binary logistic regression. In order to be able to interpret the data, besides the p-value we also need to concentrate on Ex (B), which measures the probability of a certain outcome; i.e. it measures how an increase in the variables would affect the outcome (in this case the success).

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31 Table 7 : Hie rarchi cal B in ary L ogist ic Re gression Model 1 Model 2 Model 3 Model 4 Model 5 Va ria ble S ig . Ex p (B ) Si g. Ex p (B ) S ig . Ex p (B ) S ig . Ex p (B ) S ig . Ex p (B ) C onst ant .000 0. 005 .000 0. 002 .000 0. 005 .000 0. 000 .000 0. 000 Discipli ne .585 .145 .596 .681 .471 Discipli ne _Da nc e .971 1. 040 .784 1. 402 .983 1. 024 .620 7. 508 .701 8. 559 Discipli ne _De sig n .928 1. 086 679 1. 485 .926 1. 089 .453 4. 177 .336 7. 110 Discipli ne _F il m .227 0. 516 .608 0. 748 .225 0. 513 .837 0. 812 .922 1. 100 Discipli ne _He rita ge .502 0. 577 .759 0. 769 .511 0. 582 .408 0. 306 .641 0. 496 Discipli ne _Med ia .275 0. 309 .227 0.252 .272 0. 304 .679 0. 436 .640 0. 243 Discipli ne _Musi c .765 1. 143 .134 2. 067 .681 1. 204 .373 2. 283 .113 4. 818 Discipli ne _P hotog ra ph y .786 0. 844 .900 0. 919 .773 0. 835 .450 2. 371 .494 2. 295 Discipli ne _P ubli ca ti ons .118 0. 403 .127 0. 397 .164 0. 441 .392 0. 275 .279 0. 215 Discipli ne _The atre .828 0. 892 .541 1. 406 .824 0. 889 .353 2. 609 .157 4. 634 S upe r-succ ess .994 297283452. 952 .994 239381887. 577 .994 292176879. 414 .994 29832388. 649 .993 49084650. 195 Ave ra ge investm ents .062 1. 003 .014 1. 004 .064 1. 003 .000 1. 020 .000 1. 021 P roje ct dura ti on .607 0. 995 .210 0. 988 .468 0. 993 .995 1. 000 .591 0. 988 P roje ct g oa l .003 1. 000 .000 1. 000 .003 1. 000 .000 0. 999 .000 0. 999 L ev el of Entr epr en eur ial P assi on .000 3. 723 .000 3. 878 .000 3. 792 .000 3. 234 .000 3. 431 L ev el of P re pa re dn ess .000 3. 85 4 .000 3. 504 .000 3. 875 .001 3. 633 .000 4. 107 Num b er of u p d at es (H1 ) .000 1. 332 .025 1. 331 P re viou s su cc ess (H2) 0.061 0. 069 .297 0. 029 Dive rsity in d ex (H3) .000 1. 235 .000 1. 236 Numbe r of o bse rva ti ons 778 778 778 778 778 Va li d N = 778 ( numbe r o f sa mpl es)

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Table 7 provides the output of the regression analysis (see Model 5). First, H1 proposes that the number of updates has positive association with the project success. The results show significant effect on success (p = .025), as well as Ex (B) is greater than 1, which means that hypothesis 1 is supported. According to Ex (B) an increase in the number of updates increases the probability of the success of the project by 33.1%. Second, H2 argues that previous success is positively associated with future success however Table 7 does not confirm that based on the Ex (B) (= 0.029) and the significance level (p = .297). Third, according to H3 the diversity of signals has a positive effect on the success of the projects. This is also supported by the analysis (p = .000); an increase in the level of diversity increases the probability of the success by 23.6%.

The regression analysis revealed further interesting results as well. I am describing them one by one below.

First, the descriptive analysis revealed great differences among the various crowdfunding disciplines already. This was further analyzed during the regression analysis. The Ex (B) values highly differ from each other; for instance it suggests that music and theater disciplines are the most likely to get fully funded however, according to the regression analysis there is no significant relationship between the different types of disciplines and the success of the crowdfunding projects (p > .05). Therefore I cannot confirm that there is a significant difference among the crowdfunding disciplines and their effects on the success of a project.

Second, another interesting result is that project duration is not associated with project success (p = .591). Also, even though one might argue that longer duration increases the probability that potential investors get aware of the project, which might result in an

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investment, the regression analysis shows that an increase in the project duration would rather decrease the probability of a success (Ex (B) = 0.988). This is in line with the findings of Mollick (2014) and the interview with Jelle Agema, which indicates that the quality of the project itself is much more important that the long period of investment acceptance (i.e. project duration). Mollick (2014) prove that the majority of the investments occur at the early stages of the projects’ lifetime. This might probably be explained by the fact that projects tend to fade over time; they generally recently initiated projects catch the attention of the general audience (Mollick, 2014) due to their special place on the front page.

Third, the regression (Ex (B) = 0.999, p = .000) and the correlation matrix (= -.074, p < .05) show that the project goal is negatively associated with the project success. This means that the greater the project goal is the less likely is the project to get funded.

Fourth, the level of entrepreneurial passion (Chen et al., 2009; Hobbs et al., 2016) and the level of preparedness (Elsbach & Kramer, 2003; Cardon et al., 2009) were also analyzed. The descriptive statistics have already revealed that there is a great difference between the two. The mean of the level of preparedness (= 3.42) is greater than that of the passion (= 2.93), which suggests that entrepreneurs on Voordekunst are more concerned about the depth and preparedness of the project description than the emotional content; they pay attention to provide additional information about the project and the further steps needed for the realization of the project (Koch and Siering, 2015), so that it would arouse the interest of the potential investors. The regression analysis shows that the probability of a successful crowdfunding campaign increases sharply after an increase in the level of preparedness (Ex (B) = 3.341, p = .000) and that of the entrepreneurial passion (Ex (B) = 4.107, p = .000).

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5. DISCUSSION

The present study explored the potential effects of quality signals on the success of a reward-based crowdfunding project. Even though, Ahlers et al. (2015) have already revealed that signals play highly important roles in project success, the literature on signaling theory in the context of crowdfunding is still limited. This study intended to fill the gap by focusing on the reward-based crowdfunding model – which is the most popular one (World Bank, 2013; Mollick, 2014) – and on the cultural and creative industries. Specifically, I proposed three hypotheses (about different types of signals) and conducted a regression analysis in order to be able examine their relationship with the crowdfunding projects’ success. For the setting of my study, Voordekunst was chosen, which is a Dutch crowdfunding company focusing on cultural and artistic projects. Out of the three hypotheses two (H1 and H3) were supported by the analytical research, while one (H2) was not supported by the results. Below, I discuss them one by one.

First, I proposed that the quantity of signals (i.e. number of posts) should have positive association with project success (Connelly et al., 2011; Moss et al., 2014; Kirmani and Rao, 2000, Labovitz, 2010). It is not surprising that this hypothesis was supported by both the one-way ANOVA (p = .000) and the logistic regression (Ex (B) = 1.331, p = .025). One possible explanation for this result is that by regularly providing updates about their projects for the investors, entrepreneurs might increase their projects’ observability (Connelly et al., 2011) as well as their credibility and trustworthiness (Labovitz, 2010). This result is in line with the finding of previous research (Mollick, 2014; Belleflamme et al., 2014)

Second, I proposed that previous success has positive association with future project success (Mollick, 2014; Merton, 1968; Connelly et al., 2011; Boyd, Bergh & Ketchen, 2010). However, this theoretical assumption was not supported by my research. The results show that previous success is neither correlated, nor has significant effect on the project success

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(p = .297). One possible explanation for this result is that in the whole dataset there were only 8 projects, where the entrepreneur had a previous success on the Voordekunst platform. This relatively low number could have led to the insufficient results.

Another explanation is that entrepreneurs become overconfident after having a successful crowdfunding campaign. The hubris theory of entrepreneurship (Hayward, Sheperd and Griffin, 2006) models how overconfident entrepreneurs are more likely to start new venture and how these ventures are more likely to fail. Overconfidence arises when the entrepreneurs overestimate their own capabilities and/or underestimate the necessary resources for a successful campaign. Hayward, Sheperd and Griffin (2006) propose that entrepreneurs with previous success are more likely to become overconfident without improving their skills to achieve better performance in the future.

Third, I proposed that diversity of signals has positive effect on the success of crowdfunding projects (Mollick, 2014; Ahlers et al., 2016; Koch and Siering, 2015). This assumption was supported by the research; entrepreneurs who had higher diversity indexes were more successful. This means that those who communicated different kinds of signals towards their potential investors were more likely to get funded. This result can be explained by the increased visibility of the projects due to the broader audience reached and the differentiation from the other rival entrepreneurs.

The descriptive statistics have already revealed the relatively high success rate (almost 80%), which highly exceeds Kickstarter’s (one of the most popular crowdfunding platform) success rate (48.1%) (Mollick, 2014). This is claimed to rise from Voordekunst’s entrepreneur-oriented business model. However, having a closer look at the data, the great discrepancy between the success and the so-called super-success is easy to be recognized. The overall success rate is 79.69% (i.e. 640 projects managed to reach at least 80% of their project goal), while the super-success rate is only 20.17% (i.e. 157 projects managed to reach more

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than 110% of their goal out of the 778). It is a really interesting as well as thought-provoking fact. One possible reason for such a discrepancy between the two types of success rates might arise from a natural human behavior. In case of a seemingly unsuccessful project, we might suppose that the entrepreneurs ask their families and friends to help them with the amount needed for the success, which they would transfer back to them. In this case the – otherwise – unsuccessful entrepreneurs can collect the amount funded even if they were not able attract enough investments for the real success, i.e. 80% of the project goal. Further research should focus on the success factors, which make successful entrepreneurs even more successful; an interesting and highly relevant research would examine the differences between the successful and the super-successful entrepreneurs.

5.1. Conclusion

The core objective of this thesis work was to examine the success factors in the context of crowdfunding – i.e. a novel fundraising way using the Internet without the traditional financial intermediaries. Specifically, I relied on signaling theory (Spence, 1973), according to which entrepreneurs provide and communicate various types and number of quality signals for the potential investors in order to have their crowdfunding projects realized. Based on this theory I framed three hypotheses to examine the different kinds of quality signals and their effects on the success of the crowdfunding projects.

In total I analyzed 778 recently ended crowdfunding projects on a Dutch reward-based platform, the Voordekunst, which is entirely dedicated for artistic and cultural projects, therefore the dataset fits perfectly to the focus of this thesis because of its cultural aspect. The findings of the study support two of out of the three hypotheses. First, I proposed that the number of updates should have positive association with the project success. This hypothesis was supported by the analysis. Academic literature (Kirmani and Rao, 2000; Moss et al., 2014; Connelly et al, 2011) argues that high-quality microenterprises and –entrepreneurs who

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transmit more signals are more likely to get funded, because by doing so, they create engagement as well as they increase their credibility and trustworthiness (Labovitz, 2010). Second, I proposed that previous success of the entrepreneurs has positive effect on the success of the crowdfunding projects. However, this theoretical assumption was not supported by the results. A possible explanation is that entrepreneurs become overconfident after a successful campaign. According to the hubris theory (Hayward, Sheperd and Griffin, 2006) overconfident entrepreneurs are more likely to fail because they tend to overestimate their abilities and underestimate the necessary resources. Third, I proposed that the diversity of signals should have positive association with the project success. This assumption was supported by the analysis. I argued that by communicating different kinds of signals, entrepreneurs might be able to differentiate themselves from their rivals as well as broaden their audience.

Similarly to other studies, my research has its own limitations. First, because of my focus a Dutch crowdfunding company, the data represents only crowdfunding campaigns within the Netherlands, consequently the generalizability of the study is relatively low. It would be worth conducting a similar research with other countries included, as that might provide different results. Second, not being Dutch, I needed to use online translators in order to be able to analyze the Dutch project descriptions, which might have changed the content to a certain extent. Third, the dataset contained a relatively low amount of projects where previous success occurred. It would be therefore reasonable to repeat the study with another dataset in order to be able to fully deny this hypothesis.

This study has several contributions to the literature both about signaling theory and crowdfunding. It extends the already existing academic literature about signaling theory to context of crowdfunding and emphasizes the importance of first-party signals in the success

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of crowdfunding projects. Also, it strengthens the previous knowledge about crowdfunding by identifying potential success factors of a crowdfunding campaign.

From a practical point of view, my study is relevant for the entrepreneurs as well as for the crowdfunding platforms themselves. From an entrepreneurial point of view, the research highlights those first-party signals to which the entrepreneurs need to pay special attention in order to be able to have their crowdfunding projects realized. Specifically, they need to increase their updates (H1) and have different kinds of quality signals (H3). Also, they need to pay special attention to the preparedness and passion of the project descriptions. From a managerial point of view, crowdfunding platforms may also benefit from the results as the proper use of success factors increases the success rate of the platform. Furthermore, based on the results of the study, the company advisors would be able to provide further suggestions for their clients in order to make their projects more appealing and eventually fully funded.

The present research can be extended within numerous research directions. First, the study might be repeated; a focus on an other crowdfunding platform might confirm – or deny – the results of my analysis using the dataset of Voordekunst. Second, the study might be converted into a predictive context (Koch and Siering, 2015), which would lead to techniques that are able to predict the success of crowdfunding projects based on the results of this – and other similar – studies. Third, an interesting future research might be to further analyze the difference between success and super-success; what makes certain projects even more popular and successful than the others, how to use quality signals in order to increase the projects’ investments, etc.

In conclusion, signaling theory was proven to be reasonable to use also in the context of crowdfunding; the number of updates and the diversity of signals are positively associated

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with the success of the crowdfunding projects because they increase the observability of the projects (Hobbs et al., 2011) as well as the visibility of the entrepreneurs themselves.

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6. REFERENCES

Agema, Jelle (2017): Interview on 13th February 2017 at the Voordekunst office Agrawal, A. K., Catalini, C., Goldfarb, A. (2011): The Geography of Crowdfunding. NBER Working Paper. No. 16820.

Available at: https://ideas.repec.org/p/nbr/nberwo/16820.html

Ahlers, G. K. C. et al. (2015): Signaling in Equity Crowdfunding. Entrepreneurship

Theory and Practice, Volume 39, Issue 4, Pages 955-980

Available at: http://onlinelibrary.wiley.com/doi/10.1111/etap.12157/full

Akdeniz, M. B., Calantone, R. J. and Voorhees, C. M. (2014): Signaling Quality: An Examination of the Effects of Marketing- and Nonmarketing-Controlled Signals on

Perceptions of Automotive Brand Quality. Journal of Product Innovation Management, Volume 31, Issue 4, Pages 728–743.

Available at: http://onlinelibrary.wiley.com/doi/10.1111/jpim.12120/abstract

Barasinska, N., Schäfer, D. (2014): Is Crowdfunding Different? Evidence on the Relation between Gender and Funding Success from a German Peer-to-Peer Lending Platform. German Economic Review, Volume 15, Issue 4, Pages 436-452

Available at: http://onlinelibrary.wiley.com/doi/10.1111/geer.12052/full

Baum, J. A. C., Silverman, S. B. (2004): Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups. Journal of Business Venturing, Volume 19, Issue 3, Pages 411-436. Available at: http://www.sciencedirect.com/science/article/pii/S0883902603000387

Belleflamme, P., Lambert, T., Schwienbacher, A. (2014): Crowdfunding: Tapping the right crowd. Journal of Business Venturing, Volume 29, Issue 5, Pages 585-609