Mobilizing the social network in crowdfunding

Transcription

1 Master thesis: Mobilizing the social network in crowdfunding University of Amsterdam Faculty of Business and Economics MSc. In Business Administration - Entrepreneurship and Innovation track Final version, 23 June 2017 Submitted by Kimman, Dicky ( ) Supervised by Dhr. B. (Balasz) Szatmari 0

2 Statement of originality This document is written by Student Dicky Kimman who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is 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. 1

3 Table of Contents MASTER THESIS:... 0 MOBILIZING THE SOCIAL NETWORK IN CROWDFUNDING... 0 ABSTRACT INTRODUCTION LITERATURE REVIEW CROWDFUNDING ROLE OF SOCIAL NETWORK REPUTATION IN SOCIAL NETWORK SOCIAL MEDIA METHODOLOGY PART DATA COLLECTION CONSTRUCT MEASUREMENTS VALIDITY AND RELIABILITY DATA RESEARCH MISSING DATA-POINTS AND DESCRIPTIVE ANALYSIS TRANSFORMATION AND DESCRIPTIVE STATISTICS OUTLIERS ASSUMPTIONS CHECK Variable types Multicollinearity Normality of residuals HIERARCHICAL REGRESSIONS First hierarchical regression Second hierarchical regression EXPLORATIVE RESEARCH FOR MEDIATION OR MODERATION EFFECT Moderating effects models Moderating effect model A Moderating effect model B Mediating effect models Mediation effect model C Mediation effect model D DISCUSSION DISCUSSION OF THE FINDINGS LIMITATIONS IMPLICATIONS FOR RESEARCH IMPLICATIONS FOR PRACTICE CONCLUSION REFERENCES APPENDIX A REFERENCES:

4 Abstract Purpose The purpose of this paper is to provide insights in how entrepreneurs can mobilize their social network in order to increase their chances of success in crowdfunding. Design / methodology / approach This paper reflects on the current state of the literature about crowdfunding in relation to social network theory. Additionally, it collected quantitative data from the crowdfunding platform Kickstarter and social network sites Facebook and Twitter. Consequently, the data is analyzed to reflect on the literature and to offer new insights in how an entrepreneur can tap in and mobilize the social network around him. Findings The word-of-mouth on social networks sites is an important indicator for the success rate in crowdfunding. An entrepreneur can trigger this word-of-mouth, also social media diffusion, by mobilizing his social network to spread the word by means of updates. Having more Facebook friends plus creating and linking a Facebook page to the crowdfunding campaign positively affect the social media diffusion. The reputation within the crowdfunding community is also an important indicator for the success rate. Research limitations / implications As the sample size is limited (N=83 and N=146) the findings in this paper should be interpreted with caution and future research is needed to check the validity in larger data sets. Nonetheless provide interesting insights for entrepreneurs searching for funding on crowdfunding platforms. Keywords crowdfunding, social network, reputation, social media, word-of-mouth 3

5 1. Introduction To attract financial resources, nowadays entrepreneurs can tap directly in the crowd rather than following the traditional route to funding where a single or small number of investors (e.g. banks, angel investors or venture capital funds) fund projects. It is now possible to access a huge pool of potential investors on crowdfunding platforms (Zheng et al., 2014). On these platforms, entrepreneurs can create an account, post their ideas and include a text and video to present their plans in order to collect funds. Crowdfunding entrepreneurs tend to be extremely innovative. Many important projects in consumer electronics as of 2013, such as 3D printers, electronic watches and video game consoles, were funded through crowdfunding campaigns (Mollick and Kuppuswamy, 2014). Thanks to the advancement of web 2.0 technology, crowdfunding has developed rapidly, and over 450 platforms have emerged worldwide (Cordova et al., 2015). In the era of the Social Web, crowdfunding has become an increasingly more important approach for entrepreneurs or small enterprises to raise the essential capitals from the crowd to support or kick start their projects or businesses. Crowdfunding websites such as Kickstarter and IndieGoGo behave as online intermediary agents that allow project founders to quickly reach a large number of individual investors with minimal costs. The role of social capital is stressed as important in crowdfunding (Mollick, 2014; Colombo et al., 2014; Zheng et al., 2014). As the social capital is embedded within the social network (Coleman et al., 1988) and in line with Mollick (2014) this paper will further elaborate on the social network. In addition, the role of social network is underlined as important in funding new ventures (Hsu, 2007; Shane and Cable, 2002). Friends, family and social contacts within the community are important to spread the word of the campaign (Colombo et al., 2014; Ordanini et al., 2011). However, the question remains how can a founder tap into his social network? How can he mobilize his social network? Therefore, the main research question this paper will address is: How does the social network influences the success of a crowdfunding campaign and how can this social network be mobilized? 4

6 The paper is structured as follows. It starts with a literature review defining the concept of crowdfunding. Subsequently it will elaborate on the factors social network, reputation in the social network and social media. Concluding the literature review with a conceptual model. Next, the research method and data collection method are discussed. The relationships between the constructs as pointed out in the conceptual model are tested in the following chapter by means of quantitative research on social network data. What follows is a discussion where the findings about each construct are related to theory and logic. Besides, the discussion provides implications for literature and practice, addresses the limitations of this study and points out directions for future research. To end the paper, the research question will be answered in the conclusion. 5

7 2. Literature review First this chapter will introduce the phenomenon crowdfunding. Second, the role of social network in crowdfunding is described. Thirdly, the importance of reputation within the social network is emphasized. At last, how social network sites like social media platforms influence crowdfunding success are discussed Crowdfunding Crowdfunding is a relatively new way of financing novel ventures. Crowdfunding comes forward from concepts like microfinance and crowdsourcing, but it represents its own unique category of fundraising (Mollick & Kuppuswamy, 2014). Projects vary greatly both in objective and significance, from small projects to founders searching big amounts of financial resources in seed capital (Schwienbacher and Larralde, 2010). Building on the definition of Schwienbacher and Larralde (2010), Mollick (2014) defines crowdfunding as follows: Crowdfunding refers to the efforts by entrepreneurial individuals and groups - 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 crowdfunding projects the products offered are often bought in advance when regular sale has not started yet. Based on the amount funded, backers will receive a monetary or nonmonetary reward. Belleflamme et al. (2013) argue that the involving the crowd in the production process is a major advantage of crowdfunding over traditional ways of funding projects. This involvement enhances the overall experience of the consumer. Mollick (2014) demonstrates that there are four categories of crowdfunding campaigns, namely donation-based, loan-based, reward-based or equity-based projects. For the scope of this research, only reward-based crowdfunding will be discussed in more detail. In reward-based crowdfunding funders receive a reward for backing a project. Backers are treated as early customers, given an earlier buying date or better price. Pre-selling of products to backers is another regular feature for crowdfund projects that produce more traditionally resemble entrepreneurial ventures (projects producing novel software, hardware or consumer products). 6

8 Also, participation from the backer in the product is common, such as being credited into a movie and invitations on opening nights of restaurants or even the creation of personal characters in video games Role of social network Literature around crowdfunding stresses the role of social capital (Mollick, 2014; Colombo et al., 2014; Zheng et al., 2014). Social capital is defined as the sum of the actual and potential resources embedded within, available through, and derived from the social contacts of an individual or an organization (Nahapiet and Ghoshal 1998, as cited in Colombo et al., 2014). These social contacts together are called the social network. Or in other words, the social network is the source in which social capital is embedded (Coleman et al., 1988). The success of funding entrepreneurial financing is influenced by the social network of the individuals who are looking for funding also called founders in crowdfunding. The social network provides the founder with connections to potential backers as well as endorsements of crowdfunding project quality for the potential backers (Shane and Cable, 2002; Sorensen and Fassiotoo, 2011). Kickstarter gives the opportunity to founders to link their Facebook account to their Kickstarter campaign. If founders do so, Kickstarter shows the number of Facebook friends publicly. Mollick (2014) shows that social network size, as measured in number of Facebook friends, predicts the success of the crowdfunding campaign. Contradictory, founders having only a few online connections are better off not linking their Facebook account to the Kickstarter campaign. Other research from Lu et al. (2014) state that the social network size of the founder, as measured in number of followers on Twitter, has low correlations with the crowdfunding success as it only indicates how popular a person is, not how many people are reading or acting on the posts. The social capital of a founder on crowdfunding platforms exists out of two types of social capital according to Colombo et al. (2014). The research makes a clear distinction between external social capital and internal capital. Founders can rely on social contacts outside the crowdfunding platform, such as family, friends and social media contacts. These contacts are referred to as the external social network. Founders can also benefit from the social capital within the crowdfunding platform by establishing relationships with other founders and backers. These contacts are called the internal social network; contacts 7

9 developed within a collective or community. Internal social capital is proven to influence performance of individuals and organizations, increasing the ability to complete complex projects and increase their innovative capabilities (Colombo et al., 2014). Research by Lin, Prabhala and Viswanathan (2013) on a platform for peer-to-peer lending found that borrowers who have more online friends within the platform, so a larger internal social network, are more likely to be funded, get lower interest rates and have a lower probability of ex-post default. In sum, social contacts developed within an online community increases the success getting a loan. The social contacts of a founder within and outside the Kickstarter platform increases the success of the campaign based on three mechanisms according to Colombo et al. (2014). At first, these contacts operate as promoters of the campaign, spreading information around the campaign beyond the founders own social circle. This happens by word-of-mouth, which is defined as the communication between consumers about a product, service, or a company in which the sources are considered independent of commercial influence (Arndt, 1967; Litvin et al., 2008). Word-of-mouth communication can either occur face-to-face or online. There is also a second mechanism going on within the crowdfunding platform which triggers reciprocity through a feeling of perceived obligation (Coleman, 1990 as cited in Colombo et al., 2015). This entails that social contacts that have received funding from the founder feel obliged to help the founder. This mechanism is called specific reciprocity. Faraj and Johnson (2011) add, that a founder benefits from the online community when he backed many projects in the past. Moreover, people feel obliged to support other projects as they have received funding in the past or expect to need funding in the future. This mechanism is called the norm of generalized reciprocity, which is the third mechanism (Colombo et al., 2014). Zheng et al. (2014) also state that a founder can develop internal social capital, so within the Kickstarter community, by investing in other entrepreneurs crowdfunding projects. This may trigger the willingness among other entrepreneurs to fund the founders own project (Staber, 2006). As Kickstarter publicly shows the number of projects that a member has supported in the past, this makes the reciprocating behavior visible to others in the community. This visibility is important for generalized reciprocity, where the member should be seen as a giver (Bolino, Turnley and BLooddgood, 2002). 8

10 At last, Zvilichovsky, Inbar and Barzilay (2015) researched owners of crowdfund projects, also called founders, that back other projects also called backer-owners. They found that backer-owners, are more successful in financing their campaigns compared to owners who did not back others. They also found that backer-owners are more active on the platform than other user types: they back and create more projects than other backers and nonbackers. In other words, they have more social contacts within the community. This is in line with aforementioned argument about the impact of obligation. Backing others has a cumulative effect: the more a founder backs his colleagues, the higher the number of overall backers he secures and thus the probability of financing success (Zvillochovsky et al., 2015). Accordingly, Zheng et al. (2014) found as well that backing others was a significant predictor of crowdfunding success. In line with abovementioned reasoning and research, this research expects that backing others has a positive influence on how likely a project is to succeed. Resulting in the hypothesis which captures the internal social network of a founder: H1. The more a founder backed other projects, the more likely his own crowdfunding campaign is to succeed Building on Mollick s (2014) findings on the number of Facebook friends of the founder, this research also expects that having a larger external social network has a positive effect on crowdfunding success. This results in the first two hypotheses: H2. The larger the external social network of a founder, the more likely his crowdfunding campaign is to succeed 9

11 2.3. Reputation in social network Backing others, possibly develops the founder s reputation within the crowdfunding community which could create more trustworthiness and in increase in social capital (Zheng et al. 2014). As the reputation within social networks is discussed next, Shane and Cable (2002) state that besides the private information advantage that social contacts of the founder have, there is also a social obligation rationale. It entails that investment decisions depend, in part, on the relationships themselves, rather than competence-based criteria. Therefore, Shane and Calbe (2002) suggest that investors are more likely to fund when the founder has a positive reputation from the perspective of the potential backer. Within social networks, reputation is the extent to which users can identify the standing of others in the social media setting. Reputation is a matter of trust which in social media settings is based on aggregate user-generated information. A measurement for reputation within a community can be the activity or number of posts within this community (Kietzmann et al. 2011). Translated to Kickstarter, created projects and updates are two possibilities to post within the Kickstarter community (Kickstarter, 2017). Accordingly, previous created projects can lead to an increase in social capital as Kickstarter allows founders to bulk supporters of (previous) projects (Greenberger and Gerber, 2014). In this way, the social network of the founder increases and is easy to reach when launching a second or third campaign for example. Moreover, the reputation also serves as a signal of future performance based on perceptions of past performance (Dimov and Shepherd 2005) and on the other hand the visibility of past performance or activities is an important aspect of reputation (Lang and Lang, 1988). Kickstarter does show the number of projects a founder created previously, accordingly potential backers have access to that information. Side note, it is not directly showed whether the projects were indeed successful (Kickstarter,2017). However, according to Cope et al. (2011) the success of previous attempts is less important, as Cope et al. describe that failed entrepreneurial ventures are often better prepared to proceed forwards in their next attempt due to learning experiences. Moreover, a qualitative research from Greenberger and Gerber (2014) indicated that indeed founders of failed projects felt more experienced and better prepared to crowdfund again. 10

12 In sum, this research will examine the number of projects created regardless of their success since the aim is to determine the effect of the founders reputation on the success rate of the crowdfunding campaign. Moreover, founders can uphold a reputation of a responsible and accessible project creator by addressing questions and posting regular updates (Hui et al.,2012). Frequent project updates on the project page is already empirically detected as a success predictor in prior research. As updates within the project can motivate potential backers to actual back the project (Kuppuswamy and Bayes, 2013). Also, updates within the first days of the project are positively correlated to fundraising success (Mollick, 2014). According to Kickstarter, the option updates is designed to keep backers informed of a projects progress (Kickstarter, 2017). Kickstarter further states backers appreciate regular, insightful, and honest updates. Do not be hesitant to communicate delays or changes to your original plans or to just check in. Additionally, posts within the Kickstarter community are a form of communication and frequent communication increases benevolence within the community (Cohen et al., 2010). At last, projects without an update had a significantly lower success rate (32,6%) compared to projects with updates (58,7%) in a research conducted on 8,529 campaigns by Xu et al. (2014). Combining the views of Shane and Calbe (2002) about the positive effect of reputation on investment with the views of Kietzmann et al. (2011) about reputation measured in number of posts and with the views of Lang and Lang (1988) about the visibility of past activity plus the positive relation between updates and reputation from Hui et al. (2012), this research expects that the reputation within the Kickstarter community will positively affects the success the campaign. Hence, the following hypothesis is proposed: H3. The higher the reputation of a founder within the Kickstarter community the more likely his campaign is to succeed. 11

13 2.4. Social media Founders encourage their potential backers and actual backers to share the project on their social media by means of updates. It was found that most updates (23%) contained a plea to share the project on the social media of the backers (Xu et al., 2014). Research of Ordanini et al. (2011) state that activating the backers or the social network to share the campaign in the last phase indeed increases the chances of success. Kickstarter also supports the sharing by providing the button share on the project page, by clicking on this button the project can directly be shared on the different platforms: Facebook, Twitter, Tumblr and Pinterest (Kickstarter, 2017). The word-of-mouth as aforementioned can be done effortlessly by using this share button (Colombo et al., 2014). Instead of spreading the word to a few friends in traditional forms of word-of-mouth, consumers now are able to tell thousands of other people with a simple click (Mangold and Faulds, 2009). Social media platforms such as Facebook and Twitter are often used to execute word-of-mouth marketing strategies in online environments (Groeger and Buttle, 2014). Using one s own or business social media accounts to promote the crowdfunding campaign are highly recommended by successful crowdfund founders. Moreover, also asking your online friends to post it on their social media accounts is recommended (Xu et al., 2014). Social network sites, Facebook and Twitter, are found to be important platforms for founders to connect to potential backers and friends who are willing to provide financial and informational support (Bechter et al., 2011). As potential backers, do not have the opportunity to experience the quality of the products and services before consumption it is necessary to inform and convince these potential backers, which can occur on social network sites (Zheng et al., 2014). This communication with potential investors, also backers, is an important element of leveraging social capital (Nahapiet et al., 1998; Hazleton and Kennan, 2000). Communication with potential backers happens easily on social network sites (Lambert and Schwienbacher, 2010) and according to Ordanini et al. (2011) it is a necessary to post crowdfunding projects on Facebook and Twitter to gain visibility. Earlier research already found that linking a Twitter account to the project to spread the word of one s crowdfunding campaign positively relates to the success of a crowdfunding project (Beier and Wagner, 2014). 12

14 In sum, in order to connect and communicate with potential backers and friends who are willing to offer financial or informative resources combined with the results of Beier and Wagner (2014) this research proposes the following hypothesis: H4. Having a social media page on Facebook or Twitter linked to the crowdfunding campaign the more likely the campaign is to succeed As it is not only the founder who is posting this research also analyses the effect of the posts in total as explained below. Visitors, or potential backers, can become promoters of the campaign when they promote the project among their friends through their online social networks. The research from Lu et al. (2014), which researches the diffusion of crowdfunding campaigns on Twitter, state that once a project is promoted by someone, that one is more likely to back the project in the future. Which is common-sense, as sharing a campaign comes with a certain sign of interest in the campaign. Also, in the research of Lu et al. (2014) the number of backers is highly correlated to the volume of the promotional activities, while crowdfunding success is more correlated to the design of the promotional campaign. The research concludes that reaching a lot of potential backers is done by massive promotion and crowdfunding success is established by intensive interactions with those potential backers. The question remains how to get to get these consumers to share the campaign with their social network. Mangold and Fauls (2009) conclude in their research that consumers like to network with others who share interests and desires with them. Moreover, consumers are more likely to communicate through social media and traditional word-of-mouth when they are engaged with the product, service, or idea. If a project is more persuasive to intrigue consumers to discuss it in their social network, either online or traditional, the project is also more attractive to investors. Therefore, to enhance success projects should target specific groups that have interest in the campaign. Then when a group shares this interests this group is more correlated to the funding ratio (Lu et al., 2014). The research also states that is not simply the size of the online social network of the founder but the diffusion of information through social media. This makes sense, the more people get to know a crowdfunding project, the higher the chance that a group or individual with interest in the campaign encounters the project. Accordingly, the research of Thies et al. (2014), argues that the word-of-mouth activities of external users with their personal Facebook accounts are more important to 13

15 generate traffic to the project page compared to using the Facebook account linked to the project page. This research expects that sharing or posting the link of a campaign, also called the social media diffusion, increase the likelihood of a successful crowdfunding campaign resulting in the hypothesis: H5. The higher the social media diffusion the more likely a campaign is to succeed As mentioned earlier founders encourage the visitors of their projects page and their social network to share the project link on social media (Xu et al., 2014, Ordanini et al., 2011). Zooming in on this effect, this research expects that the bigger the social network of the founder, internally and externally, will possibly influence the social media diffusion positively. As founders encourage their network and leverage their reputation to share the project on social media by means of updates or messaging their existing contacts. Therefore, this research expects that the social media diffusion acts as a mediator between reputation and social network on the one hand and success rate of the crowdfunding project on the other hand. Elaborating on that, the greater the size of the social network or reputation the greater the social media diffusion. In contrast, with the use of logic reasoning this research also expects that there is a certain trade-off or moderating effect between the social network and reputation of a founder and the social media diffusion of the campaign. Since, the more viral a campaign will go on social media, the less important the social network of the founder will become to predict the success rate of the campaign. It is expected that social media diffusion lowers the effect of the internal reputation and social network on the success rate of a crowdfunding project. As these effects are merely gut feelings, this research examines the effect of social media diffusion on the relation between the constructs reputation and social network on the success rate of the crowdfunding campaign. Hence, this research will check for moderating effects as for mediating effects. 14

16 In sum, this research researches the following hypothesis: H1. The more a founder backed other projects, the more likely his own crowdfunding campaign is to succeed. H2. The larger the external social network of a founder, the more likely his crowdfunding campaign is to succeed. H3. The higher the reputation of a founder within the Kickstarter community the more likely his campaign is to succeed. H4. Having a social media page on Facebook or Twitter linked to the crowdfunding campaign the more likely the campaign is to succeed. H5. The higher the social media diffusion the more likely a campaign is to succeed. The visualization of these hypotheses on the outcome success rate are captured in a conceptual model which is presented below (figure 1). Figure 1 Conceptual model 15

17 3. Methodology Part This research used primary data about crowdfunding extracted from Kickstarter, the most dominant reward-based crowdfunding site. Data from Kickstarter is also used in previous studies (Kuppuswamy and Bayus, 2013; Mollick, 2014; Colombo et al. 2014). To measure the social media activities around the crowdfunding projects, social media posts and pages are collected from social network sites: Facebook and Twitter. At first this chapter will briefly explain how the data is collected. Secondly, how the different constructs are measured. Lastly, validity and reliability are discussed Data collection The data used in this research is gathered from publicly available information on the Kickstarter, Facebook and Twitter websites. The dataset obtains projects launched after the 1 st of April 2017 and completed before 1 st of June This time-period captures the campaign lengths, ranging from 1 day to 60 days, as allowed by Kickstarter (Kickstarter, 2017). For each project, this paper recorded different variables as further elaborated on in the construct measurement section. The method used to gather how often a certain Kickstarter project is shared on Facebook is web scraping. A freely accessible program called shared count is used to automatically scrape data from Facebook. Other variables from the platforms Kickstarter, Twitter and Facebook are subtracted by hand Construct measurements To facilitate the understanding of the measurement of the constructs used in this paper the constructs and how they are measured will be briefly explained below. Success rate is the dependent construct used in this research, this entails the success percentage of a crowdfunding campaign. To measure the success rate, this paper measures the: Funding ratio this is the ratio between the goal as set at the beginning of the campaign and the pledged money at the end of the campaign. A funding ratio of 1 or higher indicates a successful project. Projects can also be overfunded, then the projects raise more than their goal and therefore the ratio will be higher than 1. Pledged, is the amount of money that is raised by backers for a project at the end of the campaign. Goal, is the set goal in terms of money that the creator seeks for his project. Kickstarter follows an all or nothing model where the founder can only collect the money from the backers if the funding goal is reached 16

18 (Kickstarter, 2017). Therefore, Success is a dummy variable of the success rate and it is defined that it is 1 when the amount of money raised exceeds the funding goal of the project within the set campaign duration in other words when the success rate is higher or equal to 1. For example, if a project raised 80% of the goal at the end of the campaign, this research talks about a Success rate of 0.80 and Success will be 0. Internal Social Capital, Colombo et al. (2014) argued that the internal social capital within crowdfunding sites can be measured by the number of projects that the creator had backed at the time of launching her own campaign (backed_others). The authors state that this variable shows to which degree the creator has been supportive of other projects and therefor has established social contacts within the Kickstarter community External Social Capital, is measured in Facebook friends of founder (FBF_founder). Creators can link their Facebook page with their project, if they do so, then Kickstarter shows the amount of Facebook friends that a creator has. This measurement for external social capital is conducted from Mollick (2014), his research records FBF_founder by the time of data collection rather than at the time of project initiation. Reputation, to quantify and measure the reputation of the founder within the Kickstarter community, the number of updates of the measured Kickstarter project are counted resulting in the measurement: Updates. Also, this research collected the number of crowdfunding projects that a founder previously created on Kickstarter. This resulted in the measurement: CFP_created. Social media page, to quantify the efforts from the founder to reach out to his potential (online) external social network, having a Facebook page or Twitter page linked to the campaign at the start of the campaign are measured. Having a Facebook or Twitter page offers the opportunity for potential backers outside the Kickstarter platform to be directed to the campaign and the pages also foster communication between founder and potential backer. This research measured this construct when the social media page link was either directly shown on the Kickstarter project page. Or either when typing the crowdfunding project name into the search option of Facebook or Twitter, a page with the same name showed up. For example, when a project was called Pebble watch 2 and one searched Facebook for Pebble watch 2 and the Facebook page Pebble watch or Pebble watch 2 popped up, this was counted as having a Facebook page linked. 17

19 Social media diffusion, is measured as the number of times the Kickstarter project link is shared on Facebook resulting in the measurement: KS_shares. Additional this paper will control for one control variable which is commonly used in crowdfunding research (Kuppuswamy and Bayus, 2013; Mollick, 2014; Zheng, 2014; Colombo et al., 2015) which is the duration of a project (Duration), this variable is the length of the campaign for a project measured in days Validity and Reliability To keep the measurement error to a minimum, validity and reliability are considered. Validity entails that a test measures what it set out to measure conceptually (Field, 2009). All measurements are determined from previous academic researches and therefore are considered valid content and criterion wise. Social media diffusion is applied to another social network site, and should not impact the validity of the results. Reliability is the ability of the measure to produce the same results under the same conditions (Field, 2009). Since, the constructs are the same as in previous studies, it is expected that applying these measurements and methods on a future research, the same results would be produced. 18

20 4. Data research This chapter report on the results derived from the empirical analysis. First this research describes how missing data is handled followed by descriptive statistics, normality checks and correlations for all variables. Second, the transformation of variables is described to fit normality followed by the descriptive statistics and correlations for the transformed variables. This chapter concludes with the hierarchical regression analysis and the exploration of the moderating and/or mediating effect of social media diffusion is described Missing data-points and descriptive analysis While collecting the data, the missing data points where indicated by The missing data were only related to the variable FBF_founder. If a founder of a crowdfunding project did not link his personal Facebook account to the project, this was indicated as a missing variable and was collected as number of Facebook friends from the founder. As this research examines the relationship between the external social capital of the founder and the project success rate, these missing variables might carry important information. As Mollick (2014) argues that it is better to not link the number of Facebook friends to the project if you have little Facebook friends, it might be that founders comply with these findings. To act upon this information, this research created a new variable FBF_connected, this variable is 0 when a founder did not connect his Facebook account to the project and 1 if he did. Creating this new variable possibly tackles another problem, as the sample size would be considerably reduced as there are 63 missing cases on the variable FBF_founder. The descriptive statistics can be found in table 1 below. 19

21 Table 1 Descriptive statistics Variables N M SD Min. Max. skewness kurtosis Funding_ratio Success Pledged Goal Duration Updates CFP_created backed_others FB_page FBF_connected FBF_founder TW_page KS_shares To check if the data is normally distributed, the skewness values from table 1 and the histograms of these variables are checked. Skewness values above ± 2 are considered acceptable to prove normal distributed data (Fields, 2009). Based on the skewness, there is a lack of symmetry in the variables Funding_ratio, Pledged, Goal, Updates, CFP_created, backed_others, and FBF_founder. Indicating that these variables are not normally distributed. The histogram plots, who visualize the data conform the same thing, the data is skewed to the left. As only continuous variables can be normal distributed there is no need to check the dichotomous variables Success, FB_page, TW_page and FBF_connected To check the correlations of the variables the Spearman s rank correlation efficient is used. As this method provides a non-parametric measure of rank correlation, it can be used when the data violates the parametric assumptions for normally distributed data (Field, 2009). 20

22 Correlations tell something about the relationship between the variables, which can be used to get some more understanding about the relationships and how variables interact with each other. As can be seen in table 2 some of the independent variables are related to the dependent variable. The Spearman s correlation test shows that there is a positive relationship between the dependent variable funding ratio (Funding_ratio) and the following variables: the dummy variable success (Success) (r =.863; p<.01) and the amount of money pledged (Pledged) (r =.775; p<.01) also the funding ratio is negatively related to the goal of the campaign (Goal) with r = and p<.05. The correlations between these variables are not surprising as Success is 1 for a Funding_ratio above or equal to 1 and Funding_ratio is derived from Goal and Pledged money. More interesting, is the negative correlation between Goal and Funding_ratio which could possibly indicate the more money a founder askes, the less likely his crowdfunding campaign is to succeed. As this research works with the dependent variable funding ratio, only correlations with that variable are further elaborated on. The funding ratio, is also positively related with the independent variables: number of updates (Updates) (r=.649; p<.01), number of created projects by the founder (CFP_created) (r=.221; p<.01), number of backed others projects by the founder (backed_others) (r=.343; p<.01), number of times the Kickstarter project link was shared on Facebook (KS_shares) (r=.547; p<.01) and the number of Facebook friends of the founder (FBF_founder) (r=.268; p<.05). These are promising results, as it entails that most the independent variables are related to the dependent variable. Further there are strong positive correlations between the number of updates within a project (Updates) the number of backed other projects (backed_others_ (r =.394; p<.01), whether there is a Facebook page linked to the Kickstarter campaign (FB_page) (r=.1.84; p<.01), whether there is Twitter page linked the Kickstarter campaign (TW_page) (r=.222; p<.01) and the number of times the Kickstarter project link was shared on Facebook (KS_shares)(r=.583; p<.01). 21

23 Other strong correlations are between the number of projects created by a founder (CFP_created) and the number of other projects backed (backed_others) (r=.358; p<.01). The correlation between backed other projects (backed_others) and number of times the Kickstarter project link was shared on Facebook (KS_shares) is r =.202 with p<.05. Having a Facebook page linked to the Kickstarter project (FB_page) is strongly correlated with having a Twitter page linked to the project (TW_page) (r=.377; p<.01) and to the number of Kickstarter project link shares on Facebook (KS_shares)(r=.334; p<.01). As these variables, all refer to the social media activity of the founder, these correlations are not that surprising. Lastly, having a Twitter page linked to the project (TW_page) is correlated with the number of Kickstarter project link shares on Facebook (KS_shares) with r =.210 and p<.05. If a founder has connected his personal Facebook page to his Kickstarter project, so that potential backers can see the number of Facebook friends of the founder (FBF_connected) is positively correlated with the number of times a Kickstarter project link is shared on Facebook (KS_shares) (r=.247; p<.05). The relation between FBF_connected and FBF_founder is missing as FBF_connected is a dichotomous dummy variable for FBF_founder. 22

24 Table 2 Spearman's rho correlations Variables Mean SD Funding_ratio Success ** - 3. Pledged **.689** - 4. Goal * ** - 5. Duration Updates **.534**.662** CFP_created ** backed_others **.241**.292** **.358** - 9. FB_page **.197* * FBF_connected FBF_founder *.292**.272* TW_page **.178* ** ** KS_shares **.506**.808**.330** ** *.337** *.210* - 23

25 4.2. Transformation and descriptive statistics To address the issue of non-normally distributed data, the variables have been transformed by using the natural logarithm. As the descriptive table showed, there are variables with values of zero. Since the natural logarithm of zero is undefined, there is a 1 added to these variables. The transformation then becomes Ln(1+X), where X stands for the old variable. The measurements Goal and CFP_created are transformed using the Ln (x) as they do not have values of zero. The variables Funding_ratio, Pledged, Updates, backed_others, KS_shares and FBF_founder are transformed using the Ln (x + 1) as they possibly have values of zero. Table 3 contains the new measurements and how they are computed based on the old measurements. Table 3 Computed measurements New measurement Computed as LN_Funding_ratio Ln ( 1 + Funding_ratio ) LN_Pledged Ln ( 1 + Pledged ) LN_ Goal Ln ( Goal ) LN_Updates Ln ( 1 + Updates ) LN_CFP_created Ln ( CFP_created ) LN_backed_others Ln ( 1 + backed_others ) LN_KS_shares Ln ( 1 + KS_shares ) LN_FBF_founder Ln ( 1 + FBF_founder ) After the transformation of the variables the same descriptive analyses are conducted on the data; table 4. It can be stated that the distribution improved significantly. Although still some variables appear to have relatively non-normal distributions based upon the skewness, LN_CFP_created (skewness = 2.80) and LN_backed_others (skewness = 1.98). Although this research adheres the variables as normal distributions. 24

26 Table 4 Descriptive statistics transformed variables Variables N M SD Min. Max. skewness kurtosis LN_Funding_ratio Success LN_Pledged LN_Goal Duration LN_Updates LN_CFP_created LN_backed_others FB_page FBF_connected LN_FBF_founder TW_page LN_ KS_shares To check the correlations of the transformed variables the Pearson s correlation efficient is used. As this method provides the correlation between normally distributed variables (Fields, 2009). As can be seen in table 5 the correlations do not change a lot. However, the correlation between the number of Facebook friends of the founder (LN_FBF_founder) is not statistically significant related with LN_Funding_ratio anymore compared to the Spearman s correlation. Also, the correlation between number of created projects (LN_CFP_created) is not correlated significantly anymore with the number of updates (LN_Updates). 25

27 4.3. Outliers Observations in a data set are considered outliers when they are distinctively different in from main trend of the other observations or data (Field, 2009). Outliers can be data points which are type errors and then should be excluded from the regression analyses as they would bias the results. However, outliers can also be unique cases which carry unique characteristics. If these unique cases are excluded from the analyses these unique characteristics would get lost or will not be discovered (Field, 2009). Moreover, by transforming the data with the natural logarithm significant different observations which can cause skewness will be reduced (Field, 2009). Therefore, this research decides to keep all the data points as they did not carry any typing errors. 26

28 Table 5 Pearson's associations Variables Mean SD LN_Funding_ratio Success ** - 3. LN_Pledged **.658** - 4. LN_Goal * * - 5. Duration LN_Updates **.523**.619** LN_CFP_created ** * - 8. LN_backed_others **.254**.249** **.577** - 9. FB_page **.193* * FBF_connected LN_FBF_founder c TW_page ** * ** LN_KS_shares **.508**.792**.246** ** *.312** * - ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). c Cannot be computed because at least one of the variables is constant. 27

29 4.4. Assumptions check To draw conclusions bout data based on a regression analysis done in a sample several assumptions must be true (Berry,1993; Field, 2009). The variable types, multicollinearity and normality of residuals are discussed in this chapter. Variable types All variables included in the regression analyses should be quantitative or categorical (with two categories), and the outcome variable must be quantitative, continuous and unbounded. In this research, all variables used meet these requirements. Accordingly, the predictor variables are either dichotomous variables or ratio (discrete) variables, the dependent variable is a ratio variable. Multicollinearity As there should not be a perfect linear relationship between two or more predictors. So, the predictor variables should not correlate too highly (Field, 2009). Therefore, the variance inflation factors (VIF) of the variables are checked. Variables should have VIF s below 10 and correlations below.90 (Field, 2009), which were met by all variables; table 5 above and table 16 and 17 in Appendix A. The VIF s were checked after this research ran the regressions as reported on in chapter 4.5. In sum, the assumption of no multicollinearity is met for all variables. Normality of residuals The assumption to be met is that the residuals in the model are random normally distributed variables with a mean of 0. In order to test this assumption, the histogram and P- P plot of the standardized residuals are examined; which can be found in Appendix A. The histogram indicates that the standardized residuals of LN_Funding_ratio are normal distributed. Also, the P-P plots roughly indicates that the standardized residuals are normally distributed. Lastly a scatterplot of the standardized residuals and standardized predicted value is presented in Appendix A. The scatterplot also meets the assumptions of randomly and evenly dispersed throughout the plot (Field, 2009). Hence, the assumptions of the normality of the residuals is met. 28

30 4.5. Hierarchical regressions This research will examine the relation between the independent variables and the dependent variable Funding_ratio. To check the direct effects of the independent variables on Funding_ratio. A hierarchical regression analysis is used to examine the relation between the independent variable and Funding_ratio. This type of regression was conducted to research the ability of the independent variables to predict the crowdfunding Funding_ratio, after controlling for campaign Duration. As a first step of the regression, this control variable Duration is entered as predictor. This was done so that a shared variability of this variable with the independent variable Funding_ratio can be controlled. Thus, the observed effect of the independent variables on Funding_ratio is independent of the effect of this control variable. As the sample size will differ when using either the dichotomous predictor FBF_connected or the continuous predictor LN_FBF_founder, this research will analyze two hierarchical regressions. One with FBF_connected used as predictor and one with LN_FBF_founder used as predictor. First hierarchical regression The first hierarchical regression was conducted to predict project Funding_ratio for 82 projects using Duration, LN_Updates, LN_CFP_created, LN_backed_others, FB_page, LN_FBF_founder, TW_page, LN_KS_shares as predictors. In the first step of hierarchical multiple regression, one predictor was entered: Duration. This model was not statistically significant F (1, 82) = 0.75; p =.39 thus p > 0.05 this model explained 0,9% of variance in Funding_ratio. After entry of the other independent variables at Step 2 the total variance explained by the model as a whole was 46,6% F (8, 82) = 8.07; p <.001. The introduction of the independent variables explained additional 45,7% in variance in Funding_ratio, after controlling for Duration (R2 Change =.457; F (7, 81) = 9.04; p <.001). In the final model two out of eight predictor variables were statistically significant, with LN_Updates recording a higher Beta value (ß =.34, p <.001) than LN_CFP_created (ß =.19, p <.05). In other words, if LN_Updates increases for one, the LN_Funding_ratio will increase for 0.34 units. On the other hand, if LN_CFP_created increases for one, then LN_Funding_ratio will increase for 0.19 units. 29

31 The results derived from step 2 in the model indicate that the relationship between the more other project a founder backed and the success rate of the project is statistically insignificant and has a negative relationship. This result implies that the number of projects backed by the founder does not impact the success rate of his own project. Hence, hypotheses one (H1)) is not supported. Also, the number of Facebook friends of the founder are not statistically significant related to the success rate of the crowdfunding project. Indicating that the external social network of a founder does not impact the success rate of the project. Hence, the second hypothesis (H2) is not supported. This research did found support for hypothesis 3 (H3). As the number of updates has a strong and positive relation with the success rate of the campaign. This entails that when increasing the number of updates, the success rate is increased as well. Also, when the number of previous projects created by the founder are increased the success rate will increase as well, since they are strongly and positive related. However, hypothesis 4 (H4) and hypothesis 5 (H5) are not supported by this regression as having a Facebook page or Twitter page connected to the Kickstarter project do not have a statically significant relation with the success rate. Moreover, the number of times a Kickstarter project link is shared on Facebook is also statically insignificant related to the success rate of a crowdfunding project. The first hierarchical regression model can be found in table 6 below. 30

32 Table 6 First hierarchical Regression LN_Funding_ratio Predictor variable R R 2 R 2 Change B S.E. ß t Step Duration Step ***.46*** Duration LN_Updates.34*** LN_CFP_created.19* LN_backed_others FB_page LN_FBF_founder TW_page LN_ KS_shares Note statistical significance *p <.05; ** p <.01; ***p <

33 Second hierarchical regression Next a second hierarchical regression is conducted were the independent variable LN_FBF_founder is replaced with the dichotomous variable FBF_connected. The useful sample size increases to 146 by doing so. So, LN_Funding_ratio is now predicted based on the independent variables LN_Updates, LN_CFP_created, LN_backed_others, FB_page, FBF_connected, TW_page and LN_KS_shares while controlling for Duration. In the first step of hierarchical multiple regression, one predictor was entered: Duration. This model was not statistically significant F (1, 145) = 1.33; p = 0.25 thus p > 0.05 this model explained 0,9% of variance in Funding_ratio. At step two the other independent variables were added to the model. The total variance explained by the model as a whole was 46,2% F (8, 145) = 14.72; p < The introduction of the independent variables explained additional 45,3% in variance in Funding_ratio, after controlling for Duration (R2 Change =.453; F (7, 144) = 16.48; p < <.001). In the final model three out of eight predictor variables were statistically significant, with LN_Updates recording a higher Beta value (ß =.32, p < 0.001) than LN_CFP_created (ß =.18, p <.01) and LN_KS_shares (ß =.07, p <.01).In other words, if LN_Updates increases for one, the LN_Funding_ratio will increase for 0.32 units. On the other hand, if LN_CFP_created increases for one, then LN_Funding_ratio will increase for 0.18 units. Lastly, if the number of LN_KS_shares is increased with one, LN_Funding_ratio will increase with 0.07 units. In line with the first hierarchical regression with LN_FBF_founder as predictor, the first hypothesis (H1) is not supported as backing others is insignificantly related to the success rate of a crowdfunding project. As the second regression, does not capture the relation between the number of Facebook friends of the founder and the success rate, drawing conclusions on hypothesis 2 (H2) cannot be done. However, based on the outcomes there can be concluded that connecting the personal Facebook account from the founder to the Kickstarter project so that potential backers can assess and see the number of Facebook friends of the founder is statistically insignificant related to the success rate of a project. 32

34 Hypothesis 3 (H3) is also supported by the second regression as number of created projects and number of updates are both strongly statistically significant and positive related to the success rate of a project. Also, the second regression did not found any statistically significant relation between having a Facebook page or Twitter page linked to the Kickstarter project and therefore hypothesis 4 (H4) is not supported. Interestingly in the second regression the number of times the Kickstarter campaign is shared on Facebook becomes statistically significant related to the success rate of a project. As this relation is positive, the more times a Kickstarter project link is shared on Facebook the more likely a crowdfunding project is to succeed. Hence, hypothesis 5 (H5) is supported by this second regression. The second hierarchical regression model can be found in table 7 below. Table 7 Second hierarchical regression LN_Funding_ratio Predictor variable R R 2 R 2 Change B S.E. ß t Step Duration Step ***.45*** Duration LN_Updates.32*** LN_CFP_created.18** LN_backed_others FB_page FBF_connected TW_page LN_KS_shares.07** Note statistical significance *p <.05; ** p <.01; ***p <

35 4.6. Explorative research for mediation or moderation effect Next this research will explore the effect and relation of the social media diffusion on the different statistically significant relationships between the independent variables, also predictors, and the dependent variable success rate. Thus, the relationship between reputation of the founder and success rate is explored. To explore this effect this research uses the statistical program PROCESS, which is an add-on for SPSS designed by Andrew F. Hayes (2012). Possible moderating and mediating relationships between the different independent variables and the dependent variable funding ratio will be explored. Moderating effects models As in PROCESS, to examine the single moderation (model 1) effect one can only enter one independent (X), one dependent (Y) and one moderator variable (M), this research ran several analyses. Every analysis will cover one statistically significant independent (X) variable while controlling for the other independent variables. Additionally, in line with the earlier hierarchical regressions the analyses are run with the number of Facebook friends of the founder LN_FBF_founder as control variable and with FBF_connected as control variable.in sum, there will be four analyses for the moderation effect of social media diffusion. Firstly, an analysis with the number of updates (Updates) as independent X variable, which will be referred to as moderation model A, containing the number of Facebook friends of the founder (LN_FBF_founder) as one of the control variables. Secondly, the same moderation model 1 will be explored but with FBF_connected as one of the control variables instead of LN_FBF_founder. Thirdly, the moderation effect of social media diffusion on the relationship between the number of created projects by the founder (LN_CFP_created) will be explored, this model will be referred to as moderation model B. This model will be explored with LN_FBF_founder as one of the control variables, thereafter the same model will be explored but now with FBF_connected instead of LN_FBF_founder as one of the control variables. 34

36 Moderating effect model A To check for a possible moderating effect of Social media diffusion measured as LN_KS_shares (M) on the relation between reputation measured as LN_Updates (X1) the single moderation model in PROCESS is used. The dependent variable Success is measured as LN_Funding_ratio (Y). This entails model 1 in PROCESS with the control variables LN_CFP_created, LN_backed_others, LN_FBF_founder, Duration, TW_page and FB_page. A conceptual visualization of model A is shown in figure5 in Appendix A. The regression coefficient for X1M is c3 = and is statistically different from zero (t (83) = -1.99, p<0.05). Thus, the effect of reputation as measured in number of updates (LN_Updates) on the success rate (Funding_ratio) is negatively influenced by the social media diffusion measured in number of times a Kickstarter project link is shared on Facebook (LN_KS_shares). This effect is also plotted in figure 6 (Appendix A). The outcomes of the explorative analyses for a possible moderating effect in model A with control variable LN_FBF_founder are presented below (table 8 and table 9). Table 8 Moderating effect model A with control variable LN_FBF_founder Coefficient SE t p Intercept i LN_KS_shares (M) c LN_Updates (X1) c <.001*** LN_Updates * c * LN_KS_shares (X1M) LN_CFP_created (X2) c LN_backed_others c LN_FBF_founder c Duration c TW_page c FB_page c R 2 =0.421 p<.001*** F(9, 83) =

37 Table 9 Conditional effect model A with control variable LN_FBF_founder Conditional effect of LN_Updates (X1) on LN_Funding_ratio (Y) at levels of LN_KS_shares (M) LN_KS_shares (M) Effect SE t p LLCI ULCI *** *** R 2 increase due to interaction effect = p=0.0504* F(1,74) = In line with earlier hierarchical regressions this research also explores the possible moderating effect of Social Media diffusion (M) on the relation between reputation measured as LN_Updates (X1) and the dependent variable success rate (Y) as measured as LN_Funding_ratio, but now with FBF_connected instead of LN_FBF_founder as one of the control variables. Hence, the sample size went from 83 projects to 146 projects by doings so. The PROCESS model remains model 1 as this research explores the moderating effect. Controlled variables are: LN_CFP_created, LN_backed_others, Duration, TW_page, FB_page and FBF_connected. The regression coefficient for X1M is c3 = and is not statistically different from zero (t (146) = -0.83, p = 0.406). Thus, the effect of reputation as measured in number of updates (LN_Updates) on the success rate (Funding_ratio) is not influenced by the social media diffusion measured in number of times a Kickstarter project link is shared on Facebook (LN_KS_shares). Therefore, the relation of reputation on success rate is not moderated by the social media diffusion. The outcomes of the possible moderating effect in model A with control variable FBF_connected are presented in table 10 and

38 Table 10 Moderating effect model A with control variable FBF_connected Coefficient SE t P Intercept i <.001*** LN_KS_shares (M) c ** LN_Updates (X1) c <.001*** LN_Updates* LN_KS_shares (X1M) c LN_CFP_created (X2) c ** LN_backed_others (X3) c Duration c TW_page c FB_page c FBF_connected c R 2 = p<.001*** F(9, 146) = Table 11 Conditional effect model A with control variable FBF_connected Conditional effect of LN_Updates (X1) on LN_Funding_ratio (Y) at levels of LN_KS_shares (M) LN_KS_shares (M) Effect SE t p LLCI ULCI <.001*** <.001*** <.001*** R 2 increase due to interaction effect = p=.406 F(1,137) =

39 Moderating effect model B As number of created projects is a statically significant predictor of the success rate of crowdfunding projects, the possible moderation effect of social diffusion on this relationship is also explored. Figure 7 in Appendix A captures the conceptual model of this relationship also referred to as model B. Social media diffusion again measured as LN_KS_shares (M) is put into model 1 of PROCESS to check the possible effect on the relation between the reputation of a founder measured as CFP_created (X2) on the dependent variable success rate which is measured as LN_Funding_ratio (Y). Control variables are LN_Updates LN_backed_others, LN_FBF_founder, Duration, TW_page and FB_page. Note, the first analyses is with the number of Facebook friends of the founder as one of the control variables. The regression coefficient for X2M is c3 = and is not statistically different from zero (t (83) = , p =.659). Thus, the effect of reputation as measured in number of projects created by the founder (LN_CFP_created) on the success rate (Funding_ratio) is not statistically significant influenced by the social media diffusion measured in number of times a Kickstarter project link is shared on Facebook (LN_KS_shares). Results are presented in table 12. Table 12 Moderating effect model B with control variable LN_FBF_founder Coefficient SE t p Intercept i LN_KS_shares (M) c * LN_CFP_created (X2) c LN_CFP_created * c LN_KS_shares (X2M) LN_Updates (X1) c ** LN_backed_others (X3) c LN_FBF_founder c Duration c TW_page c FB_page c R 2 =0.406 p<0.001 F(9, 82) =

40 Conditional effect of LN_CFP_created (X2) on LN_Funding_ratio (Y) at levels of LN_KS_shares(M) LN_KS_shares(M) Effect SE t p LLCI ULCI R 2 increase due to interaction effect = p=.639 F(1,74) = Also, for model B the control variable LN_FBF_founder is interchanged with the control variable FBF_connected in order to increase the sample size to 146 projects. Hereby the number of projects created by the founder (LN_CFP_founder) (X2) is the independent variable, the success rate of a project (Funding_ratio)(Y) is the dependent variable and social media diffusion (LN_KS_shares)(M) is the possible moderator in model B. The regression coefficient for X2M is c3 = in model B and is statistically different from zero (t (146) = -2.67, p<0.05). Thus, the effect of reputation as measured in number of projects created by the founder (LN_CFP_created) on the success rate (Funding_ratio) is negatively influenced by the social media diffusion measured in number of times a Kickstarter project link is shared on Facebook (LN_KS_shares). This effect appears logical, as the more the Kickstarter project link is shared on Facebook, the less important the reputation measured as the number of projects created by the founder becomes. The results are presented in table 13 and table 14 below. 39

41 Table 13 Moderating effect model B with control variable FBF_connected Coefficient SE t p Intercept i ** LN_KS_shares(M) c <.001*** LN_CFP_created (X2) c ** LN_CFP_created * LN_KS_shares(X2M) c ** LN_Updates (X1) c <.001*** LN_backed_others (X3) c Duration c TW_page c FB_page c FBF_connected c R 2 =0.480 p<0.001*** F(9, 146) = Table 14 Conditional effect model B with control variable FBF_connected Conditional effect of LN_CFP_created (X2) on LN_Funding_ratio (Y) at levels of LN_KS_shares(M) LN_KS_shares(M) Effect SE T p LLCI ULCI <.001*** ** R 2 increase due to interaction effect = p=0.032* F(1,137) =

42 Mediating effect models Next this research checks if social media diffusion has a mediating effect on the relationship between the independent variables and the dependent variable; success rate. This is done using model 4 in PROCESS. In line with other analyses first the mediation effect is conducted with LN_FBF_founder as one of the control variables, afterwards the same analyses are done with the control variable FBF_connected instead of LN_FBF_founder. The first mediation analyses are done with the number of updates (LN_Updates) as the independent variable (X1), the success rate (LN_Funding_ratio) as the dependent variable (Y) and social media diffusion (LN_KS_shares) as the possible mediating variable (M2). The model as described will be called model C: LN_Updates and the conceptual model is presented in figure 9 in appendix A. As in PROCESS one can only analyze the indirect effect, through the possible mediator, of one independent X variable at the time, this research will conduct a mediation analysis on every variable that will statistically significant influence the possible mediating variable (LN_KS_shares). The second mediation analyses are done with FBF_connected as the control variable instead of LN_FBF_founder. The other variables remain the same: the independent variable LN_Updates (X1), the dependent variable LN_Funding_ratio (Y) and the possible mediator social media diffusion (LN_KS_shares). This model will be referred to as model D: LN_Updates. The conceptual model will look the same as for model C, as only the control variable changes. Mediation effect model C The first analysis of model C is with the control variables: LN_FBF_founder, LN_CFP_created, LN_backed_others, TW_page and FB_page. The results of this analysis are presented in table 15 below. The effects of LN_Updates, FB_page and LN_FBF_founder, are indicated as statistically significant related to the possible mediating variable social media diffusion. Moreover, the mediating variable social media diffusion is indicated as statistically significant related to the independent variable success rate, which is an assumption for a mediating effect (Baron and Kenny, 1986). The independent variables LN_Updates, FB_page and LN_FBF_founder, are statistically significant related to the possible mediating variable LN_KS_shares. 41

43 Therefore, this research will analyze the indirect effect of these three independent variables on success rate through the possible mediating variable social media diffusion in PROCESS separately. Accordingly, model C: FB_page is introduced where FB_page is the dependent variable (X6) and LN_Updates becomes one of the control variables and model C: LN_FBF_founder is introduced where LN_FBF_founder becomes the dependent variable (X7) and LN_Updates plus FB_page become two control variables. Mediating effect model C: LN_Updates The effect of LN_Updates a1 = means that two projects that differ by one unit on LN_Updates estimated to differ by units on LN_KS_shares. The sign of a1 is positive, meaning that those relatively higher in LN_Updates estimated to be higher in the LN_KS_shares. This effect is statistically different from zero, t = 6.277, p =.000, with a 95% confidence interval from.817 to The effect of b1 =.077 indicates that two projects who have the same of LN_Updates but that differ by one unit in their number of LN_KS_shares estimated to differ by b1 =.077 units in success rate (LN_Funding_ratio). The sign of b1 is positive, meaning that those relatively higher in LN_KS_shares estimated to be higher in their success rate (LN_Funding_ratio). This effect is statistically different from zero, t=2.298, p =.024 with a 95% confidence interval from.010 to.143. The indirect effect of a1b1 =.092 means that two projects who differ by one unit in their LN_Updates estimated to differ.092 units in their success rate (LN_Funding_ratio) because of the social media diffusion. This indirect effect is statistically different from zero, as revealed by a 95% BC bootstrap confidence interval that is entirely above zero (.013 to.189). The direct effect of LN_Updates c 1 =.211, is the estimated difference in LN_Funding_ratio between two projects that experience the same number of LN_KS_shares but who differ by one unit in their LN_Updates, meaning that the project with more LN_Updates which is equally LN_KS_shares is estimated to be.211 units higher in its LN_Funding_ratio. This direct effect is statistically different from zero, t = 3.097, p =.003, with a 95% confidence interval from.075 to

44 The total effect of LN_Updates on LN_Funding_ratio is c =.303, meaning two projects who differ by one unit in LN_Updates estimated to differ by units in their LN_Funding_ratio. The positive sign means that the project with more LN_Updates has a higher LN_Funding_ratio. This effect is statistically different from zero, t = 5.341, p =.000, or between.190 and.416 with 95% confidence. Mediating effect model C: FB_page As having a Facebook page linked to the crowdfunding project significantly influenced the social media diffusion, the effect of FB_page is further explored. Therefore, FB_page becomes the independent variable (X) in PROCESS, social media diffusion remains the mediating variable (M) and the success rate the dependent variable (Y). Accordingly, Updates becomes a control variable. A visualization of the conceptual model is shown in figure 10 in appendix A. The effect of FB_page a6 =.937 means that if a project is linked to a Facebook page as it is dichotomous variable it is estimated to differ by.937 units on LN_KS_shares. The sign of a6 is positive, meaning that projects linked to a FB_page estimated to be higher in LN_KS_shares. This effect is statistically different from zero, t = 2.573, p =.012, with a 95% confidence interval from to Also, the indirect effect a6b1 =.072 indicates that if a project linked a Facebook page to the Kickstarter page it is estimated to differ units in their LN_Funding_ratio because of the social media diffusion. This indirect effect is statistically different from zero as revealed by a 95% BC bootstrapped confidence interval that is entirely above zero (0.013 to 0.189). The direct effect of FB_page c 6 = which is not statistically different from zero, t = , p =.101, with a 95% confidence interval from to The total effect of FB_page on LN_Funding_ratio (c = ) is statistically not different from zero, t = , p =.675 or between and with 95% confidence. Thus, the effect of FB_page on LN_Funding_ratio is an indirect effect only as the mediation effect is statistically significant but the total effect is not statistically significant (Zhao et al., 2010). 43

45 Mediating effect model C: LN_FBF_founder As the number of Facebook friends of the founder statistically significantly influenced the social media diffusion, this will be further analyzed. Figure 11 in Appendix A provides a visualization of the conceptual model. The effect of LN_FBF_founder a7 =.248 means that two founders that differ by one unit on LN_FBF_founder estimated to differ by 0.248units on LN_KS_shares. The sign of a1 is positive, meaning that those relatively higher in LN_FBF_founder estimated to be higher in the LN_KS_shares. This effect is statistically different from zero, t = 2.025, p =.046, with a 95% confidence interval from.004 to.492. The indirect effect of a7b1 =.019 indicates that two projects which differ one unit in their LN_FBF_founder estimated to differ units in their LN_Funding_ratio as a result of the mediating variable social media diffusion. This indirect effect is statistically different from zero, as revealed by a 95% BC bootstrap confidence interval that is entirely above zero (0.001 to 0.061). The direct effect of LN_FBF_founder c 7 = which statistically not different from zero, t = with p =.288, with a 95% confidence interval from to Also, the total effect of LN_FBF_founder is statistically not different from zero, t = 1.72, p =.087 or between and with 95% confidence. Hence, the effect of LN_FBF_founder on the LN_Funding_ratio is also an indirect only effect (Zhao et al., 2010). In sum, the number of Facebook friends of the founder and linking a Facebook page to the Kickstarter project both have a statistically significant indirect effect on the success rate. Moreover, both variables direct effect and total effect on the success rate of a project are statistically not significant. Concluding that these are indirect-only mediation effects (Zhao et al., 2010). The number of updates within a project direct effect, indirect effect and total effect on success rate are statistically significant, hereby the indirect effect and direct effect are positive therefore this research concludes that the social media diffusion has a complementary mediation effect on the relation between updates and success rate. Hence, social media diffusion does mediate the relation between the independent variables: number of updates, having a Facebook page and the number of Facebook friends on the dependent variable: success rate. 44

46 Table 15 Mediating effect analysis: model C Consequent LN_KS_shares(M2) LN_Funding_ratio (Y) Antecedent Coeff. SE p Coeff. SE p LN_Updates (X1) a <.001*** c ** LN_CFP_created a c LN_backed_others a c Duration a c TW_page a c FB_page (X2) a * c LN_FBF_founder a * c (X3) LN_KS_shares(M2) b * Constant i i R 2 = R 2 = F(7,82) = 8.57 F(8,81) = 5.91 P<.001*** P<.001*** Effect SE p LLCI ULCI Direct effect LN_Updates c ** FB_page c LN_FBF_founder c Total effect LN_Updates c <.001*** LN_CFP_created c LN_backed_others c Duration c TW_page c FB_page c LN_FBF_founder c Indirect effect Boot SE Boot LLCI Boot ULCI LN_Updates a 1 b LN_FBF_founder a 7 b FB_page a 6 b

47 Mediation effect model D The second analysis for possible mediation effect is done on model D is with the control variables: LN_CFP_created, LN_backed_others, TW_page, FB_page and FBF_connected. The results of this analysis are presented in table 16 below. The effects of LN_Updates and FB_page are indicated as statistically significant related to the possible mediating variable social media diffusion. Again, the mediating variable social media diffusion is indicated as statistically significant related to the independent variable success rate. To analyze the indirect effects of the number of updates and the number of projects created on the success rate, one new model is introduced named: model D: FB_page. Hence, the X variable in this model becomes FB_page (X6) and LN_Updates becomes one of the control variables. Mediating effect model D: LN_Updates The effect of LN_Updates a1 = means that two projects that differ by one unit on LN_Updates estimated to differ by units on LN_KS_shares. The sign of a1 is positive, meaning that those relatively higher in LN_Updates estimated to be higher in the LN_KS_shares. This effect is statistically different from zero, t = 7.217, p =.000, with a 95% confidence interval from.840 to The effect of b1 =.071 indicates that two projects who have the same of LN_Updates but that differ by one unit in their number of LN_KS_shares estimated to differ by b1 =.071 units in success rate (LN_Funding_ratio). The sign of b1 is positive, meaning that those relatively higher in LN_KS_shares estimated to be higher in their success rate (LN_Funding_ratio). This effect is statistically different from zero, t=3.052, p =.003 with a 95% confidence interval from.025 to.117. The indirect effect of a 1 b 1 =.082 means that two projects who differ by one unit in their LN_Updates estimated to differ.082 units in their success rate (LN_Funding_ratio) as a result of the social media diffusion. This indirect effect is statistically different from zero, as revealed by a 95% BC bootstrap confidence interval that is entirely above zero (.029 to.146). 46

48 The direct effect of LN_Updates c 1 =.315, is the estimated difference in LN_Funding_ratio between two projects that experience the same number of LN_KS_shares but who differ by one unit in their LN_Updates, meaning that the project with more LN_Updates which is equally LN_KS_shares is estimated to be.315 units higher in its LN_Funding_ratio. This direct effect is statistically different from zero, t = 6.109, p =.000, with a 95% confidence interval from to The total effect of LN_Updates on LN_Funding_ratio is c =.397, meaning two projects who differ by one unit in LN_Updates estimated to differ by units in their LN_Funding_ratio. The positive sign means that the project with more LN_Updates has a higher LN_Funding_ratio. This effect is statistically different from zero, t = 8.784, p =.000, or between.308 and.487 with 95% confidence. Mediating effect model D: FB_page The effect of FB_page is a6 =.988 meaning that projects who do have a Facebook page, are estimated to gain units more LN_KS_shares. This effect is statistically different from zero, t = 3.101, p =.002, with a 95% confidence interval from to Also, the indirect effect a6b1 =.070 indicates that if a project has a Facebook page it is estimated to differ units in their LN_Funding_ratio compared to projects that do not have a Facebook page linked. This indirect effect is statistically different from zero as revealed by a 95% BC bootstrapped confidence interval that is entirely above zero (0.020 to 0.152). However, also in model D the direct effect of FB_page c 6 = is not statistically different from zero, t = , p =.220, with a 95% confidence interval from to The total effect of FB_page on LN_Funding_ratio (c = ) is also statistically not different from zero, t = , p =.651 or between and with 95% confidence. Thus, the effect of FB_page on LN_Funding_ratio is an indirect-only mediation effect (Zhao et al., 2010), as the indirect effect is significant but the total and direct effects are not. 47

49 Table 16 Mediating effect analysis: model D LN_KS_shares(M2) Consequent LN_Funding_ratio (Y) Antecedent Coeff. SE p Coeff. SE p LN_Updates (X1) a <.001*** c <.001*** LN_CFP_created a c ** (X2) LN_backed_others a c Duration a c TW_page a c FB_page a ** c FBF_connected a c LN_KS_shares(M2) b ** Constant i <.001*** i R 2 = R 2 = F(7,145) = p<.001*** F(8,145) = 14,71 p<.001*** Effect SE p LLCI ULCI Direct effect LN_Updates c <.001*** LN_CFP_created c ** Total effect LN_Updates c <.001*** LN_CFP_created c * LN_backed_others c Duration c TW_page c FB_page c FBF_connected c Indirect effect Boot SE Boot LLCI Boot ULCI LN_Updates a 1 b LN_CFP_created a 7 b

50 5. Discussion This chapter will discuss the findings from the data research compared to theory and logic. It also points out how it adds to the existing literature and implications for real-world practice are offered. Further, the limitations of the study and the recommendations for future research are discussed Discussion of the findings As new firms face difficulties in attracting external finance at their very initial stage, tapping into the crowd by means of crowdfunding is an alternative to not only attract finance, but also to receive feedback and public attention on the initial product or service. Within the literature, the role of social capital of the founder is stressed as important for success in crowdfunding (Mollick, 2014; Colombo et al., 2014 and Zheng et al., 2014). The source of social capital is the social network, as social capital is embedded in the social network (Coleman et al., 1988). Colombo et al. (2014) argued that there is a certain internal social network and an external social network. Moreover, reaching the crowd often happens by using social network sites like Facebook and Twitter (Belleflamme et al., 2012). Therefore, the question this research addresses is: how does the social network influences the success of a crowdfunding campaign and how can this social network be mobilized. The findings of the analyses conducted in this research are interesting and sometimes contradictory to results of previous research. Obviously, the findings should be interpreted with caution as the sample is small (N differs between 83 and 146). Internal social network. Previous research from Zvilichovsky et al. (2015) stated that backing other projects will result in a higher number of overall backers and thus the probability of financing success. In line with Colombo et al. (2014) this research relates backing other projects to the internal social capital. However, the results did not support that internal social capital positively influenced the likelihood that a campaign succeeded therefore hypothesis 1 (H1) is not supported. As this research, did not incorporated the number of backers in the results, this could possibly be one of the reasons why the analysis on this data sample is contra dictionary to the findings from Zvilichovsky et al. However, Zheng et al. (2014) stated that backing others was a significant predictor of crowdfunding success, 49

51 as crowdfunding success is a dummy variable of the funding ratio used in this research, the results in this research are also in contrast of the findings by Zheng et al. (2014). Moreover, this research did not found any support for the specific or generalized reciprocity mechanisms triggered by backing others as mentioned by Colombo et al. (2014). External social network. Mollick (2014) stressed the role of the size of the founders external social network, measured in number of Facebook friends, on the success of a crowdfunding campaign. Mollick also stated that it is better to not connect the personal Facebook page of a founder to the Kickstarter project page if the founder has few Facebook friends. In the dataset of this research 57% of the founders connected their personal Facebook page to the Kickstarter project. In the sample (N=83) with a connected personal Facebook page, the number of Facebook friends did not have a statistically significant direct effect on the success rate of the campaign therefore this research does not support hypothesis two (H2). This contradicts the significant effect of external social network on the success of a crowdfunding campaign in Mollick s research (2014). Do note this research did not differ or separate between many and few Facebook friends as Mollick did, which may have caused the contrasting results. Another cause can be, as social contacts like Facebook friends, particularly play a key role in the early stages of fundraising (Agrawal et al., 2011). Afterward this early stage, the beneficial effect of the external social network might lose importance to other factors in predicting the success rate. For example, potential backers are now able to monitor and assess the quality of a project by the already accumulated funding. This is in line with the herding behavior among backers as described by Zhang and Liu (2012). However, the number of Facebook friends, also external social network, did have a statistically significant indirect-only mediation effect on the success rate of crowdfunding campaigns by means that it increased the social media diffusion which affected the success rate positively. This can be related to the word-of-mouth mechanism between social network and crowdfunding success as pointed out by Colombo et al. (2014). 50

52 So, this research argues that the beneficial effect of the external social network on early funding makes room for the indirect mediation effect on the success rate by means of the word-of-mouth mechanism resulting in social media diffusion. To get a deeper understanding how the effect of number of Facebook friends on crowdfunding success might shift during the campaign future research is needed. Reputation. Shane and Cable (2002) state that investors are more likely to fund when the founder has a positive reputation and Kietzmann et al. (2011) argued that a measurement for reputation within a community is the activity or number of posts. Within this research the reputation relates on the one hand to the experience of the founder measured in projects created and on the other hand to the intention of the founder to keep the crowd informed by means of updates. This research finds support for the third hypothesis (H3): a positive relation between reputation of the founder and the success rate of the project. This is an interesting managerial finding, as the reputation is in the hands of the founder to address. Keeping the audience updated as mentioned by Kickstarter (2017) does relate to the likelihood of success in a crowdfunding campaign. Also, the experience of created projects in the past positively relates to the success rate. When previous projects failed, the founder has a good change to do it right on his next attempt according to Greenberg and Gerber (2014) who found that 43% of relaunched projects eventually succeed. Founders of relaunched projects asked their social network and backers for feedback to adjust their campaign to the needs of the market. Also, they reported that they learned about the importance of marketing and communication in building a successful campaign. Indicating that a crowdfunding project is not only a call for financial support, but also a certain marketing-tool to create awareness and a minimal viable product to receive feedback from the crowd. This reasoning is in line with Belleflamme et al. (2010) as they indicate crowdfunding as a tool to increase consumer awareness, disseminate product information and estimate the willingness to pay of consumers. The positive relation between the number of projects created and the funding ratio, can possibly be seen as a causal relationship as the number of projects do not differ during the campaign. 51

53 Updating the project as mentioned can help to uphold a positive reputation (Hui et al., 2012). Screening the updates indicates that founders also ask their crowd to share the project on social media. Sharing possibly lead to more awareness plus a bigger audience and therefore lead to more funding. This is underlined by the research of Xu et al. (2014) that found that indeed 23% of updates contained a plea to share the project on social media. Considering that updates might relate to achieving intermediate (funding) objectives, this research does not state that there is a fully causal relationship from number of updates to the success rate. For example: already half way there in only 4 days can be such an update on achieving intermediate funding objectives. In view of this thinking, Xu et al. (2014) pointed out that the three update themes: remembering the crowd to fund, a report on progress and a new stretched reward, offered slightly more significant influence to predict crowdfunding success than a plea for social promotions; which was also significant. These three themes are closely related to achievement of intermediate (funding) objectives, indicating that the cause of an update can be the achievement of intermediate success. However, while exploring for the possibly mediation effect of social media diffusion the results underlined the expected partly causal relationship between updates and social media diffusion. This complementary mediation effect on success rate had a statistically significant indirect. The direct and total effect from updates on the success rate were also statistically significant and positive. Accordingly, asking the crowd by means of updates to share the project on social media is demonstrated to support the success rate. 52

54 Facebook and Twitter page. Next this research analyzed the efforts of the founder to communicate and connect to potential backers by means of creating and linking a Twitter or Facebook page to the crowdfunding campaign. Having a social media page linked to the crowdfunding campaign did not have a statistically significant effect on the success rate, so this research did not find any support for hypothesis 4 (H4). A logical reason that this effect is not significant could be that founders are able to post about the crowdfunding project on social media with their personal social media pages without creating or linking a Facebook or Twitter page especially for the crowdfunding project. However, while exploring for a mediation effect of social media diffusion having a Facebook page was statistically significant and positive related to the social media diffusion. Having a Facebook page had an indirectonly mediation effect on the success rate of a crowdfunding project, indicating that having a Facebook page for the crowdfunding project does help to create awareness among the crowd. Although, having this Facebook page linked, is not a predictor of success. Explore mediating or moderating effect. On the other hand, the social media diffusion does statistically significant and positively relates to the success rate consequently supporting hypothesis 5 (H5). This underlines the argument of Thies et al. (2014) that the word-of-mouth activities of external users are more important to generate traffic to the project page compared to only use the Facebook page linked to the campaign. Likewise, conform the research of Lu et al. (2014) about crowdfunding projects going viral on Twitter, the results are not that surprising. Since, the more interest there is for a project, the more likely the crowd is to share this project on social media which activates word-of-mouth mechanisms. A logical reasoning could be, the more a project is shared, the more awareness is created, the bigger audience is reached and therefore the possibility to reach a potential backer who is interested in the project is increased. This reasoning is in line with theory as Burt et al. (2013) state that the advantage of the social network lies in the bridging and distribution of sticky information. Bridging entails to communicate information from one network to another. Bridging is done by weak ties, which are loose connections between individuals who may provide useful information, resources or new perspectives for one another but typically not emotional support (Granovetter, 1983). Strong ties refer to friends and family who provide emotional support as well (Burt et al., 2013). 53

55 On crowdfunding platforms, strong and weak ties both participate by means of providing information or financial support. As social media network sites like Facebook support the maintenance of existing social ties and the formation of new connections (Ellison, 2007), the social media diffusion of a project possibly provide founders with more weak ties. In addition, weak ties are more important and dominant in the distribution of information online (Bakshy et al., 2012). Moreover, consumers like to network with others who share interests with them (Mangold and Faulds, 2009). Concluding that if a backer shares the project with his social network, the project is exposed among more weak ties who share the interests of the backer and therefore interest in the project. The founder can pro-active promote the project on Facebook. This can be done by asking friends, family and backers to share the project on their social media pages. More interestingly, one can pay Facebook for advertising the crowdfunding campaign among Facebook users with specific interests. A trade-off between the money spend on advisement and money received on the crowdfunding project, a so-called conversion ratio, is food for future research. The explorative part of this research on the possible moderating or mediating effect of social media diffusion provided some intriguing results. The mediation effects of Updates, Facebook friends of the founder and having a Facebook page specifically for the crowdfunding project on the success rate are already discussed. These findings are in line with research from Beier and Wagner (2014) as they found that linking a Twitter account to the crowdfunding project to spread the word is positive related to crowdfunding success. In this research, having a Facebook page linked to the campaign could function as a starting point to spread to word. 54

56 When exploring for a moderation effect of social media diffusion this research encountered contrasting findings. In the smaller dataset (N=83), where the number of Facebook friends of the founder was one of the control variables, there was a statistically significant moderation effect of social media diffusion on the relation between the number of updates and the success rate. The moderation effect was negative signed indicating that the more social media diffusion of a crowdfunding project the less important the number of updates became. Obviously, as the more shares, thus interest there already is for a campaign the less important the updates become. However, in the larger data set (N=146), where control variable Facebook friends of the founder was switched to whether the personal Facebook page of the founder was connected to the project page, the moderation effect was not statistically significant. Concluding that a possible moderation effect needs to be further analyzed in future research. Another finding in the larger dataset, is that the positive relation of the number of projects created on the success rate of a project is negatively influenced by the social media diffusion. Arguing that the reputation and experience within the community becomes less important as the social media diffusion is higher. This is in alignment with earlier argument that the social network of the founder possibly makes room for the beneficial effect of social media diffusion. Accordingly, previous created projects entailing an increase in the social network (Greenberger and Gerber, 2014). 55

57 5.2. Limitations This research has several limitations, which are addressed below. The main limitation of this research is the small sample size; some analyses are done on 83 projects and others on 146 projects. Hence, the results might be not robust and not generalizable to all crowdfunding projects. The results therefore should be interpreted with caution and conclusions are rather explorative and indicative then predictive. Other limitations are on the data collection part. This research did not incorporate the number of backer although earlier research around social network effects on the success of crowdfunding are based on the number of backers as dependent variable. As such, some effects might not be supported by this research. In addition, this research did directly control for the goal of the project as it integrated the goal in the dependent variable. As such, the analyses might be biased as an unproportional large goal could have deterrent effect on potential backers. In addition, while analyzing the data this research did not made a distinction between certain phases during the crowdfunding campaign. Accordingly, this research cannot disentangle or detect certain effects as they might only be present in a certain phase of the campaign, for instance in the early phase. Besides this research did not collected data from all possible social media platforms, Instagram, LinkedIn and Pinterest for example potentially affect the social capital of a founder. Also, blogs or news sites who mention the crowdfunding project potentially affect the social media diffusion. Therefore, collecting data from these online platforms are possible implications for future research. Lastly, this research merely looked at the size on one s social network. However, the quality of such a network is most likely of more importance Implications for research As crowdfunding is still a nascent topic within the entrepreneurial finance research the insights provided in this research are valuable for future research. Incorporating the online platforms as mentioned in the limitations section should be part of future analysis on crowdfunding. As Kickstarter offers founders a tool that provides insights in how and from where potential backers landed on the Kickstarter project page. 56

58 One could interview the founders to gain better insights in which platform generates the most traffic to the project page. The explorative findings on the mediating role of social media diffusion should also be researched more closely. Whether, the possible causal relations for social media diffusion are also found in a more comprehensive dataset could be interesting. Moreover, social media activity related to the crowdfunding project before the actual launch of the project are also valuable in getting a better understanding how founders can mobilize and expand their social network. A time-series research on crowdfunding, the updates and the social media diffusion in general could possible disentangle and relate the efforts of a founder to the success of a crowdfunding project. As mentioned before, this research points out that analyzing a certain conversion ratio between money spend on marketing and money raised for the crowdfunding project could lead to intriguing results Implications for practice The outcomes of this research give some practical insights and are useful for entrepreneurs who seek entrepreneurial finance on crowdfund platforms. In general, the outcomes are useful for individuals involved in crowdfunding. First, it is critical for founders to leverage their social capital. Therefore, reaching out to their social network and beyond their social network is advised. Social network platforms, like Facebook, can be powerful tools to do so. Hence, these platforms should be utilized to the full extend. This entails creating a Facebook page for the crowdfunding project and asking one s social contacts to share the project on social media. Second, to take advantage of the Kickstarter community it is advised to gain experience on the platform by creating more projects. This can either be by relaunching failed campaigns as their will be a learning effect or by dividing a bigger project in smaller projects. Besides, founders should keep the crowd informed by means of updates. Active asking the crowd to share and to fund the project is also advocated. 57

59 5.5. Conclusion The aim of this research was to provide insights in how one can mobilize the social network in reward-based crowdfunding. Therefore, the main question was how does the social network influences the success of a crowdfunding campaign and how can this social network be mobilized? As pointed out, this research did not find any support for a direct positive relation between internal social network or external social network of a founder and the success rate of a crowdfunding project. However, this research does support the hypothesis that the reputation of a founder, measured in number of updates and number of projects created on the platform, positively relate to the success rate. Besides, social media diffusion appeared to be a statistically significant predictor of the success rate. Furthermore, social media diffusion operates as a complementary mediating variable between number of updates and the success rate. Creating for or linking a Facebook or Twitter page to the crowdfunding project is not directly related to the success rate of a crowdfunding project. Although, this research found that creating or linking a Facebook page and the number of Facebook friends of a founder are related to the social media diffusion. Even though both effects were indirect-only mediating effects as they did not have statistically significant total effect on the success rate of a crowdfunding project. These effects are nonetheless interesting for a founder as he can stimulate the social media diffusion by creating and linking a Facebook page. Moreover, the number of Facebook friends of the founder as well has a positive relation with the social media diffusion indicating that having more online friends stimulates the social media diffusion. As such the external social network influences the success of a crowdfunding campaign in an indirect manner as it positively affects the social media diffusion More to the point, one can mobilize the social network by updating the project and asking the crowd to share the project on social media. Experience in creating crowdfunding projects also enhances the likelihood of a successful crowdfunding campaign as it increases the social network. 58

60 6. References Agrawal, A. K., Catalini, C., & Goldfarb, A. (2011). The Geography of Crowdfunding, Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research,, Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web, pp Bechter, C., Jentzsch, S., & Frey, M. (2011). From wisdom to wisdom of the crowd and crowdfunding. Belleflamme, P., Lambert, T., & Schwienbacher, A. (2010). Crowdfunding: An industrial organization perspective. Prepared for the Workshop Digital Business Models: Understanding Strategies, Held in Paris on June, pp Belleflamme, P., Lambert, T., & Schwienbacher, A. (2013). Individual crowdfunding practices. Venture Capital, 15(4), Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29(5), Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29(5),

61 Berry, W. D. (1993). Understanding regression assumptions Sage Publications. Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: Foundations and frontiers on advantage. Annual Review of Psychology, 64, Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: Foundations and frontiers on advantage. Annual Review of Psychology, 64, Cohen, J. H. (2010). Cooperation and community: Economy and society in oaxaca University of Texas Press. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95-S120. Cope, J. (2011). Entrepreneurial learning from failure: An interpretative phenomenological analysis. Journal of Business Venturing, 26(6), Cordova, A., Dolci, J., & Gianfrate, G. (2015). The determinants of crowdfunding success: Evidence from technology projects. Procedia-Social and Behavioral Sciences, 181, Dimov, D. P., & Shepherd, D. A. (2005). Human capital theory and venture capital firms: Exploring home runs and strike outs. Journal of Business Venturing, 20(1), Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer Mediated Communication, 13(1), Faraj, S., & Johnson, S. L. (2011). Network exchange patterns in online communities. Organization Science, 22(6),

62 Field, A. (2009). Discovering statistics using SPSS Sage publications. Fisk, R. P., Patrício, L., Ordanini, A., Miceli, L., Pizzetti, M., & Parasuraman, A. (2011). Crowdfunding: Transforming customers into investors through innovative service platforms. Journal of Service Management, 22(4), Fisk, R. P., Patrício, L., Ordanini, A., Miceli, L., Pizzetti, M., & Parasuraman, A. (2011). Crowdfunding: Transforming customers into investors through innovative service platforms. Journal of Service Management, 22(4), Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory,, Greenberg, M. D., & Gerber, E. M. (2014). Learning to fail: Experiencing public failure online through crowdfunding. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp Groeger, L., & Buttle, F. (2014). Word-of-mouth marketing influence on offline and online communications: Evidence from case study research. Journal of Marketing Communications, 20(1-2), Hayes, A. F. (2012). PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling, Hazleton, V., & Kennan, W. (2000). Social capital: Reconceptualizing the bottom line. Corporate Communications: An International Journal, 5(2),

63 Hsu, D. H. (2007). Experienced entrepreneurial founders, organizational capital, and venture capital funding. Research Policy, 36(5), Hui, J. S., Gerber, E., & Greenberg, M. (2012). Easy money? the demands of crowdfunding work. Northwestern University, Segal Design Institute,, Kickstarter. (2017)., 2017, from Kickstarter. (2017)., 2017, from Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? get serious! understanding the functional building blocks of social media. Business Horizons, 54(3), Kuppuswamy, V., & Bayus, B. L. (2015). Crowdfunding creative ideas: The dynamics of project backers in kickstarter. Lang, G. E., & Lang, K. (1988). Recognition and renown: The survival of artistic reputation. American Journal of Sociology, 94(1), Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3),

64 Lu, C., Xie, S., Kong, X., & Yu, P. S. (2014). Inferring the impacts of social media on crowdfunding. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp Lu, C., Xie, S., Kong, X., & Yu, P. S. (2014). Inferring the impacts of social media on crowdfunding. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29(1), Mollick, E. R., & Kuppuswamy, V. (2014). After the campaign: Outcomes of crowdfunding. Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), Schwienbacher, A., & Larralde, B. (2010). Crowdfunding of small entrepreneurial ventures. Schwienbacher, A., & Larralde, B. (2010). Crowdfunding of small entrepreneurial ventures. Shane, S., & Cable, D. (2002). Network ties, reputation, and the financing of new ventures. Management Science, 48(3),

65 Sørensen, J. B., & Fassiotto, M. A. (2011). Organizations as fonts of entrepreneurship. Organization Science, 22(5), Staber, U. (2006). Social capital processes in cross cultural management. International Journal of Cross Cultural Management, 6(2), Thies, F., & Wessel, M. (2014). The circular effects of popularity information and electronic word-of-mouth on consumer decision-making: Evidence from a crowdfunding platform. Xu, A., Yang, X., Rao, H., Fu, W., Huang, S., & Bailey, B. P. (2014). Show me the money!: An analysis of project updates during crowdfunding campaigns. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp Xu, B., Zheng, H., Xu, Y., & Wang, T. (2016). Configurational paths to sponsor satisfaction in crowdfunding. Journal of Business Research, 69(2), Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), Zhao, Q., Chen, C., Wang, J., & Chen, P. (2017). Determinants of backers funding intention in crowdfunding: Social exchange theory and regulatory focus. Telematics and Informatics, 34(1), Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2),

66 Zheng, H., Li, D., Wu, J., & Xu, Y. (2014). The role of multidimensional social capital in crowdfunding: A comparative study in china and US. Information & Management, 51(4), Zvilichovsky, D., Inbar, Y., & Barzilay, O. (2015). Playing both sides of the market: Success and reciprocity on crowdfunding platforms. Zvilichovsky, D., Inbar, Y., & Barzilay, O. (2015). Playing both sides of the market: Success and reciprocity on crowdfunding platforms. 65

67 Appendix A Table 17 Multicollinearity statistics first hierarchical regression Predictor variable Tolerance VIF Step 1 Duration Step 2 Duration LN_Updates LN_CFP_created LN_backed_others FB_page LN_FBF_founder TW_page LN_KS_shares Table 18 Multicollinearity statistics second hierarchical regression Predictor variable Tolerance VIF Step 1 Duration Step 2 Duration LN_Updates LN_CFP_created LN_backed_others FB_page FBF_connected TW_page LN_KS_shares

68 Figure 2 Histogram standardized residuals LN_Funding_ratio Figure 3 P-P Plot Regression Standardized Residual LN_Funding_ratio 67

69 Dependent variable Figure 4 Scatterplot Standardized Residuals LN_Funding_ratio Figure 5 Conceptual model, model A Low LN_Updates High LN_Updates Low LN_KS_shares Figure 6 Moderation effect model A with control variable FBF_connected 68

70 Dependent variable Figure 7 Conceptual model, model B Low LN_CFP_created High LN_CFP_created Low LN_KS_shares Figure 8 Moderation effect model B with control variable FBF_connected 69

71 Figure 9 conceptual model C: LN_Updates Figure 10 conceptual model C: FB_page Figure 11 conceptual model C: LN_FBF_founder - 70