“Crowdfunding: Sending the Right Entrepreneurial Orientation Signals to
Influence Funding Success”
A research on the impact of entrepreneurial orientation on crowdfunding success
M.G. van der Slikke (S2808412)
Master thesis
Business Administration Small Business and Entrepreneurship University of Groningen
Faculty of Economics and Business
Groningen, June 2016
Supervisor: I. Singaram MSc Co-supervisor: Dr. Ir. J. Kraaijenbrink
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Preface
This thesis serves as completion of the master Business administration Small Business and Entrepreneurship at the University of Groningen. This thesis has been written from February 2016 till June 2016. I chose this master because of my interests in small and medium enterprises (SMEs) and entrepreneurship in general. SMEs are known for their important contribution to job creation and a country’s gross domestic product. Since many SMEs have trouble getting financed new, alternative forms of financing like crowdfunding are being established. There is changing a lot at the moment. To be able to have a better idea of what is going on regarding crowdfunding nowadays, I decided to explore the topic myself.
I am grateful to the people that helped this thesis come to fruition. Especially Raja Singaram has contributed significantly to this thesis. He was always available to help me when I was confronted with problems. I, therefore, would like to thank him for the guidance and support. I would also like to thank my friend Bram Peters and my other colleague students for their help, creative input and feedback during the process.
Rick van der Slikke.
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Abstract
Traditional financing by bank and venture capitalists has become more difficult. This is due to increased risk-adversity and the information asymmetry between the financial provider and the company/entrepreneur in need for money. This is an concerning situation since entrepreneurs and small and medium enterprises (SMEs) contribute to the economy, job creation and gross domestic product. Additionally, electronic payment, technology and internet in general have developed. Due to these developments alternative financing methods (e.g., crowdfunding) have emerged.
Crowdfunding investors are challenged since they often don’t have experience with investing and don’t have extensive information of a crowdfunding project’s goal, quality and intentions. Therefore we believe that the signals given in a project’s narrative are important in the investment process. This research examines the effectiveness of signals that crowdfunding projects use to convince backers to pledge financial resources in a reward based crowdfunding context. We examine the impact of entrepreneurial orientation (EO) on funding success. We use the EO dimensions: autonomy, competitive aggressiveness, innovativeness, proactiveness and risk taking propensity. We conduct our analysis on a sample of 446 crowdfunding projects via the online crowdfunding platform Kickstarter.
Our results indicate that being proactive and having a risk taking propensity can be interpreted as effective signals and can positively impact funding success. Contrary to our expectations, autonomy, competitive aggressiveness and innovativeness have no impact on funding success. Furthermore, we find that backers respond positively to project’s with a short duration, which are being featured by Kickstarter, have added a video, have a realistic goal, have an extensive description of the purpose, have placed updates frequently and have created interaction in the comment section.
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Table of Content
1 INTRODUCTION... 6 2 LITERATURE REVIEW... 8 2.1 Crowdfunding ... 8 2.2 Signaling Theory ... 9 2.3 Entrepreneurial Orientation ... 10 2.3.1 Autonomy ... 11 2.3.2 Competitive aggressiveness ... 11 2.3.3 Innovativeness... 11 2.3.4 Proactiveness ... 122.3.5 Risk taking propensity ... 12
2.4 Conceptual Model... 12
3 RESEARCH METHODOLOGY ... 14
3.1 The Kickstarter investment process ... 14
3.2 Data Collection... 14 3.3 Measurements... 15 3.3.1 Independent variables ... 15 3.3.2 Dependent variables ... 15 3.3.3 Control variables ... 15 3.4 Analysis ... 17 4 RESULTS... 17 4.1 Data Cleaning... 17 4.2 Descriptive statisctics... 17
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4.4 The Effect of EO Competitive Aggressiveness... 19
4.5 The Effect of EO Innovativeness... 19
4.6 The Effect of EO Proactiveness... 20
4.7 The Effect of EO Risk Taking Propensity... 20
4.8 The Effect of Overall EO... 20
4.9 The Effect of The Control Variables... 21
5 DISCUSSION... 26
5.1 Conclusions... 26
5.2 Practical Implications... 28
5.3 Limitations... 30
5.4 Future Research... 30
5.5.1 Other projects attributes ... 30
5.5.2 Performance after funding ... 31
5.5.3 Unobservable influences ... 31
6 REFERENCES... 32
7 APPENDICES... 39
7.1 Entrepreneurial Orientation Dictionary... 39
7.2 SPSS Output One-Way ANOVA Descriptives... 41
7.3 SPSS Output Levene’s Test of Homogeneity of Variances... 43
7.4 SPSS Output One-Way ANOVA... 44
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Introduction
Due to the lack of a stable cash flow and other securities, getting (starting) capital for entrepreneurial ventures is difficult nowadays (Berger and Udell, 1998; Cosh, Cumming and Hughes, 2009). Traditional financing by bank and venture capitalists has become more difficult to get due to increased risk-adversity and the information asymmetry between the financial provider and the company/entrepreneur in need for money (Khatiashvili, Gvaramia and Kamkamidze, 2009; McCahery and Vermeulen, 2010). This is an concerning situation, since entrepreneurs, startups and small and medium enterprises (SMEs) in general contribute significantly to the economy, job creation and gross domestic product (Birch 1981, 1987; Neumark, Wall, and Zhang 2011; Haltiwanger, Jarmin, and Miranda 2009). However, these developments, in combination with the improvements of technology, electronic payment and the internet in general, have paved the way for alternative financing methods such as crowdfunding and microfinance (Rubin, 2012, Zhang, 2012; Giudici, Nava, Lamastra and Verecondo, 2012). Research of Massolution (2013) illustrates crowdfunding’s growth and it's potential. The crowdfunding market grew to a total of $16.2B in 2014 while prognoses currently are exceeding $35B and the limits of its potential has not been reached yet (Crowdsourcing.org, 2015; Massolution, 2013).
In recent years, crowdfunding has grown substantially to become a real alternative form of financing for entrepreneurs that have a difficult time getting traditional funding (Belleflamme, Lambert and Schwienbacher 2014; Belleflamme, Peitz, 2010; Schwienbacher and Larralde, 2010; Mollick, 2012). ´Crowdfunding involves an open call, mostly through the internet, for the provision of financial resources either in form of donation or in exchange for a future product or some form of reward and/or voting rights’ (Belleflamme, Lambert and Schwienbacher 2011, p.7). The financial provision is typically done by small individual financial contributions, which are pooled together, to support a particular goal.
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(Moss et al., 2014) and this is even more the case for technology firms since they usually require a higher level of EO (Su, 2013). Therefore, there can be argued that signaling EO characteristics positively influences a project’s funding success.
Despite the increasing role of crowdfunding in the financing of new ideas and ventures, the dynamics are not well understood (Griffin, 2012). By way of illustration we use two examples: Revols, founded in 2014, placed a fundraiser on Kickstarter to raise money for its quick custom-fit wireless earphones. The goal of $100.000 was met and even exceeded substantially by $1.735.320. A comparable funder on the same platform is Snug ear phones. A company that produces (wireless) earphones that can be customized to fit the ear perfectly. They were less successful: investors only pledged £4.110 of the £50.000 goal. Many possible explanations play a part in either failure or success of a fundraiser. This raises the question what makes it to a success or not. More specifically the following research question (RQ) emerges:
What is the effect of entrepreneurial orientation on funding success of technology startups in crowdfunding context?
Researchers have been using signaling theory (Spence, 1973) to help explain how information asymmetries influence decision making. The last years this theory has been researched quite extensively because signaling can be important if there are information asymmetries between groups (Conelly, Certo, Ireland and Reutzel, 2011). However, the link with signaling theory and crowdfunding has only been made by Allison, Davis, Short and Webb (2015) and Moss et al., (2014). This paper contributes to the existing literature by further extending signaling theory to crowdfunding theory and analyzing the role of EO in the signaling process and crowdfunding success. This paper is one of the first to investigate the influence of EO on reward based crowdfunding success. We examine which observable EO signals send by crowdfunding projects make investors actually invest money into a project. To examine this phenomenon and answer the research question, 446 projects between February 2016 and May 2016 from Kickstarter were analyzed. This crowdfunding platform suits this study well since its size is sufficient and its location, the USA, is progressive regarding crowdfunding (Song, 2015, Douw and Koren, 2013). Finally this paper extends the work on computer-aided text analysis (Short, Broberg, Cogliser and Brigham, 2010) by testing whether text in crowdfunding projects can influence crowdfunding success.
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the presentation of the findings of the conducted research. Concluding, the implications of the results are discussed for practitioners and future research.
Literature Review
In this section we discuss the crowdfunding market and the different types of crowdfunding. Subsequent, the conceptual framework is developed. The framework is based on signaling theory in combination with the project’s entrepreneurial orientation. Concluding this section, we conduct hypotheses for how dimensions of entrepreneurial orientation influence the success of a fundraiser.
Crowdfunding
Crowdfunding has derived from crowdsourcing (Howe, 2008). Despite similarities with microfinance, crowdfunding can be seen as a unique category of fundraising. Instead of raising ideas, feedback and solutions like crowdsourcing, crowdfunding is created to raise funds (Lambert and Schwienbacher, 2010). Crowdfunding is growing rapidly and more and more becoming a mainstream way of financing al kind of projects. The customer has become the investor (Ordanini, Micel, Pizzetti and Parasuraman, 2011). Research of Massolution illustrates this growth and its potential. Crowdfunding grew to a total of $2.67B in 2012. A growth of 81%. In 2013 the total amount of crowdfunding projects grew to $6.1B while the following year it made another impressive growth to $16.2B (Massolution, 2013). Current prognoses exceed $35B and the limits of its potential have not yet been reached (Crowdsourcing.org, 2015).
Since many investors implies many different goals (Mollick, 2012), both the funding in general and the relationship between the funder and investor differs from traditional finance sources (e.g., venture capitalists or business angels). Despite its potential, it still has to be seen to what extend crowdfunding is able to replace traditional funding sources (Belleflamme et al., 2012). This because there are some issues like legal limitations or a bad internet connection in certain countries (Lambert and Schwienbacher, 2010; Griffin, 2012; Brabham, 2008; Kleemann and Voß, 2008; Gerber, Hui and Kuo, 2012).
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compensation (Schwienbacher et al., 2010; Bradford, 2012). There are also hybrid forms available but these forms are less adopted by both the funders and investors (De Buysere, Gajda, Kleverlaan and Marom, 2012). Ahlers et al. (2015) argue that not all types of crowdfunding are suited for empirical research: investors in donation based crowdfunding for example typically focus less on the startup ability or financial returns. They focus more on the philanthropic aspects, for the reward based investors the most important aspect if whether the owner of the project is able to deliver its reward or not. Especially the equity and lending based forms are suitable for empirical research since they primarily focus on financial returns. Based on these arguments we make a distinction between different types of crowdfunding since there are differences in the suitability for empirical research.
Signaling Theory
In the introduction we discussed that there are information asymmetries between investors and (entrepreneurial) funders (Conelly, Certo, Ireland and Reutzel, 2011; Cuming & Johan, 2009). Stiglitz (p. 469) argues that different people know different things. So, certain people poses information that may be relevant for other individuals when making decisions. In situations where there are information asymmetries like described, people with private information may want to signal information to others in order to reduce these asymmetries. Researchers have been using signaling theory (Spence, 1973) to help explain how information asymmetries influence decision making. Lester, Certo, Dalton and Cannella (2006) for example examined how an entrepreneurial firm can use the characteristics of the management team to signal value.
Two types of private information stand out in decision making: (1) private information about (strategic) intent of a firm/entrepreneur and (2) private information about the firm’s characteristics (e.g., value, potential, quality) (Stiglitz, 1990). In the context of crowdfunding, investors are unknown with the (strategic) intent or the characteristics of a crowdfunding project (Backes-Gellner & Werner, 2007; Busenitz, Fiet, & Moesel, 2005; Michael, 2009). This may be the cause that projects with high potential not get funded (Ahlers et al., 2015). Therefore, in order to get funded, funders try to signal its (strategic) intent and characteristics to the investor. That is why we believe that signaling theory will matter in the crowdfunding decision making process.
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investors. Costly basically means that the costs of sending the signal may not exceed the benefits of the signal. So signals send by crowdfunding projects should be strategically managed because different signals imply different benefits and opportunity costs (Austen-Smith & Banks, 2000).
In the signaling theory, literature research has been done on signals that do not meet these two criteria. For example Farrell and Rabin (1996), Almazan, Banerj and DeMotta (2008) and Payne et al., (2013) investigated that less costly signals are also able to reduce information asymmetries between funders and investors. Despite the fact that signals on crowdfunding platforms doesn’t look to be costly, we beg to differ for the following reasons: First, usually crowdfunding platforms’ terms give them the right to ban crowdfunding projects if they provide misleading (cheap talk) information. So the long term costs (possibility of being banned) outweighs the short term incentive for providing misleading information. Second, Conelly et al., (2011) argues that misleading signals lose their value in the long run. Finally, crowdfunding projects can choose to send different signals. If they choose to signal (strategic) intent or certain project characteristics, this is at the cost of not transmitting another. So, for these reasons, we see text in crowdfunding projects as costly signals.
In the last years many projects got funded via crowdfunding, while there are also plenty of projects that failed to raise their goal. This implies that investors are able to infer certain characteristics of projects. Quality of startups usually cannot be observed right away, which makes that investors need to evaluate startups based on observable characteristics that infers its (lack of) quality (Stuart, Hoang and Hybels, 1999). In this research we assume that funders act rationally and try to send as many effective signals to investors as possible to reduce the information asymmetry (Michael, 2009). Literature argues that Entrepreneurial Orientation (EO) is able to give a venture a competitive advantage (Lumpkin and Dess, 1996). Following this reasoning, we argue that the EO of crowdfunding projects will increase the likelihood of getting funded. The following paragraph elaborates on this discussion.
Entrepreneurial Orientation
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conceptualized EO in the following dimensions: autonomy, competitive aggressiveness,
innovativeness, proactiveness and risk taking propensity.
There can be argued that technology firms require more EO than non-technology firms since those technology firms have some specific characteristics compared to conventional firms (Su, 2013). For example they usually have a high percentage highly educated professionals (Carnoy, 1985), invest substantially in research and development (Balkin and Gomez-Mejia, 1984), endorse innovations and have a high growth and death rate (Nijkamp and Poot, 1991).
Autonomy. Autonomy refers to the ability of firms to independently create and
materialize new ideas (Lumpkin and Dess, 1996). Autonomy gives actors freedom in their pursuit to new opportunities. This freedom enables firms to innovate, create new opportunities and move quickly to exploit opportunities. So it seems likely that autonomy positively influences a firm’s performance (Lumpkin, Cogliser Schneider, 2009). Less autonomous firms can make this shift less quickly so they are likely to be less successful (Moss et al., 2014). Therefore we hypothesize:
Hypothesis 1. Signaling autonomy to investors positively affects a technology project’s crowdfunding success.
Competitive aggressiveness. This dimension is about the (aggressive) tactics that aim
to improve the competitive position or maintain the current position (D’Aveni, 1994). Argued is that organizations that aggressively faceoff with competition tend to perform well in the marketplace (Lumpkin and Dess, 1996). The reason here fore is that they use decisive actions to be able to exploit business opportunities (Hamel, 2007). So, aggressiveness should have a positive relation with the acquisition of resources (Prahalad and Hart, 2002; Tauer and Harackiewicz, 2004). Therefore, ventures displaying competitive aggressiveness are more likely to perform better than their competitors (Moss et al., 2014). Following this reasoning, we propose the following hypothesis:
Hypothesis 2. Signaling competitive aggressiveness to investors positively affects a technology project’s crowdfunding success.
Innovativeness. This next dimension indicates the ability of a firm to invent new ideas,
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cycle, which bring a high level of uncertainty (Vohora, Wright, and Lockett, 2004). Wiklund and Sheperd (2005) argue that (technology) firms with a high innovativeness are able to discover and reap advantage of market opportunities. Therefore, we suggest that innovative ventures are able to outperform less innovative ventures. Subsequently to this discussion, we propose the following hypothesis:
Hypothesis 3. Signaling innovativeness to investors positively affects a technology project’s crowdfunding success.
Proactiveness. Proactiveness refers to an organizations ability to anticipate future
changes and therefore the ability to position itself in advance (Lumpkin and Dess, 1996). Technology firms need to be proactive to keep ahead of its competitors in the often dynamic environment in which they are active (Cottrell and Sick, 2002). Proactive ventures are able to become market leaders by developing and implementing new technologies before their competitors (Moss et al., 2014). Firms which are proactive often are able to earn above average profits because they enjoy first mover advantages and are able to build a reputation. This combination may result in an increase in market performance (Porter, 1980). This suggests that proactive firms outperform less proactive firms, which results in the next hypothesis:
Hypothesis 4. Signaling proactiveness to investors positively affects a technology project’s crowdfunding success.
Risk taking propensity. Risk taking propensity refers to how comfortably a firm faces
uncertainty. McClelland (1960, p210) states that ‘entrepreneurship, by definition, involves some risk’. Hence that (calculated) risk is often an entrepreneurial characteristic (Brockhaus, 1980). Since technology changes quickly, technology firms generally face more risks than non-technology firms. Apart from a potential short term loss, this can create opportunities in the long run (Dess and Lumpkin, 2005). Research indicates that firms who successfully take risks are more successful compared to those who do not (West et al., 2008). Therefore, it can be argued that risk taking firms outperform risk averse firms (Moss et al., 2014). Thus we hypothesize:
Hypothesis 5. Signaling risk taking propensity to investors positively affects a technology project’s crowdfunding success.
Conceptual Model
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outperform competitors. Crowdfunding projects that portray themselves as autonomous ventures that proactively and aggressively develop and implement new ideas in order to cut off competitors in an innovative way are more likely to get investors’ attention and thus have more funding success.
This paper builds on the work of Moss et al., (2014), Ahlers et al., (2015) and Allison et al., (2015) by examining crowdfunding through a signaling theory perspective. These studies have looked at determinants of crowdfunding success and signaling theory in microfinance. However, this is the first paper to study commercial reward based crowdfunding through a signaling theory perspective by looking at the EO of crowdfunding projects.
Figure 1 presents the conceptual framework that visualizes the relation between the independent and dependent variables.
Figure 1
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Research Methodology
This papers sample consists of 500 crowdfunding projects from the platform Kickstarter. As of May 2016 $2.361.624.233 has been pledged and there have been 29.355.636 individual pledges. That makes Kickstarter one of the biggest crowdfunding platforms in the world and a valuable context for this research.
The Kickstarter Investment Process
Kickstarter gives investors the opportunity to invest in projects categorized in fifteen different categories. The creator of the project describes the value of his or her project in order to make investors pledge. The creator also has the opportunity to add a video to enlighten the purpose of the project. Furthermore he or she can add updates which can inform investors about relevant progress that has been made and the creator can give comments to answer questions from investors. Each of Kickstarter’s projects has a funding goal and an expiration date. The goal needs to be reached by the time this date has passed in order for the creator to get the money that has been pledged. This is due to Kickstarter’s “all-or-nothing” funding principle. This funding principle implies that the backers get their money back in case the project’s funding goal is not met by the time a project has ended.
Data Collection
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and (17) overall EO. The descriptive statistics and correlation matrix for all these variables can be found in table 1 and 2.
Measurements
The following variables will be used to test the hypotheses:
Independent variables. We will measure EO in Crowdfunding projects. EO will be
operationalized by using the ENTREPRENEURIAL ORIENTATION dictionary developed by Short et al., (2010). This dictionary gives us the ability to assess which crowdfunding project signals one or more EO dimensions. This paper investigates al five EO dimensions. In the literature review we conceptualized the dimensions as separate constructs. Therefore we will also each EO dimension separately. The five EO dimensions should be seen as separate constructs when they are being analyzed with computer-assisted text analysis (Covin and Wale’s, 2012). This is in line with Lumpkin and Dess’ conceptualization. The complete word list can be found in appendix A.
We will copy the text located in the project’s description, rewards and updates and convert this textual content into plain text in order to be able to conduct computer-assisted textual analysis and statistical software (CATA; Short, Broberg, Cogliser and Brigham, 2010). This is a qualitative method that is able to classify text (Krippendorff, 2004; Weber, 1990). Originally, management literature used this method by examining how language in business documents influences investors and market participations (Allison, McKenny and Short, 2013; Shamir, Arthur and House, 1994; Short and Palmer, 2008) Furthermore, content analysis is usually used to study for example annual reports and letters (Moss, Short, Payne and Lumpkin, 2011; Palmer and Short, 2008). After collecting the plain textual content of the projects we will use CATScanner to analyze how many words of the textual content are EO related. Since some of the projects will have significantly more words than others we will control for word count. Per EO dimension we will calculate the number of hits with the words in the textual content with the words in the relevant EO dictionary. Re-using a word also counts as a hit. The variable (17) overall EO is will be calculated by taking the sum of the scores of the five EO dimensions.
Dependent variable. In order to measure the funding success of a crowdfunding project
we will follow the differentiation made by Ahlers et al., (2015). We will use the dichotomous variable funded to indicate whether a project has reached its goal or not. By using this variable we will be able to research whether successful projects significantly differ from failed projects.
Control variables. Among others, Mollick (2012) argues that certain functions like
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changes of funding success. Therefore, in addition of the independent variables, we will include eleven control variables since it may be that they also influence the dependent variable.
(1) The project duration. The duration of a project set by the creator in advance. Until this end date investors can make a pledge. The project duration will be measured in days.
(2) Whether a project has been featured by Kickstarter. If a project has appeared on the page Staff Pick, Most Funded, Recently Launched, Staff Picks, Curated Pages, Popular or
Ending Soon it has been featured by Kickstarter. Mollick (2012) found that being featured by
Kickstarter influences funding success.
(3) Whether there is a video present. Videos can substantiate the textual description. (4) The videos duration. It is said that shorter videos contribute more to a projects success than long videos. According to Kickstarter itself a videos should ideally not last longer than two minutes. The duration of the video will be measured in minutes.
(5) Whether the project is connected with Facebook. Shane and Cable (2002) and Venkataraman (1997) argue that entrepreneur’s social ties influence the decisions of investors. So it could be useful for a creator to connect the Kickstarter project with his or her Facebook.
(6) The goal of the project. The goal (in $) set by the creator that he or she wants to collect with this crowdfunding campaign.
(7) The amount of pledge levels. Creators can decide how many different pledge levels they want to use for their projects.
(8) The minimum pledge amount. The minimum amount a backer can pledge.
(9) The amount of updates. The total number of updates made by the creator. Updates can indicate the progress a creator has made since launching the project and this can seduce backers to pledge. Will be measured up until the end date.
(10) The amount of comments. The total number of comments made by the creator and/or the backers. These comments are publicly visible. However, some comments are for backers only. Will be measured up until the end date.
(11) The total amount of words. Number of words in the description, rewards and updates combined. Will be measured up until the end date.
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Analysis
Computer-assisted textual analysis and statistical software will be used to find scores of EO. Subsequently we will conduct an One-way ANOVA analysis to investigate whether and how successful and failed projects differ in terms of EO and additional control variables.
Results
Data Cleaning
Before we present the results we will discuss the measures we have taken to clean the data. After selecting only the technology projects which have reached their end date a sample of 732 projects remained. Due to time restrictions we reduced this sample by taking a random sample of 500 projects. Before we started with the analysis we looked if they were outliers. We detected outliers in the variables (11) total number of words, (12) EO autonomy, (13) EO competitive aggressiveness, (14) EO innovativeness, (15) EO proactiveness, (16) EO risk taking propensity and (17) overall EO. We have removed the outliers of these variables from our dataset. Via this way we ended up with a final sample of 446 projects.
Descriptive Statistics
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Table 1
Descriptive statistics
N Minimum Maximum Mean
Std. Deviation
Duration 446 7,08 91 40,168 16,517
Featured by Kickstarter 446 0 1 ,22 ,416
Video present 437 0 1 ,81 ,395
Video duration (mins) 437 0 10,45 2,297 1,939
Facebook connected 446 0 1 ,40 ,491
Goal 446 $99 $390.000 $16.491,27 $32.118,451
Amount of pledge levels 446 1 22 7,11 3,378
Minimum pledge amount 446 1 100 6,88 9,665
Number of updates 446 0 71 6,37 8,964
Number of comments 446 0 1.576 26,90 110,692
Total number of words 446 199 3.651 1.262,81 830,944
EO autonomy 446 0 6,4 ,774 1,042
EO competitive aggressiveness 446 0 5,1 ,210 ,537
EO innovativeness 446 0 9,8 2,394 1,828
EO proactiveness 446 0 4,2 ,333 ,573
EO risk taking propensity 446 0 1,3 ,116 ,250
Overall EO 446 0 10,5 3,827 2,226
Note: This table displays the mean, standard variable, minimum value and maximum value for both the control- and independent variables.
The Effect of EO Autonomy
We conducted a Pearson correlation to investigate the relationship between the control variables, dependent variables and independent variables. We found a moderate positive relation between EO autonomy and overall EO (r = ,402, n = 446, p < ,000).
Hypothesis 1. (Signaling autonomy to investors positively affects a technology project’s crowdfunding success.) is tested by conducting an One-way ANOVA. The results of this
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homogeneity of variance and conclude that there is a significant difference between the variances of both groups. Subsequently, since the variable EO autonomy violated the Levene’s test of Homogeneity of Variances we use an adjusted F statistic that is provided by both the Welch and Brown-Forsythe statistic. This analysis compares the effect of EO autonomy on its funding success. The results of the analysis of variance, which can be found in table 3, show that the effect of EO autonomy on the success of a crowdfunding project is insignificant, F(1, 400) = 3,669, p = ,056. Fully funded projects use less EO autonomy (M = ,679 SD = ,850) than not fully funded projects (M = ,868 SD = 1,200). Since this result is not significant we are not able to support hypothesis 1.
The Effect of EO Competitive Aggressiveness
The Pearson correlation found only one variable that has a weak but significant positive relation with EO competitive aggressiveness: overall EO (r = ,186, n = 446, p < ,000).
A One-way ANOVA has been conducted to compare the effect of EO competitive aggressiveness on failed and successful projects (Hypothesis 2. (Signaling competitive
aggressiveness to investors positively affects a technology project’s crowdfunding success).
This analysis of variance shows that the effect of EO competitive aggressiveness on the success of a crowdfunding project is not significant, F(1, 444) = 2,439, p = ,119. Fully funded projects use more EO competitive aggressiveness (M = ,249 SD = ,634) than not fully funded projects (M = ,170 SD = ,417). But since the result is insignificant we did not find evidence to support hypothesis 2.
The Effect of EO Innovativeness
The results of the Pearson correlation suggest that there is a weak positive relation between EO innovativeness and EO risk taking propensity (r = ,103, n = 446, p < ,029). Similar as with the previous independent variables, the analysis shows that there is a strong positive relationship between EO innovativeness and overall EO (r = ,817, n = 446, p < ,000).
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is not significant we are not able to support Hypothesis 3. (Signaling innovativeness to investors
positively affects a technology project’s crowdfunding success).
The Effect of EO Proactiveness
Table 2 shows that there is a weak positive relation between EO proactiveness and being featured by Kickstarter (r =,157, n = 446, p < ,001). Also, there is a weak positive relation found between EO proactiveness and overall EO (r = ,304, n = 446, p < ,000).
Furthermore, table 3 shows that there is a significant effect of EO proactiveness on funding success F(1, 444) = 6,777, p = ,010. Hypothesis 4. (Signaling proactiveness to investors
positively affects a technology project’s crowdfunding success) is therefore supported. Fully
funded projects use more EO proactiveness (M = ,404 SD = ,590) than not funded projects (M = ,263 SD = ,549).
The Effect of EO Risk Taking Propensity
The Pearson correlation found a weak positive correlation between EO risk taking propensity and a project’s amount of pledge levels (r = ,104, n = 446, p < ,028), the number of updates (r = ,161, n = 446, p < ,001), the total number of words (r = ,181, n = 446, p < ,000) and EO innovativeness (r = ,103, n = 446, p < ,029) . Additionally EO risk taking propensity has a weak positive relationship with overall EO (r = ,159, n = 446, p < ,001).
Similar as with EO autonomy and innovativeness, the significance value of EO innovativeness for the Levene’s test of Homogeneity of Variances is less than our α of ,05 (p < ,05) with a score of F(1, 444) = 5,192, p = ,023. So here we also need to reject the null hypothesis and use an adjusted F statistic provided by the Welch and Brown-Forsythe statistic. The One-way ANOVA furthermore investigated Hypothesis 5. (Signaling risk taking propensity to
investors positively affects a technology project’s crowdfunding success). The analysis of
variance shows that the effect of EO risk taking propensity on the success of a crowdfunding project is significant, F(1, 442) = 4,613, p = ,032. Fully funded projects use more EO risk taking propensity (M = ,141 SD = ,256) than not funded projects (M = ,091 SD = ,241). This outcome supports hypothesis 5.
The Effect of Overall EO
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Even tough researching overall EO is not the main goal or even a hypothesis of this paper, we have investigated whether overall EO influences funding success. Overall EO scores F(1, 444) = 8,452, p = ,004 for the Levene’s test of Homogeneity of Variances. Again, the value is less than α of ,05 (p < ,05) and therefore we use an adjusted F statistic by using the Welch and Brown-Forsythe test. The result of the final analysis of variance shows that the effect of overall EO on the success of a crowdfunding project was not significant, F(1, 428) = 2,637, p = ,105. Just as the trend was for most of the previous EO dimensions, successful projects use more overall EO (M = 3,998 SD = 1,994) than unsuccessful projects (M = 3,656 SD = 2,428).
The Effect of The Control Variables
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Kickstarter (r = ,323, n = 446, p < ,000), if there is a video present (r = ,251, n = 438, p < ,000), the videos duration (r = ,236, n = 438, p < ,000), a projects goal (r = ,217, n = 446, p < ,000), the amount of pledge levels (r = ,488, n = 446, p < ,000), the number of updates (r = ,583, n = 446, p < ,000), and the number of comments (r = ,297, n = 446, p < ,000) and a weak, negative relation with the minimum pledge amount (r = -,143, n = 446, p < ,002).
Similar as for the independent variables, a One-way ANOVA was conducted to compare the effect of the projects duration, being featured by Kickstarter, if there is a video is present, the videos duration, being connected with Facebook, the projects goal, the amount of pledge levels, the minimum pledge amount, the number of updates, the number of comments and the projects total number of words on the funding success of a crowdfunding project.
Table 3 shows that the projects duration violated Levene’s test of Homogeneity of Variances by scoring F(1, 444) = 8,862, p = ,003. Therefore we assume that there is no homogeneity of variance and we need to work with an adjusted F statistic which is given by the Welch and Brown-Forsythe tests. The analysis of variance shows that the effect of a projects duration on its success was significant (in a negative way), F(1, 434) = 9,465, p = ,002. Successful projects (M = 37,633, SD = 15,050) have a shorter duration than failed projects (M = 42,401, SD = 17,586).
The next variable, being featured by Kickstarter, scored F(1, 444) = 1.169,153, p = ,000 on the Levene’s test, which is lower than our α of ,05 (p < ,05). Here we also reject the null hypothesis and worked with an more robust F statistic that is provided by both the Welch and Brown-Forsythe statistic. It turns out that being featured by Kickstarter has a significant influence on funding success F(1, 277) = 118,251, p = ,000. On average successful projects (M = ,41, SD = ,493) are more often featured by Kickstarter than failed projects (M = ,03, SD = ,175).
With a score of F(1, 444) = 86,764, p = ,000, Levene’s test also was significant for the third control variable (if there is a video present). So we assume that there is a significant difference between the variances of both groups and use an adjusted F statistic. Table 3 also shows that the effect of adding a video to a project on its success was significant, F(1, 400) = 19,398, p = ,000. Projects who have added a video turned out to be successful more often (M = ,89, SD = ,316) than projects who did not add a video (M = ,73, SD = ,447).
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that is provided by both the Welch and Brown-Forsythe statistic. The result of the One-way ANOVA is that the effect of the project’s goal on its success was significant (in a negative way), F(1, 307) = 13,773, p = ,000. The initial goal (M = 10.926,29 SD = 18.213,455) of successful projects is significantly lower than that of failed projects project (M = 22.056,25 SD = 40.913,197).
Furthermore the analysis of variance showed that the effect of a project’s amount of pledge levels on its success was significant, F(1, 444) = 22,565, p = ,000. Funded projects had more pledge levels (M = 7,86 SD = 3,415) than projects which were not successfully funded (M = 6,37 SD = 3,179).
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Table 3
One-way ANOVA
Note: This table displays the differences in means of the successful and failed projects. ** and * indicate statistical significance at the one percent and five percent levels
Variables Successful project (mean) Unsuccessful project (mean) Difference test (Successful project vs unsuccessful project) Levene’s test of Homogeneity of Variances Welch / Brown-Forsythe Controls Duration 37,633 42,401 -4,768** ,003 (violated) ,002
Featured by Kickstarter ,41 ,03 ,38** ,000 (violated) ,000
Video present ,89 ,73 ,16** ,000 (violated) ,000
Video duration (mins) 2,430 2,163 ,267 ,002 (violated) ,150
Facebook connected ,43 ,38 ,05 ,060 - Goal 10.926,29 22.056,25 -11.129,96** ,000 (violated) ,000 Amount of pledge levels 7,86 6,37 1,49** ,343 - Minimum Pledge amount 6,39 7,36 -,97 ,037 (violated) ,288
Number of updates 11,37 1,36 10,01** ,000 (violated) ,000
Number of comments 51,22 2,59 48,63** ,000 (violated) ,000
Total number of words 1.636 889,61 746,39** ,000 (violated) ,000
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Discussion
Lumbkin and Dess (1996) and Wiklund and Shepherd (2005) argue that a venture’s entrepreneurial orientation (EO) is related to its growth, performance and overall success. A firm tends to be more successful with a strong entrepreneurial orientation (Moss et al., 2014). Furthermore entrepreneurial orientation is able to give a venture a competitive advantage (Lumpkin et al., 1996).
Crowdfunding represents an increasingly important alternative financing method for entrepreneurs who have a difficult time getting funded by a bank or venture capitalist (Belleflamme et al., 2014; Belleflamme et al., 2010; Schwienbacher et al., 2010; Mollick, 2012). There are information asymmetries between creators of crowdfunding projects and investors (Conelly et al., 2011; Cuming et al., 2009). Conelly et al., (2011) argue that signaling can be important if there are information asymmetries between groups. Investors are unknown with the characteristics of a crowdfunding project (Backes-Gellner et al., 2007; Busenitz et al., 2005; Michael, 2009). Quality of projects usually cannot be observed right away, which makes that investors need to evaluate them based on observable characteristics that infers its quality (Stuart et al, 1999). Therefore, in order to get funded, creators may want to signal a project’s attributes to investors.
This paper was one of the first to investigate the influence of entrepreneurial orientation on reward based crowdfunding success. We try to answer the following research question: What
is the effect of entrepreneurial orientation on funding success of technology startups in crowdfunding context? The answer on this research question is that signaling EO proactiveness
and EO risk taking propensity positively influence the probability of a project getting funded. The EO dimensions autonomy, competitive aggressiveness and innovativeness did not significantly influence the probability of getting funded.
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project’s characteristics that influence the chance of it getting funded. Concluding, this research extends CATA literature by using it to investigate project’s attributes in a crowdfunding context. We will now discuss the contributions in more detail below, followed by practical implications, limitations and suggestions for future research.
In line with our expectations, investors are more likely to invest in projects that signal EO proactiveness and EO risk taking propensity. Literature suggests that signaling strong EO dimensions should increase a project’s funding chances. However, our study suggests that most of the EO dimensions do not significantly influence investors. Even though we are not able to interpret insignificant results, it may be in line with the findings of Allison et al., (2015) who argue that it is more effective to focus on why a crowdfunding project would be intrinsically satisfying for the investor than to state the project as a pure investment opportunity. Since EO is not per se focused on intrinsically satisfying investors or customers but instead focuses on the strong assets of a project/organization this could come off as looking from an investment perspective (which is apparently not that affective). Furthermore Mollick (2012) argues that funders may be motivated by non-monetary rewards. These are dominant effects in reward-based crowdfunding like Kickstarter. This could be an explanation why signaling EO in reward based crowdfunding does not influence a project that much.
Another important factor which could have played a role is that crowdfunding dampens the strength of the EO dimensions. For example, in the literature background we discussed that
autonomy refers to the ability of firms to independently create and materialize new ideas
(Lumpkin and Dess, 1996). This so called strong ability to independently create and materialize new ideas and opportunities might lose credibility right at the moment that a projects needs crowdfunding to realize its goal. Some (recreational) investors use crowdfunding just for ‘fun’ and maybe even charity. They could think that the autonomous projects who are strong and independent don’t need the help of the ‘normal people’ since they should be able to fund it either by themselves or in a traditional way. Therefore, projects and companies might lose their credibility by using crowdfunding to get financed. This reasoning can also apply for innovativeness and competitive aggressiveness since investors might think that the real innovations of this word and the competitive aggressive projects/organizations would be able to survive without being dependent of the funding of recreational investors. So, this study helps to reveal contextual conditions under which certain EO dimensions may or may not influence financing.
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while others do not. These findings underline the need for future research to investigate how the five EO dimensions vary in different contexts since most studies treat EO in general (Rauch, Wiklund, Lumpkin and Frese, 2009).
Among others, Moss et al., (2014) and Mollick (2014) showed that signaling can be useful in a crowdfunding context. This study extended signaling theory within crowdfunding literature to a more informal setting. This is an important contribution since it gives us more insight in the use of signaling theory in a less defined setting such as crowdfunding instead of the traditional finance setting, where it is studied more extensively. Besides the project’s narratives on Kickstarter, investors don’t possess information about the project. Since some of the signals significantly influence funding probability, it suggests that backers in some way assess the potential of the Kickstarter project. So results of this study reaffirms the importance of signaling when there is an information asymmetry between one or more parties. It highlights that signals need to be strategically managed since different signals imply different benefits and opportunity costs (Austen-Smith & Banks, 2000).
This research provides insight in how entrepreneurs can use certain characteristics, attributes and narratives to get financed. Studies which extend these types of literature enable entrepreneurs and SMEs to get more knowledge and tools to get funded. This knowledge can be important for them since crowdfunding for example can take away some of the restrictions (e.g., the geographic location becomes less important with online financing) that entrepreneurs and SMEs have to deal with. This is a relevant addition since entrepreneurs and SMEs can be important for developments as job creation, Gross Domestic Product and the economy in general (Birch 1981, 1987; Neumark et al., 2011; Haltiwanger et al., 2009).
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Practical Implications
Our results suggest that Kickstarter projects achieve higher probabilities of funding when they signal the EO dimensions proactiveness and risk taking propensity. Subsequently, the creators should take a look at the EO proactiveness and EO risk taking propensity dictionary (Short et al., 2010) in order to see which words they should use to build their projects narrative with.
The results of the control variables also have implications for creators. Before a project goes live, the creator needs to set the end date until investors can pledge. The duration of a project should not be too long. Results show that a project’s funding success is negatively influenced when the duration is too long. Projects who set a short duration signal a certain level of confidence. As if they know that they are going to be funded and the message is that investors should capitalize on this opportunity before it is too late. Furthermore, Kickstarter gives creators tips on its website how they can increase its chances on getting featured by them. Since our results suggest that being featured by Kickstarter significantly influences funding success, creators should take these tips seriously. An explanation for this result can be that projects who are featured by Kickstarter get more attention and also the credibility of a project increases. Moreover, our results suggest that a creator should add a video to its project since this increases probability of funding. Adding a video enables a creator to enlighten the narrative of a project. A video can add elements to influence backers that plain text cannot.
Additionally, our results indicate that creators should keep their project’s goal obtainable since a high goal negatively influences its funding probability. A creator should only ask for the amount of money that he or she needs to execute the project and a certain amount to be able to deliver the rewards to the backers. It is better to reach the goal and perhaps even exceed it, than to aim high and fail. Especially with the all-or-nothing principle that Kickstarter uses. Subsequently our study suggests that a creator should add enough pledge levels. Sometimes the cheap pledge levels with additional perks run out fast. In this way a project risks losing backers who cannot afford more expensive pledges. Furthermore, following our results that successful projects use more words than failed projects, it is better to use enough words to describe the purpose of a project clearly than using less words and describe a project only briefly. If the creator fails to radiate a project’s purpose, chances of a backer making a pledge get slimmer.
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It is also important to answer backer’s questions in the comment section since removing uncertainties can persuade them to pledge. Besides answering questions, placing comments and responding to other people’s comments can prevent the vibe in the comment section becoming negative. Moreover, in which manner and how fast a creator comments and responds to backers says a great deal about his or her attitude.
Limitations
Just as all other studies, our paper comes with its limitations. First and most important, the conclusions are based on the way the crowdfunding projects present themselves. We are not able to draw conclusions based on actual venture quality (Moss et al., 2011). Subsequently, since we used EO dictionaries to measure EO related word count, we are not able to measure them in their original context (Loughran and McDonald, 2011). It might have happened that we have missed some relevant information. (Duriau, Reger and Pfarrer, 2007). Since we specifically investigated the crowdfunding, these results cannot be generalized to other types of financing like banks, venture capitalists and business angels.
Besides the CATA related imitations there are some general limitations. We investigated projects which ended June 2012 or earlier. Although Kickstarter in general remained the same and the results are still relevant, this could hurt the generalizability of the results. Furthermore, we were limited by the deadline set by the University of Groningen. Ideally we would have had more projects to investigate. Another consequence of the time pressure is that we only investigated one of the fifteen Kickstarter categories which could harm the generalizability. Finally, our study was limited to investigating narratives of the crowdfunding projects and did not implement the content of project’s images and/or videos. Additionally, since we have only gathered information until the project’s end date we do not have data about what happens after projects got funded or not. Due to this limitation we are only able to give implications from the creator’s perspective.
Future Research
Based on the results of this paper, we give three potential areas for future research.
Other projects attributes. We have only investigated narratives in the project’s
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Stephen, 2011; Pope and Syndor, 2011). So it could be fruitful to extend it to crowdfunding. If studies start implementing this type of research they might get more insights regarding impression management in a crowdfunding context. Additionally, it could also be interesting to research the sentiment in a project’s updates and comment section. It could be that a negative or positive sentiment influences the probability that a backer pledges (or not).
Performance after funding. We have only investigated the funding process. Our
knowledgeabout the process and performance of projects which got funded via crowdfunding is limited. Future research could investigate how the projects that got funded perform afterwards. Then we are able to give information from the backer’s perspective.
Other influence. As mentioned before, with CATA and many other statistical tests the
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