UNPACKING CROWDFUNDING: SIGNALING IN THE EARLY YEARS OF U.S.
UNACCREDITED EQUITY CROWDFUNDING
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A Thesis
Presented to
The Honors Tutorial College
Ohio University
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In Partial Fulfillment of the Requirements for Graduation from the Honors Tutorial College with the Degree of
Bachelor of Business Administration
______by
Alexander Schlosser
April 2020
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This thesis has been approved by
The Honors Tutorial College and the College of Business
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Dr. Ikenna Uzuegbunam
Associate Professor, Management
Thesis Adviser
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Dr. Raymond Frost
Professor, Analytics and Information Systems
Director of Studies, Business Administration
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Dr. Donal Skinner
Dean, Honors Tutorial College
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Table of Contents
ACKNOWLEDGEMENTS ...... 6 ABSTRACT ...... 7 INTRODUCTION...... 8 PROPOSITION DEVELOPMENT ...... 15
CONCEPTUALIZING SUCCESS IN ENTREPRENEURIAL FINANCE ...... 15 SIGNALING THEORY IN THE CONTEXT OF ENTREPRENEURSHIP ...... 23 A BROAD VIEW OF SIGNALS IN EQUITY CROWDFUNDING ...... 24 FINANCIAL & NON-FINANCIAL SIGNALS ...... 27 DATA AND METHODS ...... 32
DATA AND SAMPLE ...... 32 MEASURES ...... 35 TEXT MINING OF VENTURES’ CEO MESSAGE ...... 40 INDUSTRY ASSIGNMENT THROUGH CONTENT ANALYSIS OF VENTURE TAGS ...... 41 ROBUST VARIABLE TRANSFORMATION ...... 42 STATISTICAL METHODS ...... 45 RESULTS ...... 47
SUMMARY STATISTICS ...... 47 MULTIVARIATE REGRESSION ...... 56 ROBUSTNESS CHECKS ...... 72 DISCUSSION ...... 75 LIMITATIONS AND FUTURE RESEARCH ...... 78 WORKS CITED...... 82 APPENDICES ...... 95
APPENDIX 1: CONCEPTUAL SIGNALING MODELS IN CROWDFUNDING LITERATURE ...... 95 APPENDIX 2: SOCIAL TAG LEXICON ...... 96 APPENDIX 3: FINANCIAL STATEMENT VARIABLE BINS ...... 96 APPENDIX 4: TEXT MINING STATISTICS & VARIABLES ...... 97 APPENDIX 5: FULL MODEL PREDICTIVE VALUE OF PRIOR YEAR DEBT ...... 98 APPENDIX 6: OUTLIER SENSITIVITY ROBUSTNESS FOR NEGATIVE BINOMIAL MODELS 100 APPENDIX 7: PREDICTED MEAN AMOUNT OF FUNDING RAISED – 95% CIS ...... 105 APPENDIX 8: PREDICTED MEAN PER CAPITA INVESTMENT – 95% CIS...... 106 APPENDIX 9: MULTIVARIATE REGRESSION ECONOMIC SIGNIFICANCE ...... 108 APPENDIX 10: LOG-TRANSFORMED VARIABLE INTERPRETATION ...... 114
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List of Figures
FIGURE 1: A FRAMEWORK FOR SUCCESS SIGNALS ...... 21 FIGURE 2: INVESTOR SOPHISTICATION SPECTRUM ...... 27 FIGURE 3: 2019 REGULATION CF MARKET SHARE ...... 34 FIGURE 4: RAW VARIABLE DISTRIBUTIONS ...... 43 FIGURE 5: SOCIAL SIGNALS & PER CAPITA INVESTMENT ...... 54 FIGURE 6: FINANCIAL SIGNALS & TOTAL AMOUNT OF FUNDING ...... 59 FIGURE 7: NON-FINANCIAL SIGNALS & TOTAL AMOUNT OF FUNDING (P-VALUE < 0.01) ...... 60 FIGURE 8: FINANCIAL SIGNALS & TOTAL NUMBER OF INVESTORS ...... 63 FIGURE 9: NON-FINANCIAL SIGNALS & TOTAL AMOUNT OF FUNDING (P-VALUE < 0.01) ...... 64 FIGURE 10: FINANCIAL SIGNALS & PROPORTION OF MAX FUNDING RAISED ...... 66 FIGURE 11: NON-FINANCIAL SIGNALS & PROPORTION OF MAX FUNDING RAISED ...... 67 FIGURE 12: FINANCIAL SIGNALS & PER CAPITA INVESTMENT ...... 70 FIGURE 13: NON-FINANCIAL SIGNALS & PER CAPITA INVESTMENT ...... 71 FIGURE 14: FULL MODEL PRIOR DEBT & TOTAL AMOUNT OF FUNDING (95% CIS) ...... 99 FIGURE 15: FULL MODEL PRIOR DEBT & TOTAL INVESTORS (95% CIS) ...... 99 FIGURE 16: PRIOR DEBT VERSUS VENTURE AGE ...... 99 FIGURE 17: FINANCIAL SIGNAL DISTRIBUTION OF OBSERVATIONS (FIRST 10 BINS) ..... 101 FIGURE 18: SIGNIFICANT FINANCIAL SIGNAL PREDICTIONS (P-VALUE < 0.01) ...... 101 FIGURE 19: NON-FINANCIAL SIGNAL DISTRIBUTION OF OBSERVATIONS (FIRST 10 BINS) ...... 102 FIGURE 20: NON-FINANCIAL SIGNALS & TOTAL AMOUNT OF FUNDING (OUTLIER BIAS) ...... 103 FIGURE 21: PREDICTED TOTAL AMOUNT RAISED – ALL VARIABLES (P-VALUE < 0.01) 103 FIGURE 22: FINANCIAL SIGNALS & TOTAL NUMBER OF INVESTORS (OUTLIER BIAS) .. 104 FIGURE 23: PREDICTED TOTAL INVESTORS – ALL VARIABLES (P-VALUE < 0.01) ...... 105
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List of Tables
TABLE 1: INDUSTRY CLASSIFICATION CATEGORIES ...... 42 TABLE 2: INDUSTRY CLASSIFICATION PROCESS ...... 42 TABLE 3: DESCRIPTIVE STATISTICS ...... 48 TABLE 4: DESCRIPTIVE STATISTICS BY STATE ...... 50 TABLE 5: DESCRIPTIVE STATISTICS BY ASSIGNED INDUSTRY ...... 51 TABLE 6: DESCRIPTIVE STATISTICS AT DIFFERENT LEVELS OF TEAM COMPOSITION ..... 53 TABLE 7: CORRELATION MATRIX – SIGNIFICANCE ...... 55 TABLE 8: NEG. BINOMIAL – TOTAL AMOUNT OF FUNDING RAISED ...... 58 TABLE 9: NEG. BINOMIAL – TOTAL INVESTORS ...... 62 TABLE 10: OLS REGRESSION – PROPORTION OF MAX FUNDING GOAL ACHIEVED ...... 65 TABLE 11: OLS REGRESSION – PER CAPITA INVESTMENT ...... 69 TABLE 12: BLAU INDEX & FEMALE TEAM PRESENCE COMPARISON ...... 74 TABLE 13: INCREMENTAL IMPACT – PREDICTED MEAN AMOUNT OF FUNDING RAISED110 TABLE 14: INCREMENTAL IMPACT – PREDICTED MEAN TOTAL INVESTORS ...... 111 TABLE 15: ECONOMIC SIGNIFICANCE - PROPORTION OF MAX FUNDING GOAL ACHIEVED ...... 113 TABLE 16: ECONOMIC SIGNIFICANCE - PER CAPITAL INVESTMENT ...... 113 TABLE 17: LOG-TRANSFORMED ECONOMIC SIGNIFICANCE ...... 114
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Acknowledgements
I would first like to thank my thesis advisor, Dr. Ikenna Uzuegbunam for his support, guidance, and insight in the academic research process. Dr. Uzuegbunam has helped me to grow appreciation and understanding of business, entrepreneurship, finance, writing, and academic research. Moreover, I would be remiss to fail to mention the passion and excitement Dr. Uzuegbunam brings to his own work and has encouraged me to bring to mine. More recently, I am thankful for the over two-hour virtual long meetings we have had to review each part of this thesis.
I also must thank Dr. Raymond Frost, my director of studies for the Ohio
University Honors Tutorial College. Dr. Frost has given valuable feedback academically and practically in this work. He also has been an invaluable mentor throughout my undergraduate education.
I would also like to thank several close friends who have been valuable reviewers to several sections of this thesis for clarity and practical attentiveness. Michelle Black,
Derek Tittle, Noah Barr, John McWilliams, Ian Klaus, and Ryan Bruss have all helped to shape this work and provided support along the way.
Finally, I would like to thank my parents and sister. Their unwavering support extends far beyond this work but has been specifically paramount in fostering my education and ambition.
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Abstract
Equity crowdfunding is a growing venture financing tool in the United States, generating ~$300 million for ventures since 2016. However, this form of financing that allows many investors to take an ownership stake in a private company is relatively new and small compared to financing vehicles like venture capital, which had a market size of
~$136.5 billion in 2019 alone. This study leverages data from a novel equity crowdfunding platform (WeFunder) to distinguish the role of financial and non-financial signals on crowdfunding success. Success here is measured holistically to include the amount of funding raised, the number of investors attracted to the venture, and the amount of funding the average investor contributed. The results of a multivariate analysis support that both financial and non-financial measures positively impact success.
Moreover, number of updates and number of questions (non-financial signals) appear to be more economically significant than other entrepreneurial signals. These findings suggest the virtue of a more holistic view of both signaling and equity crowdfunding success. The theoretical and practical implications of these findings are also discussed.
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“Money is like gasoline during a road trip. You don’t want to run out of gas on your trip, but you’re not doing a tour of gas stations.” - Tim O’Reilly, Founder of O’Reilly Media Introduction
Entrepreneurs face many challenges on the path to exploit an entrepreneurial opportunity. Beyond the obvious challenge of developing an idea, entrepreneurs face the task of resource mobilization. Of the components of resource mobilization, obtaining capital to initiate and run novel businesses is often cited as a top issue plaguing entrepreneurs for a plethora of reasons (Drover, Busenitz, Matusik, Townsend, Anglin, &
Dushnitsky, 2017; Hwang, Desai, & Baird, 2019; Lee, Sameen, & Cowling, 2015;
Mullen, 2019). Common challenges related to obtaining capital include investor-driven challenges like struggling to solicit interest from investors in the venture’s mission or struggling to meet an agreeable valuation in the investment negotiation process.
However, entrepreneurs also deal with internal challenges in understanding the appropriate timing of investment, the amount of capital needed, identifying the appropriate investment vehicles to pursue, or even exploring the full range of investment opportunities. As alluded to by Tim O’Reilly in the quote above, an entrepreneur wants to have enough money, without derailing the mission of the venture by focusing solely on capital. This study aims to educate entrepreneurs (and investors to an extent) about one novel investment tool, unaccredited equity crowdfunding. In doing so, the focus of this work will gravitate toward entrepreneurial signaling and fundraising success, helping entrepreneurs better understand a novel gas station for their new ventures.
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With financing options including venture capital (including government and corporate VC), angel investment, and crowdfunding (including philanthropic, debt-based, rewards-based, and equity-based), entrepreneurs have several options to choose from.
Although scholars have worked avidly to explore and consolidate the intricacies of different types of entrepreneurial finance1, research integration shortcomings leave ample room for further research (Cumming & Johan, 2017). Moreover, novel topics like equity crowdfunding remain less understood (Ahlers, Cumming, Gunther, & Schweizer, 2015;
Cumming & Johan, 2017; Drover et al., 2017; Lukkarinen, Teich, Wallenius, &
Wallenius, 2016; Mollick, 2014; Vulkan, Astebro, & Sierra, 2016).
Crowdfunding of new ventures2 has grown substantially over the last few years and is expected to expand rapidly throughout the next decade, with the number of ventures running a crowdfunding campaign expected to double by 2023. During 2019, funding raised globally through crowdfunding is estimated to have grown over 33%, with
6.4 million firms running a campaign at a success rate of 22.4% (Shepherd, 2019). While crowdfunding, in general, is a relatively novel financing vehicle, equity crowdfunding is even more nascent as the youngest form of crowdfunding. (Ahlers et al., 2015; Cumming
& Johan; Drover et al., 2017). Despite becoming a financing option in several global
1 Each of these financing options can be geographically bound, industry-specific, sensitive to venture age or timing, or differ based on strategic direction, the amount of capital required, and the extent of involvement from the investor (Drover et al., 2017). Thus, it is essential for entrepreneurs to understand the financing needs of their ventures (i.e. full funding round or bridge capital), their individual willingness to give up equity in exchange for capital, and other fit characteristics (i.e. industry, geography, social considerations). 2 This study focuses on the modern interpretation of crowdfunding that takes place via the internet and involves transactions on platforms designed for different types of crowdfunding (rewards, debt, philanthropic, equity). However, crowdfunding (or ideas like it) can be traced back throughout history. For example, a portion of the Statue of Liberty was funding through a newspaper ad by 160,000 individual donors (The Statue of Liberty, 2013). Likewise, origins can be traced back further to the Irish Loan Fund, which was a way to pool capital from wealthy citizens and distribute loans to impoverished citizens (The Startups Team, 2018).
9 markets since 20073, equity crowdfunding for unaccredited investors is new to the United
States and our understanding of this novel tool is immensely underdeveloped compared to legacy forms of venture financing.
The United States’ journey to unaccredited investor crowdfunding began in 2012 with President Barrack Obama signing the Jumpstart Our Business Startups (JOBS) Act into law. Title III of the JOBS Act, Regulation Crowdfunding (Regulation CF), deals with crowdfunding for the unaccredited investor. An unaccredited investor is defined as a person with less than $1million in net worth (not including their primary home), or less than $200,000 in annual income ($300,000 combined with a spouse) (Chen, 2018). Title
III was the final section of the JOBS Act to be implemented by the Securities &
Exchange Commission (SEC) on May 16, 2016 (Crawford, 2019; Lukkarinen et al.,
2016; Rampton, 2016). Since the enactment of Regulation CF, 2,187 funding campaigns have raised over $330 million from more than 430,000 investors in the United States
(Crowdwise, 2020). While this may seem impressive on the surface, the U.S. venture capital market surpassed $130 billion in both 2019 and 2018 (Miller, 2020). While this divide can be attributable to equity crowdfunding’s nascency, entrepreneurs are largely uneducated regarding the risks, purpose, requirements, and possibility funding through
Regulation CF. Associated process complexities and regulation can deter and confuse participants in entrepreneurial finance (Rampton, 2016; Vulkan et al., 2016). Although unaccredited investors are considered to make up 97% of potential investors in the United
States, Regulation CF is likely to be under-leveraged by the base of investors the
3 The first online equity crowdfunding platform was launched in 2007 through Australia’s Enable Funding (Ahlers et al., 2015; Enable Funding, 2020).
10 legislation intended to reach. Equity crowdfunding can be leveraged in many ways by entrepreneurs and has distinct nuances that should be explored, or at least considered by all entrepreneurs and investors regardless of their prior experience or sophistication in entrepreneurial finance (Drover et al., 2017; Vulkan et al., 2016).
Equity crowdfunding presents several unique features to ventures that differ from traditional avenues of funding. Some of the distinctions include the possibility to raise both seed stage funding and late round bridge financing, the ability to seek funding for a wider variety of industries (not just the dominant technology and pharma industries that captured over 90% of VC funding in 2015), and to access pools of willing investors regardless of whether the venture or investors is located in a major entrepreneurial cluster like Silicon Valley (Meisler, Rojanasakul, & Diamond, 2016; Rampton, 2016). However, such nuances come at the cost of heightened information asymmetries between entrepreneurs and investors since equity crowdfunding is conducted predominantly online, potentially between hundreds of stakeholders rather than a handful, and with investors that may or may not come from an educated investment background (Ahlers et al., 2015; Drover et al., 2017; ). Scholars are currently leveraging Spence’s (1978) signaling theory to better discern success drivers of equity crowdfunding and to satisfy prior calls for further exploration into this nascent segment of entrepreneurial finance
(Cumming & Johan, 2017; Drover et al., 2017).
Another point of importance is the vast motivational divide between well-known crowdfunding vehicles (i.e. the rewards-based crowdfunding platform, Kickstarter) and traditional venture financing options (VCs, angels, etc.). For example, VC funding has several limitations due to its tendency to focus on profit (despite the recent emergence of
11 social impact or dual focus funds), geographic concentration, and lack of diversity (e.g. gender diversity). Crowdfunding, in contrast, has been shown to reduce or eliminate such constraints, yet represents a minor portion of the entrepreneurial finance market
(Azevedo, 2018; Block, Colombo, Cumming, & Vismara, 2018; Hwang et al., 2019;
Meisler et al., 2016; Mollick, 2014). In addition, the VC structure can also lead to agency issues4 between ventures and investors initially, or in future strategic endeavors. By contrast, crowdfunding provides entrepreneurs an avenue to seek out investors with similar goals, thereby lessening agency issues (Greenberg & Mollick, 2017; Mollick,
2014; Pahnke, Katila, & Eisenhardt, 2015). Yet, the interaction between investors and entrepreneurs is not adequately understood in equity crowdfunding, especially given the large number of potential principals (investors) who may be attracted by different signals in different ways (Yang, Kher, & Newbert, 2020).
The primary objective of this study is to examine the effects of entrepreneurial signals that may be used to alleviate information asymmetries between ventures and investors in equity crowdfunding. Consequently, understanding the nature and importance of such signals will serve to indicate how entrepreneurs can succeed in raising capital through equity crowdfunding. In following this mission, the work intends to expand on existing crowdfunding and entrepreneurial finance knowledge through presenting a large-scale empirical study of equity crowdfunding in the United States
4 There is a decent amount of literature that highlights the entrepreneur-investor relationship is a principal- agent relationship (Drover et al., 2017). Simply put, the principal-agent issue is one of goal incongruence between owners and managers of a firm. Typically, management (in this case, the entrepreneur) would be the agent, making decisions and operating the venture in a way that satisfies the goal of the principal (the investor). However, if goals are different before the relationship is established, or diverge post-investment, the entrepreneur and investors face agency risk.
12 using a hand collected dataset from WeFunder5. This purpose is rooted in helping entrepreneurs of all backgrounds, and with all missions find fundraising success.
Crowdfunding presents a unique opportunity to startups of all types in various types of industries, including social enterprises. This study uses univariate, bivariate, and multivariate statistical analyses to examine the research propositions. Notably, this work uses abductive reasoning to explore the novel dataset in WeFunder. Given the nascency of unaccredited equity crowdfunding, the findings from this research are an initial look into this platform. Thus, descriptive approaches will be paired with predictive methods to highlight nuances with this novel equity crowdfunding platform.
This study makes scholarly and practical contributions to the field of entrepreneurial finance. First, this study proposes a framework which extends prior work on entrepreneurial signals in the context of equity crowdfunding on WeFunder. Prior studies identify ways to compartmentalize types of entrepreneurial signals or determinants of investor solicitation (Ahlers et al., 2015; Kang et al., 2016; Xiaoyu,
Mingru, Yanjun, & Jihong, 2017)6. This study builds on prior work to offer more conceptual simplicity and practicality as it relates to equity crowdfunding. For example, some signals are not mutually exclusive in terms of either providing support for venture quality or reducing investor uncertainty, so this framework allows for flexibility in the analysis of such signals. Second, this study introduces average investment per investor as a success concept for entrepreneurs in an equity crowdfunding setting. Alternate studies use a wide array of success variables including total investment, total investors,
5 A major Regulation CF portal. 6 Appendix 1 compares the lens used in this study to both Ahlers’ and Xiaoyu’s. The appendix makes comparisons on geography, timing, portal selection, adaption of traditional venture financing theory to crowdfunding, and study goals.
13 proportion of funding goal met, and campaign duration (Ahlers et al., 2015; Block,
Hornuf, & Moritz, 2016; Lukkarinen et al., 2016; Mollick, 2014; Vulkan et al., 2016;
Xiaoyu et al., 2017). Only Mollick (2014) alluded to the importance of funding per investor, but in a brief manner. Significant positive correlations between this success concept and other success metrics used throughout prior literature provides validity for its use moving forward. Third, by drawing on a novel dataset, WeFunder, this study reveals new insights that contribute to the conversation surrounding equity crowdfunding signaling. For example, a novel text mining approach examines WeFunder specific fields and their associated signaling effect.
Fourth, this study evaluates the relative practical importance of financial versus non-financial signals. Economic significance comparisons are made between both sets of variables. These comparisons suggest that communication-based variables (i.e. number of updates and number of questions) have stronger economic impact than the prior financial information (i.e. prior year revenue and prior year assets). Finally, this study builds on the work of Cholakova and Clarysse (2015) who find that rewards and social signals are not significant motivators to attract investors in a crowdfunding campaign. While this may be true in certain cases, it is not generalizable. The analysis on the WeFunder dataset reveals the magnitude of investment increases given non-financial signals associated with founding team diversity and social sentiment. Arguably, this finding suggests the impact of non-financial signals calls for further exploration, potentially at a time when the knowledge and adoption of unaccredited equity crowdfunding grows to a more substantial level in the United States. These findings are useful for theory, but also paramount for entrepreneurs in practice. Understanding how entrepreneurs raise money
14 through WeFunder, and the profiles of current entrepreneurs using the platform can assist other struggling entrepreneurs to find an appropriate funding vehicle.
The remainder of this work is as follows. The next section develops two propositions, leveraging three core assumptions and building on frameworks established for signaling in entrepreneurial finance. Following the proposition development, the data and methods section discusses the data set, variables, and empirical analyses conducted.
The last two sections display model results and discuss the conclusions drawn from the study. The conclusion also presents avenues for future research.
Proposition Development
Conceptualizing Success in Entrepreneurial Finance
The question of how new ventures can gain success in entrepreneurial finance is the fundamental purpose of this study. Specifically, this study aims to identify how entrepreneurs achieve success on equity crowdfunding in the U.S., so that future entrepreneurs (potentially those who fail to succeed or are underrepresented in alternative capital markets) may properly and effectively leverage unaccredited equity crowdfunding. This study takes a multifaceted view of funding success by considering four perspectives of success (see Data & Methods section for metrics). Prior literature indicates that a primary way of assessing success in entrepreneurial finance is the total amount of investment raised by a venture’s funding round or the extent to which a venture achieves its intended funding goal (e.g. Ahlers et al., 2015; Ciuchta, Letwin,
Stevenson, & McMahon, 2016; Cholakova & Clarysse, 2015). This metric is useful from an investor perspective since its indicative of other investors’ interest in the venture. It
15 shows that a venture met the funding threshold at a certain level or generated enough monetary interest to support prior goals or needs. The same can be said from the perspective of the entrepreneur. The funding goal is determined by the venture, and success is thus directly tied to the initial goal(s) of the venture. With this concept, additional investment (i.e. additional dollar amount above the base investment goal) can be considered “better” success. However, this total amount raised, or goal met concept has limitations. For example, when considering the structure of the crowdfunding model
(i.e. Regulation CF), some jurisdictions require a minimum and maximum goal. Prior to the minimum goal, no funding can be captured. Following the minimum goal, the investor can solicit and capture all funding through the maximum goal. By having a binary, yes or no success concept, the goal boundaries are largely overlooked. Another major limitation of this concept is the failure to measure success in generating publicity, or awareness of the venture.
The number of investors attracted is used as a success proxy in several works.
Typically, this success concept extends beyond the capital involved to include achieving success for alternate goals like community building, marketing, and advertising (e.g.
Agrawal, Catalini, & Goldfarb, 2015; Lukkarinen et al., 2016; Wang, Mahmood,
Sismeiro, & Vulkan, 2019). This type of success metric is important because (a) it allows an entrepreneurs to build or grow a network, (b) it promotes the decentralization of investor power (i.e. an overarching goal in crowdfunding is democratization and decentralization of funding from few large investors), and (c) future investors may view community building as a proof of concept. While this success concept pairs well with the aforementioned funding concept, opportunities still remain to explain success in the
16 context of unaccredited equity crowdfunding. For example, the number of investors attracted concept still fails to address the boundaries of funding goals set by entrepreneurs. Further, this concept is often unrestricted. With no maximum on the number of investors attracted in a funding round, entrepreneurs can face issues in addressing the feedback or concerns of each shareholder and managing such a large pool of interested parties can be a logistical nightmare.
Some studies view entrepreneurial financing success as binary for entrepreneurs
(e.g. Cholakova & Clarysse, 2015; Vulkan et al., 2016; Yang et al., 2020). For example,
Vulkan et al. (2016) deem campaigns successful upon reaching a minimum achievable goal, as some platforms like SEEDRS, a European equity crowdfunding platform, are considered an “all or nothing platform.” However, others incorporate a proportion of success metric based on some sort of funding cap set in place by external regulation, the fundraising platform, or the entrepreneurs themselves (Greenberg & Mollick, 2017;
Mollick, 2014; Xiaoyu et al., 2017). One reason for gauging success based on an entrepreneur-set boundary is the founder’s dilemma7. Prior corporate finance literature highlights the risks and benefits of the entrepreneur-investor dyad from the standpoint of both groups of actors (Denis, 2004; Wasserman, 2016). For entrepreneurs, relinquishing control (via equity investment to investors) can result in scope creep away from an initial mission to achieve higher returns or exit valuations. Thus, at some level of funding,
7 A trade-off between power and capital is inherent in an entrepreneur’s decision to finance their venture through equity investment. Pahnke et al. (2015) reveal that agents of capital are systematic in the type of venture they target and often impose institutional logics on said ventures, some of which may conflict with the intentions of the entrepreneur. Thus, success should be measured by which an entrepreneur can solicit investment either from the proper community of thinkers or achieve the proper amount of capital necessary at the lowest relinquishment of control possible (Belleflamme, Lambert, & Swienbacher, 2014; Wasserman, 2008). Therefore, to best capture and control for entrepreneur tolerance to relinquish power for capital, fundraising success is measured through proportion of max funding goal achieved.
17 entrepreneurs may no longer deem raising additional capital a success if too much power is relinquished in the fundraising process. Alternatively, some entrepreneurs may set a minimum boundary so low as to guarantee they receive some sort of funding, while the actual funding needs of the business may be far greater and determined by a maximum goal8. By assessing proportion of the fundraising maximum, the entrepreneur’s amount of success relative to their initial goals of the founders are better illuminated. Otherwise, this success concept mirrors the first concept, the basic goal of equity crowdfunding is to raise capital. In addition to not measuring alternative reasons for crowdfunding (e.g. marketing and community building) the proportionate concept doesn’t fully explain why an entrepreneur may fail to reach a minimum goal (i.e. setting an unrealistic base, or lack of venture quality). Moreover, several fundraising portals force entrepreneurs to abide by funding restrictions9. This may mean that investors require several rounds of funding to fully meet its goals.
Given the novel nature of equity crowdfunding, especially in the context of U.S.
Regulation CF, we are uncertain if prior success concepts can fully explain and satisfy goals of entrepreneurs. In an attempt to contribute to a more complete theory of equity crowdfunding success, a novel perspective of entrepreneurial funding success is advanced in this study. This view of success is referenced by Mollick (2014), but to a minimal extent. While equity crowdfunding inherently involves funding from the masses, having too many investors (owners) in the nascent years of new venturing can cloud and
8 Several platforms have an “all or nothing” concept that only guarantees investors funding if they can meet a certain threshold of fundraising. 9 For example, Regulation CF in the U.S. prohibits ventures from raising capital above $1,070,000 in a single round of funding or within one year.
18 overload entrepreneurs, ultimately hindering organizational action10. This is a major difference from alternative forms of crowdfunding because rewards-based and philanthropic platforms do not afford investors ownership in a company. Amassing many investors can create logistical nightmares for reporting results, leaves the organization more susceptible to information leaks, and can “crowd out” future equity investment
(Jones et al., 1997; Moules, 2011). Further, when investors take ownership in a company, they have a claim on the cashflows of that company. Sometimes, entrepreneurs find themselves struggling to satisfy the wants and needs of other owners.
One notable example of entrepreneurs and crowd investors diverging on opinion can be found in the story of Oculus Rift, a virtual reality headset company. Oculus Rift began raising funds on Kickstarter, a rewards-based platform in 2012. In March of 2014, the firm was bought out by Facebook, a move that infuriated investors. Those that committed capital for as little as a mere thankyou note in some cases were now watching the founder “sellout” for $2 billion without having any claim on the exit capital
(Benedictus, 2014). In an equity crowdfunding platform, the entrepreneur might be restricted with a move like this, given that each investor would have some claim (or vote) on any exit decision. So, it is likely that an entrepreneur would want to limit the number of voices to which they would have to report to, or that may oppose an organizational action that may be necessary to grow or pivot the company.
To further illustrate the burden that can be placed on an entrepreneur from having too many investors, consider the case of the initial public offering (IPO) process. While it
10 While related more toward governance, Jones, Hesterly, & Borgatti (1997) found that amassing large networks with control or decision-making power required frequent communication between parties.
19 is not perfectly akin to the IPO process, granting equity to hundreds (or thousands) of individual investors in a private venture is the next closest thing in the entrepreneurial finance world. When you open a company to the world in an IPO, everything becomes more difficult. Disclosures increase, the company is placed under a microscope, and most importantly, there are ample investors who look to profit off the business. Alibaba co- founder Jack Ma was the largest individual shareholder in the firm prior to retiring in
2019 (Zucchi, 2020). He reported wishing the firm never went public despite a record
IPO, following the onslaught of scrutiny by public investors, regulators, and media
(Egan, 2015). Thus, this study argues that while equity crowdfunding inherently opens up a venture to many investors, having a higher amount of investment per investor can be a success criterion for entrepreneurs to reduce the volume of obligation and conflict an entrepreneur may have to traverse (Jones et al., 1997)11.
To provide a comprehensive analysis of equity crowdfunding, signals are explored against all success variations in this study. Figure 1 is a visual representation of the signaling framework used in this study. Following the framework is the theoretical development of propositions 1 and 2 that attempt to explain the impact of financial and non-financial signals, respectively. In order to arrive at these propositions, three key assumptions are introduced concerning the nature of equity crowdfunding, the first is listed below Figure 1.
11 A main advantage to equity crowdfunding is shifting power in the entrepreneur-investor dyad towards the entrepreneur by replacing a few investors, holding large ownership, to a diverse pool of small ownership stakes. However, Drover et al. (2017) notes that financial and management literature has explored issues that can arise with too much ownership dispersion. For example, having too many stakeholders may hold the entrepreneur liable to unreasonable reporting standards. Since equity crowdfunding is already an investment vehicle that promotes a power advantage for the entrepreneur, this study views the reduction of extraneous dispersion issues as a positive (i.e. a higher investment per investor is more favorable).
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Figure 1: A Framework for Success Signals in Equity Crowdfunding
Venture & Portal Information Funding Campaign Success
P1a+ Total Amount of Funding Raised Financial Signalsᵃᵇ P2a+ P1b+ Financial Capital P2b+ Total Number of Investors
P1c+ P2c+ Non-Financial Signalsᵃᵇ Max Funding Goal Reached Intellectual Capital, Human Capital, Social P1d+ P2d+ Capital/Communication
Social/Impact Investing Signals Per Capita Investment
ᵃHigh Venture Quality ᵇReduced Investment Uncertainty
An often-debated assumption in the entrepreneurial literature is to what extent entrepreneurs are effective in transmitting information to investors. Regulatory focus theory assumes that information can either be conveyed with a promotion or prevention focus. In a promotion focus, information or communication between investors and entrepreneurs is aimed at illuminating the positive aspects of a given topic (i.e. the venture). A prevention focus may portray information in a negative light, focusing only on risk elements in the business, or setting up an entrepreneur for failure. Regulatory focus is not centric to this study. However, it helps explain the theoretical linkage between entrepreneurial signals and subsequent outcomes. One example of regulatory focus discusses gender’s effects on investment decision, and how investors project prevention and promotion information on entrepreneurs (Ciuchta et al., 2016; Kanze,
Huang, Conley, & Higgins, 2018). However, several studies point to self-regulation in the entrepreneurship process (Brockner, Higgins, & Low, 2004; Hmieleski & Baron,
2008).
21
A primary reason for assuming a promotion focus is the online nature of equity crowdfunding in this study, is the online nature of equity crowdfunding. Choosing to go online and post information about an entrepreneur’s venture is by itself consistent with promotion focused behavior, primarily because the democratization of the funding process (i.e. reaching the broadest audience) and the search for an appropriate community aligns like-minded entrepreneurs with like-minded investors (Brockner et al., 2004;
Cesario, Grant, & Higgins, 2001; Higgins, 1998)12. Further, promotion self-regulated mindsets are most effective in uncertain environments (Hmieleski & Baron, 2008), wherein equity crowdfunding is arguably one of the most uncertain environments in the entrepreneurial finance landscape. The design of online platforms, and equity crowdfunding, is meant to be a democratizing mechanism, which levels the playing field by encouraging both prevention focused and promotion focused entrepreneurs to only highlight the positives. For example, WeFunder’s13 main landing page for each venture includes all entrepreneur-generated information, most of which is from a consistent template. Some example sections include reasons why an investor may want to invest, a
12 First, Higgins (1998) found that people (i.e. entrepreneurs) with a promotion focus tend to avoid failure of omission and aspire for growth, versus a prevention focus that is centric to avoiding loss and attracting the wrong fit. Promotion focus aligns better with the nature of online equity crowdfunding as equity crowdfunding is a funding tool that promotes growth and the democratization employs attracting hundreds of small investors in attempt to reach the widest audience (i.e. avoid omission). In a state where people are exerting a promotion-based self-regulation, Cesario et al. (2001) propose that like-minded individuals (promotion-focused) are most likely to offer support or interest in these ventures, implying that the interaction between investors and entrepreneurs should be centered around promotion regulation. Finally, by having the ability to refine a pitch, edit the online venture portal, and provide thoughtful response to investors through online communication, equity crowdfunding offers a better opportunity for entrepreneurs to circumvent self-prevention opportunities in a web-based platform. Rather, they can focus on persuasion, growth aspirations, and highlights that make a venture attractive, all of which is promotion focused (Brockner et al., 2004). 13 An equity crowdfunding platform in the United States.
22 message from the CEO, deal terms, founder/team profiles, and prior investment round information14. Hence, there is limited incentive to be prevention focused online.
Assumption 1: In equity crowdfunding environments, entrepreneurs tend to have a
promotion focus.
Signaling Theory in the Context of Entrepreneurship
Signaling theory was originally used in Spence’s (1973) work concerning job market signaling. In his work, Spence (1973) argued that the amount and quality of information present are what individuals use to make decisions. In the absence of information, or lack of credible or complete information, people will look to signals that can be used to reduce uncertainty and the information asymmetries inherent in any kind of decision-making process. While signaling has been extrapolated to several other contexts, one main application for this theory is entrepreneurial financing (e.g. Clough,
Fan, Vissa, & Wu, 2019). Initially, signaling theory was adapted in the study of legacy forms of entrepreneurial finance like VC and angel investment (Baum & Silverman,
2004; Plummer, Allison, & Connelly, 2015). Traditionally, in alternate forms of entrepreneurial finance (VC, Angels, etc.), capital markets available to entrepreneurs are directly related to the number of locally based investors and willingness of investors to monitor and fully understand the venture (Drover et al., 2017). However, prior literature reveals several distinct characteristics of crowdfunding from other forms of venture
14 One caveat to this assumption (and limitation of this study) is that prevention focused language may creep into a venture’s profile through rhetoric dictated to entrepreneurs by investors. Investors can post questions and comments in different locations on the page that may be biased towards prevention (Kanze et al., 2018). However, the entrepreneur has much more control over such biases in an online equity crowdfunding setting versus other forms of equity venture financing and can take a more reflective approach to addressing investors’ concerns in this environment.
23 financing (Ciuchta et al., 2016; Drover et al., 2017; Mollick, 2014; Xiaoyu et al., 2017).
Most differences can be traced to the virtual nature of crowdfunding, less experienced investors (unaccredited and retail investors), and variety of use cases for funding (e.g. seed funding, product testing, bridge financing). So, scholars have continued to leverage applications of signaling theory to uncover the logic behind investors’ decisions to support a crowdfunding campaign (Agrawal et al., 2015; Ahlers et al., 2015; Ciuchta et al., 2016; Block et al., 2016; Vulkan et al., 2016; Wang et al., 2019; Xiaoyu et al., 2017).
A Broad View of Signals in Equity Crowdfunding
Throughout the exploration of signaling in entrepreneurial finance, several scholars have introduced signals to predict the success of crowdfunding campaigns. For example, Ahlers et al. (2015) adapted Baum & Silverman’s (2004) framework to highlight the impact of venture quality and the level of uncertainty on funding success through human capital, intellectual capital, social capital, equity share, and financial projections. However, this study overlooks the component of social interaction between actors (investors and entrepreneurs, or even investors and investors) on a crowdfunding platform, and how that component influences successful outcomes. While some studies do include heightened focus on social interaction on platforms like WeFunder, they focus solely on social signals or avoid comprehensive sets of signals (Block et al., 2016;
Ciuchta et al., 2016). Moreover, while Xiaoyu et al. (2017) found support for communication (and social capital), risk, and other company characteristics of viable crowdfunding signals, the study left out a broad array of other effective signals related to venture quality and investor certainty (human capital, intellectual capital, and financial capital). One possible explanation for omitting or overlooking certain sets of signals can
24 be the idea that not all signals are credible or impactful to investors in a financing environment.
Assumption 2 is consistent with Spence’s notion that it is less difficult for a high- quality entrepreneur to convey such signals than a low-quality actor. In unaccredited equity crowdfunding, there is likely a myriad of entrepreneurial actors across the quality spectrum. Moreover, signals are expected to be more credible than in other entrepreneurial financing concepts given stringent requirements under Regulation CF, including SEC (form C) filing requirements. Furthermore, WeFunder is regulated by
Financial Industry Regulatory Authority, Inc. (FINRA) and the SEC, complying with over 1,000 pages of regulation, and ensuring it complies with the requirements of a transparent intermediary (WeFunder, 2019). The WeFunder platform also commits to a consistent layout for each venture and screens each venture and subsequent venture profile for fraud. Finally, since this information is being conveyed online, entrepreneurs are likely to exercise caution in the form of information they convey knowing that misrepresentation in transparent environment is both legally and ethically costly.
Belleflamme et al. (2014) also find that profit-sharing (equity) crowdfunding campaigns are likely to inherently signal high-quality ventures.
Assumption 2: In equity crowdfunding environments, entrepreneurial signaling
are likely to be credible.
Another area where studies have failed to fully explore includes social entrepreneurship themes as signals in equity crowdfunding. Cholakova and Clarysse
(2015) presented inductive research that outlined helping others, being part of a community, and supporting a cause as three of four motivators in investing rewards-
25 based crowdfunding. While they failed to find support for non-monetary motivators in a survey analysis, few studies have explored the topic further in a crowdfunding context.
Yang et al. (2020) explored the meaningful signals of social start-ups for social impact accelerator (SIA) selection, finding that gender rule congruity theory (GRTC) enhanced both economic and social signals’ efficacy on SIA selection. Moreover, philanthropic crowdfunding is inherently a social cause motivated platform (Lukkarinen et al., 2016).
Both communication signals and cause-driven signals can contribute to the current understanding of equity crowdfunding given that the underlying investor base is professionally and psychologically different than the investor base underlying VC and
Angel capital markets.
Yang et al. (2020) highlights developments in signaling theory as they relate to entrepreneurial finance. One major concept underlying signaling theory is that not all receivers of signals interpret information similarly. Thus, it should be important to interpret signals differently in an accredited investor context (VC, Angel, and some accredited crowdfunding) versus unaccredited crowdfunding. Arguably, unaccredited equity crowdfunding should better mirror other aspects of funding in private companies done by unaccredited investors (i.e. philanthropic and rewards-based crowdfunding) versus equity crowdfunding done by accredited investors. Investor populations vary in financial capacity, population size, investment sophistication (see Figure 2), and investment motivation (Belleflamme et al., 2014; Goethner, Luettig, & Tobias, 2020;
Wang et al., 2019; Yang et al., 2020). Goethner et al. (2020) leverage a European equity crowdfunding (or crowd investing) platform to distinguish among the variety of investors that committed capital in such an environment. This work illuminates key differences
26 amongst the three distinct crowd investor groups including the extent to which they are motivated by financials, venture characteristics, and venture quality. This introduces the third and final assumption.
Assumption 3: Though heterogenous, investors in equity crowdfunding platforms
are presumed to be rational in their interpretations of signals.
With unaccredited investors who are used to investment platforms like
GoFundMe.com15 versus non-public equity investment platforms (i.e. Regulation CF crowdfunding), and may lack existing biases rooted in traditional accredited investors
(VC, Angels, and Accelerators), exploration of potentially cause-based, or other social support on success outcomes is warranted. Moreover, despite heterogeneous motivations, each investor is expected to be rational, choosing the investment that best aligns with their motivations.
Figure 2: Investor Sophistication Spectrum
Equity-based Crowd Investors Venture Angel Accredited Unaccredited Capitalists Investors
High Investor Sophistication Low Investor Sophistication
Financial & Non-financial Signals
Given the addition of intrinsic investment motivation (investors investing for a cause or utilitarian reward) to extrinsic investment motivation (economic-based reward) in a study aiming to explore efficacy of entrepreneurial signals, a new signaling
15 GoFundMe.com is the largest platform for online giving with over $5 billion raised backed by more than 100 million investors and has had no restriction on accreditation to give since 2010, making it approximately 6 years older than Regulation CF (Why GoFundMe Charity, n.d.).
27 framework was deemed necessary to develop (see Figure 1). The approach used in this study was aimed at simplifying existing frameworks (e.g. Ahlers et al., 2015; Xiaoyu et al., 2017) for practical use, placing emphasis on U.S. equity crowdfunding specific characteristics (e.g. unaccredited and accredited investors, communication-based platform, etc.), and the exploration of social entrepreneurship themes in equity crowdfunding. The signals were categorized through prior literature, but also through ad hoc analysis of variable categories available on WeFunder. Propositions were then developed analyzing the efficacy of financial and non-financial signals on equity crowdfunding success metrics.
Financial signals show some form of economic viability and predominantly reduce investment uncertainty (Ahlers et al., 2015). Cholakova and Clarysse (2015) furthers this concept to include proof of fundamental growth or aspirations of growth in a company that provides an investor with a strong inclination of a future payoff, as equity investment typical requires external rewards, or financial return. However, more stringent filing requirements in U.S. unaccredited equity crowdfunding moderate the impact of less precise information related to financials or offering information. Some investment uncertainty may remain in projections of future growth. In addition, ambiguous financial information may also arrive in entrepreneurs’ conversations with investors. However, financial information listed in required financial filings through Regulation CF can be considered signs of venture quality, by giving investors the ability to monitor annual financial ratios or benchmark (e.g. investors may look at asset turnover, debt to equity, etc. compared to other companies in an industry or relative to the stage of growth to interpret venture quality. Moreover, prior investment, also a financial signal, can be a
28 sign of venture quality when considering the financial viability and investment scrutiny a venture must endure to receive investment (Agrawal et al., 2015; Ahlers et al., 2015;
Yang et al., 2020)16. Thus, strong financial signals are likely to reduce investment uncertainty and prove to be signs of high venture quality, both of which have been found to enhance a ventures chance at crowdfunding success (Ahlers et al., 2015; Ciuchta et al.,
2016). Additionally, sophisticated or experienced investors tend to focus on several criteria prior to investment that includes early traction, financial literacy, and realistic valuation and projections (Harroch & Kane, 2019). Several studies point to financials, financial signal, or prior financial scrutiny as signals or motivators for crowd investors
(Goethner et al., 2020; Lukkarinen et al., 2016; Mollick, 2014; Yang et al., 2020)
Proposition 1: Financial signals have a positive relationship with equity crowdfunding success across the following success concepts:
Total funding (P1a); Total investors (P1b); The percentage of max funding goal
achieved (P1c); Per capita investment (P1d).
Non-financial signals in this study are indicative of any piece of information where the primary purpose is not to convey financial data, offering information, or project economic viability. Rather these signals leverage entrepreneurs’ experience and intellect (human capital), capacity and proof of innovation (intellectual capital), network ties (social capital) and communication, and business motivations that are alternative to profit-centric missions (social signals and hybrid approaches). Each of these signals
16 Although this is fair to assume in an entrepreneurial finance context, given heterogeneity of crowd investors, it may not always be the case that a venture has undergone rigorous scrutiny from experienced investors.
29 highlight the quality of the venture or the entrepreneur(s) of the venture. Whilst prior financial information can enhance the amount of funding received by ventures, especially in crowdfunding contexts, qualities of an entrepreneur or venture team are traditionally the most important selection criteria for legacy forms of entrepreneurial finance or with certain investor clusters (Ahlers et al., 2015; Goethner et al., 2020; Lukkarinen et al.,
2016; Mollick, 2014).
An entrepreneur’s industry experience and education compose human capital, which has been proven significantly affect the success of novel ventures and can be indicative of firms that succeed in soliciting capital (i.e. human capital is a proven signal in entrepreneurial finance) (Coleman, Cotei, & Farhat, 2013; Colombo & Grilli, 2005;
Hsu, 2007; Uzuegbunam, Liao, Pittaway, & Jolley, 2017). Likewise, intellectual capital consists of the knowledge of a firm either through research and development or intellectual property (patents, copyrights, trademarks). While some studies have found intellectual capital alone (i.e. without human capital) to be ineffective in advancing growth, intellectual capital still has the potential to offer financing or networking benefits, both of which satisfy aforementioned success concepts (Coleman et al., 2013;
Uzuegbunam et al., 2017). In a prior crowdfunding signaling study, Ahlers et al. (2015) did not find a significant impact on success with intellectual capital. However, the study was limited to patents granted, so further exploration is warranted.
Third, the rise of crowdfunding has placed an enormous emphasis on two components of entrepreneur-investor interactions, social capital and communication.
Several scholars focus on how networks are built via crowdfunding, the signaling effect of third party investors (can be strong or weak depending on the prior investor), and the
30 process entrepreneurial network building through a crowdfunding round (i.e. speed of investment, investor traction, and herding behavior) (Ahlers et al., 2015; Ciuchta et al.,
2016; Greenburg & Mollick, 2017; Lukkarinen et al., 2016; Mollick, 2014). However, arguably more novel with crowdfunding, virtual communication efficacy and signaling has come to the forefront of literature. While all signals can be thought of as a form of communication, this study (in congruence with extant literature) alludes to the interaction of investors and entrepreneurs (i.e. through messaging, updates, etc.) as online communication. Scholars have found that frequent interaction between entrepreneurs and investors via online communication reduce both uncertainty and introduce additional venture quality information (Drover et al., 2017; Mollick, 2014; Xiaoyu et al., 2017).
Finally, some studies provide little empirical support for social impact-based signals or investors investing in a venture given social or hybrid motivations (e.g.
Cholakova & Clarysse, 2015). However, a growing list of scholars have pinned social motivations as an emerging concept and focus in investors across entrepreneurial finance
(Goethner et al., 2020; Greenburg & Mollick, 2017; Miller & Wesley, 2010; Mollick,
2014; Yang et al., 2020). Thus, further exploration is warranted, especially given high levels of heterogeneity of investor motivations in equity crowdfunding and the newness of the U.S. Regulation CF market.
As mentioned earlier, this is one section of the framework that was developed via analysis of WeFunder prior to fully completing the framework. Whereas other platforms or investment vehicles may allude to intellectual property, incorporate consistent and full management team descriptions, or signal relationships through prior investor connections, WeFunder content is relatively unstructured in terms of content, providing
31 freedom to the entrepreneur. Therefore, communication is highlighted as the primary vehicle to solicit aspects of founder experience, intellectual property, and other venture quality components. Moreover, cause-driven signals are clustered within communication, but are emphasized in other WeFunder specific signals inherent across venture portals.
Proposition 2: Non-financial signals have a positive relationship with equity crowdfunding success across the following success concepts:
Total funding (P2a); Total investors (P2b); The percentage of max funding goal
achieved (P2c); Per capita investment (P2d).
Data and Methods
Data and Sample
The data used in this study is primarily from WeFunder, with appended venture data from Securities and Exchange Commission (SEC) Form C filings17. WeFunder is a public benefit corporation (PBC), with the mission of drawing wisdom from the crowd to invest in a broader range of entrepreneurs than venture capital. WeFunder also aims to allow investors to distribute capital more broadly (“WeFunder is a Public Benefit
Corporation,” n.d.). Much like other crowdfunding platforms, WeFunder seeks to encourage the decentralization of capital from entrepreneurial clusters like Silicon Valley through investors and entrepreneurs. However, one problem existed for this mission to be
17 Any venture looking to conduct a Regulation CF campaign must submit a Form C filing to the SEC’s EDGAR system, along with the intermediary offering the fundraising round. The issuer must disclose several pieces of information including information about officers/directors/major shareholders, a description of business, offering characteristics (security type, price, target funding, funding deadline, use of funding), and financial conditions (financial statements are included, but at varying levels of scrutiny depending on the amount being raised). Four associated forms are supplemental to the Form C including Form C/A (amendment), Form C-U (funding progress update), Form C-AR (annual financials), and Form C-TR (termination) (U.S. Securities and Exchange Commission, 2019).
32 successful, unaccredited investors were not legally allowed to invest equity in private companies. WeFunder’s mission began as a lobbying platform, which became one of the leading forces behind the United States JOBS Act, signed in April of 2012 by President
Obama. Although signed in 2012, the rollout of Regulation CF did not occur until 201618.
(Crawford, 2019; WeFunder, 2019). During this delay of the implementation of
Regulation CF, WeFunder joined the Y Combinator in Silicon Valley as a consultant for firms looking to draw capital, adding to its lobbying work. Shortly after, it leveraged current law to develop an accredited equity crowdfunding platform. This gave WeFunder some experience with regulatory bodies, more time to lobby for quicker enactment of
Regulation CF, and experience facilitating web-based investment with little in-person entrepreneur-investor interaction. Following the enactment of Regulation CF, WeFunder transitioned to include unaccredited equity crowdfunding (WeFunder, 2019).
WeFunder was chosen as the primary data source for this study for a few distinct reasons. First, it is an investment platform domicile in the United States and one of the first to legally pursue unaccredited equity crowdfunding. The United States is of interest in this study as it is nascent compared to equity crowdfunding markets in Europe and
Australia. Also, reiterating part of the purpose driving this study, not much is known both academically and practically about unaccredited equity crowdfunding in the United
States. As of December 2019, only 2,099 of 6 million business in the U.S. have leveraged this form of financing, so this offers entrepreneurs insight into an alternate investment vehicle (Crawford, 2019; 2020). Second, its experience in accredited investor equity
18 Regulation CF was the last portion of the 2012 JOBS to be adopted by the SEC in late 2015, becoming effective on May 16, 2016 (U.S. Securities and Exchange Commission, 2019; WeFunder.com, 2019).
33 crowdfunding prior to Regulation CF allotted WeFunder the tested infrastructure to successfully go live with unaccredited equity crowdfunding. Third, WeFunder is the largest Regulation CF funding portal in the United States by number of investments and investment volume. Investment volume exceeded $30 million in 2019, giving WeFunder more than 30% of the Regulation CF market (Crowdwise, 2020; WeFunder, 2019).
Figure 3 shows the relative market share of top competing Regulation CF portals19.
Finally, the nature of its mission, including its success as a lobbyist for novel unaccredited equity crowdfunding, aligns tremendously with the focus of this study.
Since WeFunder’s operations prior to Regulation CF provide no empirical relevancy to this study, the dataset contains observations beginning in May 2016 through
October 2019. Data was collected using consistent fields across 241 Regulation CF venture portals and supplemented with data from each venture’s Form C filing on record with the SEC. The final dataset includes only 204 ventures that finished fundraising as of
November 1st, 2019 (when the data collection process was completed for this study).
Figure 3: 2019 Regulation CF Market Share
19 Outside of WeFunder.com’s reported statistics, equity crowdfunding portal rankings are difficult to find. However, Start Engine recently published a 4Q19 index summary calculating the top portals in Regulation CF. While Start Engine led the 4th quarter in funding volume, its market share numbers were consistent with WeFunder.com’s, having much of the market controlled by 4 top portals (WeFunder.com, Start Engine, Republic, and SeedInvest) (Crawford, 2020). Further, Crowdwise (2020) recently published 2019 statistics, citing WeFunder as the top platform by investment volume, ventures, and investor (only) base size. In 2019, WeFunder boasted $32.87 million raised by 21.8% of ventures that filed Form C’s with the SEC. StartEngine was the only platform that trailed closely behind with 20.4% of the ventures that filed in 2019 and ~$28.59 million raised by those ventures (Crowdwise, 2020).
34
5.0% 9.6%
WeFunder 31.1% 6.8% StartEngine Republic SeedInvest 19.0% NetCapital Other 27.1%
(Crowdwise, 2020)
Measures
Dependent Variables: Funding Success. Determining what factors drive funding success underpins the goal of this study. However, entrepreneurs elect to use crowdfunding platforms for a plethora of reasons including raising initial capital, bridge financing, product testing, and awareness (Johnson, 2014; Lukkarinen et al., 2016).
Funding success is further divided into the following four variables.
Total amount of funding raised. While success can vary depending on the goals of the venture and founder(s) of a venture, the primary, or most general goal of fundraising is to solicit capital from investors, thus one metric commonly used to measure funding success across multiple forms of crowdfunding is the total amount of funding raised
(Ahlers et al, 2015; Lukkarinen et al., 2016). This variable is derived at the closing of the fundraising round and includes capital only from the round being analyzed. Under
Regulation CF, funding rounds close upon reaching the maximum fundraising goal implemented by the entrepreneur, the maximum eligible fund ceiling of $1,070,000
35 allowed through Regulation CF, or when the allotted time to raise capital expires (U.S.
Securities and Exchange Commission, 2019).
Total investors. Another metric used as a proxy for success is the total investors who participate in the funding round by investing at least the minimum investment amount set by the entrepreneur. Prior crowdfunding literature uses this metric as a proxy of success primarily because it satisfies the entrepreneur goal of using crowdfunding as a marketing or awareness channel (Agrawal et al., 2015; Lukkarinen et al., 2016; Wang et al., 2019).
Proportion of max funding goal achieved. This metric was chosen to highlight success as it relates to entrepreneurs’ appetite for funding. An entrepreneur can choose to raise the maximum amount of funding legally possible if they do not subscribe to the founder’s dilemma, or the entrepreneur can set a lower ceiling if there is no strategic purpose to raise above a certain amount, but must also set a base goal in any scenario.
Thus, this metric builds on the total amount of funding raised metric to include boundary analysis.
Per capita investment. Although crowdfunding inherently aims to source funding from a wide array of pooled investors, entrepreneurs may also encounter a situation where having many investors, no matter the strategic intent or involvement of each investor, can hinder organizational action. Ultimately, raising funds with many investors can be considered less of a success than raising funds with fewer investors. Since an entrepreneur typically achieves some benefit from a larger investment base in crowdfunding, this study argues that having a higher investment per investor reduces some disadvantages that may arise with too many investors. Therefore, the final success
36 metric we analyze is per capita investment, or the total amount invested divided by the total number of investors. To further support the inclusion of this success metric, a significantly positive correlation with total investment (r = 0.43; p-value < 0.01) and proportion of funding goal met (r = 0.27; p-value < 0.01) was found leveraging
WeFunder’s dataset.
Independent variables: Financial and Non-Financial Signals. In this study, signals are used to define the characteristics and work displayed by each venture on WeFunder that has an impact on investors investment behavior. As mentioned in the proposition development financially oriented signals, for the purposes of this study, signal financial appropriateness or viability. Using WeFunder and Form C filings, five financial independent variables have been identified: prior year revenue reported, prior year assets reported, prior year debt reported, total prior round funding amount, and financially oriented sentiment. These variables align with Ahlers et al.’s (2015) uncertainty portion of their success determinants framework, as all of the above fields serve as economic signals (Yang et al., 2020). Arguably, these financial signals can also be representative of venture quality, as reported assets and evidence of prior investment imply a venture owns or has access to resources to leverage for growth (Gilbert,
McDougall, & Audretsch, 2006; Yang et al., 2020). Regardless of their contribution to venture quality or alleviating uncertainty, these variables have some connection to financial viability. For example, total prior round funding amount can signal to investors that a firm has passed financial scrutiny from a series of other investors20. Additionally,
20 While prior work has explored the effects of different forms of investment (debt, equity, or philanthropy; Angels, VC, or friends and family) on future investor’s intention to provide follow-on funding, this work focuses solely on total prior investment due to limitations of the dataset, but also provided that prior
37 the financial statement variables can be used as a basis for growth projections, or work with the monetary sentiment to validate valuation or support reliability of growth estimates. Data was also present to include a prior year’s net income variable, but it was excluded as investors often value growth in novel ventures over profitability, and the correlation of ventures’ net income to all success metric variables was highly insignificant relative to the other independent variables21.
In contrast to financial signals, non-financial signals tend to be non-economic in nature. They are signals that tend to attract investors for reasons involving social factors, environmental factors, human, intellectual, or psychological factors in a company. The variables used to measure non-financial signals are number of updates from entrepreneurs, number of questions asked by investors, Blau diversity index (gender), and number of social tags listed. The diversity index was calculated using the sum of the squared proportions of male and female team members, and then subtracting this value from one. The social tags listed were self-generated by entrepreneurs to attract investors in the search function of WeFunder and are listed at the top of each venture portal. While this variable contains a wide array of tags, the social tag variable is not inclusive of industry-related tags or economic signals. Appendix 2 contains the universe of social words identified. While both variables were chosen to represent pure social impact signals, the update and questions variables serve a slightly different, hybrid purpose.
These communication-based variables act as bridges to information asymmetries for
literature has found financial scrutiny to be a cornerstone for a wide array of investment vehicles and investors (Bertoni, Colombo, & Quas, 2017; Wang et al., 2019; Yang et al., 2020). 21 Gilbert et al. (2006) discusses new venture growth and reveals that most new ventures reference growth in terms of revenue or sales, employment, and market share. While firms ultimately can grow net income, revenue growth is far more widely used considering that new ventures invest more in resource expansion and capability development.
38 investors, potentially revealing a blend of insight into a firm’s human, social, and intellectual capital, along with socially driven information22. While they may offer information related to economic outcomes, it is assumed that most economic information can be derived from the required financial filings prior to, during, and after equity crowdfunding rounds23.
Control Variables: Venture age, industry, geography, and prior fundraising rounds. This study aligns with prior literature that include controls to regulate sector- based differences in funding aspirations and required capital (Xiaoyu et al., 2017).
Geography is one control that is paramount to include in empirical analyses of entrepreneurial finance. Traditionally, an entrepreneurs’ ability to raise funding is greatly constrained by location or proximity to investors (Agrawal et al, 2015; Drover et al.,
2017; Mollick, 2014). The internet-based nature of crowdfunding can reduce the geography concentration of investment but has not been shown to eliminate geographic constraints completely (Agrawal et al., 2015). Therefore, dummy variables were included for California, Colorado, Massachusetts, Nevada, New York, and Texas. By controlling for areas with more than nine listed WeFunder ventures, the impact of entrepreneurial clusters providing outsized funding for select ventures on this study’s results is mitigated
(see Table 4 for breakdown of state-level characteristics). This study also accounts for
22 For example, a random set of questions from one venture on WeFunder.com included dialogue related to margin goals (financial capital), patents (intellectual capital), environmental impact of products and distribution (environmental capital), and health concerns related to the product (social impact). One important fact to note is that the CEO deferred current financial update questions to the official release of financials required by Regulation CF, through Form C-AR. 23 Updates and questions on WeFunder.com can also be used to communicate financial progress to some investors, but is not the primary avenue for projections, reported financials, or other economic related information. While outside the scope of this study, it would be worth exploring the proportion of questions and updates that are rooted in financial oriented signaling and the subsequent success of entrepreneurs with a greater volume financially oriented updates or questions.
39 the differences in venture age, calculated from Form C founding dates. Finally, a prior number of fundraising rounds variable is used as a control to account for the experience entrepreneurs have in raising funds, and the level of existing shareholders.
Text Mining of Ventures’ CEO Message
As mentioned in the measures section, financially oriented sentiment was derived via text mining a field on WeFunder that listed a brief description, tagline, or reason(s) to invest in the venture in consideration. This field was chosen for analysis given the high potential for impact it may have on investors who are browsing venture portals for investment. The field is listed near the top of the page, immediately following the image and name chosen to represent each venture. Moreover, this field is generally authored by the company CEO, founder, or other high-ranking executive, and is targeted as direct communication with investors. Block et al. (2016) found that verified communication providing more information on predicted financial success was most likely to enhance investors’ decision to invest. The CEO or founder of an organization provides veracity in this scenario. Following selection of this data field, Python software was used to pre- process and run text mining analysis on the data. This enhances Block et al.’s (2016) work by providing a more consistent coding system, comparing results of their study to
WeFunder specifically, and moving beyond recurring updates.
A natural language processing (NLP) library was used from the NLTK Python package to process the data (Bird, Klein, & Loper, 2009). Pre-processing steps involved eliminating excess space, punctuation, and stop words. To ensure consistency in library construction, the data was also normalized to include all lowercase characters and was further pre-processed using lemmatization, which uses vocabulary and completes
40 morphological analysis to transform words into their roots (Manning, Raghavan, &
Schütze, 2018). While other pre-processing techniques are common in NLP like stemming, rare and common word removal, the final output of the analysis doesn’t warrant additional pre-processing. Finally, the analysis of the CEO message included basic feature extraction (i.e. total word count, total character count, total stop words, average words) and two versions of sentiment analysis. Details pertaining to the text analysis can be found in Appendix 4.
Industry Assignment through Content Analysis of Venture Tags
While there is no direct industry classification for ventures on WeFunder, the industry control in this study was derived through a content analysis process that leveraged the set of self-selected tags also used for the social tag variable. After analyzing the tags for commonalities, 10 industry classification groups (see Table 1) were generated using the BLS North American Industry Classification System (NAICS) as a starting point. The author and an independent reviewer classified each venture’s industry based on the set of tags present in the 10 generated categories. A reasonable agreement between the initial classification and the independent reviewer’s classification was reached at ~81%, as prior literature recommends 80% agreement (Krippendorff,
2004). Following the independent classification process, any disagreements were remedied through discussion between the two coders to finalize each venture’s industry.
The industry assignment process involved the following basic rules: (1) Place each venture in the category with the most associated words in the set of tags presented, (2)
The first listed tag is considered the most important in the event of that multiple industries are present, and (3) If words are present that explain multiple industries (ex:
41
Marketplace can be associated with the Food, Retail Trade, or Information categories), select the more specific industry. The third rule was implemented to narrow the number of companies that self-identify as technology companies, as several operated in other industries leveraging technology. Table 2 depicts examples of the industry classification process.
Table 1: Industry Classification Categories
Industry Number of Ventures Information 64 Alcohol 35 Leisure & Hospitality 23 Food 21 Retail Trade 18 Goods-Producing 15 Education & Healthcare 16 Financial Activities 6 Other 4 Professional & Business Services 2
Table 2: Industry Classification Process
Industry Tag 1 Industry Tag 2 Industry Tag 3 Industry Tag 4 Industry Tag 5 Final Category food minority owned cafe entertainment retail Food y combinator tech social impact community n/a Information b2c paradigm shift learn new things lifestyle health and fitness Education & Healthcare software infrastructure techstars services marketplace Information software blockchains tech bitcoin cryptocurrency Information blockchains b2b food moonshots marketplace Food
Robust Variable Transformation
Given the unique nature of each independent variable, raw variables were used in the initial models since OLS and negative binomial models make no assumptions concerning the normality of independent variables (of the two, only OLS assumes
42 normality of the dependent variable for small sample sizes24). However, transformations were made in robustness checks to explore the nature of independent variables and subsequent effects on the dependent success metrics. The main transformation occurred with two non-normally distributed variables (Figure 4). The number of updates and number of question variables were log transformed by taking the natural logarithm. Prior to log transforming the variables, 0.0001 was added to each raw field. This provision was appropriate to maintain the number of zeros that were in the initial dataset. These natural log-transformed variables better fit the OLS regression modeling technique used for the models displayed in Tables 10 and 11.
Figure 4: Raw Variable Distributions
The revenue, debt, and asset variables are continuous and exhibited severe non- normal distributions with several young ventures lacking a financial history and few older
24 Non-normally distributed dependent variables do not harm the efficiency or bias of a regression model, rather the normality assumption is vital in the calculation of p-values to a certain extent. If the sample size proves to be sufficiently large (n > 30), the Central Limit Theorem states that the normality assumption is no longer needed to properly approximate the p-value (LaMorte, 2016). The WeFunder.com dataset can be assumed to be sufficiently large at n = 204.
43 ventures reporting large statistics. For example, the prior year revenue variable had ~61% of its observations having values of less than $100,000 and nearly 40% of observations reporting $0 in prior year revenue. This same variable, though, had 7 observations over
$3,000,000, 30 times the previously mentioned 61%. Prior year debt and assets follow the same pattern as the prior year revenue, but to a slightly lesser extent. A discretization process (transforming more continuous variables to a set of discrete values or generating bins) was used for each variable to better understand the nature of the economic independent variables. Although discretization can lead to a loss of information or power, it is appropriate in this study primarily given that small differences in financial information is less likely to materially affect investment decisions (Analytics Vidhya
Content Team, 2015). For example, a rational investor analyzing two firms that reported prior year debt of $1,000 and $1,001 respectively should be indifferent. However, debt values of $1,000 versus $100,000 is likely to lead to a more differentiated response. From
$0 to $1,000,000, 29 equally sized bins were created. A final bin was created for any venture with revenue, assets, or debt greater than $1,000,00025. While this final bin represents approximately 11% of the observations for each variable, the observations that fall in this final bin are, on average, twice as old as the average WeFunder venture. At
10.66 years for assets, 9.16 years for debt and 10.03 years for revenue, these ventures far exceed the accepted eight-year cutoff for the ventures to have a liability of newness
(Neubaum, Mitchell, & Schminke, 2004). Moreover, agencies like the Small Business
Administration, have used similar methods when classifying venture revenue bins where
25 The discretization robustness check was carried out at two cutoff levels, $1,000,000 and $500,000. The $1,000,000 level is described above (see Appendix 3) but note that robustness weakened further for when 29 equal bins were created for the $500,000 cutoff.
44 all firms above $1,000,000 were included in one bin (Lowrey, 2009). While this work aims to capture a comprehensive view of signaling in the equity crowdfunding environment, the primary objective is to aid novel entrepreneurs in achieving funding.
Thus, younger and less experienced ventures can be assured through robustness checks that general study results hold true when emphasizing younger firms with the discretization approach.
Statistical Methods
The methods employed to study signals and success outcomes in the study include abductive analysis, leveraging scholarly assumptions with practical applications.
Moreover, financial and non-financial signals were regressed against the four main success metrics in this study through ordinary least squares (OLS) and negative binomial regression. All statistical tests assume two-tailed approaches unless otherwise specified.
Some instances and analysis will involve the use of one-tailed tests to explore marginal significance and the possibility of uniqueness of the U.S. equity crowdfunding market.
Negative binomial regression models were leveraged to predict total investors and total amount of funding raised. Both dependent variables are count data and proved to be non-normally distributed. While log transforming the variable would have provided an opportunity to leverage OLS regression models, methods that prevent the loss of data were considered first. Ideally, Poisson models could have been used for both variables, but the data was also over-dispersed, as all Pearson Chi-squared dispersion statistics were significantly greater than one, indicating that the mean and variance are unequal
(Wadhwa, Phelps, & Kotha, 2016). Given the overdispersion, the negative binomial approach was the most appropriate since it operates like a Poisson approach, but includes
45 an extra parameter to account for overdispersion. Stata’s generalized linear model (glm) function was used to conduct the analysis. Maximum likelihood optimization, and the option to “search for good starting values” were selected in addition to default options.
The other two dependent variables, proportion of max funding goal achieved and per capita investment, were predicted using ordinary least squares (OLS) regression.
Typically, a variable like proportion of max funding goal achieved could be analyzed using Probit and Logit regression models, since it should involve data between discrete values of one and zero. However, approximately 8% of the dataset can be considered an anomaly to this assumption because that portion exceeds the maximum value.
Considering the rules of crowdfunding, a venture can only raise a certain amount of funding as determined by the entrepreneurs or the maximum allotted fundraising dictated via Regulation CF. However, if a venture sets a maximum ceiling below the Regulation
CF cutoff, the initial target can be amended during the fundraising process. Moreover, if the funding period extends beyond a single year, ventures can raise more than the
Regulation CF maximum (U.S. Securities and Exchange Commission, 2019). In agreement with the mission of this study, OLS regression was used to best understand the full dataset, any anomalies. This type of approach has been documented by prior work as important in examining novel ventures and financing (Drover et al., 2017).
Additionally, all four multivariate analyses include an exploration and discussion of economic significance. While both financial and non-financial signals are expected to positively impact the success outcomes of equity crowdfunding, the extent by which each is economically significant is likely to vary. Practically speaking, prior funding is likely to be the strongest economically significant variable, given that prior funding implies
46 prior scrutiny of venture viability, likely from one or multiple accredited investment sources. Also, in prior literature concerning financial signals in settings more established than seed stage capital, it is expected that financial history will carry a higher magnitude of significance relative to the non-financial signals. However, in the context of equity crowdfunding, it is more likely to find firms seeking funding for a variety of reasons and at different points in the venture stage. Moreover, with less venture monitoring than capital markets like VC or Angel investment, communication is greatly relied on as a sign of both venture quality and reducing investment ambiguity. Thus, financial signals may be less effective, overall, in attracting better success than non-financial signals.
Furthermore, due to the heterogeneity of investor motivation and sophistication, non- financial signals may outweigh financial signals.
Results
Summary Statistics
Table 3 lists the descriptive statistics for all dependent, independent, and control variables26. For the dependent success variables, the average amount of total funding raised falls just below $280,000, which is approximately a quarter of the maximum amount of annual funding allowed via Regulation CF. Superficially, the average venture also raises less than 50% of their stated maximum goal. Together with the average amount raised, this implies that entrepreneurs often set funding goals at approximately
50% of the legal annual limit. There may be a few reasons for this observation; (1) entrepreneurs want to leave a cushion in case the need arises to raise additional capital in
26 All statistical analyses assume two-tailed tests unless otherwise specified.
47 the same calendar year, (2) financing needs typically fall below the $1.07 million mark because it is used as bridge capital for more established firms or startup financing27 for younger firms, (3) the industries leveraging WeFunder may be less capital intensive than the average firm seeking private financing, (4) WeFunder may be a platform better associated with community building than raising capital as the average venture has more than 300 investors, with the largest venture receiving investment from 3,256 investors.
The independent variables also have some unique qualities. For example, while the maximum amount for each financial statement variable (i.e. prior year revenue, assets, and debt) well exceeds $10 million, the average venture reports roughly $500,000 for each variable. This suggests that the observations are heavily concentrated below the
$500,000 mark and the data is rather skewed. This topic will be further explored in the multivariate regression section (Figure 6). Outside of the financial independent variables, communication with investors, on average, manifests in about 41 questions and nine updates. Finally, despite having the potential to introduce five social tags to investors, entrepreneurs average just below one social tag, filling this portal feature more with industry tags and other descriptive terms.
Table 3: Descriptive Statistics
27 A recent study analyzing the funding patterns of 21 VC firms and 2,982 ventures noted that the average venture raising seed capital solicited ~$5.6 million in 2018, up from only $1.3million in 2010. The study also continued to note that the traditional “seed” round of funding may be transforming to the traditional “Series A” round, as companies look for alternative sources of funding (e.g. crowdfunding) prior to seeking equity from angels or early stage VC (Loizos, 2019). This can also be thought of as startup capital.
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Standard Mean Min Max Descriptive Statistics Deviation 1. Total Amount of Funding Raised $279,911 $345,854 $0 $2,188,466 2. Total Investors 306 381 0 3,256 3. Per Capita Investment $894 $678 $0 $3,860 4. % of Max Funding Target Achieved 47.2% 57.8% 0.0% 503.3% 5. Prior Year Revenue Reported $538,611 $1,842,167 $0 $19,234,987 6. Prior Year Assets Reported $449,251 $1,170,994 $0 $13,418,957 7. Prior Year Debt Reported $445,053 $1,518,068 $0 $17,993,629 8. Total Prior Round Funding Amount $1.07E+06 $1.81E+06 $0 $1.21E+07 9. Financially Oriented Sentiment 0.01 0.02 -0.04 0.08 10. Number of Questions Asked by Investors 41.07 39.90 0.00 209.00 11. Number of Updates Provided by Entrepreneurs 9.12 12.22 0.00 96.00
12. Blau Diversity Index (Gender) 0.23 0.20 0.00 0.50 Independent Variables Independent 13. Number of Social Tags Listed 0.98 1.06 0.00 4.00 14. Alcohol Industry 0.17 0.38 0.00 1.00 15. Education & Healthcare Industry 0.08 0.27 0.00 1.00 16. Financial Activities Industry 0.03 0.17 0.00 1.00 17. Food Industry 0.10 0.30 0.00 1.00 18. Goods-Producing Industry 0.07 0.26 0.00 1.00 19. Information Industry 0.31 0.47 0.00 1.00 20. Leisure & Hospitality Industry 0.11 0.32 0.00 1.00
IndustryControls 21. Other Industry 0.02 0.14 0.00 1.00 22. Professional & Business Services Industry 0.01 0.10 0.00 1.00 23. Retail Trade Industry 0.09 0.28 0.00 1.00 24. California 0.35 0.48 0.00 1.00 25. Colorado 0.04 0.21 0.00 1.00 26. Massachusetts 0.04 0.21 0.00 1.00 27. Nevada 0.05 0.22 0.00 1.00 28. New York 0.10 0.30 0.00 1.00 State Controls State 29. Texas 0.06 0.24 0.00 1.00 30. Total Number of Prior Rounding Rounds 4.49 4.46 0.00 26.00 31. Firm Age Since Founded (Quarters) 20.76 17.98 1.47 190.63
Outside of the basic descriptive statistics, several variables were explored in the following figures and tables. Table 4 displays state-level venture information for states with at least four ventures. California and New York were the leading states for funding portals on WeFunder with 72 and 20 ventures respectively. Unsurprisingly, California claims the largest number of information industry-related ventures (along with the largest state proportion of tech ventures for states with more than nine portals). However,
California lags several states in average amount invested, average investors, and average per capita investment. This contrasts heavily with the dominance of California in the VC market, accounting for more than 62% of the United States’ largest VC rounds in 2018
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(Rowley, 2018). This may support prior literature surrounding geographic barrier-eroding properties of crowdfunding-based platforms, but also aligns with another trait of
WeFunder portals displayed in Table 5 (Agrawal et al., 2015; Mollick, 2014). This view of the data analyzes venture characteristics by assigned industry. Alcohol and goods- producing ventures have, on average, a larger amount of funding raised, more investors, and a higher investment per investor than the information industry. Texas, Massachusetts,
Florida, and Hawaii (all of which have a higher proportion of alcohol industry companies) each surpass California in at least two of the three success metrics listed. This finding may support the presence of heterogeneity of investors on crowdfunding platforms.
Table 4: Descriptive Statistics by State
% % # of Unsuccessful Avg. Avg. Avg. Per State Information Alcohol Obs. Ventures Amount Investors Cap Industry Industry CA 72 5 $272,673 271 $996 36% 14% NY 20 1 $186,440 284 $713 30% 15% TX 12 1 $353,949 343 $1,156 8% 42% NV 10 4 $207,269 216 $566 20% 20% CO 9 2 $191,565 343 $595 22% 11% MA 9 2 $414,070 329 $825 22% 22% WA 7 2 $176,821 185 $638 57% 0% FL 6 2 $374,369 502 $800 33% 17% UT 5 0 $517,365 482 $910 60% 0% AZ 4 0 $317,131 230 $1,000 75% 0% HI 4 0 $446,423 452 $1,057 0% 25% PA 4 0 $221,867 193 $1,398 0% 50% TN 4 1 $264,311 912 $356 25% 25%
Typically, entrepreneurs should expect to raise capital successfully if their venture is based in the information or healthcare sectors. Depending on the industry definition, the information sector attracts 40% to 50% of VC funding, while large scale VC funding
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(over $20 million) goes to information firms more than 70% of the time. Second only to information, healthcare sector ventures attain approximately a quarter of VC funding, and
15% to 20% of large-scale VC funding (McCarthy, 2016; Meisler et al., 2016). These statistics vary dramatically relative to the amount of funding attributable to each sector on
WeFunder. Information companies still lead the pack with total amount of funding worth approximately 29%, but this is well below later stage VC funding. Furthermore, healthcare firms represent only a fraction of WeFunder funding with the education and healthcare sector accounting for about 8% of total funding. Another interesting difference in the industry breakdown of funding is the prevalence of alcohol related firms, representing nearly 24% of the funding granted on WeFunder for its Regulation CF ventures. Moreover, these ventures (i.e. local breweries, microbreweries, recreational bars, and wineries) have the second highest number of investors of all the sectors and the second highest per capita investment of all sectors with at least 10 observations.
Table 5: Descriptive Statistics by Assigned Industry
# of Avg. Avg. Avg. Per Industry % Failed Ventures Amount Investors Cap Information 64 10.81% $258,171 324 $738 Alcohol 35 8.11% $391,156 368 $1,073 Leisure & Hospitality 23 15.15% $210,883 335 $834 Food 21 12.50% $113,286 166 $707 Retail Trade 18 4.55% $241,476 224 $871 Goods-Producing 15 0.00% $511,968 382 $1,184 Education & Healthcare 16 5.88% $289,494 297 $1,042 Financial Activities 6 0.00% $317,268 300 $1,416 Other 4 20.00% $232,256 239 $926 Professional & Business Services 2 0.00% $84,262 141 $605
A major motivation of this study is to explore how unaccredited equity crowdfunding might affect underrepresented entrepreneurs in legacy entrepreneurial
51 finance, given the potential for a more diverse pool of capital. One of the more studied and significant metrics pertaining to underrepresented minorities is the gender composition of the founding team. For perspective, in a study conducted by Bloomberg from 2009 to 2015, only 7% of founders that received at least $20 million in VC funding were women who, on average, received 23% less funding than men (Meisler et al., 2016).
Further studies support this trend with total VC fund allocation to all-women founding teams in 2018 consisting of only 2.3% of total funding, with mixed founding teams receiving only 10.3% of the total capital pool (Gross, 2019). Several explanations have been introduced to explain the gender financing gap including indirect rationales like differences in management styles, experience, education, networks, associations with certain sectors, growth aspirations, or other gender stereotypes (Verheul & Thurik, 2001;
Yang et al., 2020). These stereotypes have also led to direct, discriminatory rationales, where investors pepper women entrepreneurs with prevention-based questions (versus promotion-based given to men), or invest based on homophily (Greenburg & Mollick,
2017; Kanze et al., 2018; Verheul & Thurik, 2001).
Nonetheless, crowdfunding has been cited as a potential tool to reduce barriers for women entrepreneurs to access capital given a higher democratization of funds
(Greenberg & Mollick, 2017; Marom, Robb, & Sade, 2016) 28. Table 6 displays descriptive statistics based on the level of team gender diversity, and essentially shows that founding team diversity is rewarded from WeFunder investors. Founding teams
28 The gender gap in entrepreneurial finance also persists among investors. In the U.S. alone, approximately 75% of U.S. venture capital firms lack a single women partner, and those that have women founders rarely promote women beyond the junior level (Bamberger, 2019). Further, women make up less than 20% of U.S. business Angels (Marom et al., 2016). This is a stark contrast to the unaccredited investor base capable of investment on WeFunder.com which is composed of more than 50% women (U.S. Census Bureau, 2019).
52 composed of 20% to 40% female members solicit about 2.2 times the average amount of investment and nearly 1.5 times the number of investors compared to all-male founding teams. Despite only representing 17% of the listed ventures on WeFunder, ventures made up of more than 40% female founders have about a 37% higher average per capita investment versus all-male founding teams. Logically, any firm looking for access to capital can list for a fundraising round on WeFunder, thus legacy investor-driven biases are absent, or at minimum, diluted on crowdfunding platforms given investor heterogeneity. In the presence legacy entrepreneurial finance barriers, like that plaguing women entrepreneurs, platforms like WeFunder should be contemplated as a tool to satisfy financing needs or signal feasibility to latter-stage investors.
Table 6: Descriptive Statistics at Different Levels of Team Composition
Female % % of Failure Avg. Avg. Avg. Per % Information of Team Ventures Rate (%) Amount Investors Cap Industry 0% 35.29% 18.06% $192,446 242 $723 45.31% 20% 22.55% 13.04% $338,836 405 $874 25.00% 40% 25.00% 3.92% $419,376 374 $1,099 18.75% 60% 9.80% 0.00% $152,574 192 $971 6.25% 80% 3.92% 0.00% $166,548 220 $1,100 1.56% 100% 3.43% 14.29% $269,605 236 $821 3.13%
Two additional contributing factors of this study include the introduction of per capita investment, and the analysis of social signals’ impact on entrepreneurs’ success in equity crowdfunding. Figure 5 shows a basic relationship between the two independent social variables, the Blau index variable for gender diversity and the proportion of social tags listed variable, and the per capita investment amount. Both independent variables appear to positively impact the dependent variable, however, there are large concentrations of ventures that have no gender diversity or include few social tags.
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Figure 5: Social Signals & Per Capita Investment
$2,500 $4,000 R² = 0.0603 R² = 0.135 $2,000 $3,200
$1,500 $2,400
$1,000 $1,600
$500 $800
$0 $0 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 Social Tag Proportion Blau Index Value (Bubble Size (Bubble Size = # of Ventures) Increases with # of Ventures)
Next, the bivariate results can be found in the Table 7, a correlation matrix between dependent, independent, and control variables29. Prior year assets, prior year debt, and total prior funding had significant correlations (p-value < 0.01) with correlation coefficient for amount of funding raised, total investors, and the proportion of max funding, ranging from r = 0.18 to r = 0.47. Total prior funding is the only explanatory (or independent) variable that significantly correlated with all four dependent variables (p- value < 0.01). Outside of the financially oriented sentiment, each financial signal had at least marginal significance with each dependent variable (p-value < 0.10). The highest correlation for the financial independent variables occurred between total prior funding amount and the total amount of funding raised (r = 0.47, p-value < 0.01). Finally, financially oriented sentiment is only correlated with per capita investment (r = 0.12, p- value < 0.10).
29 All bivariate correlation results assume a two tailed statistical approach; however, marginal significance is highlighted too report a comprehensive set of relationships.
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Table 7: Correlation Matrix – Significance
Dependent Variables Independent Variables Select Controls Correlation Matrix Significance 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 1. Total Amount of Funding Raised 1.00 2. Total Investors 0.72 1.00 3. % of Max Funding Target Achieved 0.67 0.48 1.00 4. Per Capita Investment 0.43 0.03 0.27 1.00 p-value < 0.01 5. Prior Year Revenue Reported 0.32 0.17 0.16 0.15 1.00 6. Prior Year Assets Reported 0.46 0.26 0.30 0.18 0.74 1.00 p-value < 0.05 7. Prior Year Debt Reported 0.30 0.18 0.25 0.13 0.79 0.85 1.00 8. Total Prior Round Funding Amount 0.47 0.30 0.40 0.18 0.26 0.45 0.37 1.00 p-value < 0.10 9. Financially Oriented Sentiment 0.04 0.06 -0.03 0.12 0.04 -0.02 -0.02 -0.06 1.00 10. Number of Updates from Entrepreneurs 0.31 0.28 0.26 0.15 -0.01 -0.01 -0.05 0.12 0.09 1.00 11. Number of Questions Asked by Investors 0.71 0.74 0.45 0.10 0.20 0.24 0.16 0.26 0.09 0.36 1.00 12. Blau Diversity Index (Gender) 0.13 0.06 0.09 0.18 0.05 0.12 0.09 0.13 0.01 0.06 0.01 1.00 13. Number of Social Tags Listed -0.01 -0.02 -0.04 0.14 -0.03 -0.03 -0.02 -0.03 0.10 -0.10 -0.11 0.23 1.00 14. Alcohol Industry 0.15 0.07 0.11 0.12 0.08 0.14 0.10 0.00 0.01 0.10 0.06 -0.01 -0.18 1.00 15. Education & Healthcare Industry 0.01 -0.01 0.08 0.06 -0.05 0.00 -0.07 0.03 -0.09 -0.03 -0.08 0.07 0.11 -0.13 1.00 16. Financial Activities Industry 0.02 0.00 -0.03 0.13 -0.04 -0.04 -0.02 -0.04 0.05 0.01 -0.03 0.06 0.17 -0.08 -0.05 1.00 17. Food Industry -0.16 -0.12 -0.09 -0.09 0.00 -0.03 -0.03 -0.13 0.13 0.00 -0.09 -0.02 0.04 -0.15 -0.10 -0.06 1.00 18. Goods-Producing Industry 0.19 0.06 0.24 0.12 0.10 0.13 0.19 0.13 0.00 0.07 0.11 0.10 0.09 -0.13 -0.08 -0.05 -0.10 1.00 19. Information Industry -0.04 0.03 -0.14 -0.16 -0.12 -0.11 -0.09 0.04 -0.09 -0.01 0.03 -0.17 -0.12 -0.31 -0.20 -0.12 -0.23 -0.19 1.00 20. Leisure & Hospitality Industry -0.07 0.03 -0.01 -0.03 -0.08 -0.04 -0.05 -0.08 0.01 -0.09 0.02 0.00 0.20 -0.16 -0.10 -0.06 -0.12 -0.10 -0.24 1.00 21. Other Industry -0.02 -0.02 -0.05 0.01 -0.02 -0.04 -0.04 -0.02 0.17 -0.06 -0.05 0.02 -0.10 -0.06 -0.04 -0.02 -0.05 -0.04 -0.10 -0.05 1.00 22. Professional & Business Services Industry -0.06 -0.04 -0.07 -0.04 -0.03 0.00 0.07 -0.01 -0.01 -0.04 -0.06 0.13 0.05 -0.05 -0.03 -0.02 -0.03 -0.03 -0.07 -0.04 -0.01 1.00 23. Retail Trade Industry -0.03 -0.07 -0.04 -0.01 0.16 -0.01 0.00 0.04 -0.06 -0.02 -0.03 0.05 -0.09 -0.14 -0.09 -0.05 -0.11 -0.09 -0.21 -0.11 -0.04 -0.03 1.00 24. California -0.02 -0.07 -0.02 0.11 0.05 0.01 0.12 0.05 -0.01 0.02 -0.10 0.00 0.13 -0.06 -0.02 0.05 -0.08 -0.01 0.08 -0.07 0.04 0.03 0.10 1.00 25. Colorado -0.06 0.02 0.00 -0.09 0.00 -0.03 -0.04 -0.06 -0.07 0.00 0.06 0.01 -0.02 -0.03 0.11 -0.04 0.01 -0.06 -0.04 0.07 0.14 -0.02 -0.07 -0.16 1.00 26. Massachusetts 0.08 0.01 0.02 -0.02 0.00 0.17 -0.03 0.15 0.06 -0.06 0.01 -0.04 -0.02 0.03 0.11 -0.04 -0.07 0.03 -0.04 0.07 -0.03 -0.02 -0.07 -0.16 -0.05 1.00 27. Nevada -0.05 -0.05 -0.06 -0.11 0.11 -0.04 -0.01 -0.11 0.08 0.10 0.07 -0.12 0.05 0.02 0.02 -0.04 0.07 -0.06 -0.06 0.06 -0.03 -0.02 0.01 -0.17 -0.05 -0.05 1.00 28. New York -0.09 -0.02 -0.05 -0.09 -0.05 -0.06 -0.03 0.07 0.03 0.03 -0.08 -0.02 0.04 -0.02 -0.10 0.04 0.05 0.03 -0.01 -0.01 0.07 -0.03 0.01 -0.24 -0.07 -0.07 -0.07 1.00 29. Texas 0.05 0.02 -0.02 0.10 -0.03 -0.04 -0.04 -0.05 -0.02 -0.08 -0.02 0.13 -0.05 0.16 0.08 -0.04 -0.02 -0.07 -0.12 -0.02 -0.04 -0.02 0.07 -0.18 -0.05 -0.05 -0.06 -0.08 1.00 30. Total Number of Prior Rounding Rounds 0.10 -0.01 0.05 0.19 0.26 0.18 0.29 0.07 -0.03 0.00 0.06 0.12 0.03 0.07 -0.05 0.00 0.01 0.26 -0.10 -0.15 -0.06 0.03 0.06 0.07 0.03 -0.02 -0.06 -0.07 0.02 1.00 31. Firm Age Since Founded (Quarters) 0.19 0.11 0.25 0.11 0.09 0.13 0.12 0.40 -0.02 0.04 0.08 0.17 0.02 0.11 -0.03 0.01 -0.02 0.07 -0.06 -0.04 -0.01 -0.01 0.00 0.12 -0.03 -0.04 -0.15 -0.04 -0.11 0.10 1.00
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Like the financial statement variables, the number of updates and number of questions independent variables exhibited significant correlations with amount of funding raised, total number of investors, and proportion of max funding (p-value < 0.01). In fact, the number of updates variable had the highest correlation between each of the three dependent variables (p-value < 0.01) at r = 0.71, r = 0.74, and r = 0.45 respectively.
Another non-financial variable, the Blau Diversity Index for gender, had a significantly positive correlation with the per capita investment variable (r = 0.18, p-value < 0.01), but only a marginally significant positive correlation with total amount of funding raised
(r = 0.13, p-value < 0.10). The number of social tags variable only correlated with the per capita investment variable (r = 0.14, p-value < 0.05).
While the financial statement variables and prior equity funding also solicit more investment per investor, the financially oriented sentiment, Blau diversity index, and social tags listed could be tools to significantly boost dollar value per investor despite these variables not significantly contributing to overall investment amount or investor count. Basically, the presence of marginally significant correlations with the per capita investment variable, and lack of significant correlation between other variables may imply that investors may value team diversity, social mission, and strong financial language, but only after being committed to investing based on other signals.
Multivariate Regression
Although the full models present a full view of the multivariate regression models, additional analysis for each regression will consider the partial models unless otherwise specified. Table 8 shows the negative binomial model testing proposition 1a
(Models 1-6) and 2a (Models 7-11), both of which are concerning the first dependent
56 variable, total amount of funding raised. Proposition 1a was supported across four of the five independent variables. Prior year revenue (Model 1; β = 5.83E-14), prior year assets
(Model 2; β = 9.33E-14), and total prior funding amount (Model 4; β = 1.20E-13) were significant with a p-value < 0.05, while the prior year debt was the least significant predictor (Model 3; β = 5.31E-14; p-value < 0.10). The full model (Model 6) supports the partial model results of the prior year assets (β = 3.61E-13; p-value < 0.10) and total prior funding variables (β = 1.71E-13; p-value < 0.01). However, prior year revenue fails to be a full model predictor, and prior year debt’s effect becomes negative in the full model (β
= -4.08E-13; p-value < 0.01). This implies that while debt is a positive signal by itself, in the presence of other financial signals, a $1 increase in the level of prior year debt reported will decrease the difference in expected logs of the predicted amount invested by
4.08E-13. Visually, this is depicted in Appendix 5. While variance inflation factors
(VIFs) fail to raise any red flags concerning multicollinearity, the correlations between prior year debt and other financial statement variables are relatively high30. This further implies that the explanatory value of regressing multiple financial statement variables in the same model is negligible.
30 Prior year debt is highly correlated with financial statement variables, prior year assets (r = 0.85; p-value < 0.01) and prior year revenue (r = 0.79; p-value < 0.01), but not quite as high with total prior funding (r = 0.37; p-value < 0.01).
57
Table 8: Neg. Binomial – Total Amount of Funding Raised
DV = Total Amount of Funding Raised Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Prior Year Revenue Reported 5.83E-14 *** 1.44E-13 2.59E-14 1.08E-13 Prior Year Assets Reported 9.33E-14 *** 3.61E-13 ** 3.72E-14 1.89E-13 Prior Year Debt Reported 5.31E-14 ** -4.08E-13 **** 0.00E+00 1.38E-13 Total Prior Round Funding Amount 1.20E-13 *** 1.71E-13 **** 4.78E-14 5.55E-14 Financially Oriented Sentiment 5.27E-06 1.05E-05 9.33E-06 9.01E-06 Constant -1.25E-06 **** -1.36E-06 **** -1.16E-06 **** -1.27E-06 **** -1.15E-06 **** -1.74E-06 **** 4.43E-07 4.67E-07 4.22E-07 4.57E-07 4.20E-07 5.45E-07 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Included Observations 204 204 204 204 203 203 AIC 26.056 26.052 26.062 26.040 26.067 25.992 BIC -728.389 -726.612 -729.411 -726.129 -718.399 -719.902 Max VIF 2.11 2.12 2.11 2.10 2.08 5.85 Mean VIF 1.30 1.29 1.30 1.32 1.29 1.84
DV = Total Amount of Funding Raised Model 7 Model 8 Model 9 Model 10 Model 11
Number of Updates from Entrepreneurs 1.34E-08 **** 1.27E-08 **** 2.60E-09 2.80E-09 Number of Questions Asked by Investors 1.64E-08 *** 1.88E-08 ** 6.60E-09 1.14E-08 Blau Diversity Index (Gender) 7.26E-07 6.52E-07 8.17E-07 9.05E-07 Number of Social Tags Listed 2.44E-08 1.46E-07 1.45E-07 1.56E-07 Constant -2.71E-06 **** -1.64E-06 **** -1.24E-06 **** -1.09E-06 **** -3.01E-06 **** 7.05E-07 5.57E-07 4.48E-07 4.13E-07 7.16E-07 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Observations 204 204 204 203 203 AIC 25.970 26.056 26.076 26.066 25.967 BIC -710.699 -720.351 -724.074 -719.081 -691.236 Max VIF 2.09 2.10 2.09 2.09 2.11 Mean VIF 1.29 1.29 1.29 1.31 1.32 P-Values < 0.01, 0.05, 0.10, & 0.15 represented by ****, ***, **, & * respectively.
58
To determine the relative effect of each independent variable, Figure 6 displays the predicted outcomes associated with each explanatory variable from the partial model results (Models 1-4). The figure constrains each variable to relative size bins and plots the associated predicted amount of funding raised for a bin. Bin increments were
~$640,000 for prior year revenue, ~$450,000 for prior year assets, ~$600,000 for prior year debt, and ~$400,000 for total prior funding.
Figure 6: Financial Signals & Total Amount of Funding
$1,400,000
$1,200,000
$1,000,000
$800,000
Raised $600,000
$400,000
$200,000 Predicted Mean Amount Predicted Mean Amount Frunding of $0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Assets Revenue Debt Prior Funding
Superficially, it appears that the amount of prior funding causes the largest amount of dollar value increase for a sequential increase in the independent variable bin.
In extant crowdfunding literature, prior funding either at the beginning of the fundraising round or prior to the start of a fundraising round has served as an indicator of fundraising success (Lukkarinen et al., 2016; Wang et al., 2019). Furthermore, Wang et al. present cases where angels and crowd investors increase investment size following a jump in investment from a few key investors. Thus, it is reasonable to expect that prior investment, especially large amounts do provide endorsement in equity crowdfunding (at
59 least to a certain portion of the WeFunder investment base), supporting extant literature
(Goethner et al., 2020; Lukkarinen et al., 2016). Appendix 6 further develops the partial model analysis, discussing sensitivity to outliers, reduced confidence at high bin levels
(Appendix 7), and robustness of underlying results.
The arguments in proposition 2a (Table 8) were also supported with two of the four non-financial independent variables. The venture quality portion of the non-financial variables, number of updates from entrepreneurs (Model 7; β = 1.34E-8; p-value < 0.01) and number of questions asked by investors (Model 8; β = 1.64E-8; p-value < 0.05) were both significant predictors of total amount of funding raised31. Like the financial signals, both the updates and questions variables lose significance beyond a certain level primarily driven by outliers (see Appendix 6). Since the number of updates deteriorate from a confidence perspective more so than financial signals at higher bin levels, the robustness visual can be found in Figure 7 for the purpose of visual clarity. Further discussion of this phenomena (including the initially plotted data) can also be found in
Appendix 6.
Figure 7: Non-Financial Signals & Total Amount of Funding (p-value < 0.01)
31 The full model supports the results of each partial model test with the number of updates predictor remaining fully robust (β = 1.27E-8; p-value < 0.01) and the number of questions variable losing some significance (β = 1.88E-8; p-value < 0.10).
60
$650,000
$550,000
$450,000
Raised $350,000
$250,000 Predicted Mean Amount Predicted Mean Amount Frunding of $150,000 1 3 5 7 9 11 13 15 17 19 21
Questions Updates
The second negative binomial test, for the total investors success metric (Table
9), returns similar results concerning the financial signals, supporting the validity of proposition 1b (Models 1-6) with slightly more partial model significance than proposition 1a. Prior year revenue (Model 1; β = 9.93E-11; p-value < 0.01), prior year assets (Model 2; β = 1.54E-10; p-value < 0.01), prior year debt (Model 3; β = 9.85E-11; p-value < 0.05), and total prior funding (Model 4; β = 1.78E-10; p-value < 0.01) positively predict total investors. The full model supports results in the first negative binomial regression (Table 8), with prior year revenue losing significance, and prior year debt’s effect becoming negative (Table 9; Model 6; β = -5.11E-10; p-value < 0.05).
Again, this implies that while debt is a positive signal by itself, in the presence of other financial signals, higher levels of debt have a negative effect on total amount of funding raised (see Appendix 5). Figure 8 plots financial signals (in bins like the analysis conducted for the total amount of funding) against predicted mean total investors.
61
Table 9: Neg. Binomial – Total Investors
DV = Total Investors Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Prior Year Revenue Reported 9.93E-11 **** 1.59E-10 3.82E-11 1.63E-10 Prior Year Assets Reported 1.54E-10 **** 5.07E-10 ** 5.32E-11 2.72E-10 Prior Year Debt Reported 9.85E-11 *** -5.11E-10 *** 4.41E-11 2.15E-10 Total Prior Round Funding Amount 1.78E-10 **** 2.15E-10 **** 5.96E-11 7.56E-11 Financially Oriented Sentiment 1.27E-02 1.50E-02 1.08E-02 1.08E-02 Constant -1.93E-03 **** -2.05E-03 **** -1.87E-03 **** -1.73E-03 **** -1.88E-03 **** -2.22E-03 **** 5.84E-04 6.07E-04 5.70E-04 5.61E-04 5.62E-04 6.24E-04 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Included Observations 204 204 204 204 203 203 AIC 13.372 13.367 13.376 13.354 13.388 13.319 BIC -738.880 -737.170 -738.951 -735.095 -728.277 -727.964 Max VIF 2.11 2.12 2.11 2.10 2.08 5.85 Mean VIF 1.30 1.29 1.30 1.32 1.29 1.84
DV = Total Investors Model 7 Model 8 Model 9 Model 10 Model 11
Number of Updates from Entrepreneurs 2.90E-05 **** 2.87E-05 **** 3.63E-06 3.87E-06 Number of Questions Asked by Investors 3.13E-05 **** 2.60E-05 * 8.69E-06 1.71E-05 Blau Diversity Index (Gender) 6.63E-04 1.69E-03 9.76E-04 1.25E-03 Number of Social Tags Listed -5.63E-06 2.28E-04 1.89E-04 2.20E-04 Constant -5.64E-03 **** -2.53E-03 **** -1.83E-03 **** -1.70E-03 **** -6.18E-03 ****
Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Observations 204 204 204 203 203 AIC 13.170 13.352 13.396 13.401 13.173 BIC -689.641 -729.724 -733.530 -728.659 -673.414 Max VIF 2.09 2.10 2.09 2.09 2.11 Mean VIF 1.29 1.29 1.29 1.31 1.32 P-Values < 0.01, 0.05, 0.10, & 0.15 represented by ****, ***, **, & * respectively.
62
Figure 8: Financial Signals & Total Number of Investors
2,000 1,800 1,600 1,400 1,200
1,000 Investors 800 600
Predicted Mean Amount Predicted Mean Amount Totalof 400 200 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Assets Revenue Prior Funding Debt
To satisfy proposition 2b (Models 7-11), both the number of questions asked
(Model 8; β = 2.90E-5; p-value < 0.01), and the number of updates provided are significantly predictors of total investors in a fundraising round (Model 7; β = 3.13E-5; p- value < 0.01). The full model supports the results of each partial model test with the number of updates predictor remaining fully robust (Model 11; β = 2.87E-5; p-value <
0.01) and the number of questions variable falling just beyond marginal significance
(Model 11; β = 2.60E-5; p-value < 0.15). Given significance in the partial model (Model
8), this study considers a one-tailed approach for the full model (p-value < 0.075).
Additionally, prior literature points to the positive relationship between heightened communication among venture financing actors (Block et al., 2018; Mollick, 2014;
Xiaoyu et al., 2017).
Although not central to this study, these results allude to the moderating effect of the number updates variable against the number of questions. This may suggest that certain updates, potentially targeted at common investor questions, satisfy information
63 asymmetries prior to investors asking questions as the correlation between the two independent variables (r = 0.36, p-value < 0.01) is high, but nor indicative of multicollinearity issues. Similar to the depiction of predicted total amount of funding,
Figure 9 shows the robust predicted value of partial model non-financial signals towards total investor counts for the purpose of visual clarity. Much like the rhetoric surrounding the total amount of funding success metric, skewness of the number of updates and number of questions variables causes confidence to deteriorate at higher bin levels. Thus,
Appendix 6 provides additional analysis of the financial and non-financial signals’ impact on the total number of investors success metric.
Figure 9: Non-Financial Signals & Total Amount of Funding (p-value < 0.01)
1,200
1,000
800
600 Investors 400
200 Predicted Mean Amount Predicted Mean Amount Totalof
0 1 3 5 7 9 11 13 15 17 19 21 23
Questions Updates
64
Table 10: OLS Regression – Proportion of Max Funding Goal Achieved
DV = Proportion of Max Funding Target Achieved Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Prior Year Revenue Reported 4.27E-08 ** -3.11E-08 2.25E-08 3.68E-08 Prior Year Assets Reported 1.19E-07 **** 9.78E-08 3.41E-08 7.33E-08 Prior Year Debt Reported 7.99E-08 **** 2.60E-09 2.70E-08 5.85E-08 Total Prior Round Funding Amount 1.16E-07 **** 9.70E-08 **** 2.32E-08 2.61E-08 Financially Oriented Sentiment -4.61E-01 3.44E-01 2.47E+00 2.33E+00 Constant 5.20E-01 **** 4.70E-01 **** 5.16E-01 **** 5.37E-01 **** 5.31E-01 **** 4.96E-01 **** 1.32E-01 1.29E-01 1.30E-01 1.25E-01 1.37E-01 1.29E-01 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Included Observations 204 204 204 204 203 203 R-Squared 0.172 0.208 0.194 0.256 0.156 0.272 Adjusted R-Squared 0.0913 0.131 0.116 0.184 0.0730 0.184 Max VIF 2.11 2.12 2.11 2.10 2.08 5.85 Mean VIF 1.30 1.29 1.30 1.32 1.29 1.84
DV = Proportion of Max Funding Target Achieved Model 7 Model 8 Model 9 Model 10 Model 11
Number of Updates from Entrepreneurs 1.14E-02 **** 4.60E-03 ** 3.18E-03 3.19E-03 Number of Questions Asked by Investors 6.22E-03 **** 5.68E-03 **** 9.09E-04 9.80E-04 Blau Diversity Index (Gender) 9.06E-02 6.93E-02 2.11E-01 1.96E-01 Number of Social Tags Listed -3.58E-02 -1.15E-02 4.06E-02 3.77E-02 Constant 3.90E-01 **** 2.39E-01 ** 5.10E-01 **** 5.34E-01 **** 2.03E-01 * 1.34E-01 1.26E-01 1.37E-01 1.33E-01 1.31E-01 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Observations 204 204 204 203 203 R-Squared 0.211 0.326 0.157 0.159 0.335 Adjusted R-Squared 0.134 0.261 0.0746 0.0768 0.258 Max VIF 2.10 2.09 2.09 2.09 2.11 Mean VIF 1.29 1.29 1.29 1.31 1.32 P-Values < 0.01, 0.05, 0.10, & 0.15 represented by ****, ***, **, & * respectively.
65
The OLS regressions for proposition 1c (Models 1-6) and 2c (Models 7-11) can be found in Table 10. Support for proposition 1c was apparent through prior year assets
(Model 2; β = 1.19E-7; p-value < 0.01), prior year debt (Model 3; β = 7.99E-8; p-value <
0.01), and total prior funding (Model 4; β = 1.16E-7; p-value < 0.01). Prior year revenue also displayed marginal significance (Model 1; β = 4.27E-8; p-value < 0.10). The analysis of results in this study illuminates marginal significance to highlight variables that would be significant given a one-tailed approach. In the case of prior year revenue, a one-tailed test (p-value < 0.05) is reasonable to measure the impact on success given the support in prior literature concerning financial signals (Ahlers et al., 2015; Vulkan et al.,
2016; Yang et al., 2020). While the full models in Table 8 and Table 9 supported prior results, only total prior funding is a significant full model predictor in Table 10 (Model 6;
β = 9.70E-8; p-value < 0.01). Visually, initial partial model results are shown in Figure
10. Unlike the results in the negative binomial models, each bin level is statistically significant. However, the confidence intervals do expand as the bin level of increase. This relationship can be seen in Appendix 8.
Figure 10: Financial Signals & Proportion of Max Funding Raised
66
2.5
2.0
1.5
1.0 Funding Funding Achieved
0.5 Predicted Mean Proportion of Max 0.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Assets Revenue Prior Funding Debt
Proposition 2c was also supported. In accordance with Tables 8 & 9, only the number of updates (Model 7; β = 1.14E-2; p-value < 0.01) and number of questions
(Model 8; β = 6.22E-3; p-value < 0.01) variables were significant predictors of the proportion of total funding received. Figure 11 shows the initial results of Table 10 for the number of updates and the number of questions independent variables. Interestingly, after 18 bins (~58 updates) and 19 bins (~132 questions) for updates and questions respectively, the predicted proportion of max funding exceeds 100%. This is much more in-line with the number of outliers (15 observations exceed 100%) versus the financial variables that break the 100% mark, on average, at bin 15 (average value of $8.5 million), potentially implying the financial variables are more skewed in effect than the non- financial signals. The full-model displayed robustness for the number of questions variable (β = 5.68E-3; p-value < 0.01), with slight reduction in significance for the number of updates variable (β = 4.60E-3; p-value < 0.10).
Figure 11: Non-Financial Signals & Proportion of Max Funding Raised
67
1.6
1.4
1.2
1.0
0.8
0.6
Funding Funding Achieved 0.4
0.2 Predicted Mean Proportion of Max 0.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Questions Updates
The OLS regressions for proposition 1d (Models 1-6) and 2d (Models 7-11) can be found in Table 11. Support for proposition 1d was displayed across all partial models except for the model including prior year debt (Model 3). The total prior funding independent variable was the most significant predictor (Model 4; β = 6.54E-5; p-value <
0.05), while prior year assets (Model 2; β = 7.26E-5), and financially oriented sentiment
(Model 5; β = 5.34E+3) were significant with a p-value < 0.10. Again, prior year revenue displayed marginal significance with a one-tailed approach (Model 1; β = 4.31E-5; p- value < 0.075). While statistical significance of all preceding variables has displayed at some point across Table 8, 9, and 10, the financially oriented sentiment variable has yet to be statistically significant. For the per capita investment dependent variable, financially oriented sentiment displayed slight statistical significance from a two-tailed perspective. Caution should be taken before extrapolating
68
Table 11: OLS Regression – Per Capita Investment
DV = Per Capita Investment Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Prior Year Revenue Reported 4.31E-05 * 5.33E-05 2.67E-05 4.52E-05 Prior Year Assets Reported 7.26E-05 ** 1.35E-04 * 4.13E-05 9.01E-05 Prior Year Debt Reported 2.45E-05 -1.44E-04 *** 3.26E-05 7.19E-05 Total Prior Round Funding Amount 6.54E-05 *** 6.15E-05 ** 2.89E-05 3.20E-05 Financially Oriented Sentiment 5.34E+03 ** 5.37E+03 ** 2.90E+03 2.86E+03 Constant 8.77E+02 **** 8.48E+02 **** 8.78E+02 **** 8.89E+02 **** 8.13E+02 **** 7.73E+02 **** 1.56E+02 1.57E+02 1.57E+02 1.55E+02 1.60E+02 1.62E+02 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Included Observations 204 204 204 204 203 203 R-Squared 0.151 0.154 0.142 0.163 0.155 0.202 Adjusted R-Squared 0.069 0.071 0.059 0.081 0.072 0.105 Max VIF 2.11 2.12 2.11 2.10 2.08 5.85 Mean VIF 1.30 1.29 1.30 1.32 1.29 1.84
DV = Per Capita Investment Model 7 Model 8 Model 9 Model 10 Model 11
Number of Updates from Entrepreneurs 9.00E+00 *** 8.02E+00 *** 3.83E+00 4.04E+00 Number of Questions Asked by Investors 1.64E+00 9.67E-01 1.20E+00 1.24E+00 Blau Diversity Index (Gender) 4.06E+02 * 3.12E+02 2.48E+02 2.49E+02 Number of Social Tags Listed 8.14E+01 ** 8.26E+01 ** 4.69E+01 4.78E+01 Constant 7.75E+02 **** 8.06E+02 **** 8.17E+02 **** 8.65E+02 **** 6.78E+02 *** 1.61E+02 1.66E+02 1.61E+02 1.54E+02 1.66E+02 Controls (Industry, Location, Age, Prior Funding Rounds) Included Included Included Included Included Observations 204 204 204 203 203 R-Squared 0.164 0.148 0.152 0.170 0.208 Adjusted R-Squared 0.0829 0.0650 0.0690 0.0886 0.116 Max VIF 2.10 2.09 2.09 2.09 2.11 Mean VIF 1.29 1.29 1.29 1.31 1.32 P-Values < 0.01, 0.05, 0.10, & 0.15 represented by ****, ***, **, & * respectively.
69 this analysis to a one-tailed test given the lack of support in prior tests and less reliability of the financially oriented sentiment variable32. However, this statistical significance points to the possibility of this signal offering “incremental” success to entrepreneurs. For example, one possible explanation for this finding is that investors are not signaled to an investment from the financial sentiment derived in the CEO message, rather they are signaled to invest more after deciding to invest for alternative reasons. Figure 12 shows initial results for the financial independent variables. Again, the full model excludes significance from prior year revenue, but results were robust for total prior funding, prior year assets (one-tailed), and financially oriented sentiment.
Figure 12: Financial Signals & Per Capita Investment
1,900
1,700
1,500
1,300
1,100 Investment 900
700
Predicted Mean Amount Predicted Mean Amount Perof Capita 500 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Revenue Assets Total Prior Funding Financial Sentiment
The number of updates (Model 7; β = 9.0; p-value < 0.05) variable supports proposition 2d. However, Table 11 includes variation of results for other non-financial
32 In terms of reliability, the financially oriented sentiment variable was constructed with a specific lexicon that may be subject to alternate interpretations and analyzing sentiment from portal verbiage doesn’t carry the same consistency or regulation as other variables on WeFunder.com. Most other financial signals are regulated through filing a Form C with the SEC.
70 signals relative to the first 3 models. First, the number of questions asked was not a significant predictor (partial or full model). Second, like the emergence of financially oriented sentiment, the Blau index for diversity (Model 9; β = 4.06E+2; p-value < 0.15) and social tag count (Model 10; β = 8.14E+1; p-value < 0.10) signals are positive predictors of per capita investment. While there is no support for the signaling efficacy of the Blau index calculation in the first three multivariate regression analyses (i.e. Tables
8, 9, and 10), this study allows for the use of marginal significance with a one-tailed test
(p-value < 0.075) but requires further exploration. Initial predictive results for the non- financial signals are found in Figure 13. The count of social tags signal displays marginal significance but is validated through robustness in the full model regression (Model 11; β
= 8.26E+1; p-value < 0.10). Like financially oriented sentiment, these social signals may not be drivers of total investors or large amounts of funding. Rather, these signals may be incremental to the success of a venture’s fundraising round by attracting a greater amount of investment from an investor following the decision to invest in a venture.
Figure 13: Non-Financial Signals & Per Capita Investment
1,900
1,700
1,500
1,300
Investment 1,100
900
700 Predicted Mean Amount Predicted Mean Amount Perof Captia 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Updates Blau Index Social Tag
71
Robustness Checks
Several alternate tests were run on the models discussed above in attempt to ensure robustness. First, Appendix 9 discusses the practicality of each variable in relation to each other. In general, the communication variables (number of updates and number of questions) tend to deliver the strongest economic signal for investors. Second, as mentioned in the variable transformation section, financial statement variables were transformed through a discretization process to create a more normal distribution and magnify the predictive value of financial statement variables for younger, less proven ventures. Prior year revenue was robust at the $1million max cutoff for all models, and robust at the $500,000 max cutoff for Table 8 & 10. The prior year assets variable was robust for all regression models for both the $1million and $500,000 max cutoffs. Prior year debt was robust at the $1million cutoff for Table 8 and the $500,000 cutoff for
Table 10. Contrary to raw results for Table 11, prior year debt was a positive univariate predictor at both bin cutoff levels but was an insignificant predictor in all multivariate models. This further backs the idea that at low levels of debt, prior debt is a positive predictor of success (recall though, correlations between debt and other financial statements are relatively high, possibly indicating some multicollinearity issues). Third, for Table 10 & 11, the number of updates and number of questions variables were transformed by taking the natural logarithm. The results outlined in Table 10 proved to be robust with both transformed variables, including the emergence of full-model significance. Likewise, the original results in Table 11 remained robust concerning the number of updates variable. Additionally, marginal significance emerged for the number
72 of questions variable in both partial (p-value < 0.05) and full-model (p-value < 0.15) regressions through the robustness check.
Fourth, given the marginal significance in Table 11 for the Blau index for gender diversity (non-financial signal), fully understanding how diversity fits into the WeFunder and equity crowdfunding process warranted further exploration. The descriptive analysis of founding team gender diversity revealed that nearly 40% of the ventures listed had zero gender diversity. However, in firms that did have gender diversity exhibited better success statistics. Given that the Blau index variable only emerged as marginally significant in the final test, the study took an alternate approach in robustness checks.
Therefore, a new variable was created, female presence on new venture team. This variable is a binary variable (1 if there is at least one female member of the founding team) and was used in post-hoc test in place of the Blau index variable. While female team presence alone is seen as an enhancement in online settings as crowd investors are more likely to trust information from women (e.g. Marom et al., 2016), analysis of this binary metric signals that further exploration needs to be done regarding gender and entrepreneurial success on crowdfunding platforms. Significance improved in Table 8, 9,
& 11 when the Blau index was substituted out, calling for further research to explore the effects of team diversity on equity crowdfunding success (see Table 12).
73
Table 12: Blau Index & Female Team Presence Comparison33
Dependent p-value p-value Adjuste Full Model Independent Variable Variable (partial model) (full model) d R^2 Adjusted R^2
Per Capita Blau Index 0.1040 0.2120 0.0690 0.1163 Investment Female Team Presence 0.0990 0.2480 0.0695 0.1152 Total Amount of Blau Index 0.3740 0.4710 - - Funding Raised Female Team Presence 0.1390 0.4180 - - Blau Index 0.4970 0.1770 - - Total Investors Female Team Presence 0.1550 0.1350 - - *Green = p-value < 0.10; Yellow = p-value < 0.15
Fifth, variance inflation factors (VIF) reveal no issues concerning multicollinearity for any model. However, full-model regressions, paired with relatively high correlations revealed that prior debt may be susceptible to some level of multicollinearity. Each model lists the maximum and average VIF in Table 8 through 11.
Sixth, while this cross-sectional study assumes that all information was listed prior to closing of each venture’s funding round, it is possible that investors have asked questions or entrepreneurs have posted updates following the end of the funding round. Therefore, each model including the number of updates and number of questions variables
(including the associated log transformed variables) were re-run including a control variable calculating the time since each venture finished its funding round. The results for
Table 8, 9, and 11 were all robust at each model’s respective significance level after accounting for time since the end of each venture’s fundraising round. Results for Table
10 were robust for the number of questions variable, and the control variable improved the significance level to p-value < 0.01 for the number of updates variable.
33 All coefficients are positive for each of the regression models depicted in this table.
74
Finally, as mentioned in the previous section of the paper, Table 10’s dependent variable theoretically should be viable through conducting Probit or Logit analysis, since the proportion of max funding goal achieved should be bound by 0 and 1. However, 15
“rule breakers” achieved more than the max fundraising goal listed on each venture’s
Form C regulatory filing, so Table 10 employed OLS regression for the purposes of analysis. For this robustness check, the 15 “rule breakers” were converted to values of 1
(which assumes 100% of the max target was achieved) and Table 10 was re-run using a
Logit regression approach. While it is also possible to remove these observations altogether, keeping the number of observations was deemed more important since achieving more than the max target is possible. The results are consistent with the initial analysis in this study, but they are slightly less significant post-hoc.
Discussion
Despite the surge of scholarly interest in entrepreneurial finance, equity crowdfunding is still novel and underexplored, especially in the United States. This paper leveraged abductive34 (a combination of inductive and deductive) research methodology to explore the nuances of interactions between entrepreneurs and investors in the context of U.S. equity crowdfunding.
Several contributions to scholars and practitioners emerged from this study. First, a more theoretically and practically robust framework to study entrepreneurial signaling
(i.e. how entrepreneurs attract investors to their venture) in equity crowdfunding was
34 Abductive conclusions (combination of inductive and deductive) were drawn primarily for the analysis of the per capita success metric and the prior year debt variable, given the limited literature surrounding either in the context of unaccredited equity crowdfunding (Douven, 2017).
75 introduced. This framework enhances prior literature (e.g. Ahlers et al., 2015; Xiaoyu et al., 2017) by adapting signaling theory to the intricacies of equity crowdfunding. It provides a more holistic view of signaling that not only expands the conceptual categorization of signals, but also incorporates a more practical approach to building a theoretical framework. In doing so, this study responds to recent calls in the literature to address these nuances of novel forms of entrepreneurial finance (Cumming & Johan,
2017; Drover et al., 2017).
Second, this study expands the view of equity crowdfunding success by looking at the relative concentration versus dispersion of ownership in a venture. Specifically, an additional crowdfunding success metric was introduced to explore the amount of investment that the average investor contributed to each venture. Consistent with prior entrepreneurial and corporate finance literature, this metric captures the power relationship between investors (owners) and entrepreneurs (Denis, 2004; Drover et al.,
2017; Wasserman, 2016). The per capita investment metric revealed interesting findings, including the possibility of incremental signaling35 for the financially oriented sentiment, founding team gender composition and social impact tag independent variables.
Third, this study offers novel insights from data mined from WeFunder, the largest equity crowdfunding platform36 by number of investments and investment dollars.
Prior empirical research has focused on rewards-based platforms like Kickstarter (e.g.
Greenberg & Mollick, 2017; Marom et al., 2016; Mollick, 2014), the Australian equity
35 Incremental signals are valuable in determining the amount to invest after the decision is made, but do not necessarily have significant impact in the investment decision itself. 36 WeFunder leads all other Regulation CF platforms in the U.S. with ~31.1% of funding raised and ~21.8% of Form C filings (i.e. the number of ventures listed on WeFunder) (Crowdwise, 2020).
76 crowdfunding platform ASSOB (e.g. Ahlers et al., 2015), or European equity crowdfunding platforms (e.g. Goethner et al., 2020; Lukkarinen et al., 2016; Vulkan et al., 2016; Xiaoyu et al., 2017). However, not much work has covered crowdfunding in the U.S., specifically equity crowdfunding. While this study broadly explored the
WeFunder platform, which has a commitment for democratizing entrepreneurial finance, much more can be uncovered to support academia and entrepreneurs37. For example, the initial insights gained from a novel text mining approach in this study revealed that language used by the venture leader(s) did, in fact, matter statistically and economically for the amount of money an investor was going to commit to a given venture. This analysis should be continued with other information sources and through alternate lenses.
Fourth, the study also sheds some new light on a very important aspect on the interaction between entrepreneurs and investors, which are the communication variables
(i.e. number of updates and number of questions). These variables were found to be the most economically significant, providing support for their practical value. Hence, communication variables in the context of equity crowdfunding (at minimum in the context of WeFunder) are conduits for satisfying several investor motivations. For example, one update may include a question about the venture’s recent management team hire; another may provide information on the patent process on the new technology; and
37 Another example of contribution in this study has to do with a variable specific to U.S. Regulation CF firms. This work found that one prior financial statement item, prior year debt, could negatively impact success of a venture. Logically, a large debt burden can crowd out investors, but regulatory financial statement information has yet to be explored in the context of Regulation CF. A post-hoc analysis revealed that younger ventures, often also those ventures with little financial history, may be the ventures benefiting from having prior debt. However, older firms with extensive financial history (including other investments) are may be more susceptible to the downside of higher amounts of debt. Despite having reasonable VIFs, though, correlations between debt and other financial statement variables were extremely high. So, despite the finding and proposed explanation, multicollinearity may be an issue. This finding needs to be further explored.
77 another may discuss the new sustainability goals being implemented. This supports the signaling framework introduced in this study, as all the above are non-financial signals and the framework provides the opportunity to explore comprehensive variables like those that capture communication.
Finally, this work highlights dynamics relative to social enterprise and gender dimensions of equity crowdfunding platforms, using the WeFunder example. The findings of this study contrast with prior findings in the existing literature (e.g.
Cholakova & Clarysse, 2015). Descriptively, both the social tag count and the Blau index for gender diversity had positive relationships with the per capita investment success metric. Moreover, heterogenous teams proved to have the highest levels of total funding amount, total investors, and per capita investment. As a robustness test, gender was also explored from perspective of female presence on the founding team. Congruent with
Marom et al.’s (2016) rewards-based Kickstarter study, women entrepreneurs were found to be still underrepresented in information sector ventures, supporting the existence of a technology gap persisting in equity crowdfunding. Further, non-profit signals were only effective in predicting average investment per investor. These findings suggest major implications for social entrepreneurs. Future impact on social welfare signaling should be explored more precisely as the unaccredited equity crowdfunding market expands.
Limitations and Future Research
While this study offers insight into unaccredited equity crowdfunding in the
United States, the results displayed are more correlational than causal. Moreover, caution should be taken before extrapolating this study to other equity crowdfunding platforms,
78 especially those outside of the United States (see Cumming & Johan, 2017). Signals may vary depending on the crowdfunding website by design, underlying entrepreneurs and investors, and the ability to record and analyze such signals. As mentioned in the introduction, this form of venture financing is still relatively underexplored. As more investors leverage this platform, new insights may emerge.
Another limitation of this study pertains to the nature of the data in this study.
Ideally, this study should have incorporated a comprehensive set of entrepreneurial signals and success metrics as identified through prior literature. Given the limits of the data, collection process, and scope of this study, it was not possible to conduct a longitudinal analysis. For example, speed of raising capital, presence of different investors at different points of the fundraising round, investor signaling during the investment round, and percentage funded at different points of a campaign have all been used to study successful crowdfunding campaigns (Agrawal et al., 2015; Ahlers et al.,
2015; Ciuchta et al., 2016; Vulkan et al., 2016). Further, while WeFunder offers valuable data of entrepreneurial signals, the platform also has investor data such as location, prior investments, investment syndication, and profile information. Investor information was beyond the scope of this study but could also provide valuable information to better understand communication and behavioral patterns of investors.
Avenues for future research include expanding on the variables used in this study to represent equity crowdfunding success, financial signals, and non-financial signals.
Furthermore, future studies may choose to explore the same variables in greater detail.
For example, financially oriented sentiment in this study analyzes one specific field on
WeFunder (the CEO message). Future research could leverage this text mining approach
79 used in this study with a more refined lexicon or a larger sample size. Text mining may also be extrapolated to other fields on WeFunder (or other crowdfunding portals) like the
Our Team or Why You May Want to Invest sections.
Another possible application of this technique could be to parse sentiment or signal content (i.e. human capital, social capital, etc.) from an otherwise hybrid signal like the number of questions and updates variable used in this study. Right now, the communication variables used in this study were solely count variables. For example, by using text mining on the updates and questions fields, future research could extract specific evidence that a company is promoting human capital versus intellectual capital.
Additionally, text mining could be useful in exploring the content of the investor side of communication. Currently, the number of questions variable in this study only captures entrepreneurial signaling. By examining the investor side of the communication variables, scholars can achieve a better understanding of whether or not investors deploy a promotion or prevention focus towards entrepreneurs (see Kanze et al., 2018).
Further, scholars should explore the power dynamic between entrepreneurs and crowd investors in greater detail. More specifically, how investor sophistication plays a role in how entrepreneurs go about setting fundraising goals or how they value amount of investment per investor. Crowdfunding literature can also be expanded by leveraging investor data on platforms like WeFunder. For example, scholars may study the location of investors versus the geography of ventures, crowd investors investment patterns, or which groups of investors invest in which ventures.
Additionally, social impact investor motivations should be further explored. As entrepreneurial finance expands, we have seen the emergence of social venture capital
80
(SVC), philanthropic crowdfunding, and a multitude of research surrounding social enterprise. However, there is more to be discovered, especially as some crowdfunding platforms strictly fund socially driven ventures (e.g. Triodos Crowdfunding38). Finally, the interaction between a company’s previous investors (or creditors) and current investors should be further explored. In the context of equity crowdfunding, scholars should aim to explore what types of prior investment (debt, equity, etc.) signal crowd investors to invest in current fundraising rounds.
Equity crowdfunding in the United States provides entrepreneurs and investors with a path to democratize venture financing. In particular, the WeFunder platform represents a great opportunity to expand equity crowdfunding, as over $30 million was raised from nearly 50,000 investors in 2019. Beyond the obvious differences in a web- based platform versus traditional equity venture financing (i.e. lack of geographic barriers, greater number investors), unaccredited equity crowdfunding offers a broader range of investor heterogeneity in terms of motivation and sophistication. Given crowdfunding’s virtual domain, web communication played a greater statistical and economically significance role than legacy forms of venture financing, alleviating information asymmetries. However, much is left to be discovered about the underexplored U.S. equity crowdfunding market. Together, we should continue to examine the intricacies of these tools as the marketplace grows and more actors leverage this exciting form of venture financing.
38 Triodos Crowdfunding has funded 68 “impact” projects in the United Kingdom, ranging from sustainable energy to local food (Triodos Bank, 2020).
81
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Appendices
Appendix 1: Conceptual Signaling Models in Crowdfunding Literature
Determinants of Funding Success (Ahlers et al., 2015)
Venture Quality
Human Capital
Social (alliance) Capital (+)
Intellectual Capital
Funding Success Level of Uncertainty
Equity Share (-) Financial Projections
A Conceptual Model of Overfunding (Xiaoyu et al., 2017)
Risk Level - Target Equity Offering (-) - Target Funding Amount (-) Project Quality - Finishing Percentage - Absolute Funding Amount Social Capital & Investor Communication - Number of Investors - Length of Video (+) - # of Video Sharing on LinkedIn (+) - # of Video Sharing on FaceBook (+)
Company Characteristics - # of Appointments (+) OVERFUNDING - Company Age (+) - Sectors (+/-)
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There are several differences between the models depicted above and the one used in this study. (1) Both platforms use structurally and geographically different platforms, the Australian platform that’s been in operation since 2006, ASSOB, and a 2-year snapshot (2014-2016) from the UK’s oldest platform, Crowdcube. Not only are these platforms capturing different venture markets, but this data is sourced before the legal inception of unaccredited equity crowdfunding in the U.S. (2) Both frameworks fail to fully encompass the nature of equity crowdfunding. Ahlers et al.’s (2015) model leaves out communication, which is arguable one of the greatest differentiators between crowdfunding and alternative forms of venture financing. Xiaoyu et al.’s (2017) model excludes aspects of venture quality and investment uncertainty captured by the prior model. Both frameworks fail to include social entrepreneurship signaling. (3) The framework in this study is more concise for practical purposes (i.e. financial viability and non-financially oriented signals) and allows for forgiveness in portal signals that contain multiple venture quality pieces.
Appendix 2: Social Tag Lexicon
Comprehensive List of Social Tags y combinator social impact beauty moonshots pbc & bcorps main street leisure family learn new things get help from experts female founder sustainability community solar clean tech recreation crowdfunding sharing economy education energy lifestyle immigrant animals vegan enterprise minority owned clean-tech art 500 startups minority founder animal welfare invest in my town safety zero waste vegetarian aloe women woman paradigm shift
Appendix 3: Financial Statement Variable Bins
Bin Number Bin Min Bin Max Revenue Assets Debt 1 $0 $34,483 106 69 97 2 $34,483 $68,966 11 20 12 3 $68,966 $103,448 8 7 13
96
4 $103,448 $137,931 9 10 4 5 $137,931 $172,414 3 14 11 6 $172,414 $206,897 5 7 5 7 $206,897 $241,379 3 6 5 8 $241,379 $275,862 3 7 3 9 $275,862 $310,345 1 1 2 10 $310,345 $344,828 5 6 3 11 $344,828 $379,310 5 4 2 12 $379,310 $413,793 1 1 6 13 $413,793 $448,276 3 3 1 14 $448,276 $482,759 2 3 5 15 $482,759 $517,241 1 2 0 16 $517,241 $551,724 1 3 1 17 $551,724 $586,207 2 4 3 18 $586,207 $620,690 0 2 2 19 $620,690 $655,172 1 1 0 20 $655,172 $689,655 0 1 0 21 $689,655 $724,138 1 2 1 22 $724,138 $758,621 1 2 2 23 $758,621 $793,103 2 1 0 24 $793,103 $827,586 1 1 0 25 $827,586 $862,069 1 1 1 26 $862,069 $896,552 2 1 1 27 $896,552 $931,034 0 1 0 28 $931,034 $965,517 3 0 0 29 $965,517 $1,000,000 0 1 2 30 $1,000,000 up 23 23 22
Appendix 4: Text Mining Statistics & Variables
Standard Metric Average Min Max Deviation Character Count 257.51 62.34 44.00 344.00 Word Count 69.27 16.29 14.00 91.00 Dollar Sign 0.18 0.46 0.00 2.00 Percent Sign 0.17 0.48 0.00 3.00
Percent Top 5 Industries Characters Words Dollar Sign Sign Information 262.683 70.587 0.254 0.159 Alcohol 271.114 75.057 0.314 0.086 Leisure & Hospitality 228.913 62.000 0.174 0.043 Food 263.143 69.095 0.190 0.286 Retail Trade 259.111 67.278 0.111 0.333
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Percent Top 6 States Characters Words Dollar Sign Sign California 254.347 67.375 0.250 0.181 New York 261.600 69.700 0.100 0.100 Texas 294.083 76.000 0.250 0.250 Nevada 215.700 61.100 0.300 0.000 Colorado 280.889 75.000 0.000 0.222 Massachusetts 269.889 70.222 0.333 0.000
Appendix 5: Full Model Predictive Value of Prior Year Debt
Figure 14 and 15 display the predictive value of total prior debt for total amount of investment and total investors. To further support the analysis of prior year debt in
Tables 8 & 9, the full model analysis in Table 11 reveals significance for prior year debt
(Model 6; β = -1.44E-4; p-value < 0.05). Despite failing to be a significant predictor in the partial model analysis, prior year debt has a negative effect on the dollar amount invested per investor. Since per capita investment measures success in terms of the “how” and entrepreneur raises capital and attracts investors (versus other success metrics that just analyze the “what”; i.e. total amount, total investors, proportion of max funding), high levels of debt can be associated more with how much an investor is willing to invest.
Since standalone prior debt can be indicative of prior funding and associated financial rigor, it is reasonable to see positive effects on other success metrics. However, investors may be hesitant to invest higher dollar amounts of investment, after making the decision to invest, in the presence of a lofty debt burden. One possible lens to evaluate this phenomenon is through age of the venture. Figure 16 shows firm age plotted against prior debt. While logical, the WeFunder data shows that younger ventures are typically less likely to have debt, and if they do its smaller than that of older ventures. For example, the average debt amount of firms over five years old is 2.28 times the average
98 debt for firms younger than five years. Therefore, prior debt may act more like total prior investment in younger firms, whereas prior debt may serve as a negative signal in older firms with other financial information (revenue, assets, other forms of prior funding).
Figure 14: Full Model Prior Debt & Total Amount of Funding (95% CIs)
300,000
250,000
200,000
150,000 Raised
100,000
50,000 Predicted Mean Amount Predicted Mean Amount Funding of 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Full Model Debt
Figure 15: Full Model Prior Debt & Total Investors (95% CIs) 400
350
300
250
200
Investors 150
100
50 Predicted Mean Amount Predicted Mean Amount Totalof
0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Full Model Debt
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Figure 16: Prior Debt Versus Venture Age39
$2,500,000
$2,000,000
$1,500,000
$1,000,000 Prior YearPrior Debt
$500,000
$0 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 Firm Age (Quarters)
Appendix 6: Outlier Sensitivity Robustness for Negative Binomial Models
Across most independent variables (financial statement and communication variables), the WeFunder dataset includes a bias towards outliers. The bias can be displayed in Figure 17 and Figure 18. The image in Figure 17 shows the distribution of ventures that fall in the first 10 bins (~33% of the total bins used in the mean predicted total amount of funding analysis) of each significant financial signal. Approximately 94% of the observations for total prior funding fall within the first 10 bins, implying that the confidence of prediction beyond bin 10 is significantly reduced compared to the first 10
(Appendix 7 contains initial plots of each variable and associated 95% confidence intervals). Likewise, the other three variables have approximately 99% of total observations falling within the first 10 bins, creating greater skewness. Therefore, the margin analysis used to predict the mean amount of total funding raised was revisited to
39 22 ventures lie outside of the bounds in place for the visual. However, the 22 outliers are still represented in the trendline calculation, the exclusion of these observation is for visualization purposes.
100 include only the observations that were significant. The graph in Figure 18 displays the financial signal predictions with a confidence interval of 99%. In the case of total prior funding, the predictive value of the variable is robust through the 19th bin. The other three variables are significant at the 20th bin. Furthermore, this analysis better reveals the predictive value of each financial signal relative to one another.
Figure 17: Financial Signal Distribution of Observations (first 10 bins)
100%
80%
60%
40%
20% Percentage of Obs. = (n 204) 0% 1 2 3 4 5 6 7 8 9 10 Bin Number
Prior Assets Prior Revenue Prior Debt Total Prior Funding
Figure 18: Significant Financial Signal Predictions (p-value < 0.01)
$450,000
$400,000
$350,000
Raised $300,000
$250,000 Predicted Mean Amount Predicted Mean Amount Frunding of $200,000 1 3 5 7 9 11 13 15 17 19
Assets Revenue Debt Prior Funding
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Figure 19 depicts the non-cumulative distribution of each non-financial variable in the first 10 bins, while Figure 20 plots the initial analysis of the number of updates and questions alongside predicted mean amount of total funding (recall that the significant- only visual can be found in Figure 7). The number of updates variable shares a similar distribution with the financial variables with ~96% of observations falling in the first 10 bins, though the distribution in the first 10 bins is more normalized. The number of updates variable has the least amount of skewness with only ~83% of observations in the first 10 bins. After receiving ~154 questions and generating ~58 updates, confidence in the predictive value of an additional update or question deteriorates, whereas the financial variables’ trend holds true (even after confidence erodes). For example, at ~202 questions asked the confidence range expands and the predicted mean amount of funding actual becomes negative (Figure 20). This result is not practical given the nature of the data
(only one observation exists above 202 questions) and the trend prior to 202 questions asked. Finally, for the purpose of economic significance (Appendix 9) robust results of non-financial signals are plotted alongside financial signals in Figure 21.
Figure 19: Non-financial Signal Distribution of Observations (first 10 bins)
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60% = = 204)
n n 40%
20% Percentage of Obs. ( 0% 1 2 3 4 5 6 7 8 9 10 Bin Number
Number of Updates Number of Questions
Figure 20: Non-Financial Signals & Total Amount of Funding (Outlier Bias)
$10,000,000
$0 1 4 7 10 13 16 19 22 25 28 -$10,000,000
-$20,000,000
-$30,000,000 Raised -$40,000,000
-$50,000,000
-$60,000,000 Predicted Mean Amount Predicted Mean Amount Frunding of -$70,000,000
Questions Updates
Figure 21: Predicted Total Amount Raised – All Variables (p-value < 0.01)
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$650,000
$550,000
$450,000
Raised $350,000
$250,000 Predicted Mean Amount Predicted Mean Amount Frunding of $150,000 1 3 5 7 9 11 13 15 17 19 21
Questions Updates Assets Revenue Debt Prior Funding
Confidence in Table 9’s results are structurally like the confidence in Table 8’s results (Figure 22 shows the initial analysis of non-financial signals that was omitted from the body of the study). The only difference lies in the significance of each variable when predicting the mean number of investors. The number of questions variable is significant (p-value < 0.01) for bin 23 in the analysis of predicted total investors but not in the analysis of predicted amount of funding, and the number of updates variable is significant (p-value < 0.01) for bin 18 in the analysis of predicted amount of funding but not in the analysis of predicted total investors. Figure 23 (like Figure 21) shows the results that are statistically significant for both financial and non-financial signals.
Figure 22: Financial Signals & Total Number of Investors (Outlier Bias)
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30,000
25,000
20,000
15,000
10,000
5,000 Investors 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 -5,000
Predicted Mean Amount Predicted Mean Amount Totalof -10,000
-15,000
Questions Updates
Figure 23: Predicted Total Investors – All Variables (p-value < 0.01)
1,200
1,000
800
600 Investors 400
200 Predicted Mean Amount Predicted Mean Amount Totalof
0 1 3 5 7 9 11 13 15 17 19 21 23
Assets Revenue Prior Funding Questions Updates
Appendix 7: Predicted Mean Amount of Funding Raised – 95% CIs
Financial Signals:
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Non-Financial Signals:
Appendix 8: Predicted Mean Per Capita Investment – 95% CIs
Financial Signals:
106
Non-Financial Signals:
107
Appendix 9: Multivariate Regression Economic Significance
With the first two multivariate regression models (Table 8 & 9) economic significance was determined using the same bins approach in the initial analysis and in
Appendix 6. While Figures 21 & 23 (see Appendix 6) serve as visual comparisons of the predicted mean amount of funding or investors solicited, Tables 13 & 14 show the incremental predicted mean amount of funding and investors attracted for each change in bin level.
Visually, with the total amount of funding dependent variable (Figure 21 &
Table 13), total prior funding and non-financial variables appear to predict smaller mean amounts of funding at lower bin levels, before surpassing the mean amount of funding predicted at higher bin levels. For example, at bin 10 for total prior investment (~$4 million in prior investment) the economic viability of total prior funding surpasses that of prior debt and at bin 16 (~$6.5 million in prior investment) total prior investment generates the highest predicted mean amount of funding. Thus, economic significance is further analyzed with the rate of change jumping from bin levels of relative size. Total prior funding outpaces incremental funding of all other financial signals, along with the number of updates variable. However, after the 11th bin level, the number of questions asked becomes incrementally more significant than all variables40, eclipsing the number of updates variable after more than 77 questions are asked and 35 updates are generated.
One finding consistent across all variables, although more pronounced in the non- financial variables, is the increasing incremental value for each bin level. Essentially, at
40 The number of questions asked had lower incremental predictive value than two financial variables initially, but surpassed prior assets after the 4th bin (~28 questions; ~$1.8 million in assets) and total prior funding after the 7th bin level (~49 questions; ~$2.9 million in prior funding).
108 higher levels of any signal, it appears that an additional jump in bin level (i.e. ~$450k of reported assets or ~7 questions) will solicit more funding from crowd investors than a jump in bin level (the same increment) at lower bin levels. The change in predictive value is most pronounced in the number of questions asked by investors. The incremental predicted amount of investment for an increase in bin level to bin 18 (118 to 125 questions) is 5.77 times the incremental predicted value of bin 2 (7 to 14 questions). This exceeds the comparable analyses for the number of updates variable (4.06 times) and financial signals (average of 2.85 times). While this study is cross-sectional in nature
(where it is not possible to claim causality between the independent and dependent variables), one possible explanation for this behavior is the nature of the question field on
WeFunder. Questions are initiated by investors, and don’t necessarily have to be answered by entrepreneurs. This is the main clash between WeFunder questions and updates; since the updates are entrepreneur generated signals, it can be argued that updates are better aligned with tackling information asymmetries plaguing a wider base of investors than questions asked by individual investors. Moreover, both the questions and responses vary drastically in terms of how well one instance can contribute to bridging information asymmetries. For example, information concerning a venture’s financials has been proven to serve as an effective signal in crowdfunding studies, while information centric to rewards associated with the investment opportunity is considered negligible (Ahlers et al., 2015; Vismara, 2016). Thus, the likelihood of a firm with fewer questions asked to have information valuable enough to bridge wide-spread information asymmetries is low in comparison to a firm with a large volume of questions asked. It’s also likely for a firm with a high volume of questions, regardless of level of efficacy, to
109 build confidence in the preparedness and communication of the venture, both of which have been cited as effective characteristics for crowdfunding success (Mollick, 2014;
Xiaoyu et al., 2017). Finally, returning to the conversation on regulatory focus, it is likely that with high volumes of investor questions, crowd investors are more likely to identify how compatible the venture is with their self-regulation (Cesario et al., 2001).
Given the assumption of promotion across online equity crowdfunding platforms, it is logical to see the presence of many questions translating to greater support through high volumes of funding.
Table 13: Incremental Impact – Predicted Mean Amount of Funding Raised
Revenue Total Prior Number of Number of Bin Number Asset Bins Debt Bins Bins Funding Updates Questions Bin $446,667 $640,000 $600,000 $403,333 3.2 7.0 Increments 2 $5,874 $5,432 $4,809 $6,094 $6,542 $5,665 3 $6,195 $5,703 $5,018 $6,466 $6,974 $6,102 4 $6,543 $5,995 $5,240 $6,873 $7,451 $6,592 5 $6,922 $6,310 $5,478 $7,319 $7,978 $7,142 6 $7,335 $6,650 $5,732 $7,811 $8,563 $7,765 7 $7,786 $7,019 $6,005 $8,353 $9,214 $8,473 8 $8,279 $7,419 $6,297 $8,955 $9,944 $9,283 9 $8,821 $7,855 $6,611 $9,623 $10,763 $10,214 10 $9,418 $8,330 $6,949 $10,369 $11,687 $11,293 11 $10,078 $8,849 $7,314 $11,206 $12,737 $12,553 12 $10,810 $9,418 $7,709 $12,148 $13,934 $14,035 13 $11,624 $10,045 $8,136 $13,214 $15,308 $15,797 14 $12,534 $10,736 $8,600 $14,427 $16,896 $17,912 15 $13,556 $11,500 $9,105 $15,815 $18,745 $20,484 16 $14,707 $12,350 $9,655 $17,414 $20,915 $23,651 17 $16,011 $13,297 $10,257 $19,267 $23,485 $27,615 18 $17,498 $14,357 $10,917 $21,434 $26,559 $32,666 19 $19,201 $15,550 $11,643 $23,987 - $39,242 20 $21,165 $16,898 $12,443 - - $48,028 21 - - - - - $60,134 22 - - - - - $77,480
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While predominately consistent with results originating from Table 8, the results from Table 9 vary slightly. Table 14 depicts the cadence of change in predicted total investors for each bin like the process outlined in Table 13. Again, total prior funding leads all other financial signals with strong incremental investors for an increase in bin level (only trails the number of updates consistently and the number of questions at higher bin levels). However, several variables prove to be more economically viable at all bin levels (Figure 23). While the visual analysis of the number of updates and number of questions shows that financial signals are generally more economically significant, the slope acceleration further points to the incremental value of the communication variables.
Using the same analysis from the previous dependent variable, the incremental predicted number of investors for an increase in bin level to bin 17 (~118 questions and ~54 updates) is averages a multiple of 5.71 times the incremental predicted value of bin 2 (~7 questions and ~3.2 updates) for the non-financial signals
Table 14: Incremental Impact – Predicted Mean Total Investors
Total Prior Number of Number of Bin Number Asset Bins Revenue Bins Funding Updates Questions Bin $34,483 $34,483 $416,667 3.2 7.0 Increments 2 4.28 2.81 7.52 9.43 6.81 3 4.44 2.88 7.98 10.21 7.37 4 4.61 2.94 8.49 11.09 8.00 5 4.79 3.01 9.06 12.09 8.72 6 4.98 3.07 9.67 13.22 9.54 7 5.19 3.15 10.36 14.53 10.47 8 5.40 3.22 11.12 16.04 11.56 9 5.63 3.30 11.96 17.80 12.81 10 5.88 3.37 12.91 19.86 14.29 11 6.14 3.46 13.97 22.30 16.04 12 6.42 3.54 15.17 25.22 18.12 13 6.71 3.63 16.53 28.76 20.65 14 7.03 3.72 18.08 33.10 23.73 15 7.38 3.82 19.86 38.50 27.57 16 7.74 3.91 21.92 45.34 32.42 17 8.14 4.02 24.31 54.18 38.68
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18 8.57 4.12 27.12 - 46.94 19 9.03 4.23 30.45 - 58.16 20 9.53 4.35 34.42 - 73.95 21 10.08 4.47 - - 97.18 22 10.67 4.59 - - 133.39 23 11.32 4.72 - - 194.49
Given the more straightforward interpretation of OLS regression, the economic significance analyses of Tables 10 & 11 are shown in Tables 15 & 16. The calculation used for economic significance for the OLS regression models involved dividing the product of the independent variable’s partial model coefficient in the regression and the variable’s standard deviation by the dependent variable’s mean (Goethner et al., 2020).
Total prior funding carried the greatest significance, causing a 44.42% increase in the proportion of max funding goal achieved for a one standard deviation increase in prior funding. This aligns with findings in the prior analyses of total amount of funding and total investors, showing that prior funding provides investors with a better signal than financial statement history. It also supports prior literature that advocate for the efficacy of prior funding as a proxy for financial viability and scrutiny (Bertoni et al., 2017; Wang et al., 2019; Yang et al., 2020).
Regarding economic significance (see Table 15)41, the number of updates and number of questions signals imply a 29.45% and 52.58% increase in the dependent variable for a one standard deviation increase in the respective independent variables.
41 Recalling general transformations made to the variables for robustness (the updates and questions variables were transformed with the natural logarithm), additional steps were taken to understand the economic significance associated with this part of analysis stemming from the model in Table 10. These steps are explained in detail in Appendix 10 for the purposes of this study and future studies aiming to calculate economic significance while using log-transformed dependent and independent variables. However, the interpretation of a log-transformed variable differs from the approach taken with the financial signals, so the analysis of the raw number of updates and number of questions variables were presented to maintain the like-to-like comparison in this section.
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Interestingly, the average implied percent increase in the independent variable is higher for the non-financial signal set of variables (avg. = 41.02%) versus the financial signal set
(avg. = 30.72%) for a one standard deviation increase in the independent variables.
Table 15: Economic Significance - Proportion of Max Funding Goal Achieved
Independent Standard Economic Coefficient Mean Variable Deviation Significance Number of Questions 6.22E-03 41 40 52.58% Total Prior Funding 1.16E-07 $1,065,232 $1,810,936 44.42% Prior Year Assets 1.19E-07 $449,251 $1,170,994 29.57% Number of Updates 1.14E-02 9 12 29.45% Prior Year Debt 7.99E-08 $445,053 $1,518,068 25.69% Prior Year Revenue 4.27E-08 $538,611 $1,842,167 16.66%
Like the analysis for the proportion of max funding achieved, economic significance was calculated for each significant partial model predictor for the per capita investment dependent variable. Results are displayed in Table 16. Again, total prior funding proved strong, surpassed the economic significance of all non-financial variables along with financial signals. Alternatively, while the number of updates still carried a high economic significance relative to other signals, the dispersion of economic significance across signals was less pronounced relative to interpretations of the first three dependent variables. This may be a derivation of the type of success solicited from per capita investment. While non-financial variables, specifically those oriented towards communication, are strong predictors of funding raised and total investors solicited, other signals outshine the communication variables when it comes to level of investment by investors.
Table 16: Economic Significance - Per Capital Investment
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Independent Standard Economic Coefficient Mean Variable Deviation Significance Total Prior Funding 6.54E-05 $1,065,232 $1,810,936 13.26% Number of Updates 9.00E+00 9.1225 12.2189 12.30% Financial Sentiment 5.34E+03 0.0124 0.0166 9.92% Social Tags 8.14E+01 0.9803 1.0622 9.67% Prior Year Assets 7.26E-05 $449,251 $1,170,994 9.51% Blau Index 4.06E+02 0.2272 0.1966 8.92% Prior Year Revenue 4.31E-05 $538,611 $1,842,167 8.89%
Appendix 10: Log-Transformed Variable Interpretation
Simplistically, understanding log-transformed variables requires a researcher to revisit the coefficients left in the model, and re-transform the variables. For models where the dependent variable is the only variable to be log-transformed, the researcher must exponentiate the coefficient of the independent variables, then subtract one and multiple by 100. This will effectively provide the percent change in the dependent variable given a one-unit change in the independent variable. The process and analysis for only independent log-transformed variables is slightly different. The researcher must multiply the independent variable coefficient by ln(1.x) for the effect of an x% increase in the independent variable on the dependent variable. This process was conducted in
Table 17.
Table 17: Log-Transformed Economic Significance
DV = % of Max Funding Implied Unit Increase in the DV = Per Capita Investment Dependent Variable (DV) --> Goal % Increase in the Independent Number of Number of Number of Number of Variable Updates Questions Updates Questions 1% $0.28 $0.46 0.02% 0.07% 5% $1.38 $2.25 0.10% 0.33% 10% $2.70 $4.39 0.20% 0.64% 15% $3.96 $6.44 0.29% 0.93% 20% $5.16 $8.39 0.37% 1.22% 25% $6.32 $10.27 0.46% 1.49%
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