Abstract
Purpose
This empirical study uses herd behavior model to explore the role of anchor investors in ensuring fundraising success and overfunding of crowdfunded ventures.
Design/methodology/approach
Qualitative comparative analysis (QCA) is applied to find the configurational patterns describing how anchor investors' information disclosure leads to successful financing and overfunding.
Findings
Even when the anchor investor's resume is not detailed or the anchor investor has little experience in entrepreneurial investment, success or overfunding can be achieved, provided the anchor investor is a corporation rather than an individual. For individual anchor investors, a detailed resume matters. Overfunding can be achieved even when an individual anchor investor makes a small relative investment, if this small relative investment is compensated for by a detailed resume. Experience in entrepreneurial investment is crucial when individual anchor investors have few previous investments. Regardless of the anchor investor's identity, investment in absolute terms is crucial for crowdfunding success when experience in entrepreneurial investment is low. Such experience must be extensive if the anchor investor's resume is not detailed.
Practical implications
Both entrepreneurs and crowdfunding platforms can benefit from the findings in relation to the design of campaigns that use anchor investors' informational cues to achieve success and overfunding.
Originality/value
The study examines the importance of anchor investors' information disclosure in digital crowdfunding environments, differentiating between individual and corporate anchor investors.
研究目的
本實證研究使用羊群行為模型, 去探究錨定投資者在確保眾籌活動可達成功籌資以及過多籌資方面所扮演的角色。
研究設計/方法/理念
研究人員以定性比較分析法、去找出描述錨定投資者的資訊公佈如何帶來成功融資和過多籌資的配置模式。
研究結果
研究結果顯示、只要錨定投資者不是個人、而是一間公司, 則即使他們的履歷不詳盡, 又或他們對企業投資的經驗淺薄, 也無礙籌資或過多籌資的成功完成。如錨定投資者為個人, 則詳盡的履歷會影響甚鉅。即使個人錨定投資者相對而言參與少量的投資, 但若這少量的投資給他們詳盡的履歷所彌補的話, 則過多籌資仍可成功達到。若個別錨定投資者原有的投資量不多的話, 則企業投資的經驗至為重要。不管錨定投資者的身份是什麼, 若他們對企業投資所持的經驗淺薄, 則按絕對值計算的投資額對眾籌能否成功至為重要。若錨定投資者的履歷不詳盡, 則這種經驗必須是豐富廣泛的。
研究的原創性/價值
本研究區分了個人錨定投資者與公司錨定投資者兩者對眾籌的影響, 就此而研究在數碼的眾籌環境裡, 錨定投資者信息公佈的重要性。
實務方面的啟示
研究結果可幫助企業家和群眾募資平台去設計可使用錨定投資者的資訊提示來達至成功眾籌和過多籌資的活動。
Keywords
Citation
Sendra-Pons, P., Mas-Tur, A. and Garzon, D. (2024), "Anchor investors and equity crowdfunding for entrepreneurs", European Journal of Management and Business Economics, Vol. 33 No. 1, pp. 20-36. https://doi.org/10.1108/EJMBE-06-2022-0167
Publisher
:Emerald Publishing Limited
Copyright © 2023, Pau Sendra-Pons, Alicia Mas-Tur and Dolores Garzon
License
Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
“It is a far, far better thing to have a firm anchor in nonsense than to put out on the troubled seas of thought.”—John Kenneth Galbraith
The mobilization of financial resources in entrepreneurial ecosystems has been identified as one of the major difficulties in the creation of new companies (Ko and McKelvie, 2018). Crowdfunding makes it possible to reach a multitude of potential backers online. The need to develop strategies for successful crowdfunding campaigns is becoming increasingly acute (Kraus et al., 2016; Moritz and Block, 2016). The main challenges in fundraising for new ventures lie in mitigating information asymmetries between entrepreneurs and potential backers and in building trust around technologies, products or services whose quality or market demand are unproven or costly to determine (Murray and Marriott, 1998; Nagy et al., 2012; Colombo, 2021; Bahlous-Boldi, 2022).
Crowdfunding success factors (e.g. campaign design and entrepreneur characteristics, motivations, biases, and culture) have been studied using many theoretical approaches. These approaches include information asymmetries, social influence, game theory, cognitive evaluation theory, impression management, signaling theory and herd behavior. Success has also been measured in different ways (Alegre and Moleskis, 2016). One approach to mitigating information asymmetries in crowdfunding is for fund-seeking entrepreneurs, as the better informed party, to convey signals about the quality of their ventures (see Ahlers et al., 2015; Piva and Rossi-Lamastra, 2018; Chakraborty and Swinney, 2021; Huang et al., 2022). Another approach is to use others' behavior to trigger imitation based on observational learning. This imitation is known as herding. In such cases, the crowd's decision-making is influenced by others' decisions to invest. The crowd uses these cues given the cognitive cost of generating a more exhaustive evaluation of the projects available for investment (see Comeig et al., 2020; Petit and Wirtz, 2022). This study focuses on herd behavior by studying the role of anchor investors (also referred to as lead investors) in triggering herding and encouraging potential backers to invest.
The present empirical study uses qualitative comparative analysis (QCA) to establish configurational patterns of anchor investors' informational cues that lead to fundraising success or overfunding in equity crowdfunding campaigns. Here, overfunding is defined as raising at least 10% above the fundraising target. The study focuses on a process of observational learning resulting in herd behavior. The informational cues that trigger herd behavior are evaluated using two models. The first explores the successful achievement of the funding target (Model 1). The second explores the achievement of particularly high levels of funding, referred to here as overfunding (Model 2). The data are from Startupxplore. This equity crowdfunding platform follows a hybrid crowd design. Anchor investors serve as experienced professionals that conduct thorough due diligence on fund-seeking entrepreneurial projects (Chen et al., 2016).
The study is original in its exploration of the role of anchor investors' financial and reputational commitment in securing syndicated equity crowdfunding success and overfunding. It is also original in that it differentiates between information disclosure strategies for individual and corporate anchor investors. This approach not only enriches the possible theoretical implications but also makes it easier to derive practical guidelines to ensure successful entrepreneurial fundraising.
Next, a discussion of the theoretical foundations is provided, building on the literature on information processing, observational learning and herd behavior. Then, the data and method are outlined, with emphasis on the configurational nature of the analysis and the relevance of this approach. The results are then presented, followed by a discussion of the optimal information disclosure strategies identified in the analysis. Conclusions are provided, before the paper closes with the theoretical contributions, limitations and practical implications of the study.
2. Theoretical background
2.1 Investors' cognitive processing in asymmetric informational settings
Credit markets for highly innovative small- and medium-sized enterprises (SMEs), including crowd-based financial environments, have high information asymmetries. These asymmetries often result in a credit rationing problem (Comeig et al., 2014). When investors are confronted with a catalog of mutually exclusive investment options or independent projects with budget constraints, cognitive processing becomes increasingly complex as more information is added to the decision-making process (Anderson, 2003). This complexity calls for the use of heuristics. Such methods are aimed at streamlining information processing and subsequent decision making under limited cognitive resources (Gigerenzer and Goldstein, 1996; Ferretti et al., 2021). The need for heuristics is extremely acute in equity-based crowdfunding, where Hemer (2011) and Ahlers et al. (2015) have found especially high levels of information asymmetries and complexity in information processing (Bade and Walther, 2021).
In equity-based crowdfunding, potential investors possess limited information prior to investing. The most well-informed party is the entrepreneur, who often provides potential investors with information on the venture to mitigate information asymmetries. This disclosure is part of a trust-building strategy (Ahlers et al., 2015). This study does not focus on the role of the entrepreneur in signaling the expected future success of an equity crowdfunding campaign in an attempt to reduce uncertainty and stimulate investment. Instead, it focuses on how the behavior of a quasi-informed party, the anchor investor, triggers a process of observational learning, leading to herd behavior. Ultimately, this process can determine crowdfunding success or overfunding. Research has shown that it is rational to rely on the decisions of others when making one's own decisions in the presence of information asymmetries (Banerjee, 1992).
In syndicated equity crowdfunding, fundraising campaigns are always endorsed by an anchor investor. After a due diligence process, this anchor investor assigns a considerable amount of money to the venture prior to its launch. Anchor investors are considered to have substantial expertise in entrepreneurial investment and due diligence processes (Zhao et al., 2021). They often provide written justification for their decisions to invest in particular projects. Accordingly, anchor investors' access to information is somewhere between the insider information of entrepreneurs and the information provided by entrepreneurs to potential investors.
The involvement of anchor investors in equity crowdfunding campaigns provides two paths to the mitigation of information asymmetries. Before the venture is available for public investment, a sizeable investment by an anchor investor reduces the remaining amount required to reach the funding target. This path lowers the risk that the funding target will not be reached. It therefore reduces the risk that the fundraising campaign will fail and that investors will incur an opportunity cost for the time their funds have been tied up in the unsuccessful campaign (financial commitment). An alternative path to mitigate information asymmetries stems from the anchor investor's personal career and written endorsement of a campaign (reputational commitment). This study focuses on both paths. It explores the role of the anchor investor's monetary contribution in relative and absolute terms (financial commitment) and the anchor investor's information disclosure based on experience and rationale for investment (reputational commitment) in triggering rational herd behavior that ultimately leads to fundraising success and overfunding.
Figure 1 illustrates this dual path to mitigating information asymmetries and generating confidence. Section A exemplifies how, before the campaign (from
The next section describes rational herd behavior dynamics. According to Hoegen et al. (2018), these dynamics are a prominent set of heuristics used in financial scenarios with multiple competing investment alternatives.
2.2 Information disclosure and rational herding dynamics
In evaluating the information structure and the awakening of rational herd behavior, this empirical study employs three subsets of cues: (1) informational cues on the investment by the anchor investor, (2) informational cues on the anchor investor's experience and (3) informational cues on the explanation that led the anchor investor to invest. Figure 2 shows that subset (1) of cues comes from the financial commitment path. Subsets (2) and (3) come from the reputational commitment path. In QCA terminology, the first subset has two conditions: absolute investment and relative investment in relation to target funding. These conditions could be used as indicators of the anchor investor's investment. The second subset has three conditions: the number of years that the anchor investor has been investing in startups, the number of investments that the anchor investor has made and the length of the anchor investor's resume displayed on the platform, used as a proxy of detail. The last subset consists of a single condition: the detail of the explanation as to why the anchor investor decided to invest. In addition, the anchor investor's identity as either an individual or a corporation is included in the analysis to add further detail to the configurational patterns. The three subsets of conditions are now described in more depth.
2.2.1 Disclosure of information on investments by the anchor investor
Just as the potential buyers in the used car market described by Akerlof (1970) knew little or nothing about car quality, potential crowdfunders require credible signals to combat the information asymmetries in this market. As noted by Pabst and Mohnen (2021), trust building through such signals is critical in crowdfunding platforms. Reputational intermediation, whereby car dealers provide warranties for used cars, was established in the used car market to prevent the “lemons” problem (Ibrahim, 2015). Likewise, in crowdfunding, the main role of such reputational intermediaries is to provide further assurance of fundraising campaign success to mitigate information asymmetries and incentivize investment.
The original lead investor–follower model was introduced by the US equity crowdfunding platform AngelList. Under this model, the crowd invests in the lead's syndicated operations (Agrawal et al., 2016). Shen et al. (2020) found that the amount of funds invested by anchor investors in the financing process matters. Thus, anchor investors' decisions trigger more investment from the crowd because these decisions are deemed to be informed and reliable. An analogy is the fact that entrepreneurs' investment in their own ventures or their decisions to retain more equity are seen as an indication of overall venture quality (Brealey et al., 1977; Vismara, 2016; Löher et al., 2018; Shen et al., 2020).
As argued by Agrawal et al. (2016), syndicates (i.e. the use of an anchor investor to whom the crowd is syndicated) help mitigate market failures by shifting the focus of the crowd's investment activities from startups (i.e. the entrepreneurs) to anchor investors. Li et al. (2016) identified information on lead or anchor investors as a peripheral cue. They observed a positive relationship between the lead investor's identity certification and the number of followers. However, they found a negative link between the percentage of money invested by the lead investor and the number of followers, probably due to the fear of collusion between the lead and the entrepreneur. In contrast, Shen et al. (2020) found that the share of the anchor investor's contribution in relation to target funding positively influenced campaign performance. Despite concerns about collusion, which affect the reputational commitment path to mitigating information asymmetries, a higher volume of investment by an anchor investor substantially reduces the amount of money still required to be raised. It thus makes it easier to achieve the funding target. Given these arguments, the following propositions are formulated.
High monetary contributions are conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
High relative levels of anchor investment with respect to the funding target are conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
2.2.2 Disclosure of information on the anchor investor's experience
Entrepreneurs' observable attributes have been recognized as valuable signals for the market (Hsu, 2007; Gimmon and Levie, 2010; Piva and Rossi-Lamastra, 2018). Regarding human capital, Piva and Rossi-Lamastra (2018) noted that entrepreneurial experience is a key factor for fundraising success in equity crowdfunding. Given that human capital is a key factor in funding new ventures, particularly young ones, firms with greater human capital (i.e. with higher expected efficiency) should attract more money (Zacharakis and Meyer, 2000; Colombo and Grilli, 2005; Unger et al., 2011; Barbi and Mattioli, 2019).
Arguably, just as the human capital of entrepreneurs who run fundraising campaigns is relevant to potential backers (Hunter, 1986; Ackerman and Humphreys, 1990; Baum et al., 2001; Ahlers et al., 2015), the human capital attributes of anchor investors are similarly important to trigger observational learning (see Unger et al., 2011). Specifically, in the same way that studies have identified entrepreneurs' crowdfunding experience as a source of credibility for potential backers (Courtney et al., 2017; Davis et al., 2017), anchor investors' experience can perhaps enhance credibility. Anchor investors are often experienced business angels or venture capitalists whose level of expertise in entrepreneurial investment is above the average among crowd investors (Xiao, 2020). Therefore, their judgment indicates venture quality. Investment by business angels and venture capitalists is crucial for startups to develop (Cánovas-Saiz et al., 2020).
As noted by Wang et al. (2019), angels' behavior in equity crowdfunding platforms can reduce information asymmetries, thus mitigating possible market inefficiencies. Platforms can enable the flow of information from angels (i.e. experienced individuals) working with such investments (see Maula et al., 2005; Ramadani, 2009; Mason et al., 2016) to the nonprofessional crowd (which is generally less experienced). They can thereby help the former convey information on venture quality (Agrawal et al., 2016). Research has not only confirmed the central role of angels in financing large ventures but also revealed the complementarity between business angels and crowd investors as a source of greater overall efficiency in highly uncertain and asymmetric information environments (Wang et al., 2019). Specifically, Shen et al. (2020) found that the lead investor's experience was positively related to fundraising success. Kim and Viswanathan (2019) concluded that experienced early investors within the app development crowdfunding market provide credible informational cues to the crowd regarding the quality of the project. This discussion leads to the following propositions.
A greater number of years of experience investing in startups are conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
A higher number of previous investments by the anchor investor are conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
A more detailed anchor investor resume (i.e. a longer resume) is conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
2.2.3 Disclosure of information explaining investment decisions
The rationale behind an anchor investor's decision to support a funding campaign financially and reputationally is vital for triggering observational learning among potential investors and thus rational herd behavior. Hence, anchor investors offer potential investors a reasoned explanation of their investment decisions. Some authors have found a positive relationship between an optimal word count in the written content displayed in a crowdfunding campaign and investment (Bi et al., 2017; Lagazio and Querci, 2018; Moy et al., 2018). This finding may imply that the word count of the explanation of an investment decision acts as a relevant informational cue. Bi et al. (2017) and Lagazio and Querci (2018) found that backers preferred detailed textual descriptions. However, an excessive word count may hinder the assessment of a project, disincentivizing backers (Moy et al., 2018).
Ultimately, it can be argued that an anchor investor's written endorsement of an entrepreneurial project can increase the perceived trustworthiness of the campaign for two reasons. It not only shows that someone has already committed considerable financial resources to that campaign but also provides a detailed justification of this investment decision. Accordingly, a comprehensive explanation of what motivated an anchor investor to place funds in a project may help build a trusting environment where an anchor investor's endorsement is seen as a credible informational cue.
A more detailed explanation of the anchor investor's decision to invest (i.e. a longer explanation) is conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
2.2.4 Anchor investor identity
One of the key methodological advancements of the current study is to provide configurational patterns of successful equity crowdfunding campaigns with either corporate or individual anchor investors. Previous research has identified the power of peer endorsement in attracting investment in crowd-based environments (Comeig et al., 2020). Hence, it could be argued that individual anchor investors would be deemed as more credible than corporate investors because prospective investors see themselves as more similar to individuals than corporations. Conversely, a corporate anchor investor may be seen as more mature or experienced (i.e. more reputed) by the crowd of potential investors (Lee et al., 2011). However, despite its importance for developing effective fundraising strategies in digital fundraising environments, the anchor investor's identity remains unexplored as an informational cue for imitation. This condition is included in Model 1 and Model 2 (i.e. the models of fundraising success and overfunding, respectively). The models thus explore how to improve the design of informational structures that effectively convey venture quality and informed imitation depending on the anchor investor's identity.
Both individual and corporate anchor investors are conducive to funding success and overfunding (i.e. exceeding the funding target by at least 10%) in syndicated co-investment campaigns.
3. Data and method
3.1 Data
The data were gathered from the website of the equity crowdfunding co-investment platform Startupxplore, based in Valencia, Spain. Startupxplore is a leading Spanish equity crowdfunding platform. It is legally constituted as Startupxplore PFP, S.L. and is authorized by the Spanish National Securities Market Commission (Comisión Nacional del Mercado de Valores, CNMV). In June 2016, only two years after its launch, Startupxplore became Europe's second largest community. At the time of writing, the platform has raised more than 14 million euros from 60 deals. Of the fundraising campaigns on Startupxplore, 85% have been successful, attracting investment from more than 70,000 investors. This crowdfunding platform was chosen to source the data because of its relevance in terms of the volume of funds it intermediates and the fact that most of its campaigns follow a syndicated crowdfunding model with an anchor investor.
The data covered all campaigns managed until late 2021, representing €9,804,879.06 in requested funding and €10,984,543.65 in raised funding. From this initial data set, campaigns with no anchor investor were discarded (representing 41% of the initial data set), as were those with missing data for any of the conditions included in the analysis (representing 15% of the initial data set). Therefore, the final sample was homogeneous in terms of intermediation by an anchor investor and the information displayed to prospective backers, with the same informational cues provided in all campaigns.
The sample comprised 24 syndicated equity crowdfunding financing operations carried out between 2016 and 2021. Requested funding amounted to €5,141,261.06, and raised funding totaled €5,695,426.90. The anchor investor provided an average share of 23.68% of the target funding for the sample. In absolute terms, the average funding provided by anchor investors was €51,855.75. In all transactions, the anchor investor was an organization or an individual male investor. No female anchor investors were found in either the sample or the original data set prior to filtering. Hence, gender was not considered in this study.
The data to perform the QCA included both dichotomous (i.e. 0 or 1) and fuzzy (i.e. continuous values ranging from 0 to 1) conditions (Sendra-Pons et al., 2022b). To calibrate fuzzy values, full membership was set at 20% above the mean, the cross-over point was set at the mean value and full nonmembership was set at 50% below the mean (Berné-Martínez et al., 2021). Publicly available data were collected by hand from Startupxplore. The authors processed the data themselves and were fully responsible for the data collection process. Table 1 explains both the outcomes and conditions. All data on the conditions were publicly displayed to all prospective backers.
3.2 Method
The method was based on two QCA models: Model 1 aimed at providing configurations of conditions resulting in campaign success [SUCC]; Model 2 aimed at providing configurations of conditions resulting in overfunding (i.e. raising at least 10% more than the target) [OVER].
Model 1:
Model 2:
QCA was first proposed by Charles Ragin (1987) as a case-oriented methodology leveraging certain valuable aspects of qualitative and quantitative methods. As explained by Marx et al. (2014), this methodology is inspired by John Stuart Mill's comparative tradition and is rooted in Boolean logic and set theory. In QCA terminology, the phenomenon under study is the “outcome”, and each independent variable is a “condition”. A combination of conditions is called a “configuration”. QCA allows for the study of configurational patterns leading to a certain outcome (Ragin, 2008; Rey-Martí et al., 2022).
The decision to use this methodology was primarily based on its suitability for the study of phenomena in the social sciences because it allows for equifinality and multifinality (Gerrits and Pagliarin, 2021). It has been widely used in business research and has the advantage of allowing for causal multiplicity (Sendra-Pons et al., 2022a). Hence, it can offer a useful way of studying reality. Moreover, it is particularly well suited to small- and medium-sized data sets ranging from 5 to 50 cases (Rihoux et al., 2013). Figure 3 summarizes the approach in this study by displaying the conditions included in Models 1 and 2.
4. Results
The results were obtained by applying QCA to the aforementioned models. Reporting begins with the analysis of necessary conditions. Table 2 reports this analysis for Model 1, where the outcome is the success of the fundraising campaign [SUCC], and Model 2, where the outcome is overfunding (i.e. raising at least 10% more than the target) [OVER].
The analysis of necessary conditions for Models 1 and 2 (see Table 2) shows that no condition is considered necessary for the presence of funding success [SUCC] and overfunding [OVER] in equity crowdfunding campaigns because consistency is below 0.9 in all cases. Even when these conditions are grouped (see notes to Table 2), the consistency is still less than 0.9. Therefore, the next step is to explore configurational patterns leading to the aforementioned outcomes.
Table 3 shows the parsimonious solution for Models 1 and 2, aimed at exploring funding success [SUCC] and overfunding [OVER]. Raw coverage refers to the percentage of the outcome that can be explained by a specific solution, whereas unique coverage refers to the percentage of the outcome that can be described by each condition within a causal configuration (Florea et al., 2019). The results show four configurations leading to funding success (i.e. achieving the funding goal within the predefined period) [Model 1]. There are also four configurations leading to overfunding (i.e. achieving at least 10% more than the target) [Model 2]. As expected in QCA, most conditions in the configurations are both present and absent (IDEN, YEAR, LRES and NINV). ABSC only appears as present and RELC as absent. Hence, Propositions 2, 3, 4 and 5 are rejected, while Propositions 1 and 7 are corroborated. Proposition 6 could not be tested because the corresponding condition did not appear in the configurations.
5. Discussion
Based on the previous results, this section follows two main lines of discussion. The first explores the configurational patterns resulting in funding success [Model 1]. The second explores the configurations resulting in overfunding [Model 2]. A distinction is made between the corporate versus individual identity of the anchor investor. Overall, the results suggest that there is causal complexity underlying the disclosure of information about the financial and reputational commitment of anchor investors.
5.1 Configurations leading to the success of entrepreneurial fundraising
One of the configurations of logically feasible conditions resulting in entrepreneurial fundraising success applies only to corporate anchor investors, another applies only to individual anchor investors, and the remaining two do not apply to a specific anchor investor identity. The configurations that apply to a particular anchor investor identity (corporate or individual) can be seen in Table 3. The theoretical and practical implications of these configurations differ depending on the identity of the anchor investor.
Finding 1: If the anchor investor is a corporate investor (i.e. a company not an individual), then the entrepreneurial fundraising campaign through equity crowdfunding can be successful even if the length of the anchor investor's resume shown on the crowdfunding website is short.
Finding 2: If the anchor investor is an individual, then the path to a successful fundraising campaign requires a much more extensive explanation of the anchor investor's resume than if the anchor investor is a corporation.
Finding 3: Two alternative paths apply to both corporate and individual anchor investors. When investment experience is low, then the absolute amount invested must be high. Conversely, if experience in entrepreneurial investment is extensive, then less disclosure may be given in the anchor investor's resume. Thus, if experience is limited (i.e. the investor has spent few years in entrepreneurial investment), then this relatively low experience should be complemented by a large absolute investment. If experience is extensive, less information can be provided in the anchor investor's resume.
These findings are especially interesting because they provide clear insights into the role of the anchor investor's identity. Findings 1 and 2 suggest that when the anchor investor is a corporate investor, it is less important to provide details on the anchor investor's curriculum vitae. The fact that the investor is a company has enough informational power to result in the success of the funding campaign. However, this informational power seems to be diluted when the anchor investor is an individual. Hence, more detail is required in the anchor investor's resume.
The raw coverage, which is the percentage of the outcome explained by a specific solution (Florea et al., 2019), implies that more than 40% of the outcome can be explained by Configuration 1. Configuration 2 explains approximately 15% of the outcome, Configuration 3 approximately 35% and Configuration 4 approximately 29%. Thus, most of the outcome is explained when the anchor investor is a corporation (Configuration 1), partly due to the dominance of this type of anchor investor. The next most explanatory configurations are Configuration 3 and Configuration 4, which are independent of the anchor investor identity. Configuration 2, which refers to individual anchor investors, explains the smallest percentage of the outcome (roughly 15%).
5.2 Configurations leading to the overfunding of entrepreneurial ventures
In reference to Model 2, all configurational paths resulting in overfunding (i.e. raising at least 10% more than the target) apply to a certain type of anchor investor (either corporate or individual). The first two (Configuration 5 and Configuration 6) refer to corporate anchor investors. The next two configurations (Configuration 7 and Configuration 8) refer to individual anchor investors. The configurations associated with corporate anchor investors can be seen in Table 3. Again, the theoretical and practical inferences from these configurations can be stated.
Finding 4: Overfunding in entrepreneurial fundraising (i.e. exceeding target funding by at least 10%) can be achieved even if the anchor investor's resume and experience are limited, as long as the anchor investor is a corporate anchor investor.
Finding 5: Overfunding can also be achieved when experience (number of years) in entrepreneurial investment is limited, as long as the number of previous investments by the anchor investor is high and the anchor investor is a corporation.
Finding 6: When the investor is an individual, overfunding can be achieved even when the relative size of investment by the anchor investor is low, as long as a detailed resume is provided.
Finding 7: Also, when the investor is an individual, overfunding can be achieved even if the anchor investor has made a small number of investments, as long as the anchor investor's experience is extensive in terms of number of years in entrepreneurial investment.
In summary, if the anchor investor is a corporation, overfunding can be achieved even if the resume provides little detail and the investor lacks experience or if experience is limited but the investor has made a large number of investments. If the anchor investor is an individual, the length of resume and experience matter to achieve overfunding, even if the relative size of investment is low or the investor has made few previous investments.
In terms of ranking how much of the outcome is explained by each configuration, the raw coverage again suggests that the configurations relating to corporate anchor investors are the most explanatory. Configuration 5 accounts for more than 40% of the explanation of the outcome, and Configuration 6 accounts for roughly 26%. Configuration 7 (roughly 14%) and Configuration 8 (roughly 15%) explain a smaller percentage of the outcome.
5.3 Visual representation of successful strategies
Overall, three configurations apply to corporate anchor investors, three to individual anchor investors and two to both types. Four configurations explain success in equity crowdfunding [SUCC], and another four explain overfunding in equity crowdfunding [OVER]. Besides investor identity, the most common conditions in the configurations (in terms of both presence and absence) are the number of years in entrepreneurial investment [YEAR] and the length of the anchor investor's resume [LRES]. Each of these conditions appears in five causal configurations (Configurations 3, 4, 5, 6 and 8 and Configurations 1, 2, 4, 5 and 7, respectively). The absence of YEAR and LRES appears three times for each condition, and the presence of each condition appears twice. Additionally, the presence of ABSC and the absence of RELC can be found in one configuration, and each of the presence and absence of NINV can be found in one configuration. LEXP does not appear in any configuration. Table 4 summarizes causal configurations leading to entrepreneurial fundraising success and overfunding.
6. Conclusions and limitations
The present study has certain theoretical and practical contributions. On the theoretical side, based on the theory of information asymmetries (Akerlof, 1970) and herd behavior theory, it contributes to a growing body of academic research on success factors of crowdfunding campaigns. On the practical side, it shows crowdfunding platforms which information is most relevant and informs potential investors about which informational elements to look for when searching for potentially successful investment projects.
To the authors' knowledge, this configurational study is one of the very few investigations exploring funding success and overfunding in online investment campaigns through syndicated equity crowdfunding. Crucially, the study accounts for the identity of the anchor investor (corporate vs. individual) to derive guidelines for campaign design. The study provides several core findings. (1) Corporate anchor investors have considerable power in driving herd behavior. Despite situations where the anchor investor's resume is poorly explained and experience is low, this power enables success or high success (Configurations 1, 5 and 6). (2) There is a need for a detailed resume when the investor is an individual. This situation was observed in Configurations 2 and 7, despite a low relative investment. There is also a need for a high number of previous investments when experience is low (Configuration 8). (3) In cases where the identity of the anchor investor is not specified, absolute investment matters when experience (years in entrepreneurial investment) is low (Configuration 3). When little information is disclosed about the anchor investor, the number of years of experience in entrepreneurial investment should be high (Configuration 4). Overall, disclosing information on an anchor investor's financial and reputational commitment is extremely valuable for reducing information asymmetries in equity crowdfunding.
This study has several limitations. (1) Although the sample was representative, the small sample size means that the results should be validated with larger samples. (2) The information provided in the anchor investor's resume and the explanation of the investment decision were characterized in a simplistic way, relying on word count. (3) The study focused on a specific type of crowdfunding, namely equity crowdfunding. Further research should seek to enlarge the sample and broaden the types of platforms considered, include discourse analysis with text processing techniques, and develop a theoretical model of anchor investor herd behavior in online financial and crowd-based environments for subsequent validation in an experimental setting.
7. Practical implications
The findings from this study have practical implications for entrepreneurs seeking funds through syndicated equity crowdfunding and for intermediary platforms. Financial technology, including crowdfunding, requires the study of information disclosure processes between those involved to ensure the optimal use of tools in digital ecosystems where there are large information asymmetries. For entrepreneurs, the use of anchor investors can considerably reduce the uncertainty and information asymmetries faced by potential backers (both ex ante or pre-investment and ex post or post-investment). This research sheds light on how anchor investors should manage information disclosure to show both financial and reputational commitment, and effectively attract investment.
Given the range of information disclosure options that can result in success or overfunding in a syndicated equity crowdfunding campaign, intermediary platforms should pay more attention to the anchor investor information they display in campaigns. This research reveals the informational cues that are important for both individual and corporate anchor investors. Active participation of platforms in the design of information displayed by anchor investors is important to maximize success and overfunding in syndicated equity crowdfunding campaigns. However, a word of caution is necessary. Overfunding can lead to cannibalization, whereby some campaigns lose visibility in favor of overfunded campaigns (Li et al., 2020).
Figures
Outcomes and conditions used in the study
| Definition | Codification | |
|---|---|---|
| Outcomes | ||
| Success [SUCC] | Whether campaign is successful (i.e. meets or exceeds target funding) | Crisp value |
| Overfunding [OVER] | Whether campaign exceeds the funding target by 10% or more | Crisp value |
| Conditions | ||
| Anchor investor's identity [IDEN] | Identity of anchor investor (1 = corporate anchor investor; 0 = individual anchor investor) | Crisp value |
| Anchor investor's absolute contribution [ABSC] | Euro denominated amount deposited by anchor investor in campaign | Fuzzy value |
| Anchor investor's relative contribution [RELC] | Ratio of anchor investor's investment in euros to campaign funding target (relative amount) | Fuzzy value |
| Years of experience [YEAR] | Years of experience in entrepreneurial fundraising | Fuzzy value |
| Number of investments [NINV] | Number of investments by anchor investor prior to campaign | Fuzzy value |
| Length of anchor investor resume [LRES] | Word count of anchor investor's resume | Fuzzy value |
| Length of anchor investor explanation of investment [LEXP] | Word count of explanation of anchor investor's decision to invest in campaign | Fuzzy value |
Note(s): Success [SUCC] was the outcome in Model 1 and overfunding [OVER] was the outcome in Model 2. Anchor investor's relative contribution [RELC] has been employed by Shen et al. (2020). Number of investments [NINV] has been employed by Li et al. (2021). Word count has been used as a metric numerous times in crowdfunding research (Bi et al., 2017; Lagazio and Querci, 2018; Moy et al., 2018)
Analysis of necessary conditions for Models 1 and 2
| Condition | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Outcome: SUCC | Outcome: OVER | |||
| Consistency | Coverage | Consistency | Coverage | |
| IDEN | 0.611111 | 0.785714 | 0.692308 | 0.642857 |
| ∼IDEN | 0.388889 | 0.700000 | 0.307692 | 0.400000 |
| ABSC | 0.532778 | 0.832465 | 0.358462 | 0.404514 |
| ∼ABSC | 0.467222 | 0.673878 | 0.641539 | 0.668269 |
| RELC | 0.630000 | 0.855849 | 0.497692 | 0.488302 |
| ∼RELC | 0.370000 | 0.619535 | 0.502308 | 0.607442 |
| YEAR | 0.316111 | 0.729487 | 0.269231 | 0.448718 |
| ∼YEAR | 0.683889 | 0.759877 | 0.730769 | 0.586420 |
| NINV | 0.298889 | 0.778582 | 0.288462 | 0.542692 |
| ∼NINV | 0.701111 | 0.73844 | 0.711538 | 0.541252 |
| LRES | 0.338889 | 0.604559 | 0.315385 | 0.406343 |
| ∼LRES | 0.661111 | 0.855500 | 0.684615 | 0.639827 |
| LEXP | 0.344444 | 0.668103 | 0.306923 | 0.429957 |
| ∼LEXP | 0.655556 | 0.801630 | 0.693077 | 0.612092 |
| ENDO | 0.642222 | 0.829268 | 0.506153 | 0.472023 |
| ∼ENDO | 0.357778 | 0.640159 | 0.493846 | 0.638171 |
| INFD | 0.594444 | 0.721510 | 0.576154 | 0.505057 |
| ∼INFD | 0.405556 | 0.796074 | 0.423846 | 0.600872 |
| EXPE | 0.533333 | 0.818414 | 0.506923 | 0.561807 |
| ∼EXPE | 0.466667 | 0.684597 | 0.493077 | 0.522412 |
Note(s): The symbol “∼” refers to the absence of a condition. For example, “∼ABSC” corresponds to a low level of absolute investment by the anchor investor [ABSC]. “ENDO”, “INFD” and “EXPE” conditions are combinations of various individual conditions. “ENDO” refers to “endowment” and comprises “ABSC” and “RELC”; “INFD” refers to “information disclosure” and comprises “LRES” and “LEXP”; and “EXPE” refers to “experience” and comprises “YEAR” and “NINV”
Parsimonious solution for Models 1 and 2
| Causal configuration | Raw coverage | Unique coverage | Consistency |
|---|---|---|---|
| Section A. Model 1 explaining crowdfunding success | |||
| [C1] IDEN*∼LRES | 0.425556 | 0.155000 | 0.998696 |
| [C2] ∼IDEN*LRES | 0.153333 | 0.104444 | 0.734043 |
| [C3] ABSC*∼YEAR | 0.354444 | 0.163333 | 0.864499 |
| [C4] ∼LRES*YEAR | 0.290556 | 0.161111 | 0.984934 |
| Note(s): Solution coverage: 0.903333→Solution consistency: 0.887554 | |||
| Section B. Model 2 explaining overfunding | |||
| [C5] IDEN*∼LRES*∼YEAR | 0.435385 | 0.265385 | 1 |
| [C6] IDEN*∼YEAR*NINV | 0.265385 | 0.095384 | 0.991379 |
| [C7] ∼IDEN*∼RELC*LRES | 0.135385 | 0.131539 | 0.649447 |
| [C8] ∼IDEN*YEAR*∼NINV | 0.147692 | 0.143846 | 0.668990 |
Note(s): Solution coverage: 0.81→ Solution consistency: 0.881171
Causal configurations leading to entrepreneurial fundraising success and overfunding
| Configuration No. | Success in equity crowdfunding [SUCC] | Overfunding in equity crowdfunding [OVER] | ||||||
|---|---|---|---|---|---|---|---|---|
| C1* | C2 | C3 | C4 | C5* | C6* | C7 | C8 | |
| IDEN | ● | ○ | ● | ● | ○ | ○ | ||
| ABSC | ● | |||||||
| RELC | ○ | |||||||
| YEAR | ○ | ● | ○ | ○ | ● | |||
| NINV | ● | ○ | ||||||
| LRES | ○ | ● | ○ | ○ | ● | |||
| LEXP | ||||||||
| Raw coverage | 0.425556 | 0.153333 | 0.354444 | 0.290556 | 0.435385 | 0.265385 | 0.135385 | 0.147692 |
| Unique coverage | 0.155000 | 0.104444 | 0.163333 | 0.161111 | 0.265385 | 0.095384 | 0.131539 | 0.143846 |
| Consistency | 0.998696 | 0.734043 | 0.864499 | 0.984934 | 1 | 0.991379 | 0.649447 | 0.668990 |
| Solution coverage | 0.903333 | 0.81 | ||||||
| Solution consistency | 0.887554 | 0.881171 | ||||||
Note(s): Configurations marked with an asterisk (*) refer to corporate anchor investors. “●” refers to the presence of a condition within the configuration. “○” refers to the absence of a condition
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Acknowledgements
Pau Sendra-Pons thanks the Spanish Ministry of Universities for funding under FPU 2019/00867 to support this research, and Fundación Cañada Blanch for funding to support a research stay at the London School of Economics (LSE), where this research was partially carried out. Alicia Mas-Tur thanks the Generalitat Valenciana for funding under Project GV/2021/121 to support this research.