Soft and hard information in equity crowdfunding: network effects in the digitalization of entrepreneurial finance

Capital markets face well-known information asymmetry and agency problems that create inefficiencies and impede investment decisions (Stigler, 1961; Jensen & Meckling, 1976; Merton, 1987; Spence, 1973; Myers, 2000). Adverse selection and moral hazard problems often result (Akerlof, 1970; Hellmann & Stiglitz, 2000; Myers & Majluf, 1984; Stiglitz & Rothschild, 1976). Consequently, potentially profitable investments may not be made because contracts that assign risks appropriately are difficult to draw up. These problems apply with particular severity to early stage entrepreneurial finance. In an ideal environment, the availability of information, its ready exchange, and ease of interpretation should reduce uncertainty. However, in early stage finance, investors know less than entrepreneurs about the new ventures in which they seek to acquire equity (Manigart & Wright, 2013). The investment process is information intensive, and investors in entrepreneurial ventures find information costly to acquire, pool, and interpret. Moreover, not only are the transaction costs of collecting accurate and relevant information high but the true risks of investing unfold only over time, often after the investment is made (Parker, 2009; Gompers & Lerner, 2001).

Over decades of collective practice, angels, venture capitalists, and other professional investors in private markets for equity have developed a suite of informal and formal tools to assess the quality of potential investments (Lerner, 1995; Gompers, 1995). The process is highly stylized and curated (Cosh et al., 2009; Parker, 2009; Hellmann et al., 2013; Leboeuf & Schwienbacher, 2018). It also relies heavily on the acquisition and interpretation of information both hard and soft. The conceptual difference between hard and soft information, which we argue is important to develop in the context of crowdfunding, brings into sharp relief the nature of certainty, verifiability, and meaning of underlying facts in the evaluation of new ventures (Bertomeu & Marinovic, 2016; Petersen, 2004; Liberti & Petersen, 2018). As we defined above, information is hard when it represents facts that are generally verifiable and cannot be changed in the short term and about which there is general agreement (e.g., the entrepreneur’s demographics data, the firm’s age, current size, location, and industry). Soft information (e.g., the firm’s valuation, growth potential) is changeable, more difficult to verify, and open to debate or alternative interpretations of its implications. This difference reflects more than the distinction between qualitative and quantitative information (Stein, 2002). That is, even if expressed quantitatively, information that is forward-looking, such as projected financial statements, or intangible and difficult to value such as brands and patents, is soft (Bertomeu & Marinovic, 2016). Hence, in the high uncertainly context of new ventures, successful investors develop industry expertise and syndicate with others not only to spread and diversify risks but also to access broader and deeper reservoirs of knowledge (Barringer & Harrison, 2000; Manigart et al., 2006; Wright & Lockett, 2003) to interpret the information they receive. Such investor communities engage in a sense-making process under conditions of high uncertainty (Weick, 1995). Sense-making allows investors to reduce complexity and come up with a “plausible understanding” of the investment opportunity. What has evolved is a relatively small, largely closed, and geographically proximate networks of investors with the capabilities to acquire and make sense of largely soft information. The inclusion of soft information in the decision-making process increases the likelihood that such investors will identify potentially successful opportunities, yet the majority of traditional equity investors face high cost of coordinating the acquisition and dissemination of soft information, which creates inefficiencies in the supply of capital to entrepreneurial firms.

Digital platforms, on the other hand, have the potential to greatly reduce the transaction costs of acquiring, disseminating, and interpreting information, including soft information (Goldfarb & Tucker, 2019; Acs et al., 2020). ECF platform architecture allows entrepreneurs and investors instantaneously and nearly costlessly to generate and pool their knowledge about a new venture (Cumming & Hornuf, 2018). Platforms require ventures to provide information about the entrepreneurs, the management team, and the business idea, as well as financial data about the current and future prospects of the company and a video explaining the business opportunity. The open access of the platform environment provides most entrepreneurs with the incentive to reveal as much information as they can and as accurately as possible because what they provide will be scrutinised by large numbers of potential investors. Thus, the architecture of the platform creates an environment where entrepreneurs reveal more hard facts and improve the quality and scale of soft information. Moreover, the information from the entrepreneur is freely available to the entire social network, which is to say to all potential investors. During ongoing pitch processes, investors also provide soft information. They reveal their willingness to pay for equity by pledging towards pitch targets. In doing so, they provide information about the current supply of funds for that project. Soft information can include the frequency with which investors view the pitch site, the number of investors following the pitch, and the nature and volume of discussion about the pitch. The architecture of the platform ensures that the entire network is rapidly informed and a process to make sense and interpret this soft information has the potential to commence.

Unlike the curated processes of traditional early stage entrepreneurial finance, the analysis and interpretation of information on ECF platforms are not constrained by time or location: exchanges can be both synchronous and asynchronous. Moreover, exchanges of information on ECF platforms leave digital traces (Nagle et al., 2020), unlike other investment processes where information is exchanged but is ephemeral that which is captured on ECF platforms is stored as artifacts and codified (Estrin & Khavul, 2016). Notably, such information can become part of the accumulated ECF process history and can influence the investment decisions of all participants in the network in the present and in the future. Nevertheless, uncertainty and information asymmetry remain in the digital space (Adner et al., 2019) and the investment process remains information intensive. However, the architecture of the ECF platform increases the volume and quality of the information provided to potential investors both through the revelation of incremental hard facts but especially by improving the availability and dissemination of soft information, for which transaction costs are inherently higher. As research on banking has shown, soft information, which is difficult to collect and transmit, benefits from a decentralized system because in the transmission across layers in an organization, and between organizations, key soft information insights about borrowers are lost (Cornée, 2019; Filomeni et al., 2021; Liberti & Petersen, 2018; Campbell, et al., 2019). Decentralizing decision-making to loan officers who were able to gather and make sense of the soft information resulted in more loans and higher performing loan portfolios (Liberti & Petersen, 2018; Gropp & Guettler, 2018). The distributed nature of the pitch process on ECF platforms allows that information to be explored, evaluated, and additional data revealed over the course of the investment window. ECF investor communities can engage in the sense-making and co-creation of meaning from the soft information to which they have access. Thus, the low costs of transmitting and exchanging information enable virtually costless transmission to all current and potential investors as well as rapid scaling of particularly soft information flows. On a scale not previously seen in entrepreneurial finance, soft information is easier to identify, disseminate, and subject to interpretation in order to assign to it meaning. Therefore, we argue that in this environment of high uncertainty, the architecture of ECF platforms can reduce the transaction costs of accumulating and interpreting soft information about new ventures, which helps investors evaluate among competing funding possibilities. We therefore hypothesize:

As we have seen, the curation of early stage investments in physical space incurs high transaction costs of information acquisition, pooling, and dissemination (Manigart & Wright, 2013). As a result, the scale at which early stage equity markets can operate is limited. In contrast, the architecture of ECF platforms strongly facilitates scaling of the network and can greatly amplify the impact of information flows (Evans & Schmalensee, 2016; Goldfarb & Tucker, 2019). Thus, digitalization of the investment process engages a much larger set of potential investors (Gomber et al., 2017). This allows ECF platforms to exploit economies of scale in the provision of information (Varian et al., 2004) as well as important network externalities (Katz & Shapiro, 1994). In a network, because network effects generate positive feedback loops (Arthur, 1990) and increasing returns to scale (Eisenmann et al., 2006), the value of an individual’s membership increases when another user joins. To be successful and stave off competition, social media-based platforms need to scale quickly and grow both sides of their networks. Acs et al. (2020) argue that the architecture of platforms primarily acts to facilitate matching via lower search costs, lower reproduction costs, and lower verification costs, relative to traditional market exchanges. Network effects mean that each of these costs is further reduced by network size (Goldfarb & Tucker, 2019). Network effects have been documented in numerous empirical studies across different industries (Birke, 2009; McIntyre & Subramaniam, 2009), but they are particularly pronounced in digital markets (Lambrecht et al., 2014). Digital two-sided platforms, like ECF, facilitate the matching of one group of users with another and show strong network effects (Evans & Schmalensee, 2016).

Two-sided platforms like ECF generate both direct (same side) and indirect (cross) network effects (Farrell & Saloner, 1988; Katz & Shapiro, 1994). The platform facilitates direct exchanges between investors, on the one side, and entrepreneurs, on the other (Evans, 2008; Rochet & Tirole, 2003, 2006; Rysman, 2009). Direct, or same-side, network effects in the ECF context occur when, for example, previous successful examples of funding in a sector, region, or peer group encourage other entrepreneurs to pitch or when the increasing size, knowledge, and experience of the investor network attracts more investors. Indirect, or cross network effects occur when a larger number and higher quality of entrepreneurs attract more investors, or the growing scale of the investor pool leads more entrepreneurs to seek funds in this way (Butticè et al., 2017)¹. Same-side investor network effects may improve members’ interpretation of information about the quality of the pitch (Spence, 2002). Such ideas also find resonance in the literature on how crowds or networks, composed of both experts and novices, impact investment decision-making (Budescu & Chen, 2015; Lin & Viswanathan, 2016; Mollick & Nanda, 2016; Müller-Trede et al., 2018; Palley & Soll, 2019). On ECF platforms, as new network members with similar investment goals join, the stock of complementary skills and knowledge within the network increases as does the opportunity for members to communicate with one another. Moreover, because the architecture of ECF platforms increases the ease of communication and reduces its costs, members of larger networks will have more connections through which to engage in pitch evaluation (Evans & Schmalensee, 2016). Hence, as the network size increases, the provision of entrepreneurial and investor information will be amplified. Same-side network effects also benefit from larger numbers of potential investors whose diverse but complementary knowledge can be brought to bear on the interpretation of information from entrepreneurs (Rochet & Tirole, 2003). Larger networks produce more investor generated information which likewise needs to be interpreted. Thus, as its size increases, the capability of the network to evaluate pitch quality also increases (Caillaud & Jullien, 2003). Since ECF platforms are efficient in lowering the cost of pooling and disseminating information (Vulkan et al., 2016), the larger the network, the greater the potential to amplify information.

However, the impact of network effects is not symmetric between hard and soft information. Moreover, because its collection, storage, and interpretation are standardized, hard information will be less dependent than soft information on the growth of the network. Hard information has less scope for interpretation; hence, there will be fewer benefits through network effects on understanding that type of information. Soft information, on the other hand, is grounded in experience and expressed as opinions, projections, and commentary. Since many firms raising equity capital via crowdfunding have only recently begun to operate, the quality and future profitability of their business model are extremely difficult to judge (Leboeuf & Schwienbacher, 2018) and investor judgements will draw relatively more on interpretation. Whereas hard information requires lower levels of interpretation from multiple perspectives, soft information benefits from a larger and wider pool of potential investors. Collectively, investors can extract meaning about the true nature of the underlying information (Weick, 1995) that soft information transmits and their ability to do so will be increased as network effects become more pronounced. In digital environments, the nonlinear increase in the exchange of information about pitch quality associated with the growth in the size of the network means that the ability of investors to use soft information to evaluate pitches increases with scale. Hence, larger networks amplify the exchange of information and the efficiency of the resulting matching process (Evans & Schmalensee, 2016), and this effect is more marked for soft than for hard information. This leads us to suggest that the complementarities as the network grows between information provision and size of the network will be more pronounced for soft information. Therefore, we hypothesize that,

How network size influences investor decisions can also be considered from the perspective of the accumulation of funding offers during each pitch. To some extent, with a focus on the shape of the accumulation function, this issue has been addressed in the literature. However, our concern is with the complementarities between information exchange and network effects. To this end, we must consider how to characterize the provision of information within the pitch. In capital market theory, this is often captured using an heuristic, adaptive expectations, whereby investors’ predictions about future outcomes are based on previously observed choices weighted by their time of occurrence (Barberis & Thaler, 2003; Hirshleifer, 2001). As in other capital market contexts, investors rely on recent historic data and information to help set their expectations (Bauman & Miller, 1997). Thus, the information about the pitch generated up to the date when the investor is making her decision is encapsulated in the history of previous investments and their weighting of previously received information.

As we have seen, an ECF platform taps into the network effects of two-sided platforms to reduce information asymmetries between investors and entrepreneurs. Unlike other early venture financing processes, the platform architecture is highly scalable and information is instantaneously available to all users. The platform can cheaply accommodate and exploit larger and larger networks. As new members join, the information contained within the network increases, as does communication between members. We have shown how same-side or direct network effects can improve the interpretation of information about the quality of the pitch, with network effects operating in a complementary fashion. Information can be amplified more effectively within a larger network. Hence, we propose that during the pitch, network effects can have multiplicative effects on the information from previous investments and further increase the supply of funds offered to entrepreneurs. As previously, this occurs because new information ripples faster and farther: larger networks will improve both the interpretation of information about past investments and the speed with which that information flows around the network. As the network increases in size, the quantity of information generated also increases, along with the opportunity for it to be discussed and evaluated by a set of investors whose expertise is increasingly likely to be more diverse.

Therefore, we expect that as the network increases in size, the quantity of information exchanged will increase as will the opportunity for it to be discussed and evaluated by investors whose expertise is more likely to be diverse. Moreover, larger networks increase the likelihood that multiple minority opinions will be presented, noticed, and assessed (Parker & Van Alstyne, 2005). Hence, we propose:

Our dataset captures the complete history of investments made by each of the investors in every equity pitch on the Crowdcube platform, 2011 to mid-2015; a network of 165,000 investors and all 835 pitches. A number of previous quantitative studies have used data gathered externally from the Crowdcube website. The samples vary in terms of the number of pitches included and the observational time period. For example, Vismara (2016) used 187 posted pitches on Crowdcube between 2011 and 2014, Walthoff-Borm et al. (2018) use a restricted set of 277 firms raising equity finance between 2012 and 2015; Vismara (2018) on 132 equity offerings in 2014; Cumming et al. (2021) 167 offerings between 2013 and 2016; Shafi (2019) used 200 campaigns between September 2015 and August of 2016; Cumming et al. (2019) used a sample of 491 offerings 2011–2015; Nguyen, Cox, and Rich (2019) 104 campaigns that between August 2015 and February 2016. Finally, Kleinert et al. (2020) examine 221 business plans from equity offerings between April 2017 and April 2018, and Ralcheva and Roosenboom (2020) whose observation window is 2012–2017 and includes 1303 Crowdcube campaigns. Our work builds on and extends previous research. We use internal platform archived investment data, which is de-identified for the purpose of the analysis. Thus, all of our variables and especially our dependent variables are actual amounts invested on the platform. Our underlying data are complete and account for such events as reversal of commitments (Meoli & Vismara, 2021). In addition, our measures of network effects depend on accurate counts of investors and investment events before and during the pitch window.

We estimate two equations. The first explores how soft information and network size affect the likelihood that a pitch is funded, namely, that offers to invest reach or exceed target of the amount that entrepreneurs request, while the second equation analyzes the impact of network externalities on information flow within each pitch.

The first estimating Equation (1) explores the determinants of success in pitches adding to the literature the unique perspective of the conceptual distinction between soft and hard information and additional data on archived data on networks. We follow the literature and model this process using a probit estimator (Maddala, 1992) to explain the likelihood that a particular pitch reaches (or exceeds) its investment target before the offers to supply funds expire. Variables in Equation (1) distinguish between soft and hard information, which allows us to test hypothesis 1, and include network effects as moderator, which allow us to test hypothesis 2. Thus,

Hypothesis 1 is tested via hierarchical model using changes in the log-likelihood and significance of the coefficients on the variables proxying for hard and soft information respectively. We propose that the effect on the probability of funding will be greater for the latter group of variables than for the former. Hypothesis 2 is also tested through Equation (1), by the sign and significance of the coefficients on the moderating effect of network size on the hard and soft information variables respectively. Hypothesis 2 implies that the moderating effects of network size on soft information will be greater than those on hard information.

Equation (2) describes the dynamic process driving the offer of funds by individual investors during each pitch, placed within the context of the pitches against which it was competing and the size of the network at the time. We propose that the information about the pitch generated up to the date when the marginal investor is making her decision to be encapsulated in the history of previous investments. We use an autoregressive (AR) model where the dependent variable is the daily accumulated funding amount per pitch. We therefore estimate an equation, specified here in a one period lag form², as: where I represent the investment into project i on day t, and e represents an iid error term. Hypothesis 3, concerning the moderating effect of network size on information flows within the pitch, implies that the coefficient β is significantly greater than zero.

The main results are reported in Tables 2 and 3. The specifications of Equation (1) are reported in Table 2. In order to test our hypotheses using hierarchical model testing, we present an expanded regression model set consisting of 9 models, which can be viewed in stages. We estimated a control model (1). Models (2–4) present the probit regressions for the main effects, independent, hard, and soft information variables. Model (5) adds to this the main effect of the network size moderator. Models (6–7) respectively include all hard and soft variable interactions as blocks, and models (8–9) present the fully specified models with all interactions: (model 8), at first, and then a final more parsimonious specification (model 9). The Wald Chi-square test shows that all the models are statistically significant at p<.001. The pseudo R-squared for all the models is reported and ranges from 0.080 in the control model (1) to 0.531 in the fully specified model (model 8) and 0.526 in the parsimonious model (model 9). The log-likelihood for each model is reported and the difference in log likelihood (chi-squared distributed) is used to test model fit for hierarchical groups of variables. In addition, Figures. 1-–5 show plots based on margins and help to interpret the interaction effects.

Table 3 contains the estimates for Equation (2). The table reports three models where the lagged investment amounts are captured in a single lag, as specified in Equation (2). In the second model, we add the direct effect of network size. In model (3), we further add the moderating effects of network size on the lagged dependent variable.

In Table 2, model (1) provides the baseline control model; model (2) adds the hard information independent variables as a group; model (3) adds the soft information as a block; and model (4) combines both. We test the relative effect of hard and soft information by comparing the change in log-likelihood between the baseline, model (1); the hard information variables in model (2) (79.77; 7df); and the soft information variables in model (3) (179.34; 5df). This reveals that at p<.001, model (2) is a significant improvement on the baseline model (1) but that model (3) fits significantly better than model (2). Thus, whether considered by itself in model (3) or jointly with hard information in model (4), soft information more significantly impacts the likelihood of a pitch being funded. Moreover, as the change in pseudo R² shows, the addition of soft information offers substantial incremental explanatory power to the model; the pseudo R² is 0.8 in model 1; 0.152 in model 2; 0.252 in model 3 and 0.32 in model 4. Thus, variables capturing soft information add more to the explanation of successful pitches on the ECF platform than variables capturing hard information, though they both have significant effects on the outcomes.

Looking at the coefficients of the variables, we note that larger firms (in terms of employee numbers) are more likely to be funded, as are firms that have raised money through crowdfunding before and have early investors in the pitch. However, the majority of hard information variables, such as the industry to which the firm belongs, the location of the firm, the gender of its founders, and the age of the firm, do not significantly affect the likelihood of the firm being funded. On the other hand, we observe that most of the indicators of soft information do significantly affect the likelihood of the pitch outcome. Firm valuation is negatively related (with p<.01) to the probability of pitch success. Firms that project a future of better business prospects as reflected in terms of projected employment growth are also more likely to be funded. Likewise, the largest amount invested during the pitch and the total number of investors who follow the pitch significantly predict funding (all at p<.01). Revaluation of the firm’s valuation during the pitch is significant when considered in model (4).

Hypothesis 2 proposes that the effects of the information generated on the likelihood of a firm being funded increases with the size of the network, more markedly for soft than hard information. We test this on estimates of Equation (1) in Table 2, models 6–9, and find strong support for the hypothesis. Model (6) additionally enters the hard information variables moderated by network size; model (7) does the same with soft information; model (8) includes both; and model 9 is a parsimonious representation of model 8. We observe that a number of the interactive effects between hard and soft information and network size are statistically significant. In terms of hard information variables, only firm size significantly interacts with network size to affect the likelihood of a pitch being funded. More consistently, we see that the interactions between network effects and soft information variables such as valuation, projected employment, largest amount invested, numbers following pitch, and revaluation of equity are significant. Assessing hard and soft information from a hierarchical approach, we find that the change between model (5) and model (6) is significant (51.62; df 7) but a corresponding change between model (5) and model (7) reflecting soft information suggests a much better fit of the model (179.28; df5). Moreover, the addition of soft information to model (6) results in a clear improvement to the fully specified model 8 (158.85; df5). Correspondingly, the change in pseudo R² moves from 0.371 to 0.372 between model 5 and 6, but to 0.500 in model 7. However, both soft and hard information are relevant in explaining pitch outcomes: the pseudo R² is even higher in model 8 at 0.531. Overall, the models are notable for explaining a significant portion of the variance in the likelihood of a pitch being funded.

Hypothesis 3 concerns the moderating effects of network size on lagged investment, which we argue encompasses the new information in the system during the pitch. It is tested on the results reported in Table 3, in which model 1 is the base model including a single lagged investment variable; model 2 adds the moderator, network size; and model 3 adds the interaction effect between the lagged investment and network size. We test hypothesis 3 using the results from model 3 of Table 3. We note that the coefficient on the interactive term between lagged investment and network size is positive and significant (p<.001) providing strong support for hypothesis 3.

We undertook a number of additional tests based on Equation (1). For example, within each category of information, we included the interactive effects singly as well as jointly as reported in Table 2. As noted above, we also ran specifications which excluded singly the soft and hard information variables within each category which were found not to be significant within this dataset. More importantly, the specification of the direct and moderating effects did not alter the sign and significance of the other main effects, despite the complexity of the specifications the structure of the results remains the same.

We conducted additional tests with soft information variables that are interesting operationalisations of the construct but are highly correlated with the soft variables we already included. For example, the number of forum posts that was offered for a given pitch is meaningful soft information but is highly correlated (r=.74) with the number of followers. In addition, we tested alternative specifications which instead of valuation use target amount of the raise and equity that the company is willing to give up. These additional variables all have predictable signs, but unsurprisingly given the collinearity, are sometimes not statistically significant. Finally, for the estimates of Equation (2), we investigated singly and jointly lag structures up to seven and have undertaken regressions including interactive effects for all lag structures up to five. The pattern of results was not affected, so we report the one lag structure.

Importantly, we also tested alternative specifications of the network size variables. These include the number of bidders in the network 3 months prior to the pitch, and specification; the number of investors who register on the platform immediately prior to the pitch launch. Moreover, we also tested the network size variable scaled by the number of pitches that are trying to raise money at the same time in order to capture the idea that as the network grows the number of entrepreneurs rises as well. These variables address concerns that the entire network built over four years may include many dormant members, who therefore do not contribute. The results from the specification of recent bidders are similar to network size, but the results for recent registrants are slightly stronger than for the other two. The conclusions with respect to our hypotheses are the same in these specifications.

We have argued that ECF is best understood as an innovation in the digitalization of entrepreneurial equity markets (Belleflamme et al., 2015; Rysman, 2009; Goldfarb & Tucker, 2019) We have identified how the microstructure of ECF reduces transaction costs and addresses market failures caused by informational asymmetries between entrepreneurs and investors (Nagle et al., 2020). While previous work has concentrated on signals, we focus on the distinction between soft and hard information. We argue that ECF reduces transaction costs via exchange of the latter because this is the informational problem caused by the uncertainty about the future prospects of entrepreneurial businesses, as well as by being scalable at low cost, allowing the investor community to benefit from network externalities. As a result, ECF platforms improve the matching between entrepreneurs looking for funding and investors looking for investments.

Our findings are consistent with these arguments. Soft information increases the probability of pitch success more than hard information. Soft information about the projected growth of the firm, the largest amount invested, and the number of investors following a pitch all have a positive effect on the likelihood of a pitch being funded. For example, as found in other studies (e.g., Moedl, 2020), the entrepreneur’s valuation of their business has a negative effect on the likelihood of pitch success. A higher valuation implies that the investor is paying more per share and is therefore being invited to accept a more ambitious business plan from the entrepreneur. In addition, a higher valuation implies that relatively more shares are offered for sale at a given price (Hornuf & Schwienbacher, 2017). This suggests that the dispersion of ownership will be larger, and the influence of any particular investor on the entrepreneur will be lower, which raises agency issues and might de-motivate investors who seek to influence entrepreneurial activity. As mentioned above, we made a preliminary exploration of agency effects by considering the effect of dual class shares (A and B shares) (see also Cumming et al., 2019). In some pitches, only if an investor purchased more than a minimum stake set by the entrepreneur would those shares be voting (A) shares. We included the A share threshold in our regressions, but the coefficient was not significant. This is probably because the lower the threshold for A shares, the greater the dispersion of effective ownership but at the same time, the more influence would be purchased from a given (below the threshold) share, and these factors are offsetting.

We turn next to analyzing the internal dynamics of the pitch. We suggest that information within the pitch can be modelled through an autoregressive process and propose that these dynamics are sensitive to network size; in particular that the impact of prior information is enhanced in larger networks. Our empirical work confirms this prediction: for example, in the single lag specification, the interaction between the lagged endogenous variable and network size has a significant positive effect. This finding is consistent with our previous results about the impact of the interaction between network size and the provision of information on the probability that a pitch will be funded. The result further strengthens the argument that factors acting to enhance the provision of information, whether they be via network externalities or larger actual information flows reduce the asymmetries of information between entrepreneurs and potential investors and enhance the prospects of successful matching.

The fact that larger networks are subject to economies of scale might lead to a concern that the investment dynamics during a pitch might be explosive as network size increases. By this, we mean that as the network grows, any initial investment gets more strongly taken up by other investors, so that the first investment generates a stream of subsequent ones, at an accelerating rate. For example, Agrawal et al. (2011) argue that, because the process of investment is sequential, with funders drawing on accumulated capital as a signal of quality, the pitch process may create an information cascade (Bikhchandani et al., 1992; Anderson & Holt, 1997; Zhang & Liu, 2012; Vismara, 2018). For Easley and Kleinberg (2010), information cascades represent a form of irrational herding behavior because they entail people making decisions on the basis of other peoples’ actions rather than their own information and judgement. Parker (2014) also highlights some of the irrational outcomes that may occur with path dependence in an equity crowdfunding context.

Our results are not consistent with that view. As one would expect, larger network size is associated with an increase in the effects of previous on current investment, but the effect tapers over time; the coefficient on the lagged endogenous variable(s) is always less than unity. For example, in model 3 of Table 3, the sum of the direct and interactive effects is 0.515 + 0.106 × network size. Within our sample, the network size is centered and in logs. At the maximum size of the network (1.67 in logs and centered), the sum of the adjustment effects is 0.692, which is much less than unity. This is again consistent with the argument that increased network size would also bring increased diversity of opinion and expertise.