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Crowdfunding as a screener for collective investment

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Abstract

Crowdfunding uses a small amount of money from many people to fund a new venture. It is essentially a collective investment characterized by diversified investors, crowd-based decision-making, and consensus threshold. The sustainability of these markets heavily depends on the screening performance of the crowd. Although empirical studies suggest crowd-based funding decisions can be wise, little is known about the underlying rationale. This paper fills this void. We employ a computational experiment to investigate the impacts of investors’ attributes, i.e. ability and heterogeneity, and the threshold mechanisms on screening performance. We firstly identify the filtering effect of the consensus threshold. The high threshold screens out most of the unattractive projects and leads to less waste of capital, which makes All-Or-Nothing always outperform Keep-It-All. Further, we find a substitution effect of heterogeneity. When the group size is big enough, heterogeneous unsophisticated groups trump same-sized well-chosen ones. Lastly, the substitution effect of heterogeneity is moderated by the effectiveness of signals. In a crowdfunding environment of low predictability, investors’ heterogeneity contributes little to the collective accuracy, while investors’ expertise remains critical. In contrast to popular perceptions of crowdfunding markets as a new financing tool or marketing tool for entrepreneurs, our research indicates that crowdfunding is also an effective screener for collective investment.

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Notes

  1. Note that in AON the entrepreneur keeps nothing unless the goal is achieved, while in KIA the entrepreneur keeps the entire amount raised regardless of whether or not they meet their goal [20].

  2. The signal effectiveness refers to the extent to which a signal maintains validity in communicating underlying quality and reducing information asymmetry across a breadth of investors or domains in a variety of exchange contexts [51].

  3. The measurements of signal effectiveness are inconsistent in prior literature and most of them are qualitative. For example, Bjornsdottir and Rule [10] argue that the predictability of signals is associated with its visibility and representativeness. In Scheaf et al.’s [51] research, the degree to which a signal is effective is a function of the characteristics of the source and receivers of the signal. Based on the theory of statistics, we quantitively measure signal effectiveness by calculating the assessment model’s explanatory power.

  4. Two signal-based judgmental processes are studied in this paper: perceive the values of proximal signals and weigh the validity of signals. The former is related to the individual’s perception ability while the latter relies on the individual’s reasoning ability, and they are generally independent of each other.

  5. The investor makes a biased estimation for a project largely because he can not perfectly perceive signals and combine the perceived signals in the wrong way, which means the smaller the differences between the real and perceived signal values or the correct and adopted weights, the less biased the investor’s estimation. To precisely portray the two kinds of differences above, we chose root mean squared error (RMSE) as the operationalization of the investors’ ability owing to its useful property of penalizing strong deviations.

  6. We prefer the pairwise coorelations in that the variance can’t fully depict the heterogeneity among individual cognitions and fails to interpret the deeper evaluating process of investors in the crowdfunding context, while the pairwise correlations precisely characterize the extent of cognitive divergences among investors.

  7. It might seem that our ceiling of \(N = 20\) is not perfectly consistent with the practice that most crowdfunding projects usually are funded by hundreds of backers [43]. Considering the constraints of computing power, we specifically chose this modest range to distinguish from the decision-making literature that focuses on small groups (the range of group size includes 5 and 12), e.g. board of directors and jury [28,26]. Generalizing the experiment by scaling up the group size is a task left to future research.

  8. Most researches on human cognition shows that owing to individuals’ cognitive limitations and cue intercorrelations or information redundancy, judgments based on a finite set of cues can be predicted with a high degree of accuracy from a simple linear combination of three or fewer cues [52].

  9. See [28].

  10. Here we follows Karelaia and Hogarth [31] and Hastie and Kameda [26] in adopting a compensatory weight dispersion. Our later sensitivity analysis shows that changing the dispersion form of weight won’t affect our main results.

  11. To focus on the effects of investors’ characteristics and consensus threshold on screening performance, we first control the signal effectiveness of a median level. Subsequently, we reran our experiment with various environmental predictability to investigate the moderating effect of signal effectiveness.

  12. Most of the crowdfunding projects raises funds with moderate threshold. The projects with extremely low or unrealistic high threshold are still a minority (see [43]. Consistent with that, we set here the expected value of T as the mid-value of group size.

  13. According to the 3σ principle of the normal distribution, the probability that normally distributed stochastic variable x falls in the interval of \(\left[ {\mu - 3\sigma ,\mu + 3\sigma } \right]\) is 99.74%. That is, our setting of standard deviation of T almost guarantees the value of T falls in the interval of \(\left( {0,N} \right)\).

  14. Note that the crowd has no loss when the project they supported fails to reach the consensus threshold, in that their contributions will be returned in full under the threshold scheme. Those failed projects do not receive any pieces of collective funds, so they have no impact on the efficiency of collective investment.

  15. Panel (b) in Fig. 8 presents a benchmark case of \(R^{2} = 0.80\) (medium signal effectiveness) and facilitates the discussion of the moderating effect of signal effectiveness.

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Funding

This study is supported by the National Natural Science Foundation of China (71771081), National Natural Science Foundation of China (72071073), Natural Science Foundation of Hunan Province (2017JJ2037).

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  1. School of Economics and Trade, Hunan University, 109 Shijiachong Road, Yuelu District, Changsha, 410079, Hunan Province, China

    Sha Zhou, Tao Ma & Zhengchi Liu

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  1. Sha Zhou
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Correspondence to Zhengchi Liu.

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Zhou, S., Ma, T. & Liu, Z. Crowdfunding as a screener for collective investment. Electron Commer Res 21, 195–221 (2021). https://doi.org/10.1007/s10660-021-09461-4

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