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Who benefits from the wisdom of the crowd in crowdfunding? Assessing the benefits of user-generated and mass personal electronic word of mouth in computer-mediated financing

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Abstract

In this work, we explore the link between electronic word of mouth in the form of user-generated content (online forum interactions on Kickstarter) and through mass personal communication (sharing information through Facebook) on the performance of crowdfunding campaigns. Our formal theoretical model implies that the efficiency of electronic word of mouth is determined by the quality of the underlying crowdfunding campaign. Using a sample of 572 project observations, we test our theoretical predictions in cross-sectional logistic regression and ancillary Granger analyses. Our results highlight the interactive contingency of social media engagement and the success of the crowdfunding campaign. While a higher quality campaign is benefitting from user generated electronic word of mouth (online comments), the returns are diminishing. For mass personal electronic word of mouth (Facebook shares), we even find a reverse causal effect. Social media activity follows a successful campaign, but does not affect the success probability of the campaign. Crowdfunding campaigns need to approach their social media activities with a certain note of sensitivity to achieve the objective of successfully reaching their campaign goal.

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Notes

  1. https://www.kickstarter.com/projects/mytorch/torch-a-simple-router-for-digital-parenting/description (accessed 20th December 2017).

  2. This Beta distribution model goes back to Pearson (1925) and Skellam (1948) and has been applied in various fields, e.g. marketing, especially in stochastic models of buying behavior (Massy et al. (1970), pp. 61ff). See Lee and Lio (1999) for further applications and references.

  3. Related work shows that information about the project, the founder, or the technology could be relevant here (Bergh et al. 2014). We elaborate in more detail on the factors impacting the quality of the project in the empirical section.

  4. The model also accounts for the fact that signaling can naturally go both ways. While attesting to a projects quality may be helpful, afterthoughts and criticisms may be raised similarly in public. To allow for the possibility that comments reduce the chance that the campaign goal will be reached the parameter \(\beta\) is affected by the comment here as well; specifically we assume that \(\beta\) increases by \(\left( {1 - i} \right)h\). Receiving a comment from an investor about a project that may link to unwanted product characteristics, for example where \(i \to 0,\) may cast doubt on the viability (or attractiveness) of the project and as a consequence only \(\beta\) increases while \(\alpha\) remains by and large unchanged. The model thus cannot only be used to model the benefits of publicly available signals, but also presents potential to explore downsides of negative signals send.

  5. For projects with a very low level of perceived quality and/or a low signaling intensity, the derivative in (3) is negative, i.e. \(it\left( {\alpha + \beta } \right) - \alpha < 0\). In this case the additional signal send through publicly voiced support is also valuable but it is bad news so that the revised estimated probability is lower than the ex-ante estimated probability.

  6. https://www.kickstarter.com/charter; accessed November 13th 2016.

  7. We randomly assigned 70 projects to all raters. Out of these 70 project URLs, we expected at least 50 valid “consumer hardware” projects. In terms of selection, there were at least 5 failed projects (lower 25 % quartile of under-funded projects), 5 very successful projects (higher 25 % quartile of over-funded projects), and 60 projects that received more than 1 USD amount pledged randomly assigned. All respondents had to evaluate the same 10 projects and at minimum two respondents had to rate 40 randomly assigned projects.

  8. We use tertiles here to indicate low, medium, and high levels. In fact, projects are very rarely rated as 7 on the scales (only 26 observations). Hence, we grouped the tertiles into 1/2, 3/4, and 5/6/7. This results in the following distribution of observations (not accounted for missing values in other explanatory variables) across the tertiles 1st = 194, 2nd = 215, 3rd = 195. Given the equal size of the categories, they are also reasonably comparable

  9. With respect to comparing statistically significant and non-significant coefficients, we follow the extant literature on comparing coefficients within binary dependent variable models. Hoetker (2007): 338, for example, notes that “If the model is estimated separately for each group, the researcher can—at a minimum -compare the statistical significance of the coefficients across groups. This is possible because the coefficients and standard errors are consistent within each group. One could report, for example, that x has a significant and positive impact for Group 1, but is not significant for Group 2.”

  10. The Toda and Yamamoto (1995) approach sometimes may suffer power in small samples (Shukur and Mantalos 2000; Kurozumi and Yamamoto 2000; Zapata and Rambaldi 1997; Dolado and Lütkepohl 1996). In particular, the MWALD test in bootstrapped experiments with T = 25 performs relatively good in identifying causality (90.4 %) but indicated lower power in correctly identifying non-causality (49.2 %) (Zapata and Rambaldi 1997). However, Giles (1997), Chowdhury and Mavrotas (2006), as well as Mavrotas and Kelly (2001) show with similar bootstrapped simulations that the approach may work fairly well in small samples.

  11. We collected all comments made on the corresponding Kickstarter page. As such, there is a theoretical possibility that creator responses are also included in the comments. Yet, this somehow only proxies for more vivid discussions as a creator would not comment alone (let alone comment frequently) but rather his unsolicited “comments” are generally reflected as updates on the main page. We therefore believe that our measure of comments proxies the signals provided by the community rather than active creator engagement.

  12. Most of the series are difference stationary, i.e. I(1), when we apply the ADF, PP and KPSS tests, allowing for a drift and trend in each series. However, the variables pledged, pledged_high are found to be I(2) when considering a conclusive result of all three test approaches.

  13. Evidently, those that did not receive any comments and very little money pledged, show high correlations but cannot help to make inferences as variance in both, independent and dependent variables are missing. Other projects have varying days of the campaign, which may results comparing projects with unequal project length. This includes projects with 45 or 60 days, or even months with 30 days. We therefore opted to compare one common time frame. Hence, projects reported here all have a campaign length of 30 days.

  14. We thank an anonymous reviewer for extensive discussions on the heterogeneity of technology and the role of crowdsourcing rather than crowdfunding when product risk increases.

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Acknowledgements

The authors would like to thank the German Federal Ministry of Education and Research for supporting the project within the framework of the exploratory project "InnoFinance" (project number 01IO1702)

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  1. RWTH Aachen University, Kackertstraße 7, 52072, Aachen, Germany

    Jermain Kaminski & Christian Hopp

  2. Friedrich Schiller University of Jena, Carl-Zeiß-Straße 3, 07743, Jena, Germany

    Christian Lukas

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  1. Jermain Kaminski
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Correspondence to Christian Hopp.

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Kaminski, J., Hopp, C. & Lukas, C. Who benefits from the wisdom of the crowd in crowdfunding? Assessing the benefits of user-generated and mass personal electronic word of mouth in computer-mediated financing. J Bus Econ 88, 1133–1162 (2018). https://doi.org/10.1007/s11573-018-0899-3

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