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Abstract
Despite the increasing relevance of online social interactions on platforms, there is still little research on the temporal interaction dynamics between electronic word-of-mouth (eWOM, a form of opinion-based social interaction), popularity information (a form of action-based social interaction), and consumer decision making. Drawing on a panel data set of more than 23,300 crowdfunding campaigns from Indiegogo, we investigate the dynamic effects of these social interactions on consumers’ funding decisions using the panel vector autoregressive methodology. Our analysis shows that both eWOM and popularity information are critical influencing mechanisms in crowdfunding. However, our overarching finding is that eWOM surrounding crowdfunding campaigns on Indiegogo or Facebook has a significant yet substantially weaker predictive power than popularity information. We also find that whereas popularity information has a more immediate effect on consumers’ funding behavior, its effectiveness decays rather quickly, while the impact of eWOM recedes more slowly. This study contributes to the extant literature by (1) providing a more nuanced understanding of the dynamic effects of opinion-based and action-based social interactions, (2) unraveling both within-platform and cross-platform dynamics, and (3) showing that social interactions are perceived as quality indicators on crowdfunding platforms that help consumers reduce risks associated with their investment decisions. These results can help platform providers and complementors to stimulate contribution behavior and increase the prosperity of a platform.
Acknowledgments
The research was partially supported by a grant from the Dr. Werner Jackstädt Foundation in Germany (Grant no. 010103/56300720).
Supplemental File
Supplemental data for this article can be accessed on the publisher’s website at http://dx.doi.org/10.1080/07421222.2016.1243967
Notes
1. In our dataset, less than 7 percent of creators had set up more than two campaigns in the past. Out of the top twenty Indiegogo campaigns, seventeen were from first-time creators.
2. While the focus of our study is not on theorizing the differential effects of eWOM based on Facebook shares and comments on Indiegogo, we present notable differences in their effects in the results and discussion sections.
3. We argue that in reward-based crowdfunding, analyzing eWOM volume is more appropriate than eWOM valence, given that consumers write their eWOM messages before the reward has been received (e.g., the product being funded and still under development). It is therefore unlikely that the backer will have had a negative experience with the reward or the project before writing the message. The appearance of many and extremely negative eWOM messages is therefore unlikely. We checked and verified the important assumption that the valence of eWOM messages shared on crowdfunding platforms is mostly positive or neutral, such that we believe a focus on eWOM volume rather than valence is warranted in our study context (Appendix 2). Furthermore, as negative shares would not generate additional backers, our approach underestimates the true effect, as we treat all shares and comments equally.
4. As the average number of comments reported in might be driven by many zeros in the data, we ran four robustness checks that excluded projects with less than 2, 3, 4, or 5 comments to account for possible skewness. The results did not significantly deviate from our main model.
5. As a robustness check, we conducted the outlier analysis with various alternative threshold values for identifying unnatural peaks, but the results remained qualitatively unchanged.
6. We deliberately chose the number of backers as the measure for backers’ decision making behavior instead of the funding amount, for a few reasons. First, using the number of backers more adequately reflects backers’ decision making behavior and the dynamic relationships among the endogenous variables in the PVAR model because funding amounts may be distorted in several ways (e.g., if backers who are closely related to the project creator donate excessive amounts to the project; individual funding amounts are also arguably driven by the distinct rewards a project offers). Second, in the long run, knowing how many individuals are interested in a specific crowdfunding project and the respective product or service could be more relevant to the creator of the project than reaching a short-term financial goal. Third, using backers instead of funding amounts ensures that all three variables in the PVAR model are measured on the same ordinal scale. Finally, as a robustness check, we conducted the PVAR analysis with funding amounts (in U.S. dollars) as a substitute for the number of backers and obtained qualitatively consistent results.
7. As a robustness check, we followed Lin et al. [Citation51], who pointed out that studies with large sample sizes should not solely rely on p-values, as this might lead to a claim of support for hypotheses with no practical significance. We therefore followed their practice and provide coefficient/p-value/sample size (CPS) charts for the PVAR main analysis in Appendix 5 to illustrate that our results are not based on sample size but hold for random subsampling.
8. Campaigns tend to either surpass their funding goal (they are successful) or fail to do so (they are unsuccessful), by a large margin [Citation60].
9. In order to split the sample based on the average funding amount per campaign, we calculated the average funding amount per backer for each campaign. We then used a median split to turn the continuous variable (average funding amount per campaign) into a categorical one with the values “low” and “high” spending.
Additional information
Notes on contributors
Ferdinand Thies
Ferdinand Thies ([email protected]) is a Ph.D. candidate in Information Systems at Technische Universität Darmstadt (TU Darmstadt), Germany. He holds a Masters’ degree in Business Administration from the University of Munich. His research interests include software platforms and crowdfunding. His work has been published in international journals such as Decision Support Systems as well as conferences such as the International Conference on Information Systems and the European Conference on Information Systems.
Michael Wessel
Michael Wessel ([email protected]) is a Ph.D. candidate in Information Systems at Technische Universität Darmstadt (TU Darmstadt), Germany. He holds a Masters’ degree in Business Information Systems from the University of Amsterdam. His research interests include web personalization and crowdfunding. His work has been published in international journals such as Decision Support Systems as well as conferences such as the International Conference on Information Systems and the European Conference on Information Systems.
Alexander Benlian
Alexander Benlian ([email protected]; corresponding author) is a chaired professor of Information Systems, especially electronic services, at Technische Universität Darmstadt (TU Darmstadt), Germany. He holds a Ph.D. in business administration and management information systems from the University of Munich. His research interests include web personalization in e-commerce, digital business models, software platforms, and software-as-a-service. His work has been published in journals such as Journal of Management Information Systems, Journal of the Association for Information Systems, MIS Quarterly Executive, Journal of Service Research, Information Systems Journal, European Journal of Information Systems, Journal of Information Technology, Decision Support Systems, International Journal of Electronic Commerce, and others, as well as in the proceedings of conferences such as the International Conference on Information Systems (ICIS) and the European Conference on Information Systems (ECIS).