Effects of Social Interaction Dynamics on Platforms

Ferdinand Thies, Michael Wessel, Alexander Benlian

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalJournal of Management Information Systems
Issue number3
Pages (from-to)843-873
Number of pages31
Publication statusPublished - 2016
Externally publishedYes


  • crowdfunding
  • Electronic word-of-mouth
  • eWOM
  • Informational cascades
  • Online platforms
  • Panel vector autoregression
  • Popularity information
  • Platform success
  • Reward-based crowdfunding

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