Reveal or Conceal: How User Information Affects Crowdfunding Results
Crowdsourcing promises winning product ideas, lower customer service costs, support to launch new projects, and armies of brand evangelists. Has all of that panned out? Crowds can be uniquely effective in particular scenarios. Research on the crowdfunding phenomenon in particular reveals insight into the dynamics of group co-creation.
Peer influence can have a major impact on money raised via online crowdfunding platforms. Earlier contribution amounts create reference points that influence later contributors. Specifically, smaller early amounts pull down subsequent contributions whereas larger early amounts create benchmarks that increase subsequent contributions.
But some platforms allow users to conceal the amount of their contribution from other users. University of Minnesota faculty member Gord Burtch and his co-authors studied what happened when users decided to conceal this information.
Working with a major crowdfunding platform (one million users and presence in 190 countries), the researchers showed how users’ decisions to hide contribution information eliminated peer influence in the network. Advanced econometric techniques allowed the researchers to rule out alternative explanations and establish a clear cause/effect relationship.
Based on these results, the authors determined that the platform’s reveal/conceal mechanisms could be designed to nudge users in a desirable way. For example, the platform could conceal smaller contributions and reveal larger ones, by default. If users happen to be indifferent, and don’t bother to override the default setting, this would remove some undesirable peer influence (or promote peer influence when desirable).
In this study, changes to the platform’s design and features affected user behavior, and consequently, outcomes. The real dollars attached to those outcomes are clearly important to both the campaign organizers and the platform operators. Taken broadly, this study demonstrates the application of econometric techniques to historical data. Such techniques can help businesses identify actual cause-and-effect relationships that simple correlational analyses cannot.
Methods & Tools
- Advanced econometrics (e.g., instrumental variables and panel fixed effects)
- MySQL, Stata, Google Analytics