Tackling Platform Competition in the Crowdsourcing: How Referral Programs Influence Activeness and Profitability

Crowdsourcing platforms are experiencing exponential growth in recent years. Despite their extraordinary capabilities in expediting resource allocation and engendering innovation, crowdsourcing platforms also suffer from declining demand due to a growing number of competitors flooding the market. Consequently, a number of crowdsourcing platforms have launched referral programs to incentivize existing customers to recruit new members. Although past studies have attested to the effects of referral programs on individual outcomes, there is a dearth of research that has examined the macro impact of such programs. To bridge this knowledge gap, we construct a four-year panel (i.e., two years before and after policy change) and employ interrupted time-series analysis to unravel the effects of referral programs on crowdsourcing platforms’ activeness and profitability. Additionally, we also take a closer look at whether these programs will influence the magnitude and variability of platform-level outcomes. Implications of our work are discussed towards the end.