Abstract
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 demand deficiency due to a growing number of competitors flooding the market. For this reason, many 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. Potential contributions and future works are discussed towards the end.
Original language | English |
---|---|
Title of host publication | PACIS 2021 Proceedings |
Editors | Doug Vogel, Kathy Ning Shen, Pan Shan Ling, M.N. Ravishankar, Xi (Jacky) Zhang |
Number of pages | 8 |
Place of Publication | Atlanta, GA |
Publisher | Association for Information Systems. AIS Electronic Library (AISeL) |
Publication date | 2021 |
Article number | 484 |
Publication status | Published - 2021 |
Event | The 25th Pacific Asia Conference on Information Systems. PACIS 2021 - Virtual Duration: 12 Jul 2021 → 14 Jul 2021 Conference number: 25 https://www.pacis2021.org/ |
Conference
Conference | The 25th Pacific Asia Conference on Information Systems. PACIS 2021 |
---|---|
Number | 25 |
Location | Virtual |
Period | 12/07/2021 → 14/07/2021 |
Internet address |
Keywords
- Crowdsourcing platform
- Referral program
- Platform activeness
- Platform profitability