An investigation of factors affecting the visits of online crowdsourcing and labor platforms

Nowadays, the economic activities have become increasingly digital since hundreds of millions of Internet users are using crowdsourcing platforms either to work at an online job as workers, or as a model of problem-solving and production as requesters. This growing workforce makes it necessary from the perspective of the online platforms, to fully understand the business dimensions of this emerging and innovative "online labor" phenomenon, which can rapidly change the future of work and work organization in the online world. This paper aims to investigate and analyze the visits of online labor platforms that offer crowdsourcing and crowdfunding services. Using websites' metrics data drawn from Alexa for the time period 2012-2016 the paper uses Ordinary Least Squares (OLS) and Fixed Effects (FE) regression analysis to examine correlations between visits and website characteristics. The research shows that the sessions of an online labor marketplace website from mobile devices have an increasing trend to be positively correlated to the quality mechanisms a website deploys as well as on location-dependent factors. The results are expected to provide insights on how the online labor website characteristics affect their traffic and thus inform about their evolution and improvement.