More than fun and money. Worker Motivation in Crowdsourcing - A Study on Mechanical Turk

Abstract

The payment in paid crowdsourcing markets like Amazon Mechanical Turk is very low, and still collected demographic data shows that the participants are a very diverse group including highly skilled full time workers. Many existing studies on their motivation are rudimental and not grounded on established motivation theory. Therefore, we adapt different models from classic motivation theory, work motivation theory and Open Source Software Development to crowdsourcing markets. The model is tested with a survey of 431 workers on Mechanical Turk. We find that the extrinsic motivational categories (immediate payoffs, delayed payoffs, social motivation) have a strong effect on the time spent on the platform. For many workers, however, intrinsic motivation aspects are more important, especially the different facets of enjoyment based motivation like "task autonomy" and "skill variety". Our contribution is a preliminary model based on established theory intended for the comparison of different crowdsourcing platforms.