Work experiences on MTurk: Job satisfaction, turnover, and information sharing

Amazon's Mechanical Turk (MTurk) is an online marketplace for work, where Requesters post Human Intelligence Tasks (HITs) for Workers to complete for varying compensation. Past research has focused on the quality and generalizability of social and behavioral science research conducted using MTurk as a source of research participants. However, MTurk and other crowdsourcing platforms also exemplify trends toward extremely short-term contract work. We apply principles of industrial-organizational (I-O) psychology to investigate MTurk Worker job satisfaction, information sharing, and turnover. We also report the top best and worst Requester behaviors (e.g., building a relationship, unfair pay) that affect Worker satisfaction. Worker satisfaction was consistently negatively related to turnover as expected, indicating that this traditional variable operates similarly in the MTurk work context. However, few of the traditional predictors of job satisfaction were significant, signifying that new operational definitions or entirely new variables may be needed in order to adequately understand the experiences of crowdsourced workers. Coworker friendships consistently predicted information sharing among Workers. The findings of this study are useful for understanding the experiences of crowdsourced workers from the perspective of I-O psychology, as well as for researchers using MTurk as a recruitment tool.