Crowd Workers' Continued Participation Intention in Crowdsourcing Platforms

C. Wu, Gabriel Leung, Vincent Cho

OAI: oai:igi-global.com:272235 • DOI: 10.4018/JGIM.20211101.oa13

The micro-task crowdsourcing marketplace, as a novel platform, has provided firms with a new way to recruit employees at a reasonable cost and with a fast turnaround. This research explores how different types of motivations affect individuals’ continued participation intention in compensation-based micro-task crowdsourcing platforms. Our theoretical model builds on expectancy theory, self-determination theory, organizational justice theory and self-efficacy theory. To validate the theoretical model, over 1,000 crowd workers participating in Amazon’s Mechanical Turk completed an online questionnaire. Distributive justice and self-efficacy were applied to moderate the relationship between different types of motivations and continued participation intention. The confirmed three-way interaction effects indicated that external regulation and intrinsic motivation on continued participation intention are contingent on distributive justice and the level of self-efficacy. The findings enrich the understanding of MCS communities and provide important guidelines for motivating crowd workers.

Global Information Technology Computer Science and Information Technology IT Research and Theory