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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. The 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.

Crowd Workers’ Continued Participation Intention in Crowdsourcing Platforms: An Empirical Study in Compensation-Based Micro-Task Crowdsourcing