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.

中文翻译：

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众工众包平台持续参与意愿

微任务众包市场作为一个新颖的平台，为企业提供了一种以合理成本和快速周转招聘员工的新方式。本研究探讨了不同类型的动机如何影响个人在基于薪酬的微任务众包平台中的持续参与意愿。我们的理论模型建立在期望理论、自我决定理论、组织正义理论和自我效能理论之上。为了验证理论模型，参与亚马逊 Mechanical Turk 的 1,000 多名群众工作者完成了在线问卷调查。分配公平和自我效能被用来调节不同类型的动机与持续参与意愿之间的关系。确认的三向交互作用表明，持续参与意愿的外部调节和内在动机取决于分配公平和自我效能水平。这些发现丰富了对 MCS 社区的理解，并为激励群众工作者提供了重要的指导方针。