Understanding Worker Perception of Pay Fairness on Microtask Crowdsourcing Platforms

Abstract

This thesis investigates the factors shaping worker perception of pay fairness on microtask crowdsourcing platforms. It employs a hybrid approach that integrates data-driven theory building with theory-driven sensemaking and empirical testing informed by organizational justice theory. Collecting and analyzing 14,553 reviews posted by Amazon Mechanical Turk workers on TurkerView, the study identifies seven latent themes each for positive and negative experiences and maps them onto distributive, procedural, and interactional justice dimensions. Distributive concerns (e.g., generous pay, bonus opportunities, easy earnings, and time efficiency) emerge as salient factors, while procedural and interactional factors reflect task design quality and requester communication. Predictive models, including stepwise econometric models and a DistilRoBERTa-based deep learning model, show that dense text embeddings capture fairness-relevant semantic information beyond topic-level abstractions in explaining pay fairness ratings. The findings advance understanding of pay fairness and inform platform design, requester practices, and labor policy in the gig economy.