Crowdwork has become a powerful tool for businesses and researchers to get work done in a fast, convenient, and cheap way. On one hand, literature suggests that high data quality can even be achieved with poor payment which has become common practice in crowdwork. On the other hand, recent research and ethical considerations suggest that poor payments and especially a low perceived fairness in pay may come at a price. Crowdworkers may put less effort in a task, stop working for a business/researcher, or even leave the crowdsourcing platform entirely. Therefore, we develop a model in this paper to understand how perceived fairness in pay is formed before task execution. If it is only measured after task execution, we miss the “voice” of those who did not even attempt to take on a task. Therefore, we test the effect of perceived fairness in pay on actual task execution and whether it changes after task execution.
Alpar, Paul and Osterbrink, Lars, "Antecedents of Perceived Fairness in Pay for Microtask Crowdwork" (2018). Research-in-Progress Papers. 32.