Understanding the Use of HIT Catchers and Crowd Knowledge Sharing: A Case Study of Crowdworkers on Amazon Mechanical Turk

Crowdsourcing platforms have become a vital component of the modern digital economy, offering a wide range of HIT (Human Intelligence Task) opportunities to workers worldwide. Meanwhile, crowdworkers' use of scripting tools and their communication with each other are continuously shaping the entire crowdsourcing ecosystem. This thesis explores the use of HIT catchers by crowdworkers and their sharing of skill-based knowledge that facilitates the popularity of such scripting tools. It is revealed that the use of HIT catchers affects the completion speed and HIT-worker diversity for the whole HIT group, while depriving job opportunities from others. This potentially undermines the stability of the platform under the current reputation system relying on numbers of approvals and approval rates. Subsequently, another study explored how work strategies under the use of HIT catchers, including HIT acceptance, backlog, and completion, affect HIT availability, completion time, and result quality. The study also found differences in work behaviours between workers using and not using HIT catchers. Finally, this thesis investigates the skill-based knowledge sharing behaviour of crowdworkers, which promotes the blooming of scripting tools including HIT catchers, to improve the fairness of work opportunities and mitigate its negative impact on HIT completion. Using PLS-SEM, we assess the factors influencing knowledge sharing in the domain of skills. The study reveals the significance of high performance expectation, low effort expectation, and the joy and satisfaction in motivating the crowd skill-based knowledge sharing. Overall, this study provides an in-depth exploration around these two types of collective behaviour, highlighting the important role of tool use and knowledge sharing in shaping the crowdsourcing ecosystem.