Crowdsourcing and microtasks are a relatively new way to issue units of work to a large group of potential workers. This form of outsourcing to a vast on-demand workforce offers the potential to significantly change the way we work. But how can design impact how both the requester and the workforce interact and benefit from these tasks? This dissertation considers four aspects of microtask design: value, engagement, context, and complexity. Through four distinct, but highly related, investigations these four facets are ex- plored, analysed and synthesised into a considered review of microtask design. First we build a picture of the demographic and financial status of these crowdworkers by surveying the US-based crowdworker labour-force on the Amazon Mechanical Turk platform. This improved understanding of the value of crowd work, not just to requesters but to workers as well, is crucial to appropriately listing tasks in a commoditised labour market. Second, worker engagement is also a significant factor, not just in quality and cost, but also in uptake and effective completion speed. By introducing a new metric, conversion rate, and contrasting a variety of differing presentational and conceptual features across two demographics, we demonstrate an improved understanding of how tasks engage workers. The increasing use of mobile devices, including among crowdworkers, offers new opportunities to collect additional context about worker behaviour. Enhancing the data gathered by requesters can be used, not only to improve quality, but also to expand the types of tasks which can be effectively crowdsourced. This third contribution highlights enthusiasm by some workers for mobile tasks, and demon- strates how previously small-scale sensor-based data collection can increasingly be carried out by the crowd. Finally, the boundary between microtasks and macrotasks is investigated. Exploring how complex tasks, such as software development, can be successfully crowdsourced offers insight into how task design can influence suitability of these larger tasks on microtask markets.

The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and time SLOs. Initial implementation and system validation has shown that taking corrective action generally leads to a better success rate of reaching the SLOs. Having a system which can dynamically adjust internal parameters in order to perform better can lead to more harmonious interactions between humans and machine algorithms and lead to more efficient use of resources.