Crowdsourcing in the form of human-based electronic services (people services) provides a powerful way of outsourcing tasks to a large crowd of remote workers over the Internet. Research has shown that multiple redundant results delivered by different workers can be aggregated in order to achieve a reliable result. However, basic implementations of this approach are rather inefficient as they multiply the effort for task execution and are not able to guarantee a certain quality level. In this paper, we are addressing these challenges by elaborating on a statistical approach for quality management of people services which we had previously proposed. The approach combines elements of statistical quality management with dynamic group decisions. We present a comprehensive statistical model that enhances our original work and makes it more transparent. We also provide an extendible toolkit that implements our model and facilitates its application to real-time experiments as well as to simulations. A quantitative analysis based on an optical character recognition (OCR) scenario confirms the efficiency and reach of our model. © 2012 World Scientific Publishing Company.
CITATION STYLE
Kern, R., Thies, H., Zirpins, C., & Satzger, G. (2012). Dynamic and goal-based quality management for human-based electronic services. In International Journal of Cooperative Information Systems (Vol. 21, pp. 3–29). https://doi.org/10.1142/S0218843012400011
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