Enabling flexible IT services by crowdsourcing: A method for estimating crowdsourcing participants

Abstract

Crowdsourcing has become an increasingly attractive practice for companies to execute business processes in open contexts with on-demand workforce and higher level of flexibility. One of the challenges is the identification of the best-fit crowdsourcing participant from a group of online candidates. This paper presents a method of AHP-TOPSIS based on Grey Relation Analysis for estimating participants of a crowdsourcing task based on their online profiles and proposals. This method is tested by an experiment on a dataset of 348 completed IT service crowdsourcing tasks. An analysis on the matching between the test result and the actual selection result reveals the accuracy and efficiency of this method. Companies can use this method to facilitate the quality control at the beginning of crowdsourcing and keeps the selection of participants easy. This paper contributes to the design of a software agent for crowdsourcing platforms to automatically rank the participants of a task.