An integrated QFD and 2-tuple linguistic method for solution selection in crowdsourcing contests for innovative tasks

Solution selection plays an important role in crowdsourcing and is an imperative work for requesters. However, to the best of our knowledge, there is few studies focus on the problem of solution selection, especially in crowdsourcing contests for innovative tasks. This paper aims to develop a methodology incorporating quality function deployment (QFD) with 2-tuple linguistic method to assist requesters to select the right solution from a large pool of potential solutions efficiently. The methodology includes three phases. The first phase, i.e. pre-selection, is to screen potential solutions by employing the rule of non-compensatory. The second phase is to construct relationships between requester's requirements and solution features using quality function deployment (QFD), and further to determine the weights of solution features using 2-tuple linguistic weighted average operator and fuzzy weighted average method. The last phase is to evaluate the performance of potential solutions with respect to solution features, and further estimate their overall performance. Finally, an illustrative application case on the crowdsourcing platform-Taskcn is presented to demonstrate the implementation and effectiveness of the proposed approach.