A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing

Abstract

Estimating proper participants is an important way to ensure the quality of tasks’ outcomes in knowledge-intensive crowdsourcing (KI-C). The choice of a proper participant is a complex decision, involving large number of alternative participants, multiple and interrelated quantitative and qualitative criteria. In this study, we offer a methodology consisting of four stages to capture alternative participants and assess them using a combined fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Specifically, we initially identify alternative participants for a specific KI-C task from a pool of participants. Subsequently, four main participant’s attributes (PAs), i.e., interests, competence, reputation, and availability to participate, and their sub-PAs are identified as criteria for participant evaluation. Further, 2-tuple linguistic method and DEMATEL method is integrated to demonstrate the relations among PAs and to determine their weights, and TOPSIS is presented to evaluate and rank the alternative participants. Finally, an illustrative case is presented to delineate the implementation and effectiveness of the proposed approach. The results of application and comparative analysis show that the proposed methodology based on fuzzy DEMATEL and TOPSIS method is suitable, effective, and easy to use to estimate participants in KI-C.