Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approach

Xuefeng Zhang (), Bengang Gong, Yaqin Cao, Yi Ding and Jiafu Su
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Xuefeng Zhang: Anhui Polytechnic University
Bengang Gong: Anhui Polytechnic University
Yaqin Cao: Anhui Polytechnic University
Yi Ding: Anhui Polytechnic University
Jiafu Su: Chongqing Technology and Business University

Electronic Commerce Research, 2022, vol. 22, issue 3, No 5, 842 pages

Abstract: Abstract In knowledge-intensive crowdsourcing (KI-C), estimating proper participants is an important way to ensure tasks crowdsourcing outcomes. Participants’ attributes (PAs) act as the main decision factors which are viewed as criteria for evaluating and estimating potential participants. Actually, multiple interdependent PAs have effect on participant estimation. It is an initial and vital work in estimating participants in KI-C to identify those PAs and measure their relationships. Consequently, this study first identifies PAs for participant estimation in KI-C by integrating PAs presented in the related academic studies and some practical KI-C sites. Subsequently, this study develops an integrated 2-tuple linguistic method and decision making trial and evaluation laboratory method to describe and measure causal relationships of the identified PAs. Identification of PAs would offer a common list of criteria for participant estimation in KI-C and aid to enrich studies in this field. Additionally, measurement of the PAs’ relationships through causality and prominence can assist requesters and managers of KI-C sites to understand and deal with those PAs in practical.

Keywords: Knowledge-intensive crowdsourcing; Participant estimation; Participants’ attributes; 2-tuple linguistic method; Decision making trial and evaluation laboratory (DEMATEL) (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10660-020-09408-1

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