Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust
<|reference_start|>Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust: Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.<|reference_end|>
@article{montes2024teranga,
    title={Teranga Go!: Carpooling Collaborative Consumption Community with
  multi-criteria hesitant fuzzy linguistic term set opinions to build
  confidence and trust},
    author={Rosana Montes, Ana M. Sanchez, Pedro Villar, Francisco Herrera},
    journal={Applied Soft Computing 67, 2018, Pages 941-952},
    year={2024},
    doi={10.1016/j.asoc.2017.05.039},
    archivePrefix={arXiv},
    eprint={2403.05550},
    primaryClass={cs.CY cs.AI}
}