Journal article
Integrated BWM-Entropy weighting and MULTIMOORA method with probabilistic linguistic information for the evaluation of Waste Recycling Apps
Applied intelligence (Dordrecht, Netherlands)
21 Apr 2022
Abstract
Based on the encouragement and increasingly recycling demands of the Chinese governments, online recycling platforms based on B2C, such as loving recycling app, waste recycling alliance app, have emerged as the times require. As an indispensable part of online recycling, recycling app evaluation plays a vital role in user acceptance of the innovative recycling way. As we all know, app evaluation is a typical multiple criteria decision making (MCDM) problem involving many complicated criteria. This paper aims to propose an integrated MCDM method to solve this issue under a probabilistic linguistic context. Firstly, a special evaluation criteria system is constructed for measuring apps' performance, which includes five main criteria namely technical feature, safety, interface design, basic requirement, service quality, as well as 12 sub-criteria. Then, we integrated the best-worst method (BWM) and entropy method with the probabilistic linguistic term to determine the subjective and objective weights. And then the comprehensive weights are calculated by multiplicative integration method. Afterwards, the MULTIMOORA method with the Borda rule, is used to rank alternatives and identify the optimal recycling app. Finally, the assessment of the four recycling apps' performance in Beijing is presented to illustrate the validity and rationality of the proposed approach in practical applications.
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Details
- Title
- Integrated BWM-Entropy weighting and MULTIMOORA method with probabilistic linguistic information for the evaluation of Waste Recycling Apps
- Creators
- Yanfang Ma - Hebei University of TechnologyYuanyuan Zhao - Hebei University of TechnologyXiaoyu Wang - Southeast UniversityCuiying Feng - Zhejiang University of TechnologyXiaoyang Zhou - Xi'an Jiaotong UniversityBenjamin Lev - Drexel University
- Publication Details
- Applied intelligence (Dordrecht, Netherlands)
- Publisher
- Springer Nature
- Number of pages
- 24
- Grant note
- G2020202008 / Hebei Natural Science Foundation; Natural Science Foundation of Hebei Province 71871175; 71702167 / Natural Science Foundation Of China; National Natural Science Foundation of China (NSFC) 21GLB032 / National Social Science Foundation
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000784720600004
- Scopus ID
- 2-s2.0-85128790091
- Other Identifier
- 991019168219004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence