Integrated BWM-Entropy weighting and MULTIMOORA method with probabilistic linguistic information for the evaluation of Waste Recycling Apps

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.