A novel q-rung orthopair fuzzy best-worst method, Shannon entropy and MARCOS method for mobile medical app service quality evaluation

Abstract

In this study, we propose a new hybrid multi-criteria decision-making (MCDM) methodology based on q-rung orthopair fuzzy sets (q-ROFSs) by integrating best-worst method (BWM), Shannon entropy method (SEM) and measurement of alternatives and ranking according to compromise solution (MARCOS). The methodology consists of two main phases: weight determination and alternative ranking. Firstly, the determination of the weight is mainly from both subjective and objective aspects. To determine the subjective weights, we propose a q-rung orthopair fuzzy best-worst method (q-ROF-BWM) satisfying additive consistency, which requires fewer comparisons for more reliable results, and define a new consistency ratio (CR) to test the consistency of comparisons. Additionally, objective weights are obtained using SEM extended to q-ROFSs, which are ignored by many MCDM methods. In the second stage, a new integrated alternative ranking approach called q-ROF-MARCOS is proposed, which does not become more complex with the increase in the number of criteria or alternatives. Finally, the proposed method is applied to the evaluation problem of mobile medical app service quality, and a new mobile medical app service quality evaluation index system based on SERVQUAL model is established. Sensitivity and comparative analysis validate the method’s effectiveness, rationality, superiority, and robustness.