An integrated probabilistic linguistic multiple attribute group decision making method for tourism mobile e-commerce service quality evaluation
The tourism industry is the business card of a city and an important source of financial revenue. As tourism is becoming increasingly popular, tourism satisfaction has become a key indicator for Yangzhou tourism. As an important business model, e-commerce has developed into a branch of mobile e-commerce. Mobile e-commerce can be combined with the tourism industry to provide services such as information access and city tours for tourism at the city level. The tourism mobile e-commerce service quality evaluation is multiple-attribute group decision-making (MAGDM). In recent times, Exponential TODIM (ExpTODIM) and VIKOR methods were employed to tackle the challenges of MAGDM. To handle uncertain data during the evaluation of tourism mobile e-commerce service quality, Probabilistic Linguistic Term Sets (PLTSs) have been adopted as a valuable tool. This paper presents the Probabilistic Linguistic ExpTODIM-VIKOR (PL-ExpTODIM-VIKOR) method as solution for MAGDM under PLTSs. Additionally, numerical case study is included to validate the proposed method within the context of evaluating the quality of tourism mobile e-commerce services. The primary contributions of this study can be summarized: (1) extension of ExpTODIM technique, based on VIKOR to PLTSs; (2) utilization of information entropy to derive weights based on expected values and deviation degree under PLTSs; (3) introduction of the PL-ExpTODIM-VIKOR technique to effectively manage MAGDM under PLTSs; and (4) execution of numerical study and comparative analysis to verify the effectiveness of PL-ExpTODIM-VIKOR in evaluating the quality of tourism mobile e-commerce services.
Authors
- Hong Li (ORCID: https://orcid.org/0009-0005-6299-1254)
Publication Details
- Journal
- International Journal of Knowledge-based and Intelligent Engineering Systems
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1177/13272314241295961
- Primary Topic
- Multi-Criteria Decision Making
- Type
- article
- Field-Weighted Citation Impact
- 0.00