A Hybrid Model for Assessing the Performance of Medical Tourism: Integration of Bayesian BWM and Grey PROMETHEE-AL

Abstract

Medical tourism (MT) is the activity of traveling domestically or abroad to receive medical services. The scope of medical treatments covers dentistry, surgery, antiaging procedures, preventive medicine, and even some health-related treatments (meditation, physiotherapy, psychotherapy, addiction treatment, psychiatry, etc.). Due to the global boom in MT, governments are actively promoting MT packages to capture this huge business opportunity. However, what are the key factors that make MT development successful or unsuccessful? How can the performance of current MT operators be evaluated? And, how can the performance of underperforming operators be improved? This paper addresses these questions by proposing a MT assessment framework that summarizes the potential key factors of MT. In addition, this study proposes a model that integrates the Bayesian Best-Worst Method (Bayesian BWM) and grey Preference Ranking Organization Method for Enrichment Evaluations based on Aspiration Level (grey PROMETHEE-AL) to assess the performance of assessed MT operators. The Bayesian BWM not only aggregates the judgments of multiple experts but also generates a set of objective group criteria weights. Besides, the modified PROMETHEE incorporates the grey theory and aspiration level concept to increase the usefulness of the original PROMETHEE. The results of the analysis show that the two most critical criteria for MT are “the operators have cloud computing systems to analyze the travelers’ sensor data in real-time and accurately to provide customized medical services” and “multilingualism and communication skills of medical travel-related personnel.” Poor performers in the travel industry can be improved by prioritizing the criteria in order of importance. The management implications of this study can be used as a basis for performance evaluation by operators and government health care organizations.

Ecotourism branding in protected areas of Iran: Using an efficient hybrid multi‐criteria decision‐making method model

“…According to some researchers, although MCDM methods provide objective and qualitative data, when the number of pairwise comparisons used in these methods is high, the accuracy rate of the research decreases and causes a waste of time (Asadpourian et al, 2020; Demir et al, 2016; Kumar et al, 2020). The fact that the BWM method, which is a new MCDM method, requires less comparison and obtains more consistent and reliable results is emphasized in many studies (Gupta & Barua, 2018; Rezaei, 2015; Stević et al, 2017; Tajer & Demir, 2022; Yang et al, 2020, 2022). However, only one study was found in which ecotourism was determined using the BWM method in protected areas (Tajer & Demir, 2022).…”

“…The high number of comparisons in this pairwise comparison method leads to a decrease in the accuracy of the research and a waste of time (Jabrayilov, 2022;Xiao et al, 2022). For this reason, many studies emphasized that the BWM method, as another current MCDM method compared to AHP, requires less comparison and can obtain more consistent and reliable results (Gupta & Barua, 2018;Rezaei, 2015;Stevi c et al, 2017;Tajer & Demir, 2022;Yang et al, 2020Yang et al, , 2022. However, no research was found in which the BWM method and/or hybrid MCDM methods were used in protected areas and within the scope of ecotourism branding.…”

Assessment of Vietnam Tourism Recovery Strategies after COVID-19 Using Multi-Criteria Decision-Making Approach

“…Moreover, the combination of Bayesian BWM and grey Preference Ranking Organization Method for Enrichment Evaluations based on Aspiration Level (grey PROMETHEE-AL) models has been applied in medical tourism, bringing high economic value for businesses. A set of four key factors and nineteen sub-criteria was constructed to estimate the performance and feasibility of medical tourism operators [33].…”

Application of a Decision-Making Tool for Ranking Wellness Tourism Destinations

“…PROMETHEE has been successfully used in a number of interesting applications, including the selection of airport location [39,40], nature-based tourism [41,42], tourism competitiveness [43][44][45][46], supplier selection [47][48][49], medical tourism [36,50], transportation planning [51][52][53], renewable energy sources [54][55][56], aerospace industry [57][58][59], logistics [60][61][62][63], equipment selection [64][65][66][67], nanotechnology [68][69][70], medicine [71][72][73], and maritime transport [74][75][76][77], among other issues.…”