This study proposes the use of consistent fuzzy preference relations to evaluate the structure of hotel sustainable business model (HSBM) dimensions and the corresponding hierarchy of evaluation indicators, and predict the overall probability of success. As fuzzy preference relations require, a group of hotel professionals in Taiwan was asked to process pairwise comparisons using linguistic variables to determine the weights of dimensions and indicators. According to the results, finances were found to be the most important dimension, followed by human capital. The number of local cultural events in the hotel was identified as the most important indicator. The predictive values revealed the possibility for successful HSBM implementation, shedding light on the vision of sustainability for the hotel industry. The results of the present study contribute to the literature on sustainability by determining the importance and weights of dimensions and indicators for hotel business models, providing an example of the use of this strategic tool in generating and modifying sustainable business models for the hotel industry.

The marked decrease in the number of mainland tourists visiting Taiwan may turn from a temporary phenomenon to a normalized one because of the potential harm it represents to the Taiwan tourism industry. The purpose of this study is to use hierarchical cluster analysis combined with entropy to derive clustering and to identify appropriate performers by measuring operational performance levels. These clusters may serve as the benchmarks for under-performing tourist hotels (classified into five groups according to the numbers of employees) to improve in response to the reduced number of Chinese tourists coming to Taiwan. This study provides a different insight into the tourist hotels industry in benchmarking comparable to previous research groups. The measurement of five criteria has been both statistically different and significant for the clusters in order to support a view that the clusters contain hotels with a similar numbers of employees while maintaining distinct performance profiles and developing strategies for improvement.

The effectiveness of preference relations in modeling decision-making processes makes it one of the most common representations of information use for solving decision-making problems. This research presents the fuzzy incomplete linguistic preference relations (Fuzzy InLinPreRa) approach as evaluated by decision-makers dealing with increasing complexity and uncertain economics, as well as social and managerial problems. By using Fuzzy InLinPreRa, the consistency measurements of decision-makers’ evaluations will provide more accurate and reasonable solutions, allowing decision-makers to consider the objective weights of both the criteria and experts. An empirical example of the measurement of brand personality is included herein to demonstrate the feasibility of this method.