| 英文摘要 |
Hotels have played a central role and nowadays been regarded as a basic and functional business within the tourism industry. It has been suggested that room rates affect, among others, hotel occupancy rate, customer satisfaction, and customer’s decision to select a hotel. Room rate pricing thus becomes a very important decision to make in the hotel business management. The present study attempts to develop a method that takes advantage of the existing database so that room rates may reflect hotel market position, perceived value, but are still within a reasonable and competitive price range. Hedonic pricing method has been employed by most of the previous studies to tackle this problem. A novel approach in which a machine learning technique (PNN) based on the Naïve Bayes classifier was used to predict hotel room rates. A total of 103 tourist hotels in Taiwan were used, and the PNN rendered a correct classification rate of 58.1% with the validation dataset. The correct rate increased to 87.1% when a prior probability computed with AHP was incorporated. Findings from this study suggest that this approach may assist hotel managers in adequately pricing room rates, and, in addition, it is well suited for cases whereas only small datasets are made available. Possible applications are discussed. |
