Developing a Forecast System for Hotel Occupancy Rate Using Integrated ARIMA Models

Abstract

International tourist hotels play a consequential role in the development of the tourism industry. The occupancy rate is usually considered a pertinent indicator in measuring the performance of the hotels. This study employs the ARIMA and ARIMA transfer function model incorporated with the Box-Cox transformation function for the forecasting of occupancy rate. The results of this research find two explanatory variables strongly affect the occupancy rate: one is the numbers of tourists and the other is the Taiwan/ Japan foreign exchange rate. The forecasting shows slow rising of the occupancy rate for the international hotels in Taiwan; it will reach 64.67% by the year 2000. The forecast of the occupancy rate provides important information tor both government agencies and hotel managers so that corresponding management strategies can be made.     

The impact of infectious diseases on hotel occupancy rate based on independent component analysis

The recent global outbreak of Influenza A (H1N1), or the more commonly known as swine flu, has negatively affected the tourism and hospitality industries in many countries. This article reports a study that applied independent component analysis, a novel statistical technique, to separate the dominant factors which determine the levels of hotel occupancy rates in Hong Kong. Empirical findings would provide useful insights on how the dynamic lodging demand reacts to epidemics based on the severity and duration of the events.     

国内游客对饭店服务质量评论的文本分析 --以e龙网的网友评论为例

本研究通过采用分层抽样法和系统抽样法抽取了易龙网上的10个城市的200家三-五星级饭店,运用文本分析法(content analysis)对每一家饭店的网友评价进行分类评分,最后运用SPSS统计软件对结果进行对比和统计.研究结果表明,中国国内旅游者同国际旅游者一样富有经验,他们对于总台接待、客房服务、房间大小这些与饭店星级紧密相关的因素非常重视.本研究为如何评价中国饭店业的服务质量提供了一个可行途径和有益借鉴.     

Determinants of Customer Satisfaction in the Hotel Industry: An Application of Online Review Analysis

This study illustrates that determinants of customer satisfaction in hospitality venues can be identified through an analysis of online reviews. Using text mining and content analysis of 42,668 online traveler reviews covering 774 star-rated hotels, the study found that transportation convenience, food and beverage management, convenience to tourist destinations and value for money are identified as excellent factors that customers booking both luxury and budget hotels consider important and for which the performance is much satisfactory to them. Customers paid more attention to, but were less satisfied with, bed, reception services and room size and decoration. Most determinants of customer satisfaction also showed a consensus over luxury versus budget hotels, except for factors referring to lobby and sound insulation. As per its findings, the article concludes by presenting theoretical and managerial implications.