Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

SUMMARY

The existing time series forecasting models either capture the informationof the last few data in the data series or the entire data series is used for projecting future values. In other words, the time series forecasting models are unable to take advantage of the last trend in the data series, which always have a direct influence on the estimated values. This paper proposes an improved extrapolative time series forecasting technique to compute future hotel occupancy rates. The performance of this new technique was tested with officially published room occupancy rates in Hong Kong. Forecasted room occupancy rates were compared with actual room occupancy rates in several accuracy performance dimensions. Empirical results indicate that the new technique is promising with reasonably good forecasting results.     

Data Mining For Hotel Occupancy Rate: An Independent Component Analysis Approach

The recent global financial crisis and the threat of a worldwide H1N1 influenza epidemic have greatly affected the tourism and hospitality industries around the world. Both hospitality practitioners and researchers are interested in finding analytical methods that enable forecasts to be made of hotel room demand under the uncertain conditions likely to affect the industry. In this article, a novel data mining technique called independent component analysis (ICA) is proposed to establish the major factors determining the hotel occupancy rate in Hong Kong. Then, extension of the model is suggested, incorporating these factors to decompose hotel occupancy rates and examine the effect of each factor on the hotel occupancy rate. Empirical findings show that outbreaks of infectious diseases, economic performance, and service price were the major determinants of the hotel occupancy rate in Hong Kong over the period studied.     

Hotel chain affiliation as an environmental performance strategy for luxury hotels

Stakeholders are increasingly aware of the potential of environmental performance to benefit their health; however, there is a paucity of sustainability studies addressing the relationship between corporate social responsibility (CSR) performance and the brand equity of hotel chains. Unlike traditional economics that treat undesired outputs such as carbon emissions as costless, this research presents an accurate, concise and clear empirical examination of hotel chain affiliation strategy through the Malmquist-Luenberger index to measure the brand competitiveness of the strategy while considering carbon emissions reduction using objective official panel data from Taiwan. The findings reveal that a group of hotel chains has a significantly higher average energy efficiency and branding value than those of a group of independent operators when holistic carbon emissions reduction is considered. Thus, this study encourages stakeholders to promote green hotel policies to independent hotel operators to achieve a higher brand value with lower carbon emissions and to adopt greater use of business intelligence to assist the decision-making of hotel operators in conformity with the United Nations (UN) Sustainable Development Goals (SDGs).