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|Title:||An analysis of online pricing behaviour of hotels in Hong Kong||Authors:||Mohammed, Ibrahim||Degree:||Ph.D.||Issue Date:||2015||Abstract:||In the past three decades, the hotel industry has witnessed an explosive growth of Internet Distribution Channels (IDCs). These channels have created a vibrant online market for hotel rooms and contributed significantly to the growth of online pricing studies. So far, existing studies have examined rate disparity among different channels and offered suggestions about the channel(s) that offer lowest prices. Other studies have also investigated the dynamic pricing structure of hotels and determined the best possible time to book a hotel room in advance. Although research interest in online pricing of hotel rooms is continuing to grow, there has been a limited attempt to characterize online pricing behaviour in a systematic way using rigorous methodologies. Especially, studies to quantify the frequency of price change, direction of price change and magnitude or size of price change are still lacking. There are also no studies to identify the factors influencing these behavioural price patterns. Yet, knowledge of this kind can contribute to the strategic decision-making of customers and revenue managers. Aiming to fill this void, this study sought to characterize online pricing behaviour of hotels in Hong Kong by examining: a) the frequency of price change; b) the pattern or direction of price change; and c) the magnitude of dynamic price dispersion within a booking window of seven days prior to check-in. The purpose was to identify market conditions, location characteristics and hotel attributes that can be used in conjunction with demand-based pricing policy to explain the possible heterogeneity in room pricing by different hotels. To address these goals, an extensive review of relevant literature was undertaken to develop an appropriate conceptual framework. The framework stipulates that pricing behaviour as described by frequency of change, direction of price change and magnitude of dynamic dispersion is spatially-dependent and influenced by market characteristics and product attributes which are reflected in locational and hotel characteristics. This framework is underpinned by the spatial agglomeration theory and structure conduct performance (SCP) theory. Appropriate to the data requirements of this study, comprehensive data were collected from different sources including an IDC (Kayak.com), Smith Travel Research (STR), the Hong Kong Tourism Board’s (HKTB) publications, and Google map. The duration of the data collection was for a period of six consecutive months, spanning from May 2014 to October 2014, a period which covers both the peak and off peak seasons. Within this period, the target days for the data collection were all Tuesdays and Saturdays. These days were purposively chosen as the typical days representing weekday (business guests) and weekend (leisure customers) businesses respectively. In the end, a balanced panel data of 126 hotels involving 26 Saturdays and 26 Tuesdays were obtained for analysis. Given the different objectives of this study and the varying properties of the data, three econometric panel data models were used. The first set of panel data models were the Poisson and Negative Binomial count data models, which were used to analyse the factors influencing the frequency of varying hotel room rates. The second set of models were the Logit and Probit models, which were used to determine the factors that make a hotel more or less likely to increase or decrease its room rate. The last set of models were the spatial models (including Spatial Autoregressive, Spatial Error model and Spatial Durbin model), which were used to examine the interaction effects between the size of a hotel’s room rate change and the effects from neighbouring hotels.
Primarily, the results of the analysis differed according to weekday and weekend. As such, the findings were presented along these lines to highlight the pricing behaviour of hotels towards leisure customers, who often stay on weekends, and business guests, who normally stay on weekdays. Based on the rankings of the hotels in terms of weekly average room rate, significant price mobility was found to be evident. That is, hotels moved up and down the cross sectional price distribution in a random fashion over time, suggesting that customers may not be able to learn from their past experience the hotels that offer the lowest or highest price. Examining the price mobility further, it was found that price fluctuations do not exhibit any consistent patterns either; room rates could go up, decline or remain unchanged. The noticeable difference however was that Saturday room rates were more likely to change frequently than those on Tuesdays. In terms of determinants, star rating, size and distance to the international airport were among the significant factors, besides demand, that influence the probability of a hotel increasing its room rate. In addition to these factors, seller density was also significant in influencing price fluctuations. Also, hotels in different administrative districts had different price fluctuations and tendencies to change price. Regarding the estimates from the spatial models, the results showed that the extent of dynamic price dispersion was positively related to market demand and the fluctuations in room rate of neighbouring hotels. Thus, hotels could be said to be practicing demand-based pricing and competitive pricing. Size of hotel, as in number of rooms, had a negative effect on the extent of dynamic price dispersion, an indication that because large-sized hotels have a lot of rooms to sell, their price variation was less substantial. Considering the findings of this study, four significant contributions to knowledge and practice can be identified. As the foremost contribution, the study has offered a comprehensive framework that academics and industry practitioners can apply to understand the factors influencing online pricing behaviour. This framework has been tested with a large volume of frequently-changing real data. Second, the study has extended the application of SCP to the field of hospitality and augmented it with spatial agglomeration theory. That is, by spatially modelling the extent of variation in room rates within the context of SCP and finding evidence to support spatial dependence, the unique contribution to the hospitality literature is that because hotels services must be consumed at the location of production, the traditional measures of competition which are not spatially-defined may not be as important in understanding hotels’ pricing behaviour as spatial competition which reflects Tobler’s first law of geography (i.e. everything is related to everything else, but near things are more related than distant things). Third, the findings have demonstrated the pricing behaviour of hotels in terms of frequency, direction and size as well as how these behaviours are related to market conditions, hotel characteristics and location attributes. In a sense, these findings can influence future hotel development as regards site selection. Last but not least, by providing empirical evidence to characterize online pricing behaviour, both hotel customers and managers can use this valuable information to enrich their knowledge and understanding of online pricing so that they can effectively make strategic decisions. In conclusion, much as this study has made some significant contributions to knowledge which can be used to improve RM practice, it has also revealed a number of viable opportunities for future research through its inherent delimitations.
Hotels -- Prices
Hotel management -- China -- Hong Kong
Hong Kong Polytechnic University -- Dissertations
|Award:||SHTM Best PhD Thesis Award||Pages:||xiii, 267 pages : illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/8151
Citations as of Jun 26, 2022
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