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While the economic benefits of the exhibition industry for the hotel sector have been addressed, the impact of exhibitions on individual hotels is unknown, especially when individual hotels’ star classification and locations are considered. This study aims to provide a better understanding of how room rates of different hotels change during different stages of the Canton Fair in China from a spatial-temporal perspective.

Room rates of 681 star-hotels within the city of Guangzhou before, during and after the Fair were extracted from websites. Through spatial interpolation and autocorrelation analysis and geographical detector (GeoDetector) technique, spatial and temporal patterns of hotel room rates and the interdependence between the convention center and the hotels with different star classification and locations were examined.

An inverse-U shape of room rate change was identified before, during and after the Fair, and the five-star hotels had the sharpest increase. Moreover, the distribution of hotel room rates followed the law of distance decay. The variation of hotel rates became larger when the distance to the convention center was larger. Spatial high-high clusters varied among hotels with different star classification.

This study contributed to the hotel literature by providing empirical evidence regarding how hotels with different star classification and locations were affected by events. This study also advanced the event literature by introducing GeoDetector. The findings of this study offered insights into the hotel location selection, pricing strategies and hotel collaboration with events.

虽然展览业对酒店业的经济效益已经得到解决, 但展览对单个酒店的影响尚不清楚, 尤其是在考虑单个酒店的星级和位置时。本研究旨在从时空角度更好地了解中国广交会不同阶段不同酒店的房价变化情况。

网站提取了广交会前、中、后广州市内681家星级酒店的房价。通过空间插值和自相关分析以及地理探测器（GeoDetector）技术, 研究了酒店房价的时空格局以及会议中心与不同星级和位置的酒店之间的相互依赖关系。

会前、会中、会后房价变化呈倒U型, 其中五星级酒店涨幅最大。此外, 酒店房价的分布遵循距离衰减规律。到会展中心的距离越远, 酒店价格的变化就越大。不同星级酒店的空间高-高集群存在差异。

该研究通过提供关于不同星级和位置的酒店如何受到事件影响的经验证据, 为酒店文献做出了贡献。这项研究还通过引入 GeoDetector 推进了事件文献。研究结果为酒店选址、定价策略和酒店与活动的合作提供了见解。

The internet has significantly changed the ways hotels distribute and price their products. The imminent success of online intermediaries caused financial problems for hotel chains since online travel agencies offered better prices than the hotel brand websites. The existing literature on hotel online distribution has focused on pricing strategies and room availability issues for different segments of hotels. This paper, however, aims to compare online room prices of global hotel chains across online distribution channels and their own brand websites.

By using only the internet, 2,800 room rates were collected and analyzed. Descriptive statistics such as means and percentage were used to answer the research questions. Personal interviews with a CEO of an e‐business company and an area revenue director of a global hotel chain were conducted to confirm our findings and to gain additional insights in the related issues.

Descriptive statistics indicated that US properties are doing a much better job than their international partners in regards to “best rate guarantee,” “rate parity,” and room availability across online channels. Rate consistency still remains a problem within US properties.

A limitation of this study is the use of convenience sampling methods, sample size, and currency conversion instruments.

Findings of this study would benefit revenue managers, general managers, hotel owners, and corporate brand managers to make decisions and to formulate new policies concerning their online distribution, revenue, and brand optimization strategies.

This study aims to examine online room rates in Hong Kong hotels. It focuses on comparing and contrasting the lowest hotel room rates that are available to customers.

Eight distribution channels and 45 hotels in Hong Kong were examined for online room rates in a 13‐month period from 2005 to 2006. The eight distribution channels represented different nature of operations including both indirect and direct distribution channels.

Empirical findings showed the web sites of local travel agents and local reservation agents offered the lowest online room rates, and that indirect distribution channels offered lower room rates than direct distribution channels.

A major limitation of this study is the geographic limitation of hotel selection.

Findings of the study are expected to provide insights for hoteliers to refine their online room rate strategy.

The objectives of this study are to investigate the relationship between hotel room demand and room rates, and to find a viable solution for the optimal room rate that maximizes the total profit.

There are various studies in the literature on how room rates affect profitability, and how the optimal room rate that maximizes the total revenue can be determined. Most of these studies assume an algebraic relationship between room rates and room demand, and obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts the alternative approach of using a model with a demand function that has been shown to be a superior causal forecasting model in some markets, and develops a new method to optimize the total profit.

The traditional method of applying calculus to the profit function based on a causal forecasting model leads to unrealistic solutions. This gives rise to the paradox that, on the one hand, there is a superior causal forecasting model based on room rates, but on the other hand, the traditional method does not yield a realistic solution for room rate optimization. This study analyzes the underlying causes of this paradox and proposes a method to resolve it.

The findings can be used by hotels to fine‐tune the room rates determined by conventional methods to arrive at a realistic and definitive value for the optimal room rate.

This study highlights the problems that arise with the traditional method of applying calculus to revenue and profit optimization and proposes a new method to resolve it.

Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method.

From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes.

The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts.

This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management.

This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.

The purpose of this paper is to analyze the implicit prices of hotel attributes in different time periods and different markets.

With data from the travel meta-search engine, this paper chose 3- to 5-star hotels in Beijing’s central business district and use hedonic price models.

The results suggest that the attributes with significant implicit prices differ in different time periods; the same attributes with different implicit prices in different time periods; the same attributes with different implicit prices in different market segments.

This study may help to explain the different findings on the relationship between the attributes and room rates of Chinese star-rated hotels in different time periods, and will be useful in both revenue optimization efforts and the design of new hotels projects.

The aim of this paper is to analyze and compare the effect of different hotel characteristics and room attributes on room rates of hotels in the cities of Lisbon and Porto, the capital and second most important city in Portugal.

Using the hedonic pricing method, hotel characteristics are decomposed and analyzed, giving us the perception of the impact of each hotel attributes on the room rates and the people’s willingness to pay for this. Ordinary least square regression analysis was applied to the hedonic price model to find which variables could explain differences in the hotel room rates in Lisbon and Porto.

The results suggest that in Lisbon and in Porto, a number of common characteristics have significant effects on consumer willingness to pay for a stay in a hotel as star rating, consumer rating and the room size. In Porto, the existence of a fitness centre and in Lisbon, the distance to the city centre are also attributes that create a premium in room rates.

The knowledge of the most valued characteristics by consumers is an important tool for hotel managers to define a price strategy. Also important is the knowledge of the attributes that provide more added value for consumers as these should be taken into account in new investment decisions.

This study highlights the implications of the way one defines and measures qualitative hotel characteristics in hedonic pricing. Although the hedonic method has been applied in several studies, to our knowledge, in Portugal, there are few studies applied only to hotel room rates, and there are no studies comparing hotel room rates in different Portuguese cities. Moreover, the research highlights the critical role of a proper definition and measurement of the variables in hedonic pricing in general and the hotel star in particular.

This study aims to measure the effect on prices through the differing characteristics and attributes of hotels.

A hedonic price model (HPM) was adopted to estimate the impact of various site and situational factors on hotel prices in Warsaw, Poland. To better understand room rates not explained by HPM, spatial analysis of residuals was used. Datasets regarding prices from three different online travel agents (OTAs) and star ratings, either official or provided by OTAs, were investigated.

A significant impact on hotel prices by star rating has been confirmed. Every additional star allows the hotel to set approximately 25 to 36 per cent higher prices, which is in line with previous studies. Moreover, two factors indicated a high but still underestimated theoretical hotel prices: location within the city centre and proximity to the international airport.

The results of this study suggest that hoteliers should use a spatial analysis of room rates offered by the competing enterprises. Moreover, managers are expected to verify their price tactics and policies according to the geographical determinants of hotel prices investigated.

The uniqueness of the study is highlighted by comparison of HPMs based on data from different OTAs, analysing differences in HPMs based on star ratings provided by OTAs and official systems and spatial analysis of residuals of estimated HPMs. Moreover, this study is among the first to examine the usage of HPM in the hospitality industry in East-Central Europe.

In recent years, neural networks have become popular in the scientific and business fields. In the hotel industry, researchers have recently devoted attention to the application of neural networks to the classification of tourist segments and the prediction of visitor behaviour. However, no previous attempt has been made to incorporate neural networks into hotel occupancy rate forecasting. This paper reports on a study about applying neural networks to the forecasting of room occupancy rates. The significance of this approach was tested with actual data from the Hong Kong hotel industry. Estimated room occupancy rates were compared with actual room occupancy rates. Experimental results indicate that using neural networks to forecast room occupancy rates outperforms multiple regression and naïve extrapolation, two commonly used forecasting approaches.

This study's goal is to examine how an intensified room rate signal affects customers' perceptions, their propensity to book, and consequently hotels' revenue maximization.

Subjects were asked to assess quality, sellout risk and the likelihood of getting a better deal based on an advanced booking scenario, where the signal's intensity was manipulated through the level of uncertainty. Analysis was conducted using a GLM multivariate procedure and a polytomous universal model.

The results suggest that high quoted rates might affect customers' perceptions, their propensity to book, and consequently the hotel's revenues. Surprisingly, the impact of the high room rate signal on the sellout risk perception reversed its direction with the intensified signal.

The results imply that the maximal achievable revenue levels reported in a previous theoretical study ought to be re‐calculated because the assumptions used were not supported by the study's findings.

The study demonstrated yet again that price affects the booking decision beyond the monetary value. It sends a signal that affects several perceptions and shapes the consumers' booking decision. Moreover, the intensity of the signal might change the direction of the impact.

The study is a first attempt to examine price signal intensity and the impact of the signal's strength in an advanced booking environment. The theoretical and practical implications underscore the need to better understand the complex impact that price changes have on consumer reactions and consequently on the effectiveness of hotels' revenue management policies.