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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 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.

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.

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.

Achieving a balance between occupancy and average room rate is now well recognised as a prime determinant of good rooms department performance. Yield management techniques are increasingly used to help maximise rooms department sales revenue. However, it can be argued that the complexity of making available different product offerings to different market segments requires not only consideration of the sales side of the profit equation but of the cost side as well. The nature of the variable costs of accommodation operations is examined and a case study approach is used to describe the contribution derived from different room types. These data are then used to construct a value engineering matrix which can identify successful and not so successful areas of operation and suggest ways of improving overall profit performance.

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 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 purpose of this study is to examine the influence of key hotel attributes on the room rates of selected hotels in the Greater Gaborone Region, Botswana.

Using hedonic pricing analysis, the effect of eight attributes collected from 80 standard double rooms on Booking.com in the area was analysed using quantile regression.

The estimated results from quantile regression suggested the importance of the 10th quantile as the best predictor of hotel room price distribution. Overall, the presence of a fitness centre and the availability of meeting and conference facilities were positively significant for the lowest- and premium-priced hotels, respectively.

The study advanced the literature in hedonic pricing models by confirming the applicability of hotel room rate attribute research in unexplored environments.

Hotel managers should be aware of the influence of key attributes, such as meeting and conference space availability and locational factors, on the pricing decisions of room rates in the Greater Gaborone Region. The study also presented opportunities for business-to-business marketing between hotel and tour operators in the region.

The study is one of the few that uses quantile regression in the hedonic pricing analysis of hotel room rates.

Channel coordination has become an essential part of researching hotel supply chain management practices. This paper develops an improved channel coordination approach to coordinate the profit distribution between hotels and online travel agencies (OTAs) achieved through an introduction of advertising fees. This direction further improves the decentralization of cooperation and achieves Pareto improvement to achieve mutual profitability.

The methodology used in this study involves Stackelberg game theory employed for the decision-making and analysis of both the hotel and OTA. The OTA, acting as the leader, offers a hotel a contract specifying the commission rate that the hotel will pay to the respective OTA. The hotel, acting as a follower, sets a self-interested room rate as a given response. A deterministic, price-sensitive linear demand function is utilized to derive possible analytical solutions once centralized, noncooperative decentralization and cooperative decentralized channel occurs.

Results show that a new channel coordination approach is possible, namely via advertising fees. Prior to channel coordination, the OTA tends to set a higher commission rate, and the hotel sets a higher room rate in response under noncooperative decentralization. As such, this results in a lower channel-wide profit for all. One way to reduce channel-wide profit loss is to use a method of cooperative decentralization, which can, and will result in optimal profit as centralization takes place. However, the lack of incentives makes cooperative decentralization unfeasible. Further improvement is possible by using advertising fees based on a cooperative decentralization agreement, which can reach Pareto improvement.

This paper helps the OTA industry and hotel owners cooperate by way of smoother coordination. This study provides practitioners with two important practical implications. The first one is that the coordination between the hotel industry and OTA through cooperative decentralization allows for the achievement of higher profitability than that of noncooperative decentralization. The second one is that this paper solves the outstanding problem of insufficient incentives characteristic of cooperative decentralization by means of an advertising fee as a new supply chain coordination approach.

This paper offers both the problem and solution regarding the lack of incentives that hamper cooperative decentralization without the use of advertising fees. This paper is unique in that it derives analytical solutions regarding commissions levied in a typical hotel supply chain under noncooperative decentralization.

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.