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Hotels are immersed in a very competitive environment and hoteliers have to plan and redesign their strategies to stay in the hospitality industry while faced with a steady rise in competition. Hoteliers can employ horizontal differentiation strategies and pricing decisions to gain a competitive advantage over their competitors. The goal of our work is to analyse the effect of pricing and horizontal differentiation strategy of a hotel on its online reputation and to analyse if the hotel location and agglomeration of competitors moderates their relationship with online reputation.

With a sample of 264 hotels from Paris, an empirical study is developed that aims to analyse, using regression techniques, the impact of price, differentiation, location and competitive environment on online ratings given by consumers in the hospitality context.

The paper provides empirical evidence of how a good location improves the online reputation of a company and how pricing strategies should take into account the location and number of competitors since a good location allows premium prices to be valued positively by consumers while an inappropriate location can produce the opposite effect. Depending on location, the number of competitors can intensify or reduce the effect of price on online reputation. Finally, online reputation only benefits from horizontal differentiation strategies when the degree of agglomeration is low.

This work provides insights about how hotels can strengthen their online reputation through pricing and differentiation strategies, incorporating elements of their environment such as location and competition in this decision-making process.

酒店處於一個競爭非常激烈的經營環境中，營運者要面對競爭日益加劇的挑戰，在這情況下，他們必須計劃並重新設計其策略，以在酒店服務業中仍能繼續其業務。營運者可採用水平分化策略和定價決策，以取得比同業更大的競爭優勢。本研究旨在分析，若酒店採用價格及水平差異之策略，這對它們的網上聲譽會起什麽效應；研究亦探討酒店所處的位置及競爭者的凝聚會否減弱其與網上聲譽的關係。

研究樣本為來自巴黎264間酒店。研究人員採用實驗研究法，透過迴歸分析法，去分析在酒店服務的背景下，價格、差異化、地點和競爭環境如何影響消費者所給予的網上評分。

本文提供了經驗性證據，證明良好的地點會改善公司的網絡聲譽； 同時，亦證明釐定價格策略時，經營者應考慮競爭者的位置和數目，這是因為良好的地點會讓消費者正面評價溢價，而不合適的地點則會導致相反的效應。根據所在的地點，消費者的數目能增強或減弱價格對網絡聲譽的影響。最後，本文也證明了網絡聲譽只會在凝聚程度低的情況下才會受惠於水平分化策略。

本研究提供啟示，使我們更明瞭酒店如何能透過價格及差異化策略，同時亦透過把它們的環境元素如地點和競爭等納入決策程序中，來強化網絡聲譽。

This research aims to reveal that accessibility of hotels to major points of attraction in the urban–territorial continuum of Catalonia, with its urban and coastal landscapes, has a direct impact on the dependent variables of customer satisfaction and average room rate.

The study collected data from 84 of 4-star hotel establishments, divided into urban and coastal hotels. Both coastal and urban hotels were differentiated depending on their distance to, the beach and the urban city center, respectively. Customer satisfaction and average room rate data were retrieved from online review platforms.

The study proves that hotels located in the urban–territorial continuum of Catalonia have different behaviors in terms of customer satisfaction and hotel pricing according to the variables of distance and landscape. The study shows that room rate and customer satisfaction are both higher in urban landscapes than in coastal landscapes. Urban hotels present significant differences in their levels of customer satisfaction and room rates depending on their location in the city of Barcelona. However, coastal hotels do not represent significant differences in room rates depending on their location, although they do represent significant differences in terms of customer satisfaction.

The originality of this study is based on how a dynamic urban–territorial model consisting of Barcelona and the Maresme coast, the hotel location and distances to main interest points impact in the variables of customer satisfaction and average room rate.

This paper aims to study the determinants of hotel performance, especially the role of location, in the Spanish hotel market.

The sample is composed of 1,034 hotels located in 97 tourist destinations in Spain during the period 2005-2011. The estimations were made by generalised least squares using panel data.

Overall, the results show that hotel attributes are the main determinant of performance. In particular, there is a minimum efficient scale in the hotel business. Location is the second most important determinant. This paper confirms that geographical location models, agglomeration models and competition models are relevant in the study of the effect of location on hotel performance. Regarding management practices, the performance is positively affected by good asset management.

Hotel managers can improve the total net revenue per available room by individually making decisions regarding its characteristics and management practices, especially size and asset efficiency. Moreover, they can collaborate with others (managers and policymakers) to manage tourist destination factors, particularly, demand level, accessibility, negative externalities and market concentration.

This research includes hotel characteristics, management practices and location as determinants of performance, by providing a broader framework of analysis than in previous studies. Regarding location, the empirical analysis considers simultaneously geographical location models, agglomeration models and competition models. The paper studies the Spanish hotel market, which is very important worldwide and which has heterogeneous tourist destinations, thereby making it a good context to analyse the relationship between location and performance.

This study aims to explore the propensity of adoption of ICT‐based facilities by hotels in the hospitality sector.

The factors that influence a hotel's propensity to adopt ICTs are broadly divided into location‐related and firm‐related. The relative impact of the factors on the ICT adoption propensity was analyzed through a survey amongst 95 hotels from seven locations in Thailand.

The findings give valuable insights for the marketers of ICTs who wish to gain entry into the hotel sector, and help them in better focusing their sales and marketing efforts.

The study provides researchers and suppliers of ICT‐based facilities with a framework for predicting the adoption propensity of potential customers.

The purpose of this study is to compare the spatial distribution of Airbnb and hotels in London and examine the relationship between demographic, socioeconomic and environmental factors and the supply of these two types of lodging supply.

Local information of Airbnb listings in London was collected through Insideairbnb.com. Gathered data were examined using geo-spatial auto-correlation analysis and spatial econometric models.

The results indicate that Airbnb predominates in the areas around popular tourist attractions and the peripheral areas of the city, while in the downtown area Airbnb and hotels are in the state of coexistence. The mono-centric model and the agglomeration model could be extended to the context of peer-to-peer accommodation. The location of Airbnb and traditional hotels capitalizes on different factors.

The study is based on secondary data due to data availability. And, it is based on the case of London, so the findings may not reflect the situation of small cities and rural destinations.

This study not only gives suggestions for local councils to regulate the location of hotels and Airbnb but also provides professional landlords with reference to choosing Airbnb location.

This study extends the hotel location theoretical models into the context of Airbnb and sheds lights on the distinction between these two business models in terms of location factors.

爱彼迎和酒店的位置：空间分布和关系

本研究旨在比较伦敦Airbnb和酒店的空间分布, 并探讨人口、社会经济和环境因素与这两类住宿供给的关系。

伦敦Airbnb房源信息通过Insideairbnb.com网站收集, 采用地理空间自相关分析和空间经济计量模型进行分析。

结果表明Airbnb主要集中在旅游景点周边和城市周边地区, 在市中心地区, Airbnb与酒店处于共存状态。单中心模型和聚集模型适用于共享住宿情境。Airbnb和传统酒店的空间分布影响因素的重要性存在差异。

本研究将酒店区位理论模型扩展到Airbnb情境下, 并揭示了这两种商业模式在区位因素方面的区别。

本研究采用二手数据。案例地为伦敦, 因此研究结果可能无法反映小城市和乡村旅游目的地的情况。

地方政府应更加重视专业公司的劳动力保障并限制旅游景点周边的短期租赁。

Ubicación de los hoteles y preferencias: distribución espacial y relaciones

El propósito de este estudio es comparar la distribución espacial de Airbnb y los hoteles en Londres y examinar la relación entre los factores demográficos, socioeconómicos y ambientales y la oferta de estos dos tipos de oferta de alojamiento.

La información local de los anuncios de Airbnb en Londres se recopiló a través de Insideairbnb.com. Los datos recopilados se examinaron mediante análisis de autocorrelación geoespacial y modelos econométricos espaciales.

Los resultados indican que Airbnb predomina en las zonas aledañas a los atractivos turísticos populares y las zonas periféricas de la ciudad, mientras que en la zona centro Airbnb y los hoteles se encuentran en estado de convivencia. El modelo monocéntrico y el modelo de aglomeración podrían extenderse al contexto de la acomodación entre pares. La ubicación de Airbnb y los hoteles tradicionales se basa en diferentes factores.

Este estudio amplía los modelos teóricos de ubicación de hoteles en el contexto de Airbnb y arroja luz sobre la distinción entre estos dos modelos comerciales en términos de factores de ubicación.

El estudio se basa en datos secundarios debido a la disponibilidad de datos. Y se basa en el caso de Londres, por lo que los hallazgos pueden no reflejar la situación de las ciudades pequeñas y los destinos rurales.

Los hallazgos sugieren que las autoridades locales deberían prestar más atención a la seguridad laboral de las empresas profesionales y restringir los alquileres a corto plazo alrededor de las atracciones turísticas.

Operations management involves utilizing given resources as efficiently as possible to deliver services to customers and meet business goals. Developing efficient business operations requires a hospitality organization to design efficient service environments, as part of its mission. This chapter articulates the key design and planning strategies for the development of a successful hospitality organization. The first section covers the process of location selection, as the most important factor leading to the success of a hospitality organization. The second section discusses strategies for estimating the number of users (service employees and customers) during peak and idle times to justify a costly financial investment. The third section concerns the readers with the topic of layout planning, with the goal of service optimization for a targeted number of customers. The fourth section deliberates workflow conditions, and finally, the last section addresses the ambience and design of the physical hospitality environment, which is crucial for customer evaluation of a hospitality organization as it creates a first impression.

The aim of this paper is to examine the impact of three main technologies on converting browsers into customers: impact of review rating (location rating and service rating), recommendation and search listings.

This paper estimates conversion rate model parameters using a quasi-likelihood method with the Bernoulli log-likelihood function and parametric regression model based on the beta distribution.

The results show that a high rank in search listings, a high number of recommendations and location rating have a significant and positive impact on conversion rates. However, service rating and star rating do not have a significant effect on conversion rate. Furthermore, room price and hotel size are negatively associated with conversion rate. It was also found that a high rank in search listings, a high number of recommendations and location rating increase online hotel bookings. Furthermore, it was found that a high number of recommendations increase the conversion rate of hotels with low ranks.

The findings show that hotels’ location ratings are more important than both star and service ratings for the conversion of visitors into customers. Thus, hotels that are located in convenient locations can charge higher prices. The results may also help entrepreneurs who are planning to open new hotels to forecast the conversion rates and demand for specific locations. It was found that a high number of recommendations help to increase the conversion rate of hotels with low ranks. This result suggests that a high numbers of recommendations mitigate the adverse effect of a low rank in search listings on the conversion rate.

This paper contributes to the understanding of the drivers of conversion rates in online channels for the successful implementation of hotel marketing.

Organizations create their environments by constructing interpretations and then acting on them as if they were true. This study examines the cognitive spatial boundaries that managers of Manhattan hotels impose on their competitive environment. We derive and estimate a model that specifies how the attributes of managers’ own hotels and potential rival hotels influence their categorization of competing and non-competing hotels. We show that similarity in geographic location, price, and size are central to managers’ beliefs about the identity of their competitors, but that the weights they assign to these dimensions when categorizing competitors diverge from their influence on competitive outcomes, and indicate an overemphasis on geographic proximity. Although such categorization is commonly conceived as a rational process based on the assessment of similarities and differences, we suggest that significant distortions can occur in the categorization process and examine empirically how factors including managers’ attribution errors, cognitive limitations, and (in)experience lead them to make type I and type II competitor categorization errors and to frame competitive environments that are incomplete, erroneous, or even superstitious. Our findings suggest that understanding inter-firm competition may require greater attention being given to the cognitive foundations of competition.

The aim of this paper is twofold. First, clustering patterns of urban hotels are explored, and, second, clustering effects on performance for upscale urban hotels are estimated.

Local indicators of spatial association (LISA) were computed using geographic information system (GIS) techniques. Clustering for the entire population of hotels in Madrid was explored visualizing LISA statistics. Then, a system generalized method of moments regression was applied to test a set of hypotheses about the performance effects of LISA statistics for a sample of upscale urban hotels.

Two significantly distinct types of clusters are identified: dense “cold spots” or clusters containing many low-priced hotels and quiet “hot spots” or clusters only containing a few high-priced hotels. And, estimates confirmed two important results: evidence of adverse selection when clustering and evidence of positive location economies for upscale hotels.

This study has a number of relevant implications for making better hotel location decisions. Specifically, the paper shows the applicability of GIS to find statistically significant clustering in the data. In the hotel sector, knowing exactly where hotel clustering occurs and of what type is of vital importance.

This paper’s novel application of LISA based on GIS techniques for hotel clustering sheds light on the effects of clustering on performance to convey the subtle nuances of the relationship for upscale urban hotels.

A successful hotelman once said that there are only three rules for profitable hotel operation: location, location, location. Although we may regard this as a somewhat over simplified prescription for success in the hotel business, it draws our attention to the prirnary importance of location in a business with a high investment in fixed capital, which cannot transport its product to the market. An hotel is an outstanding example of location in relation to the market; once an hotel is built, it cannot change its location or follow its customers, as the principal product it sells—hotel accommodation—has to be bought and consumed at the place of production.