Search results

Accommodations providers in the sharing economy are increasingly competing with the hotel industry vis-à-vis the guest experience. Additionally, experience-related research remains underrepresented in the hospitality and tourism literature. This paper aims to develop and test a model of experiential consumption to provide a better understanding of an emerging phenomenon in the hospitality industry. In so doing, the authors also expand Pine and Gilmore’s original experience economy construct.

Using data from a survey of 630 customers who stayed at a hotel or an Airbnb in the previous three months, the authors performed a multi-step analysis procedure centered on structural equation modeling to validate the model.

The authors demonstrate that the dimensions of serendipity, localness, communitas and personalization represent valuable additions to Pine and Gilmore’s original experience economy construct. Airbnb appears to outperform the hotel industry in the provision of all experience dimensions. The authors further define the pathways that underlie the creation of extraordinary, memorable experiences, which subsequently elicit favorable behavioral intentions.

The findings suggest the need for the hotel industry to adopt a content marketing paradigm that leverages various dimensions of the experience economy to provide customers with valuable and relevant experiences. The industry must also pay greater attention to its use of branding, signage and promotional messaging to encourage customers to interpret their experiences through the lens of these dimensions.

The study expands a seminal construct from the field of services marketing in the context of the accommodations industry. The Accommodations Experiencescape is offered as a tool for strategic experience design. The study also offers a model of experiential consumption that explains customers’ experiences with accommodations providers.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

This study aims to present a unique perspective on the application of the theory of planned behaviour (TPB) in the context of the green lodging industry via configurational modelling of three TPB dimensions in formulating hotel visitors’ behavioural responses. Attitude towards behaviour, subjective norms and perceived behavioural control are the three indicators of TPB used to predict guests’ continued intention to use and recommend green hotels on Cyprus, a Mediterranean island with a fragile ecological system.

A questionnaire-based survey is used to evaluate the study’s objectives. A total of 320 guests of green hotels were approached between June and July 2017 and invited to participate. Among them, 260 valid cases were obtained and used for data analysis. The structural model was tested using structural equation modelling (SEM), the configurational model was assessed using the fuzzy-set qualitative comparative analysis (fsQCA) and the necessary predictor was evaluated using the necessary condition analysis (NCA).

The SEM results revealed that attitudes regarding behaviour increased the continued intention to visit and recommend green hotels. Similarly, subjective norms enhanced the guests’ desired behavioural responses. Perceived behavioural control boosted their continued intention to visit, but this was insufficient for predicting green hotel guests’ intention to recommend. The fsQCA results indicated that two causal models explained the conditions of both high and low levels of behavioural responses. The NCA results showed that attitude towards behaviour was the only necessary condition of the two expected behavioural responses.

Several previous studies have tried to modify, decompose or merge the TPB to provide theoretical support for proposed conceptual models indicating visitors’ behaviours. Beyond such attempts, pragmatic analytical approaches (e.g. set-theoretic method) should be applied to present a comprehensive perspective on the association of TPB indicators in decoding the complexity of customers’ behaviours. To the best of the authors’ knowledge, this study is among the first in hospitality research to use three TPB indicators and three analytical approaches to extend the knowledge of guests’ behaviours related to green hotels.

This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses “forecasted demand” arrivals generated from the real data.

The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic parameters like reservations, cancellations, duration of stay, no shows, seasonality, trend, etc. and simulates forward in time the actual process of reservations to obtain the forecast. On the other hand, the proposed optimization module extends existing optimization techniques for hotel revenue management to address group reservations, while including integrality constraints and using “forecasted demand” arrivals generated from the data. The optimization model is based on large‐scale integer programming model to optimize decision rules for accepting reservations.

A case study based on three different sets of reservation records of simulated hotel data was conducted to test the operation of the system on real data. Results showed that the model is able to generate effective recommendations to maximize revenue.

The main value of this paper is that it presents an integrated framework for hotel room revenue maximization. The novelty introduced in this approach is that it is based on an advanced room demand forecast model that simulated the reservation process from its first principles and produces demand scenarios that are used by an optimization model to generate proper recommendations.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.

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.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.

Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.

Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.

To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。

使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。

综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。

本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。

Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.

Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.

La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.

Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.

This paper reports on a study about applying regression techniques to identify the determinants and functional forms of tourism hotel expenditure in Hong Kong. Annual time series data from 1983 to 1997 of average room rate, the number of visitor arrivals, the service price index, and hotel accommodation rates were hypothesised to affect tourism hotel expenditure. Seven exogenous variables were selected for regression model development in both linear and log‐linear forms. In view of the potential problems of multicollinearity between the independent variables, and therefore the associated instability of the regression coefficients, stepwise regression analyses were employed to improve the initial model. Final empirical results showed that the hotel expenditure in Hong Kong could be explained by four of the seven exogenous variables. A log‐linear form of the regression model appeared to slightly outperform the linear form.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.