Hotel demand forecasting: a comprehensive literature review

Huang, Liyao; Zheng, Weimin

doi:10.1108/tr-07-2022-0367

Cited by 13 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is robust to missing data and shifts in the trend, and effectively handles outliers (Duong, 2021). Hotel revenue performance generally shows a nonlinear trend, and its data fluctuations are clearly affected by overall trends, seasonality, public holidays and abnormal events (Huang and Zheng, 2022). Prophet can analyze data containing these trends more flexibly.…”

Section: Methodsmentioning

confidence: 99%

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

Wang

Lin

2023

View full text Add to dashboard Cite

Purpose Using a video revealing unhygienic practices in Chinese five-star hotels as the case study, this study aims to understand the impact of service failure online exposure on hotel revenue performance in terms of seriousness, magnitude and duration, as well as to identify the hotel-characteristics and hotel-responsiveness factors that influence revenue recovery. Design/methodology/approach This study uses the actual Revenue per Available Room data of ten hotels involved in the incident and five different market segments during 2016–2019. Event study method is used to investigate the effect of online exposure on hotel revenue performance. Findings This study confirms the significant negative effect of online exposure and that hotels take nearly nine months to fully recover. The results indicate that hotel size, hotel age and response strategy play an important role in reducing negative impacts. Moreover, this study reveals the dynamic spillover effects of online exposure on different hotel market segments. These effects change from a competitive to a contagious effect with a decrease in class ratings. Practical implications Low-class hotel managers should take effective actions to avoid possible negative spillovers from others’ service failure incidents. Hotel managers could consider the synergy of different strategies rather than a single response strategy to minimize losses. Originality/value This study theoretically broadens knowledge about the negative impact of online exposure on Chinese hotel revenue. Additionally, the findings examine the dynamic spillover effects on hotels in different segments. Furthermore, they extend the existing findings on the negative impact of online public opinion crises.

show abstract

Section: Methodsmentioning

confidence: 99%

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

Wang

Lin

2023

View full text Add to dashboard Cite

show abstract

“…Figure 2 confirms the literature is very recent, with the first studies dating from 2014 (except for one in 2005). Early reviews in new research fields linking tourism and technologies usually include a very limited number of studies (Huang and Zheng, 2022; Leung et al , 2013; Yung and Khoo-Lattimore, 2019). The use of UAV technology in tourism is relatively recent, which justifies the fact that the number of publications is still limited.…”

Section: Empirical Analysismentioning

confidence: 99%

Drones in hospitality and tourism: a literature review and research agenda

Sevilla

Mendieta-Aragón

Ruiz-Gómez

2023

View full text Add to dashboard Cite

Purpose Drones have become an important element within hospitality and tourism. The purpose of this study is to identify the corpus of knowledge and create a research agenda that establishes appropriate guidelines for future study of drone application in hospitality and tourism. Design/methodology/approach This work has been undertaken using a mixed-methods approach that combines quantitative and qualitative research and includes a review of the literature related to the study of drone use in hospitality and tourism. Findings The mixed-methods review identified gaps in the research, potential areas of study to enhance the scientific literature and potential uses of drones in tourism and hospitality for researchers, consumers and industry professionals. Originality/value This study makes an original contribution by establishing an integrated framework, which led to a synthesis of the research corpus and provided a holistic conceptualisation of the relationship between tourism and drones. In addition, the research agenda proposed will help boost and consolidate this emerging field of research.

show abstract

“…Existing econometric models on hotel demand forecasting include mixed‐data sampling (MIDAS), vector autoregression (VAR), and autoregressive distributed lag mode (ADLM) models. Due to the limited availability of relevant data, econometric models are less frequently used in hotel demand forecasting (Huang & Zheng, 2022). Time series and econometric models assume explicit relationships between influencing factors and hotel demand, posing challenges in dealing with large amounts of data (Claveria et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

What online review features really matter? An explainable deep learning approach for hotel demand forecasting

Zhang

2023

Asso for Info Science & Tech

View full text Add to dashboard Cite

Accurate demand forecasting plays a critical role in hotel revenue management. Online reviews have emerged as a viable information source for hotel demand forecasting. However, existing hotel demand forecasting studies leverage only sentiment information from online reviews, leading to capturing insufficient information. Furthermore, prevailing hotel demand forecasting methods either lack explainability or fail to capture local correlations within sequences. In this study, we (1) propose a comprehensive framework consisting of four components: expertise, sentiment, popularity, and novelty (ESPN framework), to investigate the impact of online reviews on hotel demand forecasting; (2) propose a novel dual attention-based long short-term memory convolutional neural network (DA-LSTM-CNN) model to optimize the model effectiveness. We collected online review data from Ctrip.com to evaluate our proposed ESPN framework and DA-LSTM-CNN model. The empirical results show that incorporating features derived from the ESPN improves forecasting accuracy and our DA-LSTM-CNN significantly outperforms the state-of-the-art models. Further, we use a case study to illustrate the explainability of the DA-LSTM-CNN, which could guide future setups for hotel demand forecasting systems. We discuss how stakeholders can benefit from our proposed ESPN framework and DA-LSTM-CNN model.

show abstract

Hotel demand forecasting: a comprehensive literature review

Cited by 13 publications

References 48 publications

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

Drones in hospitality and tourism: a literature review and research agenda

What online review features really matter? An explainable deep learning approach for hotel demand forecasting

Contact Info

Product

Resources

About