No abstract

No abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Purpose 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. Design/methodology/approach 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. Findings 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). Practical implications 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. Originality/value 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.

No abstract

Tonle Sap Lake is the largest river-connected lake, buffer area and ecological zone of Mekong River, which plays a huge role in dispelling flood peak and compensating water, and the conservation of biological diversity. The river-lake relationship between Mekong River and Tonle Sap Lake is unique and has always been a major focus in the international community. The land terrain and under-water topography were used to analyze the morphological characteristics of Cambodia Mekong Delta and Tonle Sap Lake. Long series of hydrological data of river-lake controlling stations were used to analyze the water level variation characteristics and water volume exchange pattern between Mekong River and Tonle Sap Lake, and the response relationship to river-lake morphological characteristics were also researched. The results show that: Cambodia Mekong Delta and Tonle Sap Lake Area is low-lying and flat with gentle channel gradient and water surface gradient, making the relationship between water level and area (or volume) smooth. The channel storage capacity of Mekong River and Tonle Sap River is not enough compared to the inflow, so vast flooding plain is extremely prone to be inundated, making the flood relationships between the left and right banks become very complicated. Tonle Sap Lake is a seasonal freshwater lake with water flowing in and flowing out, and the timing and intensity of water exchange with Mekong River are closely related to the water flow resistance at the exit section of Tonle Sap Lake and the cross-sectional area of Tonle Sap River, which can be reflected by the river-lake water level difference and the water level of Tonle Sap River. Affected by the river-lake morphological characteristics, the water

Since the end of 2019, the COVID-19 epidemic broke out suddenly and spread rapidly around the world. This sudden disease has seriously affected the conventional social system, especially after the media have been controlled, people cannot get sufficient and correct information, at the same time, rumors have spread widely and rapidly on the Internet, these factors led to public panic. Based on the Situational Crisis Communication Theory (SCCT), we analyze 554 comments of users in the two WeChat official accounts of Caixin and the Beijing News during the period of COVID-19, exploring the relationship between public sentiment and crisis type. The findings reveal the characteristics of crisis events causing public sentiment under different attribution types and form a preliminary model of crisis emotion guidance.

No abstract