International Journal of Contemporary Hospitality Management: Volume 33 Issue 6

Guest editorial

Tourism demand nowcasting using a LASSO-MIDAS model

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand…

Forecasting daily attraction demand using big data from search engines and social media

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

High-frequency forecasting from mobile devices’ bigdata: an application to tourism destinations’ crowdedness

This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a…

A segmented machine learning modeling approach of social media for predicting occupancy

This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions’ precision. To do so, this paper presents a framework…

Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly…

Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to…

Are environmental-related online reviews more helpful? A big data analytics approach

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what…

Listening to your employees: analyzing opinions from online reviews of hotel companies

This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel type…

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial…

Asymmetric relationship between customer sentiment and online hotel ratings: the moderating effects of review characteristics

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the…

Toward travel pattern aware tourism region planning: a big data approach

The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate…

Extracting revisit intentions from social media big data: a rule-based classification model

The examination of revisit intentions in hospitality is integral to relationship marketing and customer loyalty. Its measurement and determination have largely been done through…

Spatial-temporal evolution patterns of hotels in China: 1978–2018

This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

Destination image through social media analytics and survey method

Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the…

Do the flipped impacts of hotels matter to the popularity of Airbnb?

By building on cooperation–competition theory, this study aims to investigate the multidimensional flipped effects of neighborhood hotels on Airbnb listings’ popularity, examining…

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking…

Using social media photos as a proxy to estimate the recreational value of (im)movable heritage: the Rubjerg Knude (Denmark) lighthouse

On October 2019, the Rubjerg Knude lighthouse (Denmark) was moved 70 metres from the cliff edge. The Danish Government spent €700,000 on the rescue operation. Using the zonal…