Validating Volunteered Geographic Information: Can We Reliably Trace Visitors\u27 Digital Footprints?

The objective of this study is to evaluate the quality of Volunteered Geographic Information (VGI) data for the purpose of describing patterns of visitor movement within a tourism destination. Using a Southern United States tourism destination as a case study, this research quantifies visitor flow networks using both data mined from Instagram and data collected using a traditional online survey methodology and then conducts a series of statistical analyses to compare results. In doing so, this paper highlights the advantages of using VGI data for tourism research, but also draws attention to potential trappings. Keywords: Volunteered Geographic Information (VGI), Instagram, visitor network, digital footprin

Effects of Channel, Timing, and Bundling on Destination Advertisement Response

This research investigates the relationships between advertisement channels, the timing of travel decision making, and the interaction of individual travel decisions on destination advertising response. Based on a sample of 5,472 travelers, this study finds that neither the timing of travel decision making nor the channel of advertisement significantly correlates with the advertising response for most trip decisions. However, strong interactions are found between advertising response and restaurant and shopping trip decisions, and between the attractions and events trip decisions. These findings are important in that they suggest that destination marketing programs should bundle these aspects of the trip together when developing their promotional efforts

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

PurposeThe impact of demand fluctuation during crisis eventsis crucial to the dynamic pricing and revenuemanagement tactics of the hospitality industry. The aim of this paper is to improve the accuracyof hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in HongKong as an example.MethodologyCrisis severity, approximated by social media data, is combined with traditional time-series models,including SARIMA, ETS and STL models. Models with and without the crisis severityintervention are evaluated to determine under which conditions a crisis severity measurementimproves hotel demand forecasting accuracy.FindingsCrisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demandduring crisis. When the market is volatile, the model with the severity measurement is moreeffective to reduce the forecasting error. When the time of the crisis lasts long enough for the timeseries model to capture the change, the performance of traditional time series model is muchimproved. The findings of this research is the incorporating social media data does not universallyimprove the forecast accuracy. Hotels should select forecasting models accordingly during crises.OriginalityThe originalities of the study are as follows. First, this is the first study to forecast hotel demandduring a crisis which has valuable implications for the hospitality industry. Second, this is also thefirst attempt to introduce a crisis severity measurement, approximated by social media coverage,into the hotel demand forecasting practice thereby extending the application of big data in thehospitality literature