RevPAR

Abstract: Social media has been touted as having an increasingly important role in many aspects of the hospitality industry, including guest satisfaction and process improvement. However, one of the more intriguing aspects of social media is their potential to move markets by driving consumers’ purchasing patterns and influencing lodging performance. In the absence of a comprehensive attempt to quantify the impact of social media upon lodging performance as measured by bookings, occupancy, and revenue, this report uses the unique position of Cornell’s Center for Hospitality Research to combine data from three CHR research partners (ReviewPro, STR, and Travelocity), and two other data providers (comScore and TripAdvisor) in a first attempt at determining ROI for social-media efforts. The analysis finds the following. First, the percentage of consumers consulting reviews at TripAdvisor prior to booking a hotel room has steadily increased over time, as has the number of reviews they are reading prior to making their hotel choice. Second, transactional data from Travelocity illustrate that if a hotel increases its review scores by 1 point on a 5-point scale (e.g., from 3.3 to 4.3), the hotel can increase its price by 11.2 percent and still maintain the same occupancy or market share. Third, to measure the impact of user reviews on hotel pricing power, consumer demand, and revenue performance the study uses matched-sample data from ReviewPRO and STR. By matching ReviewPRO’s Global Review IndexTM with STR’s hotel sales and revenue data, a regression analysis finds that a 1-percent increase in a hotel’s online reputation score leads up to a 0.89-percent increase in price as measured by the hotel’s average daily rate (ADR). Similarly this 1-percent increase in reputation also leads to an occupancy increase of up to 0.54 percent. Finally, this 1-percent reputation improvement leads up to a 1.42-percent increase in revenue per available room (RevPAR).

Abstract: Understanding consumers’ needs and wants has been a major source of success for hotel organizations. Notwithstanding, investigating the valence of online reviews and modeling hotel attributes and performance is still a rather novel approach. Using partial least squares path modeling, Swiss country-level data for online reviews from 68 online platforms, together with data from 442 hotels, we test 11 hypotheses. Our research model includes three distinctive areas of the hotel: physical aspects, quality of food and drink, and human aspects of service provision. RevPAR and occupancy are employed as performance metrics. We also test for mediation effects. Results indicate that hotel attributes, including the quality of rooms, Internet provision and building show the highest impact on hotel performance, and that positive comments have the highest impact on customer demand. This study contributes to theories of valence on hotel performance and presents salient implications for practitioners to enhance performance.

Abstract: 1. A Global Industry 2. The Structure of the Conference Industry 3. Winning Conference Business: 1 4. Winning Conference Business: 2 5. Planning and Staging Successful Conferences: An Organiser's Perspective 6. Conference Management: A Venue Perspective 7. The Economic, Social and Environmental Impacts of Conferences and Conventions 8. Developing the Industry's Workforce: Creating a Profession 9. Leading Industry Organisations 10. The Future: Trends, Challenges and Opportunities

Abstract: Purpose – Tourism systems have been considered more and more in the light of complexity and chaos theories Most of the work done in this area has highlighted the reasons for and the issues regarding this approach A steadily growing strand of the recent literature uses the theories to overcome the problems of a reductionist and mechanistic view that is considered unable to provide a full understanding of the structural and dynamic characteristics of tourism systems, and specifically of tourism destinations This paper seeks to continue this approach and to provide a series of quantitative methods to assess the dynamics of non‐linear complex tourism systemsDesign/methodology/approach – The time series used in the paper contains data collected from a sample of 23 large (four‐star) hotels located in Milan, Italy For each structure daily data of occupancy, average room rate and RevPAR (revenue per available room) were recorded for the period 2006‐2009 The daily distributions of these observations are high