Online consumer reviews have become increasingly important for consumer decision-making. One of the most prominent examples is the hotel industry where consumer reviews on…
Online consumer reviews have become increasingly important for consumer decision-making. One of the most prominent examples is the hotel industry where consumer reviews on websites, such as Bookings.com, TripAdvisor and Venere.com, play a critical role in consumers’ choice of a hotel. There have been a number of recent studies analyzing various aspects of online reviews. The purpose of this paper is to investigate their effects in terms of hotel occupancy rates.
This paper measures through regression analysis the impact of three dimensions of consumer reviews (i.e. review score, review variance and review volume) on the occupancy rates of 346 hotels located in Rome, isolating a number of other factors that might also affect demand.
Review score is the dimension with the highest impact. The results suggest that after controlling for other variables, a one-point increase in the review score is associated to an increase in the occupancy rate by 7.5 percentage points. Regardless the review score, the number of reviews has a positive effect, but with decreasing returns, implying that the higher the number of reviews, the lower the beneficial effect in terms of occupancy rates is.
The findings quantify the strong association of online reviews to occupancy rates suggesting the use of appropriate reputational management systems to increase hotel occupancy and therefore performance.
A major contribution of this paper is its comprehensiveness in analyzing the relation between online consumer reviews and occupancy across a heterogeneous sample of hotels.
Web 2.0 applications enable travelers to evaluate several services and assessment attributes. Constructed websites in several languages trigger a new way of data…
Web 2.0 applications enable travelers to evaluate several services and assessment attributes. Constructed websites in several languages trigger a new way of data collections resulting in data streams leading to the accumulation of vast amounts of data, called big data. The need for analysis is in high demand. This study aims to construct a model to investigate which single attribute or interrelated ones having an impact on the performances of hotels.
The total number of 1,137 observations collected from the website HolidayCheck.de are used from the hotels in the Bavaria region in 2016. Bavaria is a region where both domestic and foreign travelers mostly prefer to visit. Fuzzy rule-based systems, which is a combination of fuzzy set theory (FST) and fuzzy logic, are used. Although the FST is used to convert linguistically expressed perceptions by travelers into mathematically usable data, fuzzy logic is used to construct a model between service attributes and price-performance (PP) to attain the set of single and interrelated attributes on the assessment of PP.
No single attribute plays a key role in PP assessment. However, two or more interrelated combinations have different impacts on PP. For example, when “Food—Drink” and “Room” moves together from average to good level, PP reaches the highest level of assessment.
Accessibility to too much data is difficult.
A model can be continuously run so that any changes can be observed during the incoming of data.
As the consumer reviews and ratings are the crucial source of information for other travelers, hoteliers must monitor and respond them on time in order to deal with the complaints.
Travelers’ perceptions or evaluations are treated with a FST that measures the impression of human beings. New modeling enables researchers to observe not only any single attribute but also interrelated ones on the PP.