This document summarizes a study on spill-over effects of online consumer reviews in the hotel industry. The study examines how online consumer ratings and room prices of hotels in London are associated with and influence neighboring hotels. Spatial econometric models are used to analyze data on ratings and prices from 689 hotels in London over 6 months in 2016-2017. The results show that both positive and negative spill-over effects exist, with consumer ratings and prices of nearby hotels impacting each other. This suggests the importance of considering regional interactions and applying spatial analysis when evaluating hotel performance and marketing strategies. The study provides the first analysis of spill-over effects between online ratings and prices in the hotel sector.

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Spill-over Effects of
Online Consumer Reviews
in the Hotel Industry
Sangwon Parka
, Jason Li Chena
a
University of Surrey, UK
sangwon.park@surrey.ac.uk
l.chen@surrey.ac.uk

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Introduction
• Numerous studies identifying factors that affect hotel
performance/satisfaction (Zhou, Ye, Pearce, & Wu, 2014)
– Hotel attributes (e.g., rooms, facilities, service quality, etc.),
– Pricing, location, and security
• The focus of extant studies about hotel management is on
their internal strategies.
– The importance of spill-over effects associated with
spatial interactions among hotels within a certain place

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Introduction
• That is, different accommodations located in the same
place interact with each other (Ritchie et al., 2013)
– The operational strategy and performance
• The development of information technology (e.g., Kayak,
Booking.com, etc.): allowing to estimate ‘value for money’
in regard to opportunity cost (Rivera et al., 2016)
– Online consumer reviews
– Variations of room rates by different channels

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Literature Review
• Spill-over effects in online consumer reviews
– The extent to which "existing information and
perceptions influence beliefs that are not directly
addressed by or related to the original information
source or perception object" (Janakiraman, Sismeiro, & Dutta, 2009; page 2).
• Two streams of prior research discussing WOM spill-overs
(Chae et al. 2016; Kozinets et al. 2010; Libai et al. 2009; Peres and Van den Bulte 2014)
– Brand diffusion research / demand spill-overs
– Spill-over of WOM from one segment to other segments.

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Literature Review
• Associative network theory (Collins & Loftus, 1975; Henderson e al., 1998)
 An entity such as a hotel is punished or rewarded for the
association with another entity (Borah & Tellis, 2016).
• Hypothesis 1: The increase in online consumer ratings of
hotels in a city is significantly associated with online
consumer ratings of neighbouring hotels in the same city.
• Hypothesis 2: The increase in online consumer ratings of
hotels in a city is significantly associated with room prices
of neighbouring hotels in the same city.

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Methodology – Global Model

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Data analysis - Local Estimation

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Data Collection
• KAYAK in mid-August 2016
• The median prices for each hotel across various online
providers from August 2016 to February 2017
• To generate the spatial weight matrix W, the latitude and
longitude coordinates of each hotel are collected.
• 1832 hotels in London  valid sample: 689

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Results
• Ordinary least squares (OLS) method
– The spatial autocorrelation in the residuals
– Moran I test
 Suitability of SDM with the inclusion of spatially
lagged terms

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Table 1. Global model estimation

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Fig. 1. Relationships between room price and consumer
rating of hotels A and B
Spatial feedback loop: the effect of spatial interaction
LeSage, and Pace (2009)

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Table 2. Summary of local estimates

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-12
-8
-4
0
W*Y
Fig. 2. Local spill-over coefficient of consumer
ratings

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Implications
• First study to identify spill-over effects in the hotel
industry with consideration of online word-of-mouth and
room prices - Both positive and negative spill-over effects
• Application of spatial econometrics in the hospitality firm
levels
• From the local estimation - spatial variations of the spill-
over effects
 Importance of regional marketing

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limitations
• Expand more destinations for generalizability of the
findings
• Suggest to conduct spatiotemporal models – in order to
address a trade-off between the sub-sample size and
parameter variability

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Thank you for your attentions!
Any questions and suggestions?