ENTER2017 eTourism Conference presentation

1. ENTER 2017 Research Track Slide Number 1
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

2. ENTER 2017 Research Track Slide Number 2
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

3. ENTER 2017 Research Track Slide Number 3
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

4. ENTER 2017 Research Track Slide Number 4
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.

5. ENTER 2017 Research Track Slide Number 5
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

9. ENTER 2017 Research Track Slide Number 9
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

15. ENTER 2017 Research Track Slide Number 15
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

16. ENTER 2017 Research Track Slide Number 16
Thank you for your attentions!
Any questions and suggestions?