Comparing guests’ key attributes of peer-to-peer accommodations and hotels: mixed-methodsapproach

Abstract

As peer-to-peer (P2P) accommodations have grown exponentially, it is critical to understand motivations for guests to choose a P2P accommodation instead of a hotel. The current study seeks to understand these motivations by using mixed-methods approach to compare online reviews for P2P accommodations and hotels. Through quantitative analysis, thematic analysis, and text mining, this study provides analysis of 800 reviews from New York, Chicago, Los Angeles, and Houston. The results consistently show that guests in P2P emphasize relationships with hosts, whilst hotel guests place more values on room attributes.

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Comparing guests’ key attributes of peer-to-peer
accommodations and hotels: mixed-methods
approach
Amanda Belarmino, Elizabeth Whalen, Yoon Koh & John T. Bowen
To cite this article: Amanda Belarmino, Elizabeth Whalen, Yoon Koh & John T. Bowen (2017):
Comparing guests’ key attributes of peer-to-peer accommodations and hotels: mixed-methods
approach, Current Issues in Tourism, DOI: 10.1080/13683500.2017.1293623
To link to this article: http://dx.doi.org/10.1080/13683500.2017.1293623
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RESEACH LETTER
Comparing guests’key attributes of peer-to-peer accommodations
and hotels: mixed-methods approach
Amanda Belarmino, Elizabeth Whalen, Yoon Koh*and John T. Bowen
Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, Houston,
TX, USA
(Received 25 November 2016; accepted 1 February 2017)
As peer-to-peer (P2P) accommodations have grown exponentially, it is critical to
understand motivations for guests to choose a P2P accommodation instead of a hotel.
The current study seeks to understand these motivations by using mixed-methods
approach to compare online reviews for P2P accommodations and hotels. Through
quantitative analysis, thematic analysis, and text mining, this study provides analysis
of 800 reviews from New York, Chicago, Los Angeles, and Houston. The results
consistently show that guests in P2P emphasize relationships with hosts, whilst hotel
guests place more values on room attributes.
Keywords: sharing economy; peer-to-peer; segmentation; motivation; mixed methods
Introduction
The sharing economy is a worldwide phenomenon, changing how people consume services
(Sundararajan, 2016). Peer-to-peer (P2P) accommodation sites such as Airbnb and Home-
away have become one of its fastest-growing segments (Jefferson-Jones, 2015). Airbnb,
whose third quarter 2015 revenue exceeded Choice Hotels, grew its revenue by 113% in
2015. It is now seen by many as a lodging alternative to hotels due to convenience and effi-
ciency (McNichol, 2015), despite safety and quality concerns (Carpenter, 2016). As P2P
accommodations grow and hoteliers want to compete, they need to understand why
guests choose a P2P accommodation instead of hotel.
In order to examine if motivations of guests who choose P2P accommodation are differ-
ent from those who choose traditional hotels, this study analysed user-generated contents
(UGC) using three methods: quantitative analyses, thematic analysis, and text mining.
UGC has been frequently analysed with text mining, yet the use of computerized software
may lose meaning because words and phrases could be misinterpreted (Banyai & Glover,
2012). Therefore, this study employed three methods to interpret and validate results for
800 online reviews from New York, Los Angeles, Chicago, and Houston. This study
will assist lodging mangers in allocating resources.
Crowd-based capitalism and P2P accommodations
Crowd-based capitalism is a term developed by economists to understand the shift from tra-
ditional economic activities to sharing economy activities (Sundararajan, 2016). This
© 2017 Informa UK Limited, trading as Taylor & Francis Group
*Corresponding author. Email: ykoh@uh.edu
Current Issues in Tourism, 2017
http://dx.doi.org/10.1080/13683500.2017.1293623

concept combines the economic and social aspects of sharing by explaining that consumers
wish to engage in more meaningful relationships in their day-to-day economic activity.
Additionally, individuals would rather share their possessions as a source of income than
working for a company. One of the most prominent forms of the sharing economy is
P2P accommodations (Belk, 2014).
Airbnb, founded in 2008, is the marketplace leader in P2P accommodations, offering
diverse accommodations: shared rooms, private rooms, and entire apartments/houses
(Airbnb, 2016). An integral part of this crowd-based experience is the regulatory effect
of UGC. A guest’s ability to book on Airbnb relies on the quantity and quality of
reviews the guests received from P2P hosts, and the hosts’future ability to sell on
Airbnb relies heavily on the UGC (Airbnb, 2016). Therefore, reviews can be studied as
a source of information regarding the quality of the guests’experience.
Research into P2P accommodation found that owners are typically motivated by econ-
omic reasons. P2P accommodations have been cited as a solution to middle-class stagnation
by providing additional income to home owners whose wages are growing at a slower rate
than the cost of living (Sperling, 2015). Consumer motivations, however, vary from travel-
ling families receiving better price/value benefits from entire apartments than multiple hotel
rooms (Harrington, 2015), guests’desire to help P2P owners (Yannopoulou, Moufahim, &
Bian, 2013), and the need for personal relationships (Tussyadiah & Zach, 2016).
Hypothesis: P2P accommodation guests’motivations are different from hotel guests’
motivations.
Methodology
Whilst UGC has been actively examined in tourism and hospitality research, including its
relationship to booking intentions (Vermeulen & Seegers, 2009) and firm performance
(Duverger, 2013), it is relatively new for the P2P segment. A recent study by Tussyadiah
and Zach (2016) shows the potential for future research in P2P UGC research. This
study further develops this line of research by expanding the geographic scope and utilizing
mixed methods to verify the findings.
The UGC for this study came from two sources: Airbnb representing P2P accommo-
dations and TripAdvisor representing traditional hotels. P2P accommodation guests only
have the booking website as a venue for browsing guest reviews (Zervas, Proserpio, &
Byers, 2014). TripAdvisor is the US leader in online travel reviews because of the
scope, depth, and reliability of their reviews (Soler & Craig, 2016). This study used
reviews only from this website because some guests leave multiple reviews over multiple
websites (Nicol, 2014), which is likely to generate methodological risk from duplicate
reviews.
This study used reviews from accommodations located in the city centres in New York,
Los Angeles, Chicago, and Houston, the four largest cities in the USA. The P2P units were
chosen by number of reviews, location, and placement on the first page: (a) if it had at least
ten reviews and (b) it was within three miles of the city centre. Hotels were matched to P2P
accommodations based on price and amenities. Extended-stay hotels were matched with
apartments/houses because they offer kitchens, comparable sized accommodations, and
comparable pricing; limited service hotels were chosen for private rooms because they
offer limited food and beverage choices, rooms with basic amenities, free parking, and com-
parable pricing. Based on this criterion, 10 P2P accommodations were chosen for each city,
5 full apartments/houses and 5 private rooms. This yielded 100 P2P reviews and 100 hotel
reviews for each city, making a total of 400 for P2P and 400 for hotels (800 combined). This
2A. Belarmino et al.

sized data set is more reliable than big data for t-tests (Moore & McCabe, 1989) and for
thematic analysis (Fugard & Potts, 2015).
A mixed-methods approach was used to interpret the UGC: quantitative comparison,
thematic analyses, and text mining. For the quantitative comparison, each review was
coded by two reviewers and reconciled by a third. Eight factors were used (see Table 1).
The first seven factors were established by Radder and Wang’s(2006) study on guest-
houses. The current research added host/employee name (HN) to study the unique
dynamic of P2P interactions in this segment. The reviews were coded with a one for a
mention and a zero for no mention. Then, t-tests were run between number of mentions
for each variable to compare P2P and hotels. Functional skills and abilities (FSA) was
removed because it was only mentioned 17 times.
The second analysis sought to find common themes in the online reviews for P2P and
hotels, respectively. Thematic analysis is commonly used in analysing the context and rich-
ness of UGC (Mkono, 2013). Two reviewers determined the themes separately and
reconciled.
Text mining was used to verify the results, following the procedure used by Godnov and
Redek (2016). City names were replaced with the word ‘city’. For Airbnb, host names were
replaced with ‘host’. For hotels, employee names were replaced with ‘staff’.
Results
When guests choose P2P, quality indicators such as brand are absent, and online reviews are
more important. Through a mixed-methods analysis, this research found that reviews for
P2P accommodations emerged with different themes than did the hotel reviews. Table 2
synthesizes the results of the three analyses. Across all three (quantitative, thematic analy-
sis, and text mining), relationships with hosts/owners emerged as the most significant factor
in P2P reviews. The actual name of the host was mentioned in 315 reviews out of 400 (often
multiple times), ‘conversations with hosts’emerged as a major theme, and the name of the
host(s), term ‘host(s)’, or adjective referring to the hosts were used 513 times in the reviews
(the most mentioned word). Conversely, hotel guests only mentioned a staff member’s
name in 42 of the 400 reviews and a staff member (by name or generically as an employee,
staff, or team member) was mentioned 299 times (3rd most mentioned word). For the hotel
reviews, room amenities emerged as the predominant theme. They were mentioned in 351
reviews, with 5 themes emerging for this category from the thematic analysis, and room
being the most used word in the reviews (545 times). General amenities were mentioned
in 278 hotel reviews, with themes of food and beverage and odour emerging, whilst this
Table 1. Attributes examined.
Attributes Name Description
PSA Professional skills and abilities Friendliness/professionalism
GA General amenities Public areas
RA Room amenities Cleanliness, safety/security, quality, noise
CS Core service Services offered, service recovery
CO Convenience Accuracy, reliability, wait time
FSA Functional skills and abilities Training/selection of staff
AB Ambience Location, attractiveness
HN Host/employee name Host or staff name
Current Issues in Tourism 3

Table 2. Consolidated results.
Quantitative analysis
Host/ employee
name Ambience
Professional
skills & abilities Room amenities Convenience Core service General amenities
* = sig at 0.05
P2P
315
(78%)
Hotel
42
(11%)
*
P2P
309
(77%)
Hotel
302
(76%)
P2P
307
(77%)
Hotel
297
(74%)
P2P
278
(70%)
Hotel
351
(85%)
*
P2P
157
(39%)
Hotel
68
(17%)
*
P2P
71
(18%)
Hotel
83
(21%)
P2P
61
(15%)
Hotel
278
(70%)
*
Thematic analysis Host/employee
name
Ambience Professional
skills &
abilities
Room amenities Convenience Core service General amenities
Hotel Themes Personal interaction Location Cleanliness
Bathroom; Noise
Room amenities
Room Specs.
Wait times
Price
Food/Beverage
Odour
Properties Staff Nearby
attractions
Clean/Dirty
Maintenance, size
Noisy/Quiet
Coffee, toiletries
Size, furniture
Check-in/out
Price/Value
Restaurant
quality Odour
Quotes ‘Very
accommodating
staff’
‘Excellent
location
convenient to
eateries and
subways’
‘Room was very
clean’
‘Loud fan in the
bathroom, tiny
shower’
‘We arrived 45
minutes before
official check-in
time. We had to
wait’
‘Breakfast was
embarrassingly
minimal’
‘It was old and
smelly’
Host/employee
name
Ambience Professional
skills & abilities
Room amenities Convenience Core service General amenities
P2P Themes Personal interaction Neighbourhoods Guidebooks Website photos/
description
Comparison to
hotels
4A. Belarmino et al.

Properties Conversations with
hosts
Local
restaurants/stores
Authentic,
personal
Matching the
description
To hotels
Quotes ‘(The host) made us
coffee in the
morning and made
my family feel
welcome’
‘There are
MANY shops,
bakeries,
restaurants, and
the subway
nearby’
‘He provided us a
lot of
information’
‘The apartment
itself is even
more beautiful
than the photos’
‘It’s nice to feel
like you are living
like local people
do instead of
being in soulless
hotels’
Text Mining Host/employee
name
Ambience Professional
skills & abilities
Room amenities Convenience Core service General amenities
P2P Word
Number
Rank
Host
513
1
Location
164
7
Place; Apart;
Clean.
258; 216; 144
3; 5; 9
Great; Nice; Stay
309; 115; 245
2; 10; 4
Apartment
216
5
Hotel Word
Number
Rank
Staff
299
3
City; Location
203; 196
7; 9
Room; Clean
545; 197
1; 8
Great; Good;
Stay
220; 175; 217
5; 10; 6
Breakfast; Hotel
247; 533
4; 2
Current Issues in Tourism 5

was less prominent in P2P reviews. Other elements emerged in both reviews but in different
ways. For ambience, for instance, P2P guests discussed neighbourhoods and local
businesses whilst hotels mentioned proximity to attractions.
By comparing the UGC from Airbnb guests and hotel guests, this study contributes to
the literature by comparing the language used to describe these accommodations. This
study demonstrates that connectedness is a consistent theme for P2P accommodation
guests but not for hotel guests, supporting the crowed-based capitalism ideologies.
Implications and suggestions for future research
For P2P accommodation owners, this study highlights the importance of interaction with
their guests. This means that investors who are seeking to earn income through P2P accom-
modations should provide interaction with their guests. Hospitality literature has shown a
direct, positive relationship between positive online reviews and increased revenue (Ogut
& Tas, 2012); therefore, this investment in guest relationships can increase income. Sec-
ondly, this study explains why guests would choose a P2P accommodation. By demonstrat-
ing that guests who stay at each of these types of accommodations generate different types
of UGC, this research indicates that P2P guests value the host, neighbourhood, and local
experience. If hoteliers want to attract theses guests, they need to put a premium on personal
interactions (Neild, 2016).
This work supports Sundararajan’s(2016) proposition that consumers in the sharing
economy are seeking to re-engage in relationships with others, where economic activities
have become increasingly impersonal since the industrial revolution. Whilst Tussyadiah
and Zach (2016) suggest that hotels are better at providing additional services and P2P
are better at building relationships, this research contends that guests at these accommo-
dations place a different value and emphasis on these benefits.
Future research should evaluate whether these are truly different groups of travellers,
and also investigate the cultural, sociological, and economic reasons behind these
choices. As crowd-based capitalism strives to add personal interactions back to our daily
economic activities (Sundararajan, 2016), future studies can investigate if guests in P2P
accommodations engage in other sharing economy activities.
This study is not without limitations. This study used Airbnb for P2P reviews and Tri-
pAdvisor for hotel reviews. Although both are recognized as authorities in their areas,
future research could compare different P2P sites to determine if different themes emerge
across different platforms. The double-blind method of reviews for Airbnb could also
impact the nature of the reviews since host and guest reviews occur simultaneously.
Disclosure statement
No potential conflict of interest was reported by the authors.
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