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Abstract

Purpose The purpose of this paper is to review the extant literature on Airbnb – one of the most significant recent innovations in the tourism sector – to assess the research progress that has been accomplished to date. Design/methodology/approach Numerous journal databases were searched, and 132 peer-reviewed journal articles from various disciplines were reviewed. Key attributes of each paper were recorded, and a content analysis was undertaken. Findings A survey of the literature found that the majority of Airbnb research has been published quite recently, often in hospitality/tourism journals, and the research has been conducted primarily by researchers in the USA/Canada and Europe. Based on the content analysis, the papers were divided into six thematic categories – Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulation, Airbnb’s impacts on the tourism sector and the Airbnb company. Consistent findings have begun to emerge on several important topics, including guests’ motivations and the geographical dispersion of listings. However, many research gaps remain, so numerous suggestions for future research are provided. Practical implications By reviewing a large body of literature on a fairly novel and timely topic, this research provides a concise summary of Airbnb knowledge that will assist industry practitioners as they adapt to the recent rapid emergence of Airbnb. Originality/value This is the first paper to review the extant literature specifically about Airbnb.
Journal of Hospitality and Tourism Technology
Progress on Airbnb: a literature review
Daniel Guttentag,
Article information:
To cite this document:
Daniel Guttentag, (2019) "Progress on Airbnb: a literature review", Journal of Hospitality and Tourism
Technology, https://doi.org/10.1108/JHTT-08-2018-0075
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Progress on Airbnb:
a literature review
Daniel Guttentag
Department of Hospitality and Tourism Management, College of Charleston,
Charleston, South Carolina, USA
Abstract
Purpose The purpose of this paper is to review the extant literature on Airbnb one of the most
signicant recent innovations in the tourism sector to assess the research progress that has been
accomplished to date.
Design/methodology/approach Numerous journal databases were searched, and 132 peer-reviewed
journal articles from various disciplines were reviewed. Key attributes of each paper were recorded, and a
content analysis was undertaken.
Findings A survey of the literature found that the majority of Airbnb research has been published
quite recently, often in hospitality/tourism journals, and the research has been conducted primarily by
researchers in the USA/Canada and Europe. Based on the content analysis, the papers were divided into
six thematic categories Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations,
Airbnb regulation, Airbnbs impacts on the tourism sector and the Airbnb company. Consistent
ndings have begun to emerge on several important topics, including guestsmotivations and the
geographical dispersion of listings. However, many research gaps remain, so numerous suggestions for
future research are provided.
Practical implications By reviewing a large body of literature on a fairly novel and timely topic, this
research provides a concise summary of Airbnb knowledge that will assist industry practitioners as they
adapt to the recent rapid emergence of Airbnb.
Originality/value This is the rst paper to review the extant literature specically about Airbnb.
Keywords Airbnb, Literature review, Sharing economy, Peer-to-peer, Short-term rental
Paper type Literature review
Airbnb
目的 文旨Airbnb-业中之一-今为
设计//132
大多Airbnb往往/
/
-AirbnbAirbnbAirbnb目的Airbnb
AirbnbAirbnb出几
宿大多究空未来
Airbnb的相
从业Airbnb
/Aribnb
AirbnbP2P
Progress on
Airbnb
Received 12 August2018
Revised 21 November2018
13 February 2019
Accepted 13 February2019
Journal of Hospitality and
Tourism Technology
© Emerald Publishing Limited
1757-9880
DOI 10.1108/JHTT-08-2018-0075
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-9880.htm
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Introduction
The emergence of Airbnb is unquestionably one of the most signicant and transformative
recent developments within the worldwide tourism sector. Although Airbnb only has existed for
roughly 10 years, by revolutionizing the age-old practice of peer-to-peer lodging with a new
technology-driven distribution platform, the company has unleashed a timely innovation that has
grown faster than virtually anyone had expected (Guttentag, 2015). Airbnb has now expanded to
the point that it is impacting some traditional tourism accommodations (Zervas et al., 2017),
transforming countless individuals into hospitality micro-entrepreneurs and making tourism
lodging a major policy issue in cities worldwide (Guttentag, 2017). Although Airbnb is far from
the only company dedicated to peer-to-peer short-term rentals, owing to its immense size and vast
impacts, most would agree that Airbnb is the most important company of its type and it has
become a poster child of the so-called sharing economy.Consequently, academic research into
peer-to-peer short-term rentals has often have looked explicitly at Airbnb, and this research area
has grown signicantly in recent years. This paper represents the rst review of the academic
literature specically focused on Airbnb.
Airbnb is an online platform through which individuals can rent out their spaces as tourist
accommodation. These spaces typically entail either an entire place(house, condominium, etc.)
or a private roomin a residence where the host is also present. Airbnbs diverse inventory
additionally includes some fairly exotic accommodations (castles, igloos, treehouses, etc.), and
Airbnb listings range from quite modest to extremely luxurious. For guests,the process of
nding and booking Airbnb accommodation is relatively similar to using an online travel agency
(e.g. Expedia), although the booking process may require some personal communication with the
host.For the hosts who own and/or manage Airbnb listings, the Airbnb platform offers the
ability to freely and almost effortlessly become a tourism lodging provider posting descriptions
and photographs of ones accommodation, communicating with potential guests and accepting
reservations and payments from around the world.
Unlike pure matchmaking platforms such as Craigslist or distribution platforms like Expedia,
Airbnb is involved in numerous aspects of the transactions it facilitates. Most obviously, Airbnb
processes the payments from guests to hosts and earns money by charging a service fee(i.e.
commission percentage) from both parties. Airbnb importantly also encourages both guests and
hosts to publically review one another, which helps foster the underlying trust necessary for such
a service to thrive (Jøsang et al., 2007). Airbnb further promotes trust and security by offering
various identity verication measures, free property damage protection (Host Guarantee), free
liability insurance (Host Protection Insurance)andaGuest Refund Policythat protects
against issues like inaccurate listing descriptions. Moreover, Airbnb has introduced various
innovations to further enhance and expand its services. Some of its more noteworthy innovations
have included an Instant bookingfeature that permits some reservations to be placed
immediately, without explicit host approval; a Superhoststatus badge for particularly active
and well-reviewed hosts; a For work tripslter that highlights listings appropriate for business
travel; and an Airbnb Plusstatus badge for top-quality listings that have been personally
inspected by an Airbnb representative. Furthermore, Airbnb has partnered with several travel
management companies in order to facilitate corporate travel (Griswold, 2016), and recently even
partnered with a real estate developer to build apartment complexes designed for Airbnb renting
(Quackenbush, 2018). Airbnb also has extended beyond tourism lodging, and now additionally
processes restaurant reservations and offers Experiences, which involve tours or other
excursions led by local guides.
Given Airbnbs diverse product portfolio and immense popularity,itiseasytoforgetthatthe
company was founded just very recently, in 2008. Airbnb usage was limited very early on, but
beginning around 2011 the company began growing very rapidly (Griswold, 2018).More
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specically, Airbnb had 140,000 guest arrivals in 2010; 800,000 in 2011; three million in 2012; six
million in 2013; 16 million in 2014; 40 million in 2015; 80 million in 2016; an estimated 115 million
in 2017; and an estimated 164 million in 2018 (Molla, 2017;Team, 2018). To accommodate these
guests, at the time of writing the company boasted over ve million active worldwide listings
(Airbnb, 2018), which was higher than the room capacity of the top ve worldwide hotel
companies combined (Hartmans, 2017). Furthermore, it recently was estimated that if Airbnb
were to go public, its market capitalization would be around $60 billion signicantly higher
than even Marriott International (Ting, 2018).
Unsurprisingly, Airbnbs rise has triggered reverberations in the tourism sector and beyond.
The companys most direct potential impacts are on the tourism lodging industry, although the
extent of these impacts are debatable. Airbnb denies it competes with hotels (Business Insider
Intelligence, 2017;Trenholm, 2015), and numerous hotel executives have dismissed the potential
threatposedbyAirbnb(DePillis, 2016;Handley, 2017;Trejos, 2018). On the other hand, media
headlines frequently highlight Airbnbs supposed threat to hotels (Griswold, 2016;Ting, 2017),
and trade groups including the American Hotel and Lodging Association (Benner, 2017),the
British Hospitality Association (Witts, 2016) and the Hotel Association of Canada (Press, 2018) all
have pushed for greater regulatory oversight and enforcement against Airbnb.
In addition to potentially disrupting the hotel sector, Airbnb also often is perceived as
disrupting communities around the world as residences for locals are transformed into tourism
lodging. These transformations have prompted countless regulatory battles, which have focused
on issues such as hoststax obligations, the safety of Airbnb rentals, Airbnbsimpactson
housing markets and residential quality of life concerns (noise, community fabric, etc.) (Guttentag,
2017). The Airbnb policy debate is quite divisive and heated, as it relates to matters people feel
very strongly about their ability to earn money, their rights regarding their homes and the
fabric of their communities. These tensions are crystallized in New York City, where protesters
from both sides have held dueling rallies at City Hall (Durkin, 2018), the State Attorney General
subpoenaed Airbnbs data and published a report criticizing the company (Schneiderman, 2014),
a State Assemblymember secretly recorded her own undercover Airbnb sting operation (Golding,
2015), Airbnb released video ads claiming it is being scapegoated for housing affordability issues
(Reader, 2018), and the Hotel Association of New York City and a hotel workers union created an
adlinkingAirbnbwithterrorism(Lovett, 2017).
With Airbnb becoming an increasingly important issue in both tourism and public policy,
scholarly research on Airbnb has increased over the past several years, scattered throughout
journals in various disciplines. Nonetheless, to date, research specically focusing on Airbnb has
never been summarized in a single literature review, so this article intends to ll that gap. The
only existing article that has somewhat summarized the extant Airbnb research is Prayag and
Ozannes (2018) review of the broader peer-to-peer accommodation literature from 2010 to 2016.
However, there is particular value in a more focused examination of solely Airbnb, as the peer-to-
peer accommodation sector is fairly diverse, comprising disparate services like Couchsurng (in
which hosts offer guests accommodation free of charge) and HomeAway (which only offers entire
home rentals and has branded itself as distinct from the sharing economy) (Vranica, 2016).
Furthermore, a plethora of Airbnb articles have been published between Prayag and Ozannes
(2018) review and the present study; in fact, only 26 of the 132 (19.7 per cent) articles included in
the present study were examined by Prayag and Ozanne.
Methods
This study sought to retrieve every peer-reviewed journal article on Airbnb published in
English by July 2018, when the literature search was conducted. To be as comprehensive as
possible, this study sought articles published in journals from any discipline. Also, like
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many literature reviews (Ip et al.,2011;Law et al., 2009), in order to focus exclusively on the
highest level of scholarship only peer-reviewed journal articles were considered (including
both full-length articles and research notes). Other publications were excluded, such as book
chapters, conference proceedings, working papers, industry reports, theses and editor
prefaces. Papers published in law review journals were excluded because such publications
are not refereed, and a few papers were excluded because they were published in journals of
dubious merit, as determined by the Australian Business Deans Councils Journal Quality
List and Google Scholars journal metrics. Finally, each paper needed to focus explicitly on
Airbnb. This focus didnot have to be exclusive (e.g. a paper could focus on both Airbnb and
Couchsurng), but Airbnb had to feature very prominently in the paper. Broader research
on peer-to-peer accommodations or the sharing economy was excluded.
Numerous online databases were searched ScienceDirect Journals, EBSCOhost Hospitality
and Tourism Complete, EBSCOhosts full database, Emerald Insight, IngentaConnect, Web of
Science and Google Scholar. Subsequently, Google Scholar was used to examine all of the articles
that had referenced the two most highly-cited Airbnb articles (Guttentag, 2015;Zervas et al.,
2017). All searches were conducted using the keyword Airbnb. Article titles, keywords and/or
abstracts were examined to determine whether an article was retrieved for further consideration,
and in this initial retrieval stage any article that seemed possibly relevant was saved, resulting in
293 saved articles. Subsequently, each article was examined more carefully to make a nal
decision on its inclusion, and 132 articles were kept and a content analysis was undertaken. As
part of this analysis, the articles were divided into thematic categories based upon the primary
topic being researched, and key attributes of each article (journal name, publication date, etc.)
were recorded.
General characteristics of Airbnb research
The vast majority of Airbnb studies were published very recently (see Figure 1), with 87.8
per cent published in 2017, in 2018 (through July), or only had been published online by July
2018. Also, the majority of these papers (58.3 per cent) were published in journals related to
hospitality/tourism. In fact, of the six journals that had published at least ve Airbnb
articles, all were hospitality/tourism-focused International Journal of Hospitality
Management,International Journal of Contemporary Hospitality Management,Current
Figure 1.
Number of Airbnb
articles published in
print each year
103
12
44
49
22
0
10
20
30
40
50
60
2013 2014 2015 2016 2017 2018
(-July)
Online
Note: Despite this literature review being conducted in July
2018, one article had already been dated 2019, but was
excluded from this chart
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Issues in Tourism,Tourism Management,Annals of Tourism Research and Journal of
Travel and Tourism Marketing. Together these six journals accounted for 39.4 per cent of all
of the articles examined (see Table I). Beyond hospitality/tourism journals, Airbnb papers
were published in journals associated with a range of disciplines, including business/
management, urban studies and information technology.
The majority of Airbnb research has employed quantitative methods (61.5 per cent), and
the remainder consists of qualitative studies (18.5 per cent); theoretical, conceptual, or review
articles (11.5 per cent); and mixed methods studies (8.5 per cent). Among the quantitative,
qualitative and mixed methods studies, data publically available on the Airbnb website (e.g.
listing attributes or guest reviews) were most commonly used. Such data were used in 48.7
per cent of the studies (keeping in mind that some studies had multiple data sources), and
was derived from manual collection, independently programmed scraping scripts, Inside
Airbnb (www.insideairbnb.com), or AirDNA (www.airdna.co). Data also commonly were
obtained from surveys, interviews and experiments. With regards to geography (again
keeping in mind that some studies collected data from multiple locations), 40.2 per cent of the
studies collected their data in the USA/Canada, 29.5 per cent in Europe, 13.4 per cent in Asia,
7.1 per cent in Australia/New Zealand, 1.8 per cent in Africa, 1.8 per cent in the Caribbean/
Latin America and 13.4 per cent from the entire world.
With regards to the geographic location of the researchers (as per the rst authorsbyline),
most Airbnb research was conducted by researchers in Europe (42.4 per cent) or the USA/Canada
(33.3 per cent), with the remainder conducted by researchers in Asia (13.6 per cent), Australia/New
Zealand (9.1 per cent) and Africa (1.5 per cent). The ve most common individual countries were
the USA (24.2 per cent), Canada (9.1 per cent), Australia (7.6 per cent), the United Kingdom (6.8 per
cent) and Spain (6.1 per cent). The departmental afliation of each lead author also was examined,
revealing that about two-thirds worked in business, management, or economics (33.1 per cent) or
hospitality, tourism, or leisure (32.3 per cent). The other authors were scattered amongst a range of
disciplines, including information technology, urban/regional planning and geography.
Research themes and literature review
The papers were divided into thematic categories based on the primary focus of each study.
Six thematic categories (plus various sub-categories) were established Airbnb guests,
Table I.
Number of Airbnb
articles published in
journals with
multiple Airbnb
publications
Journal name Articles Pct. of total (%)
International Journal of Hospitality Management 15 11.4
Intl. Journal of Contemporary Hospitality Mgmt. 12 9.1
Current Issues in Tourism 8 6.1
Tourism Management 7 5.3
Annals of Tourism Research 5 3.8
Journal of Travel and Tourism Marketing 5 3.8
Tourism Geographies 3 2.3
Urban Policy and Research 3 2.3
Anatolia 2 1.5
Government Information Quarterly 2 1.5
International Journal of Tourism Research 2 1.5
Journal of Housing Economics 2 1.5
Journal of Tourism Futures 2 1.5
Journal of Travel Research 2 1.5
Tourism Economics 2 1.5
Tourism Recreation Research 2 1.5
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Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulation, Airbnbs
impacts on the tourism sector and the Airbnb company. As can be observed in Table II,
research on Airbnb guests was by far the most common thematic category. The following
review of the literature includes every paper categorized within each theme. Papers that
generated ndings relating to more than one theme sometimes are mentioned under
multiple themes, as space allowed.
Airbnb guests
Why travelers choose airbnb. When reecting upon the rapid emergence of Airbnb, perhaps
the rst question that arises is why millions of travelers are opting to stay in the (oftentimes
unlicensed) homes of strangers found online, rather than just simply booking a hotel. In
Guttentags (2015) early look at Airbnb through the lens of disruptive innovation theory, he
proposed that even though Airbnb may underperform in comparison with hotels when
considering hotelsprimary performance attributes (e.g. service quality and security),
Airbnb offers an alternative value proposition centered around cost-savings, household
amenities and the potential for a more authentic local experience.
Numerous researchers have since tackled this motivation question empirically, and their
research has repeatedly highlighted the particular importance of Airbnbs practical/
utilitarian benets, while also sometimes recognizing the importance of experiential
benets. For example, Sthapit and Jiménez-Barreto (2018a) interviewed worldwide Airbnb
users and found price and location were the two primary drivers of Airbnb use. Likewise, So
et al. (2018) conducted a mixed-methods study that determined economic benets,
enjoyment and household benets signicantly impacted attitudes towards Airbnb, which
in turn inuenced behavioral intentions. In contrast, constructs including authenticity,
social interaction and sharing economy ethos were not found to exert meaningful inuence.
Guttentag et al. (2018) surveyed over 800 Airbnb users, mostly in Canada and the USA, and
concluded they were attracted primarily by Airbnbs practical benets (e.g. price, location
and household amenities), yet experiential ones (e.g. social interaction, authenticity and
novelty) were still important for some guests. Finally, Paulauskaite et al. (2017) interviewed
Table II.
Number of Airbnb
articles focused on
each thematic
category
Theme Articles Pct. of total (%)
Airbnb guests 55 41.7
Why travelers choose Airbnb 12 (9.1)
How guests choose their Airbnb accommodation 18 (13.6)
The Airbnb guest experience 15 (11.4)
Loyalty towards Airbnb 10 (7.6)
Airbnb hosts 25 18.9
Hostsmotivations 2 (1.5)
Hostsexperiences, strategies, and conduct 9 (6.8)
Airbnb accommodation pricing 14 (10.6)
Airbnb supply and its impacts on destinations 22 16.7
Airbnb regulation 10 7.6
Airbnbs impacts on the tourism sector 13 9.8
The Airbnb company 7 5.3
Note: The sub-category percentages, displayed within parentheses, indicate percentage of the overall total,
and sum to the percentages shown for their parent categories
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Airbnb guests and found cost-savings to be their primary motivator. Nevertheless, that
study focused explicitly on authenticity, and the researchers also found authenticity
contributed to Airbnbs appeal, as related to three key areas the accommodations, the
social interactionswith hosts and the interactions with local culture.
Guttentag et al. (2018) additionally argued that Airbnb users are not homogenous, and
the authors conducted a cluster analysis dividing their sample into motivation-based
segments. The ve resulting segments differed primarily according to the Airbnb users
desire for social interaction (with a host and other locals) and their use of entire homes
(versus shared accommodations). Focusing on the disabled traveler segment, Boxall et al.
(2018) noted that Airbnb homes are better suited than hotels for disabled people to develop
feelings of place identity, yet the authors also acknowledged that widespread availability of
disabled-access Airbnb rooms would probably only come from government intervention.
Also, two studies looked at Airbnb through the theoretical lens of the Theory of Planned
Behavior and the closely related Theory of Reasoned Action. So et al.s (2018) previously
mentioned study found Airbnb behavioral intentions also were positively inuenced by
perceived behavioral control, trend afnity and social inuence, and negatively impacted by
perceived insecurity. Amaro et al. (2018) surveyed German and Chinese Millennials and
found intentions to use Airbnb were driven, respectively, by subjective norms, an afnity
for Airbnbs unique and varied listings, positive attitudes towards online shopping and
perceived economic benets. Interestingly, Amaro et al. found economic benets to be of far
lesser import than had other studies, plus the researchers found perceived risk did not
exhibit a signicant impact on behavioral intentions, unlike So et al.s (2018) nding about
perceived insecurity.
Numerous researchers also have explored perceptions of the Airbnb brand and service,
thereby providing an alternative lens through which to understand Airbnbs appeal. For
example, very early on Yannopoulou et al. (2013) analyzed Airbnbs website and social
media, and found the companys brand identity focused on the everyday nature of its hosts,
the friendship and warmth guests can experience and the balance between authenticity and
professionalism that guests can enjoy. Lee and Kim (2018a) surveyed usersperceptions of
Airbnbs brand personality and found highest agreement with the dimensions of exciting,
sincere and competent (rather than rugged or sophisticated), and with the traits of original,
friendly, up-to-date, unique and reliable. The authors additionally found that such
perceptions were felt more strongly by users exhibiting higher levels of Airbnb
involvement. Yang et al. (2018) surveyed Airbnb users and found that gueststrust in the
Airbnb brand was fostered by their trust in individual hosts (which in turn was fostered by
interaction with hosts), hostsperceived credibility (based on reviews), perceived emotional
bonding with hosts, and individual listing attributes. Finally, Stollery and Jun (2017)
surveyed South Korean Airbnbusers and found value perceptions of Airbnb were positively
inuenced by monetary savings, hedonic benets and novelty (but not social interaction),
and were negatively inuenced by psychological risk (but not performance risk, physical
risk, or time risk).
Finally, a pair of studies have more generally compared the characteristics of Airbnb
guests with hotel guests. Volgger et al. (2018) compared international Airbnb and hotel
guests in Western Australia and found Airbnb users were comparatively more likely to be
visiting for pleasure, to visit attractions, to not be travelling alone, to be staying in semi-
peripheral regions, and to be from Singapore or Malaysia. Nevertheless, many other
characteristics were similar between the two groups, including total trip expenditure and
most trip activities, leading the authors to conclude that Airbnb users did not represent a
particularly alternative, non-mainstream type of visitor. In a somewhat similar study, Poon
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and Huang (2017) surveyed tourists who were using Airbnb and hotels in Hong Kong and
found Airbnb users were somewhat older and better educated, more focused on price and
security when choosing accommodations, more allocentric, on longer trips, and more likely
to be with friends. In contrast, the hotel users were more interested in service and more
likely to be on trips that were shorter and/or with family.
How guests choose their Airbnb accommodation. In many destinations, Airbnb guests
have a multitude of Airbnb listings to choose from, and numerous studies have examined
how such decisions are made. Gunter and Önder (2018) examined which listing attributes
inuenced Airbnb listing demand in Vienna and found that listing size, photo quantity and
host response rates increased demand, whereas price, distance from city center and host
response time decreased demand. Visser et al. (2017) surveyed Airbnb guests to Cape Town
and found the most important attributes guests focused on when choosing their
accommodations were, respectively, location, price and facilities. Similarly, Varma et al.
(2016) surveyed Airbnb users and found they placed the greatest importance on location,
price, reviews, service quality and past experience, which aligned somewhat with the
attributes seen as most important by hotel users (who also were surveyed). Abrate and
Viglia (2017), looking at several European cities, found host revenue was boosted by having
averied identication; a Superhost badge; more time on the platform; and, to a lesser
degree, higher review quantity and professional photos. Xie and Mao (2017) examined
nearly 6,000 listings in Austin, TX and found demand was impacted by hostsSuperhost
status, response rate, membership duration and number of listings, in addition to the
accommodations rating, review count, price and capacity. Also highlighting the appeal of
Superhosts, Liang et al. (2017) examined Hong Kong Airbnb listings and concluded that
listings managed by Superhosts were more likely to receive reviews (often seen as a proxy
for booking volume) and higher ratings, plus guests were willing to pay a premium to stay
with such hosts. Finally, Mauri et al. (2018) examined UK and Italian Airbnb listings and
found popularity (operationalized as a combination of rating scores, review quantity and
times saved to wish lists) was driven largely by reputation, which itself was particularly
impacted by personal narrative storytelling in hostsself-descriptions.
While price is clearly an important factor as Airbnb guests assess their options, several
researchers have instead examined the more general concept of value. Chen and Chang
(2018) surveyed both Airbnb users and non-users and found star rating had a positive
inuence on perceived value, which in turn had a positive inuence on purchase intentions,
whereas rating volume had a direct positive inuence on purchase intentions. Also, Zhang
et al. (2018) surveyed Airbnb users and found they were willing to pay a premium (versus a
hotel) based on perceived functional and social value early on in the buying process.
Acknowledging potential heterogeneity among Airbnb guests, Lutz and Newlands (2018)
explored the more general choice of entire home versus shared space renting. Through a
survey of Airbnb users, the authors found entire home renters tended to have higher
incomes and education, were more often traveling with a partner/spouse, and were less
comfortable with social interaction. In contrast, guests staying in shared spaces were more
likely to be male, have a lower income, be traveling alone or in a large group and be open to
social interaction. Looking at generational differences, Chang and Wang (2018) had
members of Generations X (ages 35-49), Y (ages 21-34)and Z (under 20) rank Airbnb listings,
and found Generations Y and Z were comparatively more focused on cost whereas
Generation X was more focused on cleanliness. Nonetheless, all generations were inuenced
by reviews, shown in part via sentiment ratio scores that the authors calculated to efciently
indicate the positivity of reviews.
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Reviews are indeed a central feature of Airbnb, because they help establish the required
trust between guest and host, and several researchers have specically investigated how
reviews impact Airbnb choices. Abrahao et al. (2017) conducted an experiment with several
thousand Airbnb users in which the authors manipulated the demographics (age, gender,
marital status and home state) and reputation (star ratings and review quantity) of
hypothetical hosts, and found that positive reputations successfully counteracted biased
distrust that arose due to social distance. The authors subsequently tested this notion by
examining one million actual Airbnb interactions, and further conrmed their initial
conclusions. Considering similar themes, Bae et al. (2017) surveyed South Korean Airbnb
users and found decreased social distance increased the perceived credibility of reviews, and
in turn purchase intentions.
Looking at reviews from a different angle, Bae and Koo (2018) conducted a netnography
and a subsequent experiment, which found that South Korean Airbnb guests did not fully
trust review content, and therefore employed various decision heuristics like focusing on
review quantity and pictures rather than review content, and using reviews primarily as an
initial anchor value for future information gathering. Also, some individuals were more
strongly impacted by text, and some by pictures. Looking more generally at the concept of
trust, Mittendorf (2018) surveyed Millennials and found that intentions to inquire about an
Airbnb listing or request a booking were driven by both perceived trust in the host and trust
in the Airbnb platform, with trust in the host having a greater impact on inquiries and trust
in Airbnb having a greater impact on booking requests. Also, Martin-Fuentes et al. (2018)
developed a machine learning classication technique based on data from several million
hotels, which generallysucceeded in assigning hotels to their correct star-rating category by
focusing on review volume, price, review score and userswish lists, rather than the criteria
that are typically used. The authors showed that this same technique could be used to
assign a hotel-like star rating to Airbnb listings (budget, mid-low range, mid-high range and
superior), which could prove more useful than existing Airbnb star ratings due to their
positive skew. Somewhat similarly, Nguyen et al. (2018) found that the ambiance of Airbnb
listings could be determined by assigning them certain adjectives either via crowdsourcing
or pre-trained neural networks.
In addition to reviews, host prole pictures also can inuence guestsdecisions. Ert et al.
(2016) analyzed Airbnb pricing in Stockholm and conducted a pair of discrete choice
experiments, which found that the perceived trustworthiness of hostsphotos increased
booking probability and prices. Perhaps surprisingly, the authors also found that review
scores had no impact on guestschoices. Similarly, Fagerstrøm et al. (2017) conducted a
conjoint experiment in which they manipulated hostsfacial expressions, and found that
neutral and positive expressions increased booking tendencies, whereas negative facial
expressions and a lack of host pictures reduced booking tendencies, even when
complemented by low prices and positive ratings.
The Airbnb guest experience. Once Airbnb guests select and book an accommodation,
they will have the experience of actually staying there, and the characteristics of Airbnb
guest experiences have proven to be another popular area of study, with researchers often
gleaning their ndings from what guests write in their (public) reviews. Such studies have
found that reviews tend to focus on a similar collection of themes relating to both the host
(e.g. hospitality and social interactions) and the accommodation (e.g. comfort and location).
For example, Cheng and Jin (2019) analyzed Sydney Airbnb reviews and found they were
overwhelmingly positive and focused chiey on location convenience; accommodation
amenities; and hostshelpfulness, exibility and communication. Somewhat similarly,
Tussyadiah and Zach (2017) cluster analyzed Airbnb reviews in Portland, USA and found
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they focused on the service, the facility, the location (convenience and neighborhood
character), feeling welcome and the comfort of staying in a home (ambiance and hospitality).
Interestingly, however, whereas Cheng and Jin (2019) interpreted their results as paralleling
hotel assessments in many ways, Tussyadiah and Zach (2017) interpreted their ndings on
the importance of hosts and feeling welcome in a home as underscoring the distinct nature of
Airbnb. Tussyadiah and Zach also found that reviews focusing on location and feeling
welcome were associated with higher rating scores. von Hoffen et al. (2018) developed a
sentiment analysis toolkit and applied it to Airbnb reviews and tweets about Airbnb, and
concluded that Airbnb guests particularly value cleanliness, bed comfort, fully-equipped
kitchens, spaciousness, a good view, a central and quiet location and a non-intrusive host.
Conrming the general consistency that is evident in the previously described studies,
Brochado et al. (2017) compared reviews of Airbnb listings in the USA, India and Portugal
and, contrary to the authorsexpectation that different cultural norms surrounding
individualism would lead to divergent review patterns, the authors detected homogeneity
between the reviews. This nding led the authors to conclude that enjoyable Airbnb
experiences were similaracross different countries.
A pair of studies also have used Airbnb reviews to better understand the Airbnb
experience through the prism of value co-creation that occurs between guests and hosts.
Johnson and Neuhofer (2017) analyzed Airbnb reviews in Jamaica and found that value
arose from a combination of the home, the surrounding community and the hosts, while the
guests also found value in traveling like a local, cooking and cleaning with the host, cultural
learning and relaxing. Somewhat similarly, Camilleri and Neuhofer (2017) analyzed Airbnb
reviews in Malta and found the reviews focused on six common themes relating to value co-
creation arriving and being welcomed, expressing positive/negative feelings, evaluating
the accommodation and location, interacting with and receiving help from hosts,
recommending the accommodation to others and thanking one another.
Moving beyond looking solely at Airbnb, several studies have compared Airbnb
experiences with hotel experiences. Belarmino et al. (2017) compared Airbnb reviews with
TripAdvisor hotelreviews and found Airbnb gueststended to focus on interactions with the
hosts, neighborhood ambiance and local businesses, whereas hotel guests tended to focus on
room amenities, property amenities and location relative to attractions. Mody et al. (2017)
added four new constructs (serendipity, localness, communitas and personalization) to the
experience economy framework, and found surveyed Airbnb guests rated each experience
construct more highly than hotel guests, yet hotels were equally able to translate guest
experiences into extraordinary and memorable outcomes that in turn led to intentions to
recommend and repurchase. In a subsequent survey study, Mody et al. (2018) added a
hospitableness construct into the experience economy framework, and found hospitableness
was not greater in Airbnb stays, prompting the authors to argue that hotels could leverage
their hospitableness to compete with Airbnb by providing memorable experiences.
Nonetheless, Birinci et al. (2018) surveyed Airbnb and hotel guests and found perceived
authenticity was higher amongst the Airbnb guests, whereas safety/security risks, time/
convenience risk and product performance risk showed no signicant differences between
the two groups.
Also using data other than review comments to examine Airbnb experiences, Priporas
et al. (2017b) surveyed Airbnb guests in Phuket, Thailand and found convenience and
assurance in accommodation quality contributed most highly to perceived service quality.
Additionally, Airbnb guests interviewed by Sthapit and Jiménez-Barreto (2018a) highlighted
how positive host interactions were strong contributors to overall enjoyable Airbnb
experiences.
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It is important to have such research on Airbnb experiences that does not rely on reviews
because Airbnb reviews tend to be extremely positive (Zervas et al., 2015), partly due to
various characteristics of the review system that seem to encourage positive reviews
(Guttentag, 2017). In fact, Bridges and Vásquez (2016) examined Airbnb reviews (by hosts
as well as guests) and found they were overwhelmingly positive, with only 2 per cent
classied as categorically negative. Nonetheless, the authors noted that reviewers
sometimes subtly indicated dissatisfaction by sandwiching a negative comment between
positive comments, or simply leaving a fairly neutral review. Also, it is important to
remember that not every Airbnb guest leaves a review. Bae et al. (2017) found that guests
became more likely to leave reviews as an Airbnb experience increasingly differed from
expectations (either positively or negatively), and as the quality of the experience deviated
from neutral (either positively or negatively).
Indeed, not every Airbnb experience will be a positive one, and several researchers have
looked specically at negative Airbnb experiences. For instance, as part of their previously
mentioned interviews, Sthapit and Jiménez-Barreto (2018b) found negative Airbnb
experiences most commonly revolved around poor communication between guests and
hosts, leading to lost time and/or money. Also, Phua (2018) analyzed complaints posted
about Airbnb on a third-party website and found the complaints tended to focus on a few
core issues poor customer service attention from Airbnb, uncertainty and a lack of trust
(e.g. last-minute cancellations and accommodations not meeting expectations) and
technological issues (e.g. the verication process). Finally, Bucher et al. (2017) interviewed
Airbnb users about how guest-host closeness can lead to interpersonal contaminations,
which were categorized as relating to poor environmental hygiene, hostspersonal objects,
interpersonal contact and privacy intrusions. Nevertheless, a subsequent survey found that
perceived authenticity overrode concerns about the rst three contaminants, thereby
suggesting that guests tolerate some undesirable issues so long as the experience is
considered authentic.
Loyalty towards Airbnb. Airbnb guestsexperiences in turn inuence their loyalty
towards Airbnb, and the question of what inuences Airbnb guest loyalty has been a
common subject of research. This research has most frequently used survey data and
structural equation modeling in which different constructs serve as antecedents for
satisfaction, which in turn is positioned as an antecedent of loyalty (or repurchase
intentions). For example, Möhlmann (2015) surveyed German Airbnb users and found cost
savings, familiarity with Airbnb, trust in Airbnb and acceptance of Airbnb as a hotel
substitute all positively inuenced satisfaction, which in turn positively impacted intentions
to reuse Airbnb (or comparable services). Numerous other constructs including the belief
that Airbnb promotes feelings of community belonging or reduces ones environmental
impact were not found to have signicant impacts. Priporas et al. (2017a) surveyed Airbnb
guests in Phuket and found service quality positively impacted satisfaction, and both
constructs in turn impacted loyalty. Liang et al. (2018a) surveyed North American Airbnb
users, examining both transaction-basedsatisfaction with the Airbnb purchase process
and experience-basedsatisfaction with the Airbnb accommodation experience, and found
transaction-based satisfaction was particularly important in inuencing both experience-
based satisfaction and repurchase intentions, which was a relationship partly mediated by
trust in the Airbnb hosts and the Airbnb company. Lee and Kim (2018b) surveyed USA
Airbnb users and determined that both perceived hedonic value and utilitarian value
positively impacted satisfaction, which in turn positively impacted loyalty, plus hedonic
value (but not utilitarian) had an additional direct impact on loyalty. Also, these
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relationships held even when taking customer involvement into account as a moderating
variable.
Taking a more theoretical approach, Wang and Jeong (2018) surveyed USA Airbnb users
with a model based on the Technology Acceptance Model and Innovation Diffusion Theory,
and found that Airbnb loyalty was signicantly impacted by both satisfaction (based on the
quality of amenities, and the hosts hospitality) and attitudes (based on perceived usefulness
of the Airbnb website, and trust in the Airbnb website, with personal innovativeness
serving as an antecedent for both). Also, Tussyadiah (2016) surveyed USA peer-to-peer
short-term rental (primarily Airbnb) users and found enjoyment, economic benets and
amenities all had a signicant positive impact on satisfaction, which in turn had a positive
impact on repurchase intentions. On the other hand, social benets and local benets were
not found to be signicant, and sustainability surprisingly had a negative impact on
satisfaction. Tussyadiah further compared entire home guests with private room guests and
found that perceived social benets promoted satisfaction in only the latter. Birinci et al.
(2018) took a slightly different approach by surveying both Airbnb and hotel users and
found that for both groups perceived authenticity inuenced satisfaction, which in turn
inuenced repurchase intentions. Also, satisfaction was further predicted by safety/security
risk among Airbnb users, whereas time/convenience risk and product performance risk
were not signicant for either group.
Although the majority of Airbnb loyalty studies have used satisfaction as an antecedent
of loyalty, several have not. For example, Liang et al. (2018b) found use of electronic word-of-
mouth, price sensitivity, perceived value and perceived risk all signicantly impacted
repurchase intentions, with the latter two constructs also acting as a mediator for the initial
two constructs and for perceived authenticity. Additionally, Mao and Lyu (2017) surveyed
USA Airbnb users with constructs based on the Theory of Planned Behavior and Prospect
Theory, and found repurchase intentions were most strongly impacted by attitudes, which
were themselves most strongly impacted by unique experience expectations. Subjective
norms also exhibited a direct positive impact on repurchase intentions, yet perceived
behavioral control did not. Finally, Lalicic and Weismayer (2018) surveyed Airbnb users
and found Airbnb loyalty was signicantly impacted by the hosts service quality and the
enjoyment of social and authentic experiences, but was not impacted by perceived economic
benets or lack of risk.
Airbnb hosts
Hostsmotivations. As the research on Airbnb guests clearly demonstrates, Airbnb hosts
are a critical part of the Airbnb experience, plus they are essentially the foundation of the
Airbnb company, as without hosts there would be no Airbnb. Several studies have
investigated why individuals become Airbnb hosts, although this topic has received far less
attention than its guest equivalent. Like Airbnb guests, the hosts seem to be attracted by
both nancial and experiential benets. For example, Karlsson and Dolnicar (2016)
surveyed Australian hosts with an open-ended motivation question, and found answers t
into three primary categories income (covering expenses or earning additional money),
social interaction (enjoying meeting new people) and sharing (utilizing unused space and
sharing ones world). Visser et al. (2017) surveyed Cape Town Airbnb hosts and found that
nancial incentives were the most common reason for hosting. Finally, Ladegaard (2018)
interviewed Boston Airbnb hosts and found they saw hosting as an opportunity to interact
with the foreign Otherand achieve cosmopolitanism, but the hosts still often cherry picked
guests for a certain degree of familiarity, which Ladegaard dubbed comfortably exotic.
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Ladegaard also found these encounters helped hosts acquire social capital related to the
global knowledge and social connections they attained.
Hostsexperiences, strategies and conduct. Whereas Airbnb host motivation research
has focused on the benets of hosting, several studies on the hosting experience have
underscored its numerous challenges. Malazizi et al. (2018) surveyed Airbnb hosts in
Northern Cyprus about their risk perceptions and found host satisfaction was negatively
impacted by nancial concerns (e.g. regarding online payments) and security concerns, and
this satisfaction in turn directly related to future hosting intentions. Such intentions also
were directly impacted by nancial concerns, security concerns and political concerns (e.g.
political conicts between a hosts and guests countries). Using a different methodological
approach, Roelofsen (2018) conducted an autoethnography in which she stayed at 11 Airbnb
listings in Soa, Bulgaria, and found that Airbnb hosting altered the very essence of home
for the hosts by blurring the lines between front stageand back stage. Roelofsen
additionally found that Airbnb homes became commodied, that certain objects were
staged for the guestsexperience, and that the privacy of intimate spaces had to be silently
negotiated. Somewhat similarly, Wilkinson and Wilkinson (2018) offer an autoethnographic
account of the authorsexperiences as Airbnb hosts in Manchester, UK The authors
described tensions associated with maintaining a sense of privacy and preserving routines
while simultaneously making guests feel welcome. The authors also highlighted the
signicant and sometimes unpleasant labor required to maintain a high standard of
cleanliness. Finally, Roelofsen and Minca (2018) critiqued how Airbnb has commercialized
peoples living spaces, which is a sacrice hosts must make in order to join the Airbnb
community. Roelofsen and Minca further argued that hosts are reduced to quantiable data
signaling their ability to provide hospitality experiences, the pinnacle of which is the
Superhost.
In addition to being hospitality providers, hosts also must market themselves to potential
guests. For example, Lutz and Newlands (2018) compared the listing descriptions written by
hosts of entire home and shared space rentals, and found entire home hosts explicitly
targeted older guests, couples, business travelers and high-income professionals, while
highlighting professional-level cleanliness and ensuring privacy. In contrast, shared room
hosts targeted younger and frugal guests, did not boast about cleanliness and assured social
interaction was a part of the experience. Tussyadiah and Park (2018) analyzed USA Airbnb
hostsprole descriptions, and found hosts tended to describe themselves either as well-
traveled individuals eager to meet new people or with a focus on their professions. In a
follow-up survey of peer-to-peer accommodation users, the authors found potential guests
perceived the hosts describing themselves as well-traveled were deemed more trustworthy,
and booking intentions towards these hosts were higher. Another way to stand out as a host
is to achieve Superhost status, but Airbnb offers only general guidance, rather than precise
detail, on how this status can be achieved. Gunter (2018) explored this subject by examining
San Francisco host characteristics and found that obtaining and maintaining excellent
reviews was easily the most important criterion for becoming a Superhost, followed by
reliable cancellation behavior, responsiveness and sufcient demand.
Once potential guests are attracted, hosts additionally can choose whether or not to
accept a booking request (unless the host is using Instant booking), and numerous studies
have explored how such decisions are made. Karlsson et al. (2017) conducted a choice
experiment in which they presented Australian Airbnb hosts with several pairs of
hypothetical booking requests, and found hosts preferred certain personal characteristics
older, female, a prole picture (although facial expression did not matter, unlike what was
found by Fagerstrøm et al., 2017), a picture of couples and a picture without kids. Also, hosts
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preferred guests who requested longer bookings, made a positive self-reference about their
behavior, were travelling with a partner, were not travelling with family/friends, were
travelling to attend a wedding, and were not celebrating their birthday.
It seems natural that hosts may exhibit preferences for guests deemed trustworthy,
compatible and protable, yet the ability to deny booking requests also permits
inappropriate forms of discrimination, like racial discrimination. In fact, numerous racial
discrimination complaints have been levied against Airbnb, and the company has
responded with various actions including the establishment of a Nondiscrimination Policy
(Benner, 2016;Glusac, 2016). Useful insight into Airbnb discrimination is provided by
Edelman et al. (2017), who conducted an experiment in which they sent inquiries to Airbnb
hosts from fake accounts with distinctively white or African American names, and
discovered the African American requests were 16 per cent less likely to be accepted than
the white ones. Discrimination was demonstrated by both male and female hosts, by both
African American and white hosts, by hosts offering entire homes and shared
accommodations, by hosts with signicant experience and multiple properties, by hosts
with high- and low-priced units, by hosts in diverse and homogenous neighborhoods, and
towards both male and female potential African American guests. Nevertheless, hosts who
had previously hosted African American guests did not discriminate, leading the
researchers to conclude that discrimination primarily was exhibited by a particular subset
of hosts. The discrimination issue has received fairly signicant media attention, and Cheng
and Foley (2018) examined reader comments to a Guardian article about Airbnbs anti-
discrimination policy. The authors found that people viewed the issue from varying
perspectives, such as in terms of economic implications for the company, governmental
regulations, and hostsrights regarding their ability to select who they accept into their
homes.
Airbnb accommodation pricing. Unlike some other sharing economy services (e.g. Uber),
Airbnb hosts have the freedom to set their own prices, and copious research has used
hedonic regression models to better understand how different host and listing attributes
inuence price. (It is worth noting that while pricing is a direct responsibility of hosts, and
therefore included in this section of the paper, pricing also relates to the consumer side of
Airbnb by highlighting what guests value most.) These studies often have been conducted
with large data sets up to nearly 500,000 listings (Benítez-Aurioles, 2018b) and have
included listings from a wide range of destinations in Canada (Gibbs et al., 2018a), Germany
(Teubner et al., 2017), Italy (Magno et al., 2018), Spain (Aznar et al., 2018a;Benítez-Aurioles,
2018a), the USA (Chen and Xie, 2017;Kakar et al.,2018;Zhang et al., 2017) and worldwide
(Benítez-Aurioles, 2018b;Wang and Nicolau, 2017).
These studies have tended to nd that price is positively associated with review scores,
offering an entire home (versus shared accommodation), bedroom count, bathroom count,
guest capacity, Superhost status, hosts time as a member, certain amenities (e.g. parking)
and photo count. In contrast, price generally has been negatively associated with distance
from a destinations city center, a host having multiple listings, more exible cancellation
policies, Instant booking availability and review count. Although these pricing patterns
have been fairly consistent, it is important to note that studies looking at multiple
destinations have found noteworthy differences between them (Gibbs et al., 2018a), plus
some studiesndings have not conformed with these general patterns. For example, Kakar
et al. (2018) did not nd that cancellation policies signicantly impacted price, and neither
Teubner et al. (2017) nor Chen and Xie (2017) found that Superhost status signicantly
impacted price. Also, several studies have acknowledged that it is somewhat
counterintuitive that exible cancellation policies, Instant booking and review count would
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negatively impact price, as such attributes should make a listing more attractive to potential
guests, and authors have rationalized this phenomenon in different ways. For instance,
Benítez-Aurioles (2018b) explained it in economic terms, claiming that hosts simply are
encouraging demand with strategies including both lower prices and more appealing
booking policies. Somewhat differently, Gibbs et al. (2018a) explained it as commercially-
oriented hosts being more likely to use Instant booking and reduce prices to ensure demand,
while casual hosts have a higher minimum threshold for the compensation that makes
hosting worthwhile. Additionally, Teubner et al. (2017) detected an interaction effect
between review scores and quantity as related to price, with the negative impact of high
review volume beinggreater for listings with low rating scores.
Also, most Airbnb price studies have looked at additional variables beyond what has
been described. For instance, Kakar et al. (2018) used their analysis to look specically at
racial discrimination and found that, all else being equal, Asian and Hispanic hosts in San
Francisco tended to charge eight to ten per cent less than white hosts. Nonetheless,
occupancy rates were the same, and neither gender nor sexual orientation had a similar
impact on price. Furthermore, Ram and Hall (2018) looked at both Airbnb listings and hotels
in Tel Aviv to explore the relationship between walkability and price, and found no
signicant relationship in either case.
Rather than examining static prices, numerous studies have examined the degree to
which Airbnb hosts have adopted revenue management practices, adjusting prices
strategically. Magno et al. (2018) found that hosts in Verona, Italy were increasing their
prices in response to increased demand, thereby demonstrating basic dynamic pricing
behavior. However, Aznar et al. (2018b) examined Barcelona Airbnb listings and hotels and
found that Airbnb hosts varied prices by season, but, unlike hotels, did not vary much by
day of week. Similarly, Gibbs et al. (2018b) examined nearly 40,000 Airbnb listings and over
1,000 hotels in Canada, and found that while dynamic pricing among Airbnb hosts was
evident by day of week and by season, many Airbnb hosts were shifting prices minimally or
never. Also, Airbnb price uctuations tended to be less pronounced than those of hotels, and
hotel rates correlated more closely with their occupancy levels. Lastly, Oskam et al. (2018)
examined dynamic pricing by Airbnb hosts in Amsterdam and found that hosts who
adjusted prices more frequently performed better in terms of occupancy levels and daily
rates.
Airbnb supply and its impacts on destinations
Complementing research on hosts and guests, numerous studies have examined Airbnb
supply in various destinations to more generally understand its characteristics and impacts.
For example, Abdar and Yen (2017) examined over 673,000 Airbnb listings from 16
countries and found that apartments and houses were the most common property types,
entire homes were the most common room type, and most listings had a 4.5 or ve star
rating. Crommelin et al. (2018) examined Airbnb listings in Hong Kong, London, New York
City, Paris and Sydney and found the majority of listings were entire homes, and a
substantial proportion were dedicated unitsavailable over 90 days per year and/or owned
by a host with multiple listings, which the authors contrasted with the supposed ethos of the
sharing economy. Also, Adamiak (2018) examined 737,000 listings in 432 European cities
and identied meaningful differences between the Airbnb supply characteristics of different
countries (e.g. more or fewer fully-dedicated Airbnb units), and concluded that Airbnb
supply volume roughly corresponded with a citys size and status as a leisure destination.
Adamiak additionally found the relative proportion of entire homes dedicated to short-term
renting was higher in major tourist destinations, and Airbnb seemed to play a particularly
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important role in rapidly growing inbound markets like Iceland. This form of large-scale
inventory analysis requires purchasing or otherwise attaining scraped Airbnb data, and
Oses Fernández et al. (2018) offered methodological guidance for such Airbnb data scraping
to assist with sector monitoring. Also, Palos-Sanchez and Correia (2018) found that the
volume of Google searches for different keyword terms associated with Airbnb could
successfully predict income from short-termrentals.
By examining large Airbnb data samples, researchers have gained insights into public
health questions as well. For example, Kennedy et al. (2018) investigated the re safety and
rst-aid features that hosts claimed at over 120,000 listings in various USA cities. The
authors found 80 per cent had smoke detectors, 58 per cent had carbon monoxide monitors,
42 per cent had re extinguishers and 36 per cent had rst-aid kits, suggesting less safety
preparedness than would be found in hotels. The authors also noted that smoke alarms and
carbon monoxide detectors were comparatively more prevalent in cities that had Airbnb
permits requiring such devices. Looking at a different public health issue, Kennedy et al.
(2017) found signicant disparity between Canadian cities regarding the percentage of
Airbnb listings permitting smoking, and the authors predicted that Airbnb may ll a gap as
hotels increasingly become smoke-free environments.
Large data sets also permit analyses of the geographic dispersion of Airbnb rentals.
This form of research has been conducted in Austin, Boston, Chicago, San Francisco and
Washington, D.C. (Wegmann and Jiao, 2017); New York City (Dudás et al., 2017a);
Barcelona and Madrid (Benítez-Aurioles, 2018a;Gant, 2016;Gutiérrez et al., 2017);Berlin
(Schäfer and Braun, 2016) and Hamburg (Brauckmann, 2017); Budapest (Boros et al.,
2018;Dudás et al., 2017b;Smith et al., 2018);CapeTown(Visser et al., 2017); Sydney and
Melbourne (Alizadeh et al., 2018;Crommelin et al., 2018); Utrecht, Netherlands (Ioannides
et al., 2018);andWarsaw(Gy
odi, 2017). With the exception of Cape Town, these studies
consistently have found that Airbnb listings arehighlyconcentratedincitycentersand
around tourist attractions and facilities. For example, Benítez-Aurioles (2018a) found that
in Barcelona and Madrid over 80 per cent of Airbnb listings were found within a three-
kilometer radius of the city centers. Consequently, this body of research has largely
concluded that Airbnb listings reect fairly similar spatial patterns to hotels, although
there are distinctions when considering proportional densities in different areas (Gy
odi,
2017;Ioannides et al., 2018), plus Airbnb is introducing tourism lodging into some
previously residential neighborhoods (Gutiérrez et al., 2017). Moreover, Wegmann and
Jiao (2017) found evidence in some cities that Airbnb listings cluster around transit lines.
This area of research also has repeatedly found that, demographically, Airbnb listings
are primarily found in relatively afuent (Alizadeh et al., 2018;Visser et al., 2017;
Wegmann and Jiao, 2017) and predominately white areas (Visser et al., 2017;Wegmann
and Jiao, 2017), aside from in Hamburg, where Airbnb was more prevalent in
multicultural neighborhoods (Brauckmann, 2017).
Some researchers have examined the consequences of Airbnbs rapid expansion
throughout destinations. Freytag and Bauder (2018) looked at Airbnb as one of various
change agents precipitating the touristicationof Paris, and determined that Airbnb
disperses tourists throughout the city and immerses them in local neighborhoods,
thereby introducing tourism into new areas of the city beyond the traditional tourist
bubbles.Also,Fang et al. (2016) examined Airbnbs impact on tourism sector
employment in Idaho, USA, and found the relationship was positive, as Airbnb brings
increased tourists to the destination. Nevertheless, the authors cautioned that this lift
could be offset as Airbnb potentially grows to the point that it reduces hotel employment.
Finally, Gurran and Phibbs (2017) examined Airbnbs impacts in Sydney by analyzing
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written submissions by local planners and other interested parties to a government
inquiry into short-term letting. The authors found greater opposition coming from urban
areas, characterized by concerns about issues like noise, trafc, parking and waste
management.
In addition to disrupting the daily life of locals, Airbnb also may impact local housing
markets by reducing supply and raising prices, which is an important issue in many of
Airbnbs regulatory battles. In fact, several of the previously mentioned studies on Airbnbs
spatial patterns and impacts suggested that impacts on housing were possible in
neighborhoods of high Airbnb density (Alizadeh et al., 2018;Brauckmann, 2017;Gurran and
Phibbs, 2017). Multiple other studies have focused specically on this issue. For example, in
Berlin, Schäfer and Braun (2016) found that over 5,500 Airbnb units were being offered
beyond the nightly limit prescribed by law, representing 0.3 per cent of Berlins housing, but
this gure increased to seven per cent in areas of particularly high Airbnb density. The
authors also found that rental rates had increased more signicantly in areas with higher
Airbnb density. In Boston, Horn and Merante (2017) determined that every standard
deviation increase inAirbnb inventory volume wasassociated with a 0.4 per cent increase in
asking rents (and up to 3.1 per cent in areas of high Airbnb density), and a 5.9 per cent
decrease in long-term rental unit supply. In simpler terms, the authors found that every 75.8
Airbnb listings equated with 4.5 fewer long-term rental units. In Barcelona, Gant (2016)
found that in high-density areas, like the Gothic Quarter, Airbnb rentals represented up to
17 per cent of homes. Gant also interviewed locals in the Gothic Quarter about Airbnbs
impacts and found nearly all of her 42 interviewees spoke of displacement concerns. Gant
summarized that displacement occurs in various forms, including housing shortages, rent
increases and frustrations with daily disruptions, which together produce a snowball effect
of residents leaving and being replaced by tourism investors. Finally, in New York City,
Wachsmuth and Weisler (2017) explained that because Airbnb creates a new opportunity to
generate revenue through residential housing, it creates and augments a rent gapin which
landlordsactual earnings are smaller than their potential earnings, leading to loss of rental
housing via direct evictions and indirect displacement as housing is made unaffordable.
With a particular focus on gentrication, the authors found that although Airbnb revenue
was greatest in wealthier areas (e.g. Times Square), some peripheral areas (e.g. Harlem)
appeared ripe for gentrication.
Airbnb regulation
Concerns over housing markets are one of just various reasons why policymakers across
the world are looking to regulate Airbnb. Existing regulatory frameworks were not
prepared for the rapid rise of a technology-driven peer-to-peer short-term rental platform
like Airbnb, and the regulatory battles that have ensued have often been highly
contentious affairs. Crommelin et al. (2018) compared short-term rental laws in ve large
global cities and found that different regulatory instruments were used, but each city
historically had regulations on short-term letting prior to Airbnb, and each city has
limited the number of nights a short-term rental could be let out annually. Ferreri and
Sanyal (2018) explored how Airbnb is pushing to shape the short-term rental regulatory
environment in London. The authors additionally highlighted the challenges associated
with identifying and collecting evidence on violators of Londons 90-night quota passed
in 2015, which necessitated data scraping and triangulation. The authors argued that
such difculties raised questions about the degree to which platform companies like
Airbnb should be involved in the rule enforcement process. Taking a more general look at
Airbnb regulation, Gurran (2018) summarized the debate from an urban policy
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perspective and highlighted key concerns with Airbnb consumer protection measures
are challenging to enforce, Airbnb disrupts local neighborhood life and Airbnb adversely
impacts housing markets. Gurran also presented a future research agenda, calling for
greater knowledge regarding how Airbnb is changing conceptualizations of home,how
Airbnb is triggering gentrication and more general neighborhood change and how well
companies like Airbnb can self-regulate.
Looking at Airbnbs approach to its regulatory battles, McKee (2017) critiqued how
Airbnb has successfully framed the regulatory debates such that the default position has
been legality, even though Airbnb listings have often been illegal and produce negative
externalities. McKee posited that Airbnbs success on this front has come from its
association with notions of open markets (that are themselves positioned as natural,
politically neutral, consensual and efcient), family and technology. Similarly, Stabrowski
(2017) examined the Airbnb regulatory debate in New York City by describing how Airbnb
had positioned itself using alternative notions of community, sustainability and governance,
with a particular focus on individual economic empowerment coming from transformed
meanings of home.
Different jurisdictions have approached Airbnb regulation very differently, and several
studies have explored these differences. Tham (2016) compared Australia and Singapore
and found that the Australian governments response has been more fragmented and
localized, whereas Singapores response has been more driven by the federal government.
Tham also noted that Singapore was more resistant than Australia to Airbnb, despite being
fairly welcoming towards Uber. Hong and Lee (2017) interviewed various government and
Airbnb employees in South Korea and found that federal government ofcials were more
open than local ones to adapting regulations in a manner favorable to sharing economy
services like Airbnb, because the local ofcials were more beholden to their local
constituents. Somewhat similarly, Hong and Lee (2018) examined Airbnb regulatory policies
in 47 USA cities and determined that political competition (i.e. degree of victory in most
recent election) was positively associated with more favorable regulatory action towards
sharing economy companies like Airbnb, which is consistent with the general phenomenon
of more entrenched government administrations tending to favor the status quo, and vice
versa.
Offering a path forward, based on their examination of Airbnb data in ve major USA
cities, Wegmann and Jiao (2017) suggested four guiding principles for Airbnb policymakers
web scraping is imperfect but still cheap and effective for data gathering, regulation should
take into account local factors including Airbnb usage patterns, regulation is only
meaningful if paired with dedicated enforcement (often nancially supported by permit fees),
and commercial operators should be distinguished from other hosts. The enforcement issue is
particularly important because it can be so challenging, as was highlighted by Ferreri and
Sanyal (2018) and Leshinsky and Schatz (2018). However, Leshinsky and Schatz (2018) also
noted that some jurisdictions are therefore enlisting the assistance of private companies, but
the authors argued that this practice raises questions about the role that private companies
should play in the enforcement of public regulations.
Airbnbs impacts on the tourism sector
One of many factions involved in the Airbnb regulatory debates has been the hotel
industry. Hotels are naturally vulnerable to competition from Airbnb, but questions
remain as to whether Airbnb has a material negative impact on hotels, with some studies
detectingimpactswhileothershavenot. In their seminal study on Airbnbshotel
impacts, Zervas et al. (2017) examined nearly seven years of Airbnb and hotel data in
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Austin and found that Airbnb supply negatively impacted hotel revenues, primarily by
pressuring hotels to lower their rates, and particularly in hotels that were budget-priced
and/or had a limited business clientele. The authors additionally determined that this
negative impact was especially pronouncedduringpeakdemandtimes,whenAirbnb
supply would surge. Also looking at Austin, Xie and Kwok (2017) found Airbnb supply
increases exerted a signicant negative impact on hotel RevPAR. However, this impact
decreased as the price differential between a hotel and nearby Airbnb listings increased
or the dispersion of Airbnb prices increased. Looking at New York City hotels, McGowan
and Mahon (2018) found that Airbnb growth had exerted small, but statistically
signicant, effects on hotel revenue, occupancy and rates. The authors additionally noted
that despite Airbnbs limited apparent impacts, hotels should nonetheless be concerned
due to Airbnbs rapid growth.
Guttentag and Smith (2017) examined the substitution question at a micro level by
surveying Airbnb guests about what they would have done if Airbnb and other similar
platforms had not existed. The authors found that nearly two-thirds indicated they would
have otherwise used a hotel, with the majority indicating a mid-range hotel. Additionally,
over 25 per cent of the respondents indicated they would have used a hostel or a bed-and-
breakfast, and fewer than six per cent claimed they would have stayed with friends/family
or not taken the trip. The authors also examined guestscomparative performance
expectations from their last Airbnb with hypothetical nearby hotels, and found Airbnb was
generally expected to outperform budget hotels/motels, underperform upscale hotels, and
have mixed outcomes versus mid-range hotels when considering traditional hotel attributes
(e.g. cleanliness and comfort). When considering attributes more closely associated with
Airbnb (experiential attributes and low price), Airbnb was expected to signicantly
outperform all three hotel classes.
Despite such ndings, other research has not found that Airbnb has a material impact on
hotels. For example, Choi et al. (2015) found that Airbnb listing volume had virtually no
impact on hotel revenues in South Korea, and Ginindza and Tichaawa (2017) found that
hotel occupancy and Airbnb occupancy rates were positively correlated in Swaziland.
Additionally, Blal et al. (2018) examined several years of San Francisco hotel and Airbnb
data, and found Airbnb supply was unrelated to hotel RevPAR, and increases in average
Airbnb prices actually were associated with RevPAR increases among luxury hotels. On the
other hand, Blal et al. also found that increases in Airbnb satisfaction (as per user reviews)
were associated with RevPAR decreases, suggesting that improvements in Airbnb quality
may impact hotels negatively.
Researchers conducting qualitative research with hotel representatives also have
found limited levels of concern over Airbnb. For example, Koh and King (2017)
interviewed representatives from Singaporean economy/mid-tier hotels and hostels and
found they tended to feel as though a stricter regulatory environment was needed,
although Airbnb was not perceived as an immediate threat. Indeed, some hostel owners
even indicated that they listed rooms on Airbnb. Likewise, Varma et al. (2016)
interviewed a dozen USA hotel executives and found they doubted Airbnb was
impacting the hotel industry, feeling instead that Airbnb was targeting a different
segment of travelers. Nonetheless, whereas the larger hotels seemed satised to simply
monitor Airbnb, smaller hotels tended to be more active in opposing Airbnb through
actions like regulatory lobbying and enhancing their hotel products. Also, Tham (2016)
analyzed tourism industry responses to Airbnb in Australia and Singapore and
concluded the responses had been quite limited in both places.
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Finally, a handful of papers have examined Airbnbs potential competition with
hotels through a more conceptual lens. Hansen Henten and Windekilde (2016) explained
the emergence of Airbnb from a transaction cost theory perspective, explaining that
Airbnb facilitates transactions that would not have otherwise occurred, due to the prior
challenges for hosts and guests to nd each other, establish trust and engage in a
transaction. The authors posited that Airbnb has created a new market that both
complements and competes with the traditional hotel industry. Also using transaction
cost theory, a proposition paper by Akbar and Tracogna (2018) predicted that Airbnbs
inherent uncertainties, lack of specicity (i.e. Airbnb listings are not built to be short-
term rentals) and low transaction frequency may push the service to evolve from a
strictly peer-to-peer platform to a more integratedplatform in which there are tighter
standards, more centralized control and asset ownership. Forgacs and Dimanche (2016)
summarized some of the reasons why Airbnb has proven so successful (e.g. strong
business model, intuitive and user-friendly website and an association with authentic
local travel), but argued that hotels have ample opportunities to compete on these same
grounds by competing on value rather than rate, enhancing their websites,
incorporating local elements into their properties and embracing customer relationship
management practices. Lastly, Sovani and Jayawardena (2017) discussed how the
Canadian tourism sector should respond to sharing economy services like Airbnb,
based on a travel conference panel discussion on the sharing economy that included a
representative from Airbnb. Sovani and Jayawardena recommended that the Canadian
tourism sector have a positive attitude towards change, carefully revise relevant laws
and regulations, encourage technological innovations, try to quantify part-time
employment in the sharing economy and think outside the box in promoting healthy
competition.
The Airbnb company
The nal category of Airbnb research comprises articles that have examined the Airbnb
company more generally. For example, Boswijk (2017) summarized how value is co-created
by Airbnb and its community, as established by the companys vision, the unique
experience of staying in a locals home, Airbnbs creation of a trusted marketplace, the
companys culture and its innovative business model. Midgett et al. (2018) posited that
Airbnb should typically be more sustainable than hotels, and the authors presented various
associated propositions regarding energy use, emissions, water use, waste production, users
economic well-being and the creation of social ties. Taking a more critical stance, ORegan
and Choe (2017) critiqued Airbnb through the prism of cultural capitalism, highlighting that
the company has contributed to the commodication of life experiences. She further argued
that, although Airbnb adopts appealing rhetoric of sharing and speaks of other ideals like
sustainability and transformative travel, the company is just another prot-driven lodging
enterprise that is introducing new market-based problems into the tourism accommodation
sector.
Looking at Airbnbs advertising efforts, Pera and Viglia (2016) interviewed several
Airbnb users about an Airbnb YouTube ad that tells a hosts story. Many viewers were
emotionally engaged by the story and even became eager to meet the host and visit the
sites presented in the video. Considering hypothetical advertising techniques, Liu and
Mattila (2017) explained that two of Airbnbs key appeals are feeling a sense of
belongingness and the uniqueness of Airbnb accommodations. The authors conducted
an experiment in which they manipulated peoples sense of power, and found that
individuals feeling a sense of powerlessness responded more favorably to hypothetical
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Airbnb advertising focusing on belongingness, whereas individuals feeling a sense of
power responded more favorably to hypothetical Airbnb advertising focusing on
uniqueness, and these relationships were mediated by individualsself-brand
connection. Such forms of experimentation align well with a data-driven tech company
like Airbnb and, indeed, Airbnb employees Bion et al. (2018) described how they use the
free statistical software program Rfor various tasks, including data analysis,
experimentation and data visualization. Finally, Oskam and Boswijk (2016) mapped out
possible future scenarios for Airbnb, based on a travel sector workshop and a Delphi
panel, and suggested that Airbnbs evolution will differ between different cities,
primarily as a function of consumer demand and regulatory policies.
Future research
The plethora of studies that have been published on Airbnb in the past couple of years
demonstrate how academia can generate a large body of knowledge on a novel phenomenon
in a fairly short time. Nonetheless, this initial wave of research still has many limitations and
leaves many questions left unanswered. As was shown, the majority of the extant Airbnb
research has been conducted by researchers in the USA/Canada and Europe, and much of it
has similarly focused on these same regions, while destinations in the Caribbean, Latin
America, Africa and the Middle East have received minimal attention. Likewise, much of the
research has focused chiey on large urban destinations (e.g. New York City, Barcelona and
Sydney) rather than destinations that are less populous and/or popular with tourists, though
the dynamics of Airbnb may be very different in such locales.
Methodologically, there is also a need for greater diversity. While myriad methods have been
used to examine Airbnb, many research questions have been looked at repeatedly using the same
methods. Generally speaking, surveys have been used to understand guest motivations,
experiments have been used to investigate accommodation choice, surveying with structural
equation modelling has been used to examine loyalty, hedonic regressions have been used to
examine the importance of different listing attributes, text mining and content analysis have been
used to analyze Airbnb reviews and proles, and scraped data with geographic information
systems have been used to examine Airbnb dispersion. Nevertheless, these same questions
undoubtedly could be examined with other approaches. For example, choice experiments could
be used to examine guestsmotivations to use Airbnb, interviews with Airbnb guests could be
used to investigate satisfaction and loyalty, questionnaires could be used to investigate why
Airbnb listings tend to concentrate in certain areas, and hostsreviews of guests could be used to
better understand host experiences. In general, quantitative methods have been most common
thus far, and have helped to establish foundational knowledge on many topics, but there is ample
opportunity for more qualitative research that could offer rich insights into the many questions
surrounding Airbnb. Indeed, a large number of studies have relied on relatively easily accessible
listing and review data that is scraped from the Airbnb website, but it is time for more
researchers to tackle their research questions in more ambitious ways.
Also, by looking back at the Airbnb research agenda that Guttentag (2015) laid out several
years ago, it is clear that many important questions remain mostly unanswered. For example,
more research is needed regarding how Airbnb impacts destinationstourism economies beyond
the lodging sector, how non-hotel accommodations (e.g. hostels and bed-and-breakfasts) are being
affected by Airbnb, how online travel agencies and guided tour companies are being affected by
Airbnb, why Airbnb reviews tend to be so positive, whether tourists actually save money by
using Airbnb and what they do with those savings, how businesses and business travelers
perceive and use Airbnb, how users(or the general publics) attitudes towards Airbnb have
shifted over time, how later adopters differ from earlier adopters, how different regulatory
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measures impact Airbnb inventory and host behavior, and how residents perceive Airbnb and
the factors that inuence such attitudes. Also, it must be recognized that because Airbnb and its
regulatory environment are evolving so rapidly, older ndings could quickly become outdated
and need to be re-examined.
Conclusion
This paper has reviewed the research progress that has been made on the topic of Airbnb
since the companys inception. The literature review grouped the extant Airbnb research
into six different thematic categories Airbnb guests, Airbnb hosts, Airbnb supply and its
impacts on destinations, Airbnb regulation, Airbnbs impacts on the tourism sector and the
Airbnb company. By reviewing this large, recent body of literature, this paper lls a
signicant research gap. It additionally has highlighted several areas of Airbnb knowledge
that are beginning to mature as consensus emerges between similar studies. For example,
repeated ndings have demonstrated the importance of money in motivating both Airbnb
guests and hosts, the importance of attributes like room type and guest capacity in
determining listing prices, and the geographical concentration of Airbnb listings in many
city centers.
This literature review offers clear theoretical and practical implications. In terms of
theoretical implications, this review advances comprehension of the peer-to-peer
economy, it adds a new layer to understandings of tourism lodging choice and the
various factors (e.g. perceived authenticity) that inuence such choice, and it similarly
offers new perspectives for thinking about innovation and value co-creation. In terms of
practical implications, this paper provides a valuable synthesis of Airbnb knowledge that
should be useful to Airbnb and other tourism lodging providers as they compete for
guests, Airbnb and other peer-to-peer short-term rental platforms as they look to attract
and retain hosts, destination marketing organizations as they seek to better cater towards
modern tourist preferences and policymakers as they look to more effectively manage the
Airbnb phenomenon.
Despite the sizeable body of research that has been covered in this review, there remain
countless knowledge gaps that need to be lled by future research, and numerous
recommendations for such research have been provided. Such research is particularly
important due to the speed of technological innovation and regulatory ux that is constantly
shifting the Airbnb landscape. Fortunately, given the rapid pace at which Airbnb research
has been conducted over the past several years, one can be optimistic that researchers will
continue generating ndings of both theoretical and practical value on the increasingly
important subject of Airbnb.
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About the author
Daniel Guttentag is an Assistant Professor in Hospitality and Tourism Management in the School of
Business at the College of Charleston (South Carolina, USA). He additionally, serves as the Director of
the DepartmentsOce of Tourism Analysis. Daniel holds a Ph.D. in Recreation and Leisure Studies
and a masters degree in Tourism Policy and Planning, both from the University of Waterloo
(Ontario, Canada). He also worked for several years as the Tourism Business Analyst for the Toronto
Convention and Visitors Bureau. Daniel is particularly interested in tourism innovations, especially
as related to Airbnb and virtual reality. Daniel Guttentag can be contacted at: guttentagda@cofc.edu
For instructions on how to order reprints of this article, please visit our website:
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... Generally, [37] argues that residents perceived P2P accommodations as having more positive than negative impacts. The P2P accommodation industry has grown to the point that countless people have been transformed into hospitality micro-entrepreneurs [38,39]. The findings of [40] study reveal that rural Airbnb listings during COVID-19 took advantage of the low density of tourism facilities and businesses and the ease of social distancing in rural areas and targeted and promoted agritourism tourists. ...
... The research team selected Airbnb firms in KSA's eastern province's rural destinations to conduct the field study. As a result of its success, the number of Airbnb studies has recently grown [39], with the platform accounting for nearly 75% of peer-to-peer accommodation studies in the academic literature [33]. Data collection was accomplished through a web-based and paper questionnaire survey. ...