Factors Affecting Online Booking Intention and Behavior: The Case of Airbnb

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The Third International Scientific Conference
TOURISM IN FUNCTION OF
DEVELOPMENT OF THE REPUBLIC OF
SERBIA
Тourism in the Era of Digital Transformation
Thematic Proceedings
I
UNIVERSITY OF KRAGUJEVAC
FACULTY OF HOTEL MANAGEMENT AND TOURISM
IN VRNJAČKA BANJA
Vrnjačka Banja, 31 May - 2 June, 2018

ii
THEMATIC PROCEEDINGS
The Third International Scientific Conference
TOURISM IN FUNCTION OF DEVELOPMENT OF THE
REPUBLIC OF SERBIA
Тourism in the Era of Digital Transformation
Publisher
University of Kragujevac
Faculty of Hotel Management and Tourism in Vrnjačka Banja
For the Publisher
Drago Cvijanović, Ph.D. - Dean
Edited by
Drago Cvijanović, Ph.D., Faculty of Hotel Management and Tourism
in Vrnjačka Banja, Serbia
Arja Lemmetyinen, Ph.D., Turku School of Economics at the
University of Turku, Finland
Pavlo Ružić, Ph.D., Institute for Agriculture and Tourism, Poreč,
Croatia
Cvetko Andreeski, Ph.D., Faculty of Tourism and Hospitality, Ohrid,
Macedonia
Dragana Gnjatović, Ph.D., Faculty of Hotel Management and
Tourism in Vrnjačka Banja, Serbia
Tanja Stanišić, Ph.D., Faculty of Hotel Management and Tourism in
Vrnjačka Banja, Serbia
Andrej Mićović, Ph.D., Faculty of Hotel Management and Tourism in
Vrnjačka Banja, Serbia
Computer Support
Vladimir Kraguljac, M.Sc., dipl.ing.
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ISBN 978-86-89949-29-2, ISBN 978-86-89949-30-8
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opinions published in the Volume. They represent the authors’ point of view.
Publication of Thematic Proceedings was financed by the Ministry of Education,
Science and Technological Development of the Republic of Serbia.

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FACTORS AFFECTING ONLINE BOOKING INTENTION AND
BEHAVIOR: THE CASE OF AIRBNB
Marija Kuzmanović
1
; Zlatko Langović
2
;
Abstract
In a modern environment characterized by a high level of digitization
where information technologies are available to a large number of users,
the way of travel planning changes its shape. Namely, users increasingly
take over the role that agencies once had; they use online platforms not
only for information but for booking both accommodation and other
travel activities. In this context, peer-to-peer (P2P) platforms have
become particularly popular in recent years. One of the most popular and
fast-growing platforms is Airbnb, founded in 2008 with the idea to enable
owners to offer their unoccupied houses or rooms for short-term rental.
The aim of this paper is threefold: first, to explore the determinants of
online booking intention and behavior, then to explore the motivation of
respondents to select Airbnb platform, and finally, to determine the
respondents' preferences towards attributes of Airbnb properties.
Key Words: peer-to-peer, Airbnb, accommodation, motives, preferences
JEL classification: Z300
Introduction
The increasing digitization and the development of internet-based
technologies have a strong impact on all aspects of the both global and
local economy (Langovic & Pazun, 2016). Advances in digital
technologies have led to the emergence of new business models, mostly
collaborative, that potentially challenge the status quo of many industries
(Täuscher, 2017). Namely, companies‘ resources and capabilities have
become more modular, connectable, and conveniently shareable (El Sawy
1
Marija Kuzmanović, associate professor, PhD, University of Belgrade, Faculty of
Organizational Sciences, Jove Ilića 154, Belgrade, Serbia,
marija.kuzmanovic@fon.bg.ac.rs
2
Zlatko Langović, associate professor, PhD, Faculty of Hotel Management and Tourism,
Vrnjaĉka Banja, VojvoĊanska 5A, 36210 Vrnjaĉka Banja, Serbia, zlangovic@kg.ac.rs

133
& Pereira, 2013). In this context, a growing number of business models in
tourism based on sharing and collaborative practices between individuals
has been observed. These innovative businesses emerge from interactive
Internet technologies and one of the most popular forms of sharing
economy in tourism are peer-to-peer (P2P) accommodation services,
provided by Web 2.0 platforms such as Airbnb, Housetrip, or 9Flats.com
(Souza, Kastenholz, & Barbosa, 2017). According to Rimer (2017), P2P
accommodation rental could change travel behavior.
Airbnb is a San Francisco-based start-up company founded in 2008 by
Joe Gebbia, Brian Chesky and Nathan Blecharczyk. It is an on-line P2P
accommodation renting platform catering to hosts and travelers. Hosts use
Airbnb to promote their underused space (an entire home, private room or
shared room) and rent it out to others, while travelers use the site to book
and stay at another person's house. Airbnb acts as a third party between
hosts and travelers, and charges fees to both parties (Nguyen, 2016).
Airbnb is growing and currently operates in about 65,000 cities across
191 countries and currently offers 4 million listings – more listings than
the top five hotel chains combined have rooms (Hartmans, 2017). Various
differences were observed between Airbnb travelers and those who were
staying in traditional accommodation, taking into consideration the length
of stay, local spending and level of use. Namely, Airbnb guests are likely
to visit and spend money in the accommodation‘s neighborhood, as
frequently the accommodations are located outside the tourist core of
destinations. As a result, Airbnb guests are closer to ‗real life‘ of
destinations by direct contact with local residents, being immersed in
authentic local experiences (Guttentag et al., 2017). Moreover, Airbnb
platform create income for the local community and assists the growth of
economy (Tussyadiah & Pesonen, 2016).
According to Edelman and Geradin (2015), Airbnb was initially
considered to represent a danger to the affordability and safety of the
local population. Secondly, government agencies and hotels also treat
Airbnb as a threat for the hospitality businesses and traditional tourism,
such as lower- priced hotels, because Airbnb hosts do not have to follow
the standards set by hotels. According to Guttentag (2015), much Airbnb
rental activity is actually illegal and there are claims Airbnb is avoiding
its full tax obligations. Namely, a host on Airbnb website acts as an
accommodation provider without permission or official registration, so
safety standard of private accommodation is not guaranteed or inspected
like in hotels or hostels. In addition, because Airbnb renting currently

134
occurs largely in the informal sector, guests can generally avoid paying
the taxes that are typically charged in the traditional accommodation
sector.
Airbnb services as low-end offers are mostly suitable for young travelers
who are familiar with technological devices, use social networks, love to
explore new things and prefer inexpensive housing. However, the use of
online services has long been associated with privacy threats ‒ sharing
personal data and information online renders Internet users vulnerable to
both accidental and intentional harm caused by other users (Lutz et al.,
2017). Surveys have shown that despite reported online privacy concerns,
users extensively use online services and share personal information
online. This apparent divergence between attitudes and behavior is known
as a privacy paradox (Savić & Kuzmanović, 2017).
A large number of both hosts and travelers from Serbia joined the Airbnb
platform and this number is constantly on the rise. The largest offer of
accommodation is in the capital Belgrade, then in Novi Sad, followed by
the mountains Zlatibor and Kopaonik. Most accommodations offer the
type of entire home, then in a private room, and a negligible number in
shared rooms. According to Airbnb, there are over 75000 reviews for
accommodation in Serbia, while the average rating is 4.5 out of 5.
The aim of this paper is multifaceted. Firstly, the determinants of booking
intention and behavior in P2P accommodation platforms will be explored.
Through the empirical research, the motivation of respondents to select
Airbnb platform will be investigated, as well as their preferences towards
features both of Airbnb service and properties.
Literature review
The academic literature on Airbnb concept remains limited, and the
phenomenon of Airbnb, in general, is being examined within the context
of ―sharing economy‖ or ―collaborative economy‖. Recent researches
address variety of the issues: Some studies focused on the hosts'
motivation and performance (Li, Moreno, & Zhang, 2015; Mittendorf &
Ostermann, 2017), legal issues (Lee, 2016), Airbnb's branding strategies
(Yannopoulou, Moufahim, Bian, 2013), while others investigated the
impact of Airbnb on the hotel industry (Fang, Ye, & Law, 2015; Neeser,
2015; Zervas, Proserpio, & Byers, 2017). A few studies also explored the
consumer view of the Airbnb experiences and the motivational factors

135
that influence their choice (Nguyen, 2016; Mittendorf & Ostermann,
2017).
Nguyen (2016) explored and identified customer perceived value inside
the sharing economy in the case of Airbnb. The results indicate that the
sharing economy offers customers alternative choices with easier
consumption methods at a lower cost, as well as a unique, personal and
socially-integrated experience. Moreover, the author finds out that
although consumers are aware of the potential costs and risks, they still
prefer using the sharing economy because of its flexibility and
uniqueness. Mittendorf and Ostermann (2017) investigated how social
motives, trust, and perceived risk of private and business customers,
influence the hosts‘ intention to accept a booking request of respective
type of customer on Airbnb. Specifically, authors evaluated whether
social motives influence the hosts‘ intentions to accept a business
customer and to accept a private customer differently. They found the
effect of trust as a positive and perceived risk as a negative direct
antecedent of the host‘ intention to accept customers on Airbnb.
According to Tussyadiah (2016), satisfaction and return intention
represent important factors for commercial sharing services such as
Airbnb, who are linked to P2P accommodation.
As in other sectors, there is a risk in tourism, and preferences of tourists
change depending on the risk perception of specific destinations (Katić,
Kuzmanović, & Makajić-Nikolić, 2017). Liang, Choi and Joppe (2018)
researched consumer repurchase intention, perceived value, and perceived
risk into the realm of the P2P economy, specifically in the context of
Airbnb. They showed that perceived risk negatively impacts Airbnb
consumers‘ perceived value and repurchase intention while perceived
value positively enhances their repurchase intention. In addition, they
found that the price sensitivity no reducing customers‘ perceived risk but
can improve their perceived value and positively influences them to
repurchase the Airbnb products. Perceived authenticity was found to have
a significant effect in reducing Airbnb consumers‘ perceived risk and
positively influencing their perceived value.
Motivation to use Airbnb
Lamb (2011) examined the motivations behind CouchSurfing and Airbnb
hosts and guests, focusing on their desire for authentic interpersonal
experiences. He found that Airbnb guests were primarily attracted to the

136
service by their desire for such experiences, while financial savings
played a small role in their decisions. Household amenities and space
have additionally been acknowledged in several studies and actually were
the two top motivations found by Quinby and Gasdia (2014). Guttentag
(2015) categorized Airbnb as a disruptive innovation. He found that low
cost is the main draw for people participating in Airbnb. According to
this author, the experiential appeal also represents a significant
characteristic to be considered in the decision of using Airbnb because of
the fact that consumers tend to search for authentic experiences where
they feel like travellers and not tourists (Rimer, 2017). According to
Möhlmann (2015), the decision of using Airbnb is based on factors such
as: economic considerations, familiarity, utility or the trustworthiness of
the host about photos, reviews and, finally, the price policy. Hamari et al.
(2016) discovered other elements such as sustainability, enjoyment, and
economic benefits.
Tussyadiah (2015) surveyed drivers and deterrents of the use of P2P
accommodation rental service from the customers‘ perspective. The
author finds out that the motivations that drive the use this type of
accommodation include the societal aspects of sustainability and
community, as well as economic benefits as most significant driver. On
the other hand, factors that deter the use of P2P accommodation rental
services include lack of trust, lack of efficacy with regards to technology,
and lack of economic benefits. In a similar study, Tussyadiah and
Pesonen (2016) examined motivations to use P2P accommodation rental
service among American and Finnish users. The authors used 12
motivation statements rooted in the collaborative consumption literature,
and an exploratory factor analysis revealed two factors – Social Appeal
and Economic Appeal – plus several items (including location
convenience and search efficiency) that did not load onto either factor
(Guttenta et al., 2017).
Stors and Kagermeier (2015) focused at the motivations and expectations
of the Airbnb guests, taking Berlin as a case study. They found that, as
expected, the monetary dimension plays an important role when it comes
to choosing share economy accommodation. However, the survey
revealed that other dimensions are at least as important. In addition to
practical reasons, the key motivation factors are aspects related to
authenticity in sense of social interaction between guests and hosts, the
location of the flats/rooms within the city (in residential quarters), and
personal contact. Mody, Suess, and Lehto (2017) conducted the study to

137
compare and contrast customers‘ experiences of hotels and Airbnb. The
authors considered eight dimensions of the experience economy:
entertainment, education, escapism, esthetics, serendipity, localness,
communities, and personalization. They found that Airbnb appears to be
leveraging these eight dimensions to a greater extent than the hotel
industry, while two of the top three areas in which Airbnb outperforms
hotels are communities and localness.
Effects on the tourism industry
Nowak et al. (2015), surveyed U.S. and European travelers in order to
gauge Airbnb‘s potential threat to hotels and online travel agencies. The
respondents who had used Airbnb within the previous year were asked
about the factors that led them to use Airbnb, and 55% indicated ―cheaper
price,‖ 35% indicated ―location,‖ 31% indicated ―authentic experience,‖
25% indicated ―own kitchen,‖ 24% indicated ―uniqueness of unit,‖ 23%
indicated ―easy to use app/site,‖ and 17% indicated ―large party
accommodation.‖ (Guttentag, 2016). Yrigoy (2016) argues that the
emergence of Airbnb is triggering a wave of tourism led-gentrification
which is rooted in a substitution of the residential rental by a tourism
rental market. The impact of Airbnb on traditional accommodations and
hotel revenues has also been studied in many studies. According to some
authors, Airbnb is expected to drive hotel rates and revenues down as the
additional supply will affect the distribution of the market power (Oskam
& Boswijk, 2016).
Zervas et al. (2017) examined the relation between changes in the volume
of Airbnb listings and hotel revenues in Texas. He estimated a 13 percent
loss of room revenue for Austin and a 0.35 percent decrease in the
monthly hotel room revenue for every 10 percent increase in Airbnb
listings for Texas in general. The authors also found that the impacts were
greater at independent and hotels without business facilities. Neeser
(2015) replicated Zervas‘s approach to examine Airbnb‘s impacts in
Norway, Sw
ede
n, and Finland. He found that Airbnb appeared to
negatively impact hotels‘ average daily rate s, but did not impact revenue
per available room, leading him to surmise that hotels were reducing rates
in an effort to maintain occupancy levels.
Oskam and Boswijk (2016) analyzed potential further development of
Airbnb in the next five years and the impact this developments will have
on tourism, on hotels and on city destinations. According to them,

138
compared to hotels, Airbnb hosts offer competitive pricing because in the
case of private residences fixed costs as rent and electricity are already
covered, the fact that Airbnb revenue is usually an additional income, and
because stays are usually not taxed.
Measurement Instrument
In January and February of 2018, an online survey was conducted in
Serbia to determine factors affecting respondents choose Airbnb platform,
their experiences and satisfaction.
Sample
Since Airbnb is relatively new platform, used by only a relatively small
part of the population, both worldwide and in Serbia, the sample for this
study (guests who have ever used Airbnb) is hard-to-reach. Therefore, we
decided to base our research on a sample of travelers in general or those
who participate in organizing and planning trips, not just Airbnb users. In
this way, it is possible to determine the percentage of respondents who
have not used or heard about Airbnb so far, and how many of them are
currently not planning to use Airbnb in the future and what the reasons
are. For that purpose, a multiple-frame sampling online non-random
approach was used to recruit an adequate number of respondents for the
analyses. The majority of the respondents were recruited via travel blogs
or social network Facebook, which proved to be effective in recruiting
respondents from hard-to-reach populations (Vukić & Kuzmanović,
2017). The online social network produced a high-quality data that was
also cost-effective. Part of the data was collected through the snowball
sampling method. We asked the respondents to share survey link on their
Facebook and ask their friends and friends-of-friends if they are interested
in participating in the research.
Survey design
The research was conducted as an online questionnaire. It consisted of
five sections, based on short open-ended questions, multiple choice or 5-
point Likert scale. Section A comprised the socio-demographic questions
regarding gender, age, level of education, employment status, and
household income level. In addition, it contains self-assessment questions
related to fluency in English, respondents‘ risk preferences, preferences
toward adventure, as well as preferences toward social aspect of

139
travelling. These questions were used to describe the sample in order to
establish a mutual relationship with their motives.
Section B contained questions regarding original communication channel
creating awareness of Airbnb, most recent Airbnb use, trip characteristics
(when, how long, on which occasion etc.), and respondents‘ overall
satisfaction on 5-point Liker scale. Respondents‘ intentions to recommend
Airbnb to others and to use the service again were used to measure a
loyalty index score. Those respondents who do not plan to use Airbnb
ever, were asked to state the reasons.
Questions related to factors affecting choose Airbnb belong to the part C
of the questionnaire. Agreement with 12 different potential motivation
factors, organized in 6 different dimensions, was measured using the 5-
point Likert scale (1 = strongly disagree to 5 = strongly agree). These
factors are derived both from existing literature (Guttentag, 2016) and
through pre-research. The first factor, Price, has been identified in the
existing Airbnb literature as a key comparative advantage of Airbnb
relative to other accommodation options. Five items relating to functional
attributes were included, based on
exi
s
ti
ng Airbnb research and
alternative accommodation research. These items related to location
suitability, access to household amenities, access to a large amount of
space, the homely feel of the accommodation, and the opportunity to
receive useful information and tips from one‘s host. Four items were
included regarding the desire for unique and authentic local experiences.
One item referred generally to the opportunity for an authentic local
experience. This dimension also included one item relating to interaction
with the host or other locals, and one item relating to the
accommodation‘s location i.e. staying in a non-touristy area and one
related to the opportunity to do something new and different. Two items
related to the philosophy of the sharing economy were included, with one
referring generally to Airbnb‘s philosophy, and one referring to
accommodation expenditure going directly to locals.
Section D contains questions relating to comparative performance
expectations. Namely, to better understand Airbnb‘s strengths and
potential weaknesses related to a hypothetical nearby hostel, budget, a
mid-range, and upscale hotel, respondents had to assess the expected
performance along various attributes. The following attributes have been
selected: cleanliness, comfort, security, ease of booking and price.
Previous research has shown that location and price represent a

140
comparative advantage of Airbnb accommodation, while comfort, safety
and cleanliness may be a potential weaknesses in relation to certain
hotels. All of these assessments were measured with the 5-point Likert
scale.
Section E was focused on the key criteria when one choosing a particular
accommodation on Airbnb. The task was to rank the eight offered factors
from the one that is most significant to the least significant one. Attributes
such as price, location, amenities, house rules and the like, as well as
photos of the host, were included.
Results
Sample characteristics
In total 214 respondents completed the survey. The sample mainly
consisted of women (59.6%). The overall sample average age is 28.02
(SD = 8.27), while the respondents are between 18 and 55 years old. The
majority of them completed high school (45.73%) or gained one of the
university degrees, and they are either students (47.66%), or employed
(46.73%). 87% perceived their household financial status as at least
―average‖. More detailed statistics regarding demographic data is shown
in Table 1.
Table 1: Demographic data
Demographic
Category
Percent
Gender
Male
40.4%
Female
59.6%
Age
18-22
34.58%
23-28
33.64%
29-34
9.35%
35-40
12.15%
>40
10.28%
Level of education
High school
45.73%
Undergraduate
24.30%
Master degree
22.43%
PhD degree
6.54%

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Employment status
Students (university)
47.66%
Unemployed
5.60%
Employed
46.73%
Household income
Well below average
0.93%
Below average
12.15%
Average
41.12%
Above Average
42.99%
Well above average
2.80%
Most respondents consider themselves to be tourists when traveling
(60.75%), 32.71% of them is declared as a traveler, and only 4.67% as
perceived themselves as backpackers. Two respondents choose option
―other‖. Even 21.5% of respondents did not hear about Airbnb and are not
aware of the services provided by this accommodation platform. The
highest percentage of respondents become aware of Airbnb through word-
of-mouth (30.81%), online word-of-mouth (10.28%), Airbnb advertising
(8.41%) and mass media (7.48%), while 20.56% of respondents do not
remember how they first heard about Airbnb.
Airbnb usage experience
Of all respondents who are aware of the existence of Airbnb, almost 40%
have already used and plan to continue using Airbnb, and 46.5% non-
users will use it in the future. Respondents who do not plan to use Airbnb
at all (14%), as the main reasons for that indicate mistrust and
uncertainty. The two respondents even cited ideological reasons as an
obstacle to use Airbnb.
Only 2.80% of the respondents have so far been Airbnb's host, but none
of them has used Airbnb as a guest until now, although everyone states
that they are planning to use it in the future. Almost 50% of current
Airbnb users are considering to offer their accommodation and being a
host.
As can be seen from Table 2, respondents perceived themselves as a very
fluent in English, stating that the social aspects of travel are very
important for them. Although they consider themselves to be adventurers

142
to a large extent, they are somewhat less willing to take the risk when
organizing trips.
Table 2: Self-assessment results
Current
users
Potential
users
Non
users
Not
aware
Average
rate
Fluency in English
4.24
4.08
4.00
3.61
4.00
Significance of social aspect
of traveling
4.18
4.08
4.08
3.39
3.93
Considering
himself/herself as an
adventurous type
3.91
3.17
4.00
3.87
3.85
Readiness to take the risk
when organizing a trip
3.70
2.92
3.31
3.30
3.38
However, there is a difference between the groups of the respondents
depending on their awareness and the usage of Airbnb (see Table 2).
Current Airbnb users say they are very fluent in English (rate 4.24 of 5).
The social aspect of traveling is more important for respondents in this
segment than for other segments (4.18) and they are most likely to take a
risk when organizing a trip (3.91). The lowest risk-seeking are those
respondents who are aware of the existence of Airbnb, but do not want to
use it. At the same time, these respondents evaluate themselves as
adventurers to a lesser degree than other segments. They mostly estimate
the household income below the average.
Respondents who are not aware of Airbnb consider their English fluency
to be considerably lower than other respondents (3.61) and the social
aspect of travel is least important for them (3.39). Almost all in this
segment are students. Although they evaluate themselves as adventurers,
above mentioned characteristics may partially represent the barrier to
using the online platforms such as Airbnb.
The first choice of accommodation of most of respondents (on a private
trip, according financial possibilities) is mid-range hotel (33.64%)
followed by Airbnb (26.17%), hostel (14.95%), and Bed&Bearkfast
(10.28%). Only 8.41% of respondents listed the budget hotel as the first
choice of accommodation, 2.80% listed the upscale hotel, and 1.87%
CouchSurfing. Table 3 shows the first choice of accommodation within
certain groups of respondents. Most of the respondents who already had

143
experience with Airbnb, referred to as their first choice, while the other
respondents prefer a mid-category hotel or hostel.
Table 3: First choice of accommodation
Current
users
Potential
users
Non
users
Not
aware
Sample
Airbnb
19.63%
0.00%
6.54%
0.00%
26.17%
Bed &
Breakfast
1.87%
0.93%
3.74%
3.74%
10.28%
Couch
Surfing
1.87%
0.00%
0.00%
0.00%
1.87%
Hostel
0.93%
2.80%
7.48%
3.74%
14.95%
Budget hotel
1.87%
1.87%
1.87%
2.80%
8.41%
Mid-range
hotel
2.80%
5.61%
14.02%
11.21%
33.64%
Upscale hotel
1.87%
0.00%
0.93%
0.00%
2.80%
Other
0.00%
0.00%
1.87%
0.00%
1.87%
Total
30.84%
11.21%
36.45%
21.50%
100.00%
For their most recent Airbnb stay, 63.64% respondents had been traveling
for leisure and 21.21% for business; 69.70% were staying in an entire
home and 27.27% in private room; 54.55% were staying for between two
and four nights (in average 5.51 nights), and 39.39% were staying with a
friends. Finally, 63.64% had used Airbnb no more than three times, and
75.85% are used it in the past year.
Figure 1: Overall satisfaction with Airbnb and intention to recommend it
As can be observed on Figure 1, overall satisfaction is very high, as 82%
of the respondents indicated that they were either very satisfied or
satisfied with their most recent Airbnb stay. Likewise, agreement with the
―Intention to recommend‖ was also very high, with over 90% of the

144
respondents indicating they were very likely or likely to recommend
Airbnb to a friend, family member, or colleague.
Motivation factors
Descriptive statistics for each of motivation items can be found in Table
4. As can be seen, respondents on average agreed with nearly all of the
proposed motivations (with 3.1 as the lowest average score on the scale
up to 5). On average, respondents agreed most strongly with the attribute
‗suitable location‘, followed by ‗Airbnb philosophy‘. They also exhibited
a fairly high level of agreement with the ‗low cost‘ and other two
functional attributes (‗large amount of space‘ and ‗useful info/tips from
host‘). Furthermore, respondents indicated moderate levels of agreement
with the motives ‗To interact with host, locals‘ and ‗for the access to
household amenities‘. Finally, respondents stated some agreement with
the motives ‗for the homely feel‘ and ‗to do something new and
different‘, and minimal agreement with the motives ‗money to locals‘ and
‗non-touristy neighborhood‘.
Table 4: Motivations to choose Airbnb
Dimension (Mo
ti
v
ati
on)
Users
Non-
users
Avg.
S.D.
Price
For its comparatively low cost
3.53
3.55
3.54
1.19
Functional attributes
For the access to household amenities
3.32
3.16
3.24
1.33
For the large amount of space
3.80
3.26
3.51
1.19
To receive useful local information and tips
from my host
3.24
3.76
3.53
1.24
For the homely feel
3.20
3.05
3.12
1.30
For the suitable location
4.16
3.86
4.00
1.11
Unique and local authenticity
To have an authentic local experience
2.90
3.11
3.02
1.29
To stay in a non-touristy neighborhood
3.16
2.89
3.01
1.30
To do something new and different
2.77
3.38
3.10
1.25
To interact with host, locals
2.94
3.49
3.24
1.35
Sharing economy philosophy
I prefer the philosophy of Airbnb
3.58
3.73
3.66
1.06
I wanted the money I spent to go to locals
2.66
3.32
3.01
1.32

145
However, there is a difference between the respondents who are already
Airbnb users and those who plan to be in the future. Namely, Airbnb
users agreed more strongly with the Functional attributes dimension
(except with ‗useful info/tips from host‗) than non-users. Also, they are
more motivated by possibility to stay in a non-touristy neighborhood than
non-users. On the other hand, non-users are mostly motivated with the
dimensions Unique and local authenticity and Sharing economy
philosophy.
Respondents’ preferences for accommodation attributes
The results of ranking eight Airbnb accommodation attributes in terms of
their significance for respondents when choosing a specific place to stay
are given in the Table 5. The first-ranked attribute is the location of the
property, followed by the price and overall rate (numbers of stars).
Cancellation policy and host‘s photo proved to be the least important
criteria when choosing a particular property.
Table 5: Attributes overall rank and frequency in top three ranked
Attribute
Overall
rank
Frequency as a
first-ranked
Frequency as a
second-ranked
Frequency as a
third-ranked
Location
1
70
34
8
Price
2
40
42
26
Overall rate
3
14
16
28
Number of
reviews
4
4
12
22
Amenities
5
6
24
24
House rules
6
2
4
18
Cancellation
policy
7
0
4
10
Host
photography
8
2
2
2
Comparative performance expectations
As can be observed in Table 6, respondents had very different
performance expectations regarding Airbnb and other accommodation
types. Moreover, there is also a significant difference in expectations
between current users and non-users of Airbnb i.e. the non-users'
expectations are lower and this may be the result of their inexperience.

146
Airbnb users expected Airbnb to outperform all other accommodation
categories in terms of easier bookings.
Table 6: Comparative performance expectations
Airbnb
Hostel
Budget hotel
Mid-range hotel
Upscale hotel
Cleanliness
users
4.12
2.88
3.00
4.03
4.64
non-users
3.81
2.94
3.17
4.11
4.75
Comfort
users
4.12
2.21
2.58
3.55
4.36
non-users
3.83
2.53
2.83
3.75
4.58
Security
users
3.82
2.82
3.27
4.00
4.52
non-users
3.75
2.61
3.03
3.83
4.39
Ease of booking
users
4.48
4.03
4.03
4.06
4.06
non-users
3.86
3.61
3.56
3.97
4.06
Price
users
4.30
4.42
3.85
3.42
2.79
non-users
4.03
4.14
3.58
3.03
2.53
In general, respondents expected Airbnb to significantly outperform
budget hotels for all attributes as well as hostels for all but one (price)
attribute. Comparing with mid-range hotels, respondents expected Airbnb
to significantly outperform them with regards to Airbnb‘s supposed
strength (price) and two supposed hotel strengths (cleanliness and
comfort). On the other hand, respondents expected Airbnb to
underperform mid-range hotels concerning security. Finally, respondents
expected Airbnb to underperform upscale hotels with regards to all
attribute except price (as was expected).
Conclusion
This paper provides a significant insight into the motives and preferences
of respondents in Serbia related to the use of the online accommodation
platform Airbnb. To our best knowledge, this is the first empirical study
to address this issues in Serbia.
The results show that the number of users of this platform in Serbia (both
as guests and hosts) will grow in the future. Namely, the current users

147
showed a high level of loyalty and repurchase intentions, while the non-
users are specially motivated by the factors related to unique and local
authenticity experience and possibility to interact with locals, beside the
price. Nevertheless, barriers have been identified for use Airbnb by a
certain portion of the population, and that are uncertainty and lack of
trust, besides to the level of English fluency. The comparative advantage
of Airbnb relative to hostel and hotel accommodations has been also
empirically investigated. It has been shown that the main competitive
advantage in addition to the price is the comfort that is considered in
literature to be the strength of the hotels.
The findings of the study have important theoretical as well as practical
implications for the various stakeholders in the tourism industry,
including policy makers. Namely, the literature related to the both
motives and obstacles for using the P2P accommodation platforms has
been enriched. Furthermore, the research findings can be of benefit to
Airbnb itself in order to improve their service, but also to those who are
considering to be hosts in the future. The results of the study indicate that
hotels are confronted with growing competition in the form of P2P
platforms primarily due to its price, location and comfort. Hotel
management can use these findings to upgrade its own competitive
advantages but also to strive to keep up with market trends and meet
customer needs better. Given the importance of tourism for the
development of the entire regions and the state itself, and bearing in mind
that the survey results show a significant growth of interests in P2P
accommodation, our results have significant implications for policy
makers also. Namely, community revenues rely in part on tax receipts
from well-regulated hotel industry. With demand shifting away from
these traditional form of accommodation, regulation and taxation of P2P
platforms becomes more challenging.
Future research should be directed towards post hoc segmentation, based
on the respondents‘ preferences. A tool that could be useful for that
purpose is conjoint analysis (Kuzmanović, 2006). The method was
originally developed to measure consumer preferences, but proved to be
very useful and applicable in many other areas including tourism and
hospitality industry (Vukic, Kuzmanovic, & Kostic-Stankovic, 2015).

148
References
1. Edelman, B., Geradin, D. (2015). Efficiencies and regulatory shortcuts:
How should we regulate companies like Airbnb and Uber. Stanford
Technology Law Review, 19, 293.
2. El Sawy, O., Pereira, F. (2013). Business modelling in the dynamic
digital space: An ecosystem approach. Heidelberg: Springer.
3. Fang, B., Ye, Q., Law, R. (2016). Effect of sharing economy on
tourism industry employment. Annals of Tourism Research, 57, 264-267.
4. Guttentag, D. (2015). Airbnb: disruptive innovation and the rise of an
informal tourism accommodation sector. Current issues in Tourism, Vol.
18, No. 12, 1192-1217.
5. Guttentag, D. (2016). Why tourists choose Airbnb: A motivation-based
segmentation study underpinned by innovation concepts. PhD Thesis.
University of Waterloo.
6. Guttentag, D., Smith, S., Potwarka, L., Havitz, M. (2017). Why
Tourists Choose Airbnb: A Motivation-Based Segmentation Study.
Journal of Travel Research.
7. Hamari, J., Sjöklint, M., Ukkonen, A. (2016). The sharing economy:
Why people participate in collaborative consumption. Journal of the
Association for Information Science and Technology, Vol. 67, No. 9,
2047-2059.
8. Hartmans, A. (2017). Airbnb now has more listings worldwide than the
top five hotel brands combined. http://www.businessinsider.com/airbnb-
total-worldwide-listings-2017-8 (20 January 2018).
9. Katić, A., Kuzmanović, M., Makajić-Nikolić, D. (2017). Analiza
uticaja rizika od terorizma na odabir turistiĉke destinacije. In
International Thematic Monograph: Modern Management Tools and
Economy of Tourism Sector in Present Era, (pp. 457-471). Association of
Economists and Managers of the Balkans; Faculty of Tourism and
Hospitality, Ohrid.

149
10. Kuzmanović, M. (2006). Kvantitativne metode u upravljanju
marketingom: Primena Conjoint analize. Monografija. Beograd: Društvo
operacionih istraţivaĉa Srbije.
11. Langovic, Z., Pazun, B. (2016). Virtualization of information
resources in tourism organizations. In Tourism in function of development
of the republic of Serbia: Spa Tourism in Serbia and Experiences of other
Countries (pp. 536-552). Vrnjaĉka Banja: University of Kragujevac,
Faculty of Hotel Management and Tourism.
12. Lee, D. (2016). How Airbnb Short-Term Rentals Exacerbate Los
Angeles's Affordable Housing Crisis: Analysis and Policy
Recommendations. Harv. L. & Pol'y Rev., 10, 229.
13. Li, J., Moreno, A., Zhang, D. (2015). Agent behavior in the sharing
economy: Evidence from Airbnb. Ross School of Business.
14. Liang, L., Choi, C., H., Joppe, M. (2018). Understanding repurchase
intention of Airbnb consumers: Perceived authenticity, electronic word-
of-mouth, and price sensitivity. Journal of Travel & Tourism Marketing,
Vol. 35, No. 1, 73-89.
15. Lutz, C., Hoffmann, C. P., Bucher, E., Fieseler, C. (2017). The role of
privacy concerns in the sharing economy. Information, Communication &
Society, 1-21.
16. Mittendorf, C., Ostermann, U. (2017). Private vs. Business Customers
in the Sharing Economy–The implications of Trust, Perceived Risk, and
Social Motives on Airbnb. Proceedings of the 50th Hawaii International
Conference on System Sciences.
17. Mody, M., Suess, C., Lehto, X. (2017). The accommodation
experiencescape: A comparative assessment of hotels and Airbnb.
International Journal of Contemporary Hospitality Management, Vol. 29,
No. 9, 2377-2404.
18. Möhlmann, M. (2015). Collaborative Consumption: determinants of
satisfaction and the likelihood of using a sharing economy option again.
Journal of Consumer Behaviour, Vol. 14, No. 3, 193-207.

150
19. Neeser, D. (2015). Does Airbnb hurt hotel business: Evidence from
the Nordic countries. University Carlos III de Madrid.
20. Nguyen, H. (2016). Customer value in Sharing economy: The case of
Airbnb. University of Tampere.
21. Nowak, B. A. (2015). Global insight: Who will Airbnb hurt more-
hotels or OTAs. Morgan Stanley Research.
22. Oskam, J., Boswijk, A. (2016). Airbnb: the future of networked
hospitality businesses. Journal of Tourism Futures, Vol. 2, No. 1, 22-42.
23. Quinby, D., Gasdia, M. (2014). Phocuswright. Retrieved from Share
This! Private Accommodation & the Rise of the New Gen Renter:
http://www.phocuswright.com/Travel-Research/Consumer-Trends/Share-
This-Private-Accommodation-the-Rise-of-the-New-Gen-Renter.
24. Rimer, R. (2017). Why do people choose to stay with Airbnb? Modul
Vienna University.
25. Savić, G., Kuzmanović, M. (2017). Behaviour and Attitudes vs.
Privacy Concerns of Social Online Networks. In Knowledge Discovery in
Cyberspace: Statistical Analysis and Predictive Modeling (121-150).
Nova Publisher.
26. Souza, L., Kastenholz, E., Barbosa, M. (2017). Tourist experience,
perceived authenticity and place attachment when consuming person-to-
person accommodation: evidence for destination loyalty. The Art of
Living Together, 7th Advances in Tourism Marketi (5-60). CEFAGE.
27. Stors, N., Kagermeier, A. (2015). Motives for Using Airbnb in
Metropolitan Tourism—Why do People Sleep in the Bed of a Stranger?
Regions Magazine, Vol. 299, No. 1, 17-19.
28. Täuscher, K. (2017). Business Models in the Digital Economy: An
Empirical Classification of Digital Marketplaces. Leipzig: Fraunhofer
Center for International Management and Knowledge Economy.
29. Tussyadiah, I. (2015). An Exploratory Study on Drivers and
Deterrents of Collaborative Consumption in Travel. In I. Tussyadiah, &

151
A. Inversini, Information & Communication Technologies in Tourism
2015 (817-830). Springer Internationa.
30. Tussyadiah, I. (2016). Factors of Satisfaction and Intention to Use
Peer-to-Peer Accommodation. International Journal of Hospitality
Management, 55, 70-80.
31. Tussyadiah, I., & Pesonen, J. (2016). Impacts of Peer-to-Peer
Accommodation Use on Travel Patterns. Journal of Travel Research, Vol
55, No. 8, 1022-1040.
32. Vukić, M., & Kuzmanović, M. (2017). Primena conjoint analize u
pozicioniranju gasrtronomskih manifestacija na turistiĉkom trţištu Srbije.
In Thematic Monograph: Modern Management Tools and Economy of
Tourism Sector in Present Era (240-256). Association of Economists and
Managers of the Balkans; Faculty of Tourism and Hospitality, Ohrid.
33. Vukic, M., Kuzmanovic, M., & Kostic-Stankovic, M. (2015).
Understanding the Heterogeneity of Generation Y's Preferences for
Travelling: a Conjoint Analysis Approach. Journal of Tourism Research,
Vol. 17, No. 5, 482-491.
34. Yannopoulou, N., Moufahim, M., & Bian, X. (2013). User-generated
brands and social media: Couchsurfing and AirBnb. Contemporary
Management Research, Vol. 9, No. 1, 85.
35. Yrigoy, I. (2016). The impact of Airbnb in the urban arena: towards a
tourism-led gentrification. The case-study of Palma old quarter (Mallorca,
Spain). Turismo y crisis, turismo colaborativo y ecoturismo. XV Coloquio
de Geografía del Turismo, el Ocio y la Recreacion de la AGE (281-289).
36. Zervas, G., Proserpio, D., & Byers, J. (2017). The rise of the sharing
economy: Estimating the impact of Airbnb on the hotel industry. The rise
of the sharing economy: Estimating the impact of Airbnb on the hotel
industry. Journal of Marketing Research, Vol. 54, No. 5, 687-705.