To Study the future of tourism with respect to AirBnB over conventional hotels
Abstract
In recent years, the emergence of AirBnB has become popular. Instead of conventional hotels
it has been found that people prefer to choose AirBnB for accommodation. The hospitality
industry has been flooded with AirBnB, As Airbnb has thoroughly transformed the way
people stay in hotels in recent years. Airbnb provide a much homely experience with a more
rational expense and also comprises higher accessibility in more isolated areas.
In the proposal, we aim to comprehend how AirBnB altered the dynamics of the hotel sector
throughout the world. And, how does the consumer preference for AirBnB over traditional
hotels affect the hotel industry, as AirBnB has totally made over the manner people stay in
hotels in the contemporary era. And lastly, to determine the components impacting a person's
preference for accommodation.
Keywords
Hospitality, Future, Preference
Introduction
Within a few years of its inception in 2009, Airbnb had become one of the most successful
sharing economy platforms. The company has developed its business model based on a
compelling value proposition. It integrates economic benefits for travellers and residents of
tourist areas via a trusted marketplace that enables the platform to scale up and leverage its
assets through network utilization. Airbnb has revolutionised the way people travel by
focusing on building a community and not just a mere distribution platform. Airbnb is a
marketplace where travellers get to stay at the property listed on the site by the local hosts.
Airbnb is a community built on sharing. Customer satisfaction and low rental prices are the
main factors which have led Airbnb on its path of success. The brand has set its own set of
community standards and they are mandatory to be followed by owners of the spaces. The
study shows how the dynamics in hospitality industry is changing since the Airbnb model has
disrupted the market
Literature Review
Hui Li, Kannan Srinivasan (2019)’s study shows that demand estimates suggest that Airbnb’s
flexible supply helps recover the lost underlying demand due to hotel seasonal pricing (i.e.,
higher prices during high-demand seasons) and even stimulates more demand in some
cities. The counterfactual results suggest that some hotel types in some cities may benefit
from conducting less seasonal pricing and even considering counter-seasonal pricing.
Market conditions (e.g., seasonality patterns, hotel prices and quality, consumer
composition, and Airbnb supply elasticity) play a crucial role in determining the impact of
Airbnb on hotel sales and hotels’ strategic response. Finally, recent Airbnb and policy
changes (e.g., higher Airbnb hosting costs due to hotel taxes or lower Airbnb hosting costs
due to third-party services and the “professionalism” of hosts) affect the competitive
dynamics. The profits of high-end hotels are the most sensitive to the changes in Airbnb
hosting costs. Airbnb’s recent attempt to behave more like hotels can increase hotels’
vulnerability to lower Airbnb hosting costs.
Tarik Dogru (2020)’s research shows that Airbnb listings in these cities have been increasing
more than 100% year over year and that the effect of Airbnb on hotel RevPAR (Revenue per
available room) and OCC (occupancy rate) is negative and statistically significant. In
particular, a 1% increase in Airbnb listings decreases hotel RevPAR by between 0.016% and
0.031% in these hotel markets. The implications of these findings for destinations and
hoteliers are discussed.
Ruggero Sainaghia and Rodolfo Baggio (2020)’s findings suggested that the two series are
widely desynchronized during the week, on workdays and trade-fair days, when hotels work
prevalently within the business segment, and when Airbnb listings mainly accommodate
leisure guests. By contrast, a partial synchronization (and therefore a potential substitution
threat) is revealed during weekends and holidays
Georgios Zervas, Davide Proserpio, and John W. Byers (2017)’s findings mention that the
causal impact on hotel revenue is in the 8%–10% range; moreover, the impact is
nonuniform, with lower-priced hotels and hotels that do not cater to business travelers
being the most affected. The impact manifests itself primarily through less aggressive hotel
room pricing, benefiting all consumers, not just participants in the sharing economy. The
price response is especially pronounced during periods of peak demand.
Tarik Dogru (2021)’s findings say that the adverse impact of Airbnb supply on hotel revenues
was mainly driven by decreases in hotel prices rather than decreases in hotel demand.
Contrary to expectations, single-unit host listings exerted greater downward pricing
pressure on hotels than multi-unit host listings. These effects were consistently observed
across hotel segments, with economy-scale hotels experiencing substitution effects in
addition to pricing pressure
Methodology
This descriptive study aims to understand preferences and factors leading to choosing
AirBnB over conventional hotels for accommodation during the recent year. After reviewing
the literature, the questionnaire was developed as a measurement scale to achieve the
objectives of the research. This section includes the research design, population size, data
collection tools and processes, and data analysis techniques.
There are three objectives of the research. First, to understand how AirBnB changed the
dynamics of the hospitality industry. Second, to determine the factors affecting individual
preferences of choosing AirBnB and lastly, to determine the consumer preference of Airbnb
over Conventional Hotels. Research strategy of this research is survey based and a
questionnaire was generated as a data collection instrument for this research which was
circulated through Google forms. The data was collected from across Bangalore with a
sample size of 140 members out of which 104 responses were valid. The type of sampling
strategy chosen is random stratified sampling under probability sampling.
The background knowledge and the industry overview were studied with the help of
secondary data. The research objective will be answered through primary data via data
collection through a questionnaire. The collected data is organised, analysed and categorized
with the help of Microsoft excel using combination of bar and pie chart.
Research Findings and Interpretation
On the basis of the questionnaire, it is observed that 89% of the respondents book the
accommodations through internet as it is easily accessible for all. Moreover, the questionnaire
was prepared in three parts as per our research objective to get a clear view of the paper.
The first objective to determine the shift in dynamics after the induction of Airbnb was
directly asked to the respondents. As illustrated in Fig. 01., 63% of the respondents agree to
the objective while 3% disagrees. Whereas, 22% stayed neutral to the motion.
22%
3%
1%
12%
63%
SA (%) A (%) N (%) D (%) SD (%)
Fig. 01: Shift in Dynamics
Findings from research “The welfare effects of peer entry in the accommodation market: The
case of Airbnb” done in 2018 by, Airbnb reduced hotel profits by up to 3.7 percent. That
year, Airbnb generated $41 of consumer surplus per room per night.
Fig. 2., explicitly determines the factors taken into consideration for the research. As
illustrated in Fig. 2., 23% of the respondents chose AirBnB as it gives a feeling of a home
away from home. Moreover, 38% of the respondents choose BnB because of the price factor
as the comparative cost of BnB is lesser than a hotel. In addition to it, 39% have chosen it
because of the app which makes booking a BnB more accessible than hotels.
A Home Away 23%
from Home
Cheaper 38%
Accessbility 39%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Fig. 2: Factors for choosing
AirBnB
Furthermore, in the questionnaire, few additional factors were added to observe the consumer
behaviour towards the other aspects to a booking. It can be observed in Fig 3., the location
and infrastructure motivate travellers to book a particular accommodation. 29% of the
respondents see the aesthetics of the property while booking whereas, 33% look for exotic
locations while choosing the accommodation which makes AirBnB more preferable over
hotels. Besides, 24% of the respondents have chosen the variety which AirBnB offers as well,
18% opted AirBnB as it is easy to compare different property on the app’s interface.
Aesthetics 29%
Exotic location 33%
Variety 24%
Comparision 18%
0% 5% 10% 15% 20% 25% 30% 35%
Fig. 3: Other factors
1% 2% 5% 8%
18% 7% 12%
100% 21%
80% 16% 41%
55% 23%
60% 55%
40% 46% 22% 43%
20% 10% 4% 7% 5%
0%
t on l s
xis re te
-e tu ati Ho
co fu nr
n th
e g e an
ca in w th
nb ed ne nb
rb ce to irb
Ai c nt A
d su ve er
an to le ref
ls d re rp
te un is he
Ho bo nb t
ra
or
e irb h
m A uc
is m
nb ld
rb ou
A i Iw
SA (%) A (%) N (%) D (%) SD (%)
Fig 4: Customer
acceptance
As illustrated in Fig 3., Respondents were asked accordingly in respect to the third objective
of the research. As seen 55% of the respondents have agreed to the fact that hotels and
AirBnB can co-exist whereas 41% of the respondents have agreed on the fact of AirBnB
succeeding in future. It can also be observed that 55% of the respondents stayed neutral when
it came to relevance of AirBnB to the contemporary generation. Eventually, when asked if
they would rather prefer AirBnB over hotels 43% of the respondents agreed while 21%
disagreed.
Finally, when we look at the correlation of whether people prefer Airbnb over a Hotel when
they mostly stay at their accommodation during most of the length of their vacation, we
found out that most of the people would rather spend their time in their Hotel rather than
Airbnbs. The Significance F came up to be 0.0185 which is positive and shows a decent fit
for the model.
Regression Statistics
0.23061
Multiple R 1
0.05318
R Square 2
Adjusted R 0.04389
Square 9
1.79708
Standard Error 6
Observations 104
ANOVA
df SS MS F Significance F
5.72921
Regression 1 18.5026 18.5026 3 0.018512798
329.410 3.22951
Residual 102 9 8
347.913
Total 103 5
Conclusion
By this data, we can conclude that Airbnb has indeed had a significant disruption in the
market and the existence of an easy to access and use app along with a centralized database
with a decentralized room availability mechanism shows that people prefer booking Airbnb
over Hotel rooms. However, we also understand that, with an overwhelming response when
asked about preference of these two kinds of accommodation, being 43%, people are still
very flexible in booking rooms during travel and money constraints being a major factor in
this.We also find out that hotels are slightly more popular among the higher income groups
but there is a serious limitation in data regarding that in this research to be conclusive.
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