Department location: Factor for Airbnb user’s valuation perception
Ubicación del departamento: factor
para la valoración percibida por los usuarios de Airbnb
Saúl
Alfonso Esparza Rodríguez
Universidad Michoacana de San Nicolás de Hidalgo (México)
sesparza@umich.mx
Jaime Apolinar Martínez Arroyo
Universidad Michoacana de San Nicolás de Hidalgo (México)
jmartinez42195@gmail.com
Fernando Ávila Carreón
Universidad Michoacana de San Nicolás de Hidalgo (México)
favila_68@yahoo.com.mx
Received: January, 2019
Accepted: June, 2019
ABSTRACT
The objective is to analyze the
impact of the location in the quality perception of customers. Data was
obtained from the stars-based valuation of Airbnb website, considering a
standardized option of accommodation just apartments of one room up to two
guest maximum, with an average cost of USD 50 per night and located in four
cities: New York and Miami in USA, and Mexico City and Cancun in Mexico, using
a chi-square analysis to identify if there is a difference in quality
perception considering if the destination place have beach or not. The results
showed than departments located in New York had the most significant difference
in valuation of quality of hosts.
Keywords: Airbnb, global accommodation,
platform economy, collaborative economy.
Jel Code: P40.
RESUMEN
El
objetivo es analizar el impacto de la ubicación en la percepción de los
consumidores. Los datos fueron obtenidos de la evaluación basada en estrellas
del sitio de internet de Airbnb, considerando como opciones de alojamiento
apartamentos de una sola habitación para uno o dos huéspedes máximo, con un
costo promedio de USD 50 por noche y ubicados en cuatro ciudades: Nueva York y
Miami en EUA y la Ciudad de México y Cancún en México, utilizando un análisis
de Chi cuadrada para identificar si hay una diferencia en la percepción de la
calidad considerando destinos que cuentan con playa o no. Los resultados
mostraron que los departamentos ubicados en la ciudad de Nueva York tienen la
diferencia más significativa en la percepción de la calidad por parte de los
huéspedes.
Palabras clave: Airbnb,
alojamiento global, economía de plataforma, economía colaborativa.
Código Jel: P40.
INTRODUCTION
Sharing economy, or collaborative
economy, has been defined as a new concept of trading between peers that can be
summarized with the following phrase “What is mine is yours, for a fee”, driven
mainly because of the rise of some technologies of information and
communication. The digital evolution that emerged as a result of rapid
technological developments has brought this concept forward, creating
opportunities for individuals to turn, their talents to money and benefit from
underutilized resources (Bozdoganoglu, 2017).
This trend of the economy has been
defined as an interaction between two or more individuals, trough using of not
of digital media, that satisfy a need (real or perceived) to one or more
people. In that sense, digital platforms stablished a framework that
facilitates exchanges with lucrative ends among users, whom can interact by
selecting a variant participation role (either client or supplier), or else in
a multiple role sense, being sometimes users and sometimes providers of a good
or service.
The definition for “collaborative
economy” may be interpreted under different labels: collaborative consumption,
shared economy, on-demand economy, peer-to-peer economy, zero-marginal cost
economy, and crowd-based capitalism are just some examples of the different
interpretations that are currently interconnected to the notion of sharing
economy (Selloni, 2017).
One of the key characteristics of
the collaborative economy is that provides an economic opportunity for
individuals to trade their underutilized assets with other individuals through
intermediaries that match supply and demand in an efficient way (Petropoulos, 2017) taking advantage of technologies
available in the internet and the greatly broad scope for business using the
web to reach a substantial amount of potential clients around the world.
Digital technologies enable sharing
what people traditionally do not use full-time, considering assets such as
houses, departments, cars and even people´s free-time, in the form of labor
potential to do specific tasks. These technology allows performing practices
that promote the use and exploitation of properties, promoting the re-use and
access instead of purchasing ownership (Grifoni et
al., 2018).
Peer-to-peer accommodation platforms
for example, are significantly changing consumption patterns, with the social
and economic appeals of this new phenomenon affecting expansion in destination
selection, increase in travel frequency, length of stay, and the range of
activities participated in tourism destinations (Tussyadiah
and Pesonen, 2015; cited by Zhu, So, & Hudson, 2017).
The activity related to sharing
resources using digital tools facilitate temporary non-ownership of resources
seeking monetary rewards, can be considered as a differentiator between the
latest generation of platform businesses and their predecessors (Breidbach & Brodie, 2017).
Literature review
The concept of renting or sharing is
changed for a more efficient way of consuming in a new mode of consumption,
were consumers do not have to own everything they need, but instead is oriented
to a new cultural concept of the possession of goods. This creates a form of
collaborative consumption that includes processes, such as the production
(crowdsourcing, collective innovation, open software, co-working,
user-generated content), financing (crowdfunding) or consumption for goods and
services (Palos-Sanchez & Correia, 2018).
For instance, a product or service
systems, allows members to share multiple products that are owned by companies
or by private persons. Examples of product-service systems are car-sharing
services (Zipcar) and peer-to-peer sharing platforms (Zilok.com), while Trends
in tangible assets include the rise of household names such as Airbnb and Uber.
Another option is related to
redistribution markets, peer-to-peer matching or social networks allow the
re-ownership of a product (NeighborGoods.com and thredUP.com). Access also can
be derived through collaborative lifestyles in which people share similar
interests and help each other with less tangible assets such as money, space or
time; this sharing is mostly enabled through digital technology (Roh, 2016). Online home-sharing is part of a
growing range of practices described variously as the “peer to peer”, or
“sharing economy”, where participants engage in “collaborative consumption” by
“borrowing/renting” rather than “buying/selling” (Hamari, Sjöklint, & Ukkonen, 2016).
This collaborative or “shared”
economy represents a human activity that seeks to generate public value
functioning by new forms of work organization, based on a kind of organization
that is more horizontal designed, that is based mainly in value creation via sharing of goods, spaces and tools (usage
rather than ownership) for citizens’ 'networks' or communities and, generally,
intermediation by internet platforms (David, Chalon, & Yin, 2016).
The current dissemination and uptake
of sharing economy platforms and services are praised for allowing various idle
resources such as homes, tools, clothes and vehicles to be used more
effectively for bringing people together, for encouraging the development of
more user-centered services and for constituting new forms of entrepreneurship
around the world (Bradley & Pargman, 2017), with more than one hundred different
companies already listing a wide variety of products including car rentals,
parking spaces, high end sports, photography equipment, musical instruments,
and lodging accommodations (Wiles & Crawford, 2017).
As part of this collaborative
economy, there is a tendency to take advantage of the innovation in some
information and communication technologies that creates what is been called as
a “platform economy”, considered as a new but fast-growing phenomenon, given
the potential for platforms to facilitate economic growth and mediate access to
various markets. (European Commission, 2015 cited by Kilhoffer, Lenaerts, & Beblavý, 2017).
The distinction between labor and
capital platforms can be traced to the value creation for potential and actual
clients, where the first allow sellers to be paid for a single task or good at
a time, the second is focused to let participants to sell goods or rent assets,
making possible a connection among workers and sellers directly to customers and allowing people to work
when they want while payment passes through the platform (Scher et
al., 2016). As a result of the diffusion of digital
technologies, particularly the Internet and smart phones applications, sharing
platforms have become sufficiently scalable to generate a critical mass of
users worldwide (Constantiou, Marton & Tuunainen 2017).
In terms of labor, the opportunities
created through platforms allowed that a sub tantial
number of people to use apps, platforms, and websites to find and perform jobs.
There are at least seven million platform workers that live all over the world,
doing work valued at US$5 billion per year outsourced via platforms or apps (Kuek et al., 2015; Heeks, 2017
cited by (Graham & Woodcock, 2018).
According to Kilhoffer et al. (2017), a platform encompasses two
essential characteristics. First, a platform contains a common “core” or
“architecture” with certain essential functions, which can be the basis of
development of new products or services (e.g. Gawer,
2007; Tiwana et al., 2010). Second, a platform is
capable of a “positive feedback loop” among its users, which is known as the
networked effect (Eisenmann et al., 2011; Gawer 2011;
Ghazawneh and Henfridsson, 2013). In order to reduce
uncertainty and facilitate trust among participants, sharing economy companies
have developed platforms that make public information about the service
providers available for free consultation at any given time (Ye, Alahmad, Pierce, & Robert, 2017).
Figure 1
Collaborative economy and its components
Source: Own elaboration.
According to World Economic Forum,
there are some related concepts to sharing economy that often are a source of
confusion and do not represent a truly economy in the market, just a way of
interactions among participants who use one platform in search of a given good,
and offers a distinction considering the following examples based on trends in
the market (WEF & PWC, 2017):
On demand economy: Economic transactions that use an online platform that facilitates the
interaction of suppliers and demanders in real times, as well as the delivery
of products or services (Spotify, Netflix).
Collaborative consumption: Economic model that is based on sharing idle assets, products or
services, enabling access over ownership and continuous interaction instead of
the traditional relationship buyer/seller (Thred Up, Helpling).
Crowd economy:
Participants connected through a platform in order to achieve a goal of shared
interest (Amazon, MyCrowd QA).
Gig economy:
Platforms that allow connection among people searching for a job with employers
looking to occupy temporary contract-based activities (Udemy, Featly).
Peer-2-Peer economy: Decentralized economic model directly dependent on an online P2P
platform (EasyRoomate).
Collaborative economy: Builds on P2P platforms to include “economic systems of decentralized
networks and marketplace that unlock the value of underused assets by matching
needs and haves, bypassing traditional institutions” (Peerby,
ParkFlyRent).
Among the community sharing
practices, the aspect related to “Trust-verification” allow people to build
trust through a model that facilitate transacting partners to limit
counterparty verification and liability expenses while reaping the benefits of
sharing. Peer review ratings, third-party validation and liability insurance
are the most common ways of establishing such trust between users and the
platform and also among users themselves (WEF & PWC, 2017), where many transactions rely on
the peer-to-peer relationships between customers and product/service providers (Yang, Song, Chen, & Xia, 2017).
Figure 2
Community transactions practices in capital sharing economy
Source: Own elaboration (WEF & PWC, 2017).
In that sense, a validation process
based on star ratings functions in a double-way sense, not only the customers
and potential client can use that information to make a decision regarding
which supplier is the best option for accommodation services, but also the
people who are opening their spaces to strangers can use it in order to decide
open the doors to some random people, even without further knowledge, but using
a trust verification system accepted for all the participants. This trust
verification systems is one fundamental basis for the
business model of Airbnb and other companies in collaborative economy, as well
as capitalizing on idle capacity and the use of technology, as the figure 2
shows.
Airbnb business model
Airbnb is a company and a software
platform dedicated to offer accommodation to individuals and tourists, that
counts with and approximated offer of two million properties, located in 192
countries and 33 thousand cities. It was founded in November 2008, in the city
of San Francisco, California, according to the information in the website[1].The
company maintains an alternative offer to the traditional accommodation
services such as chains of hotels, staying as a competitor that generates
profits in a business model that can be defined as part of the collaborative
economy, as a subset of capital economy, where is a part of businesses based in
what is being considered as platform economy.
This model of business, operated via
online platform, allowing that both provider and customer have access to
certain means to grant a “grade” or “stars based valuation” for his
counterpart, a valuation that in the case of the supplier of the service makes
easy the selection process that the consumer does because it is one of the main
criteria that people takes into account at the time of making decisions when
selecting a product or service using the internet.
Platform economy companies have
developed at a pace beyond the ability of all levels of government to pass laws
and regulations to capture tax revenues from either the corporate entities,
such as Uber or Airbnb, or the service providers who drive the cars and rent
out the rooms (Virginia Municipal League & Center for
State and Local Government Leadership at George Mason University, 2015).
When the guest search for listings
in Airbnb, swift trust is developed before their peer-to-peer interaction. Due
to the lack of personal knowledge about the trustees before sufficient
interaction, trustors have to use simple heuristics, such as the trustee’s
social categories, roles and third party information to forming trust (Hung,
Dennis, and Robert, 2004 cited by Ye,
Alahmad, Pierce, & Robert, 2017), and because people often have a
personal interaction with the owner of assets they tend to be more considerate
when using those assets. (Stemler, 2016).
Decision making of accommodation services consumers
From a destination point of view,
the fact that Airbnb represents a substitute for other types of traditional
accommodation, means that Airbnb could decrease the amount of money which
travelers spend in a destination. According to Airbnb, visitors are spending
their savings in the destination, meaning that they end up helping the economy
of the community and also the local tourist industry at the destination (Speranta, 2017).
A study conducted by Varma, Jukic, Pestek, Shultz, & Nestorov
(2016) revealed that when it comes to the factors used by customers in their
selection of a lodging facility, aspects like importance of location, past
experience, image, reputation were considered as determinant, as well as
importance of security, cleaning, loyalty programs and recommendations.
In an analysis that made a
comparison among Airbnb and Hotels performance in 13 different places such as:
Barcelona, Boston, London, Los Angeles, Mexico City, Miami, New Orleans, Paris,
San Francisco, Seattle, Sidney, Tokyo and Washington D.C., showed that Airbnb
occupancy levels were higher in places with high hotel occupancy rates, the
shares of market demand and revenue for Airbnb was generally below 4% and 3%
respectively, the rates of the platform were lower than hotels (16$ lower
considering the U.S. markets) (STR, 2017).
One of the main characteristics for
this type of business is the possibility to create trust between buyers and
sellers and to build trust and facilitate transactions, online markets
typically present information not only about products, but also about the
people offering the products (Edelman & Luca, 2014), which is a factor that can drive
the intention and preferences of potential consumers before making a decision.
The present research seeks to
contribute with evidence that supports the hypothesis that relates location as
a factor that influences in decision making process at the time to select an
option for allocation. Research question for the present work is: The location
of an accommodation service influences the perception of the service quality in
customers?
METHODOLOGY ANALYSIS
Data was obtained via the website of
Airbnb.mx, accessed the day September 25th, 2018, with and
randomized mode of collecting the information based in the number of stars
assigned to each object of study, which functions as a rating system that shows
the valuation regarding the experience of the guest, and also gives some useful
information for potential guest in order to make a decision.
Dependent variable: Perception of the quality in accommodation service, measured and
identified by evaluation that the users of the platform provide in the Airbnb
system, rated in a scope from 1 to 5 stars, and is a result of the combination
of service quality factors that groups particular validation of the following
factors: Veracity, Communication, Cleanness, Location, Arriving and Quality.
Independent variable: 4 kinds of accommodation options are considered to the analysis of the
present work:
-
Accommodation type 1: Department, 1 or 2 guest, 1 or 2 beds,
1 bathroom, average price per night equivalent to USD
50 approximately, located in Cancun, Mexico.
-
Accommodation type 2: Department, 1 or 2 guest, 1 or 2
beds, 1 bathroom, average price per night equivalent
to USD 50 approximately, located in Miami, United States.
-
Accommodation type 3: 1 or 2 guest, 1 or 2 beds, 1 bathroom, average price per night equivalent to USD 50
approximately, located in Mexico City, Mexico.
-
Accommodation type 4: 1 or 2 guest, 1 or 2 beds, 1 bathroom, average price per night equivalent to USD 50
approximately, located in New York, United States.
-
For each accommodation type and
criteria, 8 places were considered, which in total are 96 different places.
With the information obtained in the
website of Airbnb, a non-parametric analysis using Chi-squared was conducted to
analyze the valuations that are registered in the platform of Airbnb,
considering 96 different accommodation options located considering beach
destinations in Mexico and USA, as well as cities that received both business
and leisure tourism.
Table 1
Data collected in Airbnb website, with types of apartments and valuation
of host
Source: Authors, based on data
obtained in Airbnb.mx, 2018.
Those places were included
considering a valuation based on 3 different criteria: Superhost, Good Host and Normal Host,
as follows:
-
Superhost: Valuation
of 4.7 stars or more on average.
-
Good host: Valuation between 4.1 to 4.6 stars
on average.
-
Normal Host: Valuation of 4 stars or less.
The gathered data of the valuation
stars for each type of Department and the validation of host quality catalogued
in different types served as the key information to interpret the valuation of
users of Airbnb in terms of the classification for each kind of host, as the
table 1. The distribution regarding data about valuation of criteria for each
host shows the graph 1, 2,3, and 4.
Graph 1
Quantity of data
valuation for each department in each kind of host (Using D as short of
Department)
Source: Authors, based on data obtained in Airbnb.mx,
2018.
Graph 2
Valuation for normal host in each type of apartment
Source: Own elaboration, based
on data obtained in Airbnb.mx, 2018.
Graph 3
Valuation for good
host in each type of apartment
Source: Own elaboration, based
on data obtained in Airbnb.mx, 2018.
Graph 4
Valuation for superhost in each type of apartment
Source: Authors, based on data
obtained in Airbnb.mx, 2018.
Chi-square test
The complete gathered information
was ordered in an observed frequency table in order to make the Chi-square
analysis using Excel program of Microsoft Office Suite. The results obtained in the analysis showed in
the table 2 (observed, expected and calculation of test statistics value).
Table 2
Observed frequency of the data obtained
Source: Airbnb.mx, 2018.
Chart 5
Observed frequency by type of accommodation place and classification of
Host
Source: Authors, 2018.
Table 3
Expected frequency table
Source: Authors, 2018.
Table 4
Outcome frequency estimated table
Source: Authors, 2018.
Value for Chi-squared (95% probability considering 6 degrees of
freedom): 12.591587
A
post-hoc analysis was performed with the intention to deepen understand the
weight of each option in comparison with the chi-square critic value, with is
show as the table 5.
Table 5
Results of distinct test performed with the data
Source: Authors, 2018.
With the results obtained, it is
noticeable that the difference between calculated chi-square and the critic
value is considerable high, and that the higher the number, the grater the
impact of the location of the accommodation place in the valuation received by
users of Airbnb.
DISCUSSION
The most quantity of qualification
available in Airbnb website was focused in Good
host, located in Miami, USA, and the least quantity was for Regular host located in Cancun, México,
with an average of qualifications in general of 662 qualifications received
with a standard deviation of 325.62, out of a total of 7,954 qualifications
considered in the present study.
Departments located in Miami and
Mexico City received almost the same number of valuation, up to 3216 and 3206
respectively, considering all kind of host.
The most frequently valuated kind of
host were Good Host, in all kind of
types for apartments considered with 4282 valuations available for consultation
in the site of Airbnb at the time the data was collected.
The observed frequency in the data
analyzed showed an incremental trend for the accommodation type 3 located in Mexico City, that went from 469 (Normal Host), 1,257 (Good Host) to 1,480 (Superhost), and also for accommodation type
1, located in Cancun, Mexico, that went from 257 (Normal Host), 375 (Good Host),
to 722 (Superhost).
The comparison between the critic
value for the Chi-square and the calculated value of the test statistic was
noticeable high, considering that the value for the first with 95% probability
considering 6 degrees of freedom was of 12.591587,
and the second one was of 637,
showing that there is a definitive impact of the location in the valuation
received by users, mainly in the validation of the services received in the
apartment type 4, that is an
apartment with 1 room, 1 or 2 beds, with a night fee of around USD50, located
in New York, an international city with a large amount of people traveling to
spend time with purposes either for pleasure and business.
In all the test considered in the
extra analysis, apartment type 4
obtained the biggest weight when considered (test 1,4,5,6), being the last
three with the Normal Host valuation
the heaviest values.
CONCLUSIONS
The alternative hypothesis is
accepted categorically, there are changes in the user perception regarding the
location of the accommodation service, mainly among the guest that use the
accommodation service in New York, that showed a determinant weight when
calculating the chi-squared value in all the different test that were performed
with the data.
The analysis in the gathered
information also showed one case where the valuation of quality for the host
had an increasing tendency, in the case of guest that used Airbnb services in
Mexico, City, being the only one that showed that behavior in client´s
perception.
The most common valuation was “good host”, adding all the results
obtained in all the types of departments with similar characteristics such as
price, quantity of rooms and beds, with a single differentiation factor that
was location.
One factor that have to be taking
into account is that the cost of the rent, in despise of being used as a way to
give an equitable treatment to the information collected in the website of
Airbnb, could be a determinant influence factor that affects the customer
perception, considering that in big cities this kind of accommodation usually
are located far from the city´s downtown, and that can be an explanation why
the impact of the allocation in New York was the biggest factor in terms of the
user valuation of both “normal host”
and “superhost”.
One of the limitation of the analysis
was the amount of accommodation options considering aspects as price, number of
beds, location and number of guest allowed by host, perhaps the consideration
of a wider range of options and cities could give more information of the
consumer perception of the quality of service´s valuation.
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