Gaining Competitive Advantage By Exploiting Internet Of Things (IoT)
Technology
GRAMMATIKOPOULOU GEORGIA
Business Administration
International Hellenic University
Terma Magnesias str, Serres, Greece
GREECE
TSOURELA MARIA
Business Administration
International Hellenic University
Terma Magnesias str, Serres, Greece
GREECE
Abstract: - Internet of Things (IoT) refers to the billions of physical devices around the world that are now
connected to the internet, collecting and sharing data. The common managerial and theoretical concern is to
build IoT applications momentum, in order to bolster possible weaknesses and stimulate their adoption by
individuals. This research explores the influence of IoT applications in hospitality industry. Since IoT
applications are considered to add value to hotel guests, their influence, from a potential guest’s perspective,
over a hotel’s primary activities and operations is examined. Hotels, by adding value to their service provisions,
could achieve higher customer satisfaction and loyalty levels, conferring a sustainable competitive advantage.
Resourced-Based View model was used, because it concentrates on how a hotel’s key resources can create
value for its customers to meet their needs, which in turn enhances its ability to gain competitive advantage. For
current research purposes, a questionnaire was created and distributed to evaluate whether a hotel’s IoT
applications are associated with choice and guests’ preference of an IoT enabled hotel. Findings revealed that
all studied IoT applications are hotel services enhancement instruments. Prospective guests’ preference of an
IoT enabled hotel over another one, provides support that IoT applications drive guests’ satisfaction and
loyalty, by recognizing it as value for money and reason for revisits.
Key-Words: - Internet of Things (IoT); hotel IoT applications; hotel guest satisfaction; competitive advantage;
Resource-based View (RBV) model
Received: August 25, 2021. Revised: March 30, 2022. Accepted: April 27, 2022. Published: June 8, 2022.
1 Introduction
The past decade has seen a spectacular
development of the Internet worldwide. “Smart
people” built the “smart artifacts” to live a “smarter
life”. “Things” have changed, thus changing our
interaction with them. The Internet of Things (IoT)
is the concept of connecting any device to the
Internet and to other connected devices. It is a giant
network of connected things and people – all of
which collect and share data about the way they are
used and about the environment around them.
In hospitality and tourism industry, adopting
technological innovations can prove to be of a great
importance for achieving competitive advantage.
Technological innovations adoption can create
smarter destinations by facilitating improvement
and restructuring processes for tackling challenges
such as seasonality and overcrowding [15]. The
main purpose of this study is to investigate guests’
perception of a hotel’s IoT applications provision on
primary activities and operations, through “smart”
environments and systems, with a view to reaching
a competitive advantage. Results will help managers
and practitioners to define whether integrating IoT
applications in a hotel can be thought as an effective
path for enhancing the quality of provided services
and total customer perceived value. Thus,
prospective hotel guests’ intentions towards and
expressed preferences for or against these relatively
new technology functionalities are examined.
In literature IoT concept and its connection to
hospitality and tourism industry is defined revealing
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that IoT can contribute in upgrading the whole
tourism industry by informatization and
intellectualization in a wider sphere. The Resource-
Based View model is chosen to a) explain how
innovative technologies adoption reinforce a hotel’s
services quality enhancement and consequently
increase guests’ experience satisfaction and b)
reflect on IoT applications added value. Although
general features of IoT applications in hospitality
were examined by some prior studies, inadequate
holistic research has been conducted on specific
aspects such as guest’s choice of an IoT’s enabled
hotel over another.
This study will make a considerable empirical
contribution to literature concerning the perspective
of IoT applications acting value added services in
hospitality enhancing guests’ satisfaction and
loyalty. Additionally, it will add to the knowledge
by revealing the possible existence of high and low
impact IoT applications levels. Thus, the literature
on hospitality will also benefit from a
methodological contribution for managing the
information resource. Findings could be beneficial
to managers of hotels in crafting policies for
hospitality scholars, as well as creating strategies for
other hospitality stakeholders interested in
successful implementation of IoT as a mean of
services enhancement.
The study is structured into several parts. The
first part is the literature review which includes the
theoretical framework on IoT, IoT applications and
IoT applications in hospitality and tourism industry,
as well as previous work on IoT applications in
hotels. The next section explains the Resource-
Based View model, the VRIN Analysis, the research
questions and hypotheses setting. Research method
and results will be presented in subsequent sections
followed by discussion and conclusions.
2 LITERATURE REVIEW
We are living in the post-PC era where
smartphones, tablets and other ‘smart’ devices are
changing the world around us by making it more
interactive and informative. “The IoT integrates the
interconnectedness of human culture -- our 'things' -
- with the interconnectedness of our digital
information system -- 'the internet.' That's the IoT,”
[2]. The IoT aims to extend the benefits of the
regular internet – constant connectivity, remote
control ability, data sharing, and so on – to goods in
the physical world [17].
An IoT ecosystem consists of web-enabled smart
devices that use embedded processors, sensors and
communication hardware to collect, send and act on
data they acquire from their environments. IoT
devices share the sensor data they collect by
connecting to an IoT gateway or other edge device,
where data is either sent to the cloud to be analyzed
or analyzed locally. Sometimes, these devices
communicate with other related devices and act on
the information they get from one another. The
devices do most of the work without human
intervention, although people can interact with the
devices -- for instance, to set them up, give them
instructions or access the data.
2.1 IoT Applications
Based on what [4] say about the IoT value in
various industries, [14] present that “the IoT
generate $14,4 trillion in value, the combination of
increased revenues and lower costs will migrate
among companies and industries from 2013 to
2022.” From an industry perspective, there are four
industries that make up more than half of $14,4
trillion in value, with manufacturing (27%), retail
trade (11%), information services (9%), finance and
insurance (9%) to be the leading ones [14].
Referring to [14], they present what Gartner (2014)
foresees, that is “the IoT will reach 26 billion units
by 2020, up from 0.9 billion in 2009, and will cause
impact to the information available to supply chain
partners and how the supply chain operates.”
Currently, IoT is thoroughly applied and being
developed in every area of life and industry. The
IoT is the hot trends in the tech and business world.
[16] states that, “IoT is a fast-growing constellation
of Internet-connected sensors attached to a wide
variety of things”. There are numerous real-world
applications of IoT such as smart homes, wearable
devices, smart healthcare, smart buildings, smart
city, smart farming, smart appliances and so on,
ranging from consumer IoT and enterprise IoT to
manufacturing and industrial IoT (IIoT). IoT
applications span numerous verticals, including
automotive, telecommunications, energy and more.
2.1.1 Hospitality IoT
The IoT technology will play an extremely
important role in the development of smart tourism
industry in general and the hotel sector in particular.
IoT can contribute in upgrading the whole tourism
industry by informatization and intellectualization in
a wider sphere. Research conducted in China
express ideas and experience on the technological
structure of IoT and its implementation to ‘smart’
industries, has contributed on deeper understanding
of IoT concept from the perspective of sensor
network of things and helped to introduce this
technology on the global stage and international
strategic plans [10].
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Cloud computing and IoT technology are significant
components smart tourism can focus on, so as to
“apply intelligent perception of all kinds of tourism
information, like tourist resources, tourism
economy, tourism activities, tourism participants,
among others to realize the acquisition and
adjustment of real-time tourism information through
mobile Internet or Internet terminal equipment”
[10]. IoT technology can be considered as the core
carrier of the smart tourism information system.
Through IoT technology, systems can identify the
real-time positioning of visitors and automatically
point them at the resort map, which can lead to
facilitate an exchange of information through alarms
and messages. Such example can totally be
indicative of how IoT is going to be used in a hotel
resort [10].
An increasing number of hotels tend to adopt IoT,
with the expectation that new ICT-based
technologies and processes will result in enhancing
companies’ operations and consequently customer
service levels. ICT-based products and processes
help hotels enhance operating efficiency and
improve the service experience, as well as to
provide a secure access to global markets [22]. The
time that takes for a firm to accept, adopt and use
the new technology is called adoption propensity. A
firm with high level of adoption propensity will
adopt the new technology much earlier than its
competitors, which also means that at the same time
it is a firm which takes risks. “The firm’s adoption
propensity is fueled by its attitude towards and
belief in innovativeness as a source of competitive
advantage.” [18].
In the hotel industry, the geographical location is
considered to be a major determinant for its
operations and profitability. More specifically, a
hotel’s geographical location indicates the profile of
its guests, the size of the market and the level of
competition. These three factors play a significant
role in hotel’s adoption propensity of ICTs, as well
as they are closely related to what hotel expects
about the added value that ICTs can provide to its
customers, and additionally to what it believes about
expansion of the target market through ICTs [18].
As the hospitality industry is characterized of high
competitiveness, it is obvious that hotel companies
tend to implement ICTs innovations so as to
improve their image, their customer perceived
quality, and consequently to achieve brand loyalty.
So, in accordance with the innovativeness, it is
essential for hotels to pay much attention to their
customer-based brand equity in order to remain
competitive in their market [19].
It is supported and recognized that providing with
the highest quality of services and aiming to highest
customer satisfaction are two of the most crucial
factors, in order for the hotel to add value to its
product, and subsequently, gain increased levels of
customer loyalty and retention. “Faced with intense
competition in the marketplace, it is imperative for
hotels to tailor hotel services to the changing needs
and lifestyles of customers with a view to increasing
customer loyalty and retention.” [13]. IoT
technological application can elevate the
competitive advantage of a service organization
such as a hotel business by helping the hotel’s
employees to develop their capability to offer their
best services to the customer [13].
2.2 IoT Hospitality Applications
There are many research papers that argue towards
the importance of smartification of destinations,
hotels, restaurants, entertainment and transportation.
According to [22] and [10], a smart tourism central
management platform can be successfully achieved
through many applicable examples with the most
indicative to be “[…] integrated electronic ticketing
system, tourist flow monitoring, analysis system of
scenic spots, vehicle monitoring system, tour guide
management system of travel agents, digital room
service and operation management system of hotels,
and destination marketing system” [10].
In smart tourism development, the application of
IoT mainly includes intelligent hotel management
system, scenic spot intelligent ticketing system,
intelligent remote video monitoring system,
intelligent tour guide system and intelligent travel
agency system [22]; [10]. [7] proved that the IoT
technology enhances the convenience of tourism.
Tourist destination selection, tourist routes planning,
hotel bookings, and integration management of
tourist attractions could be included in the IoT
information system.
[6] attempted to design a real time positioning
system based on “received signal strength
indication” (RSSI) ranging and the IoT technology.
Test results showed that this design could identify
real time positioning of visitors and automatically
mark them on the resort map, which could facilitate
an exchange of information (e.g., alarm messages).
[23] established a model of tourism commodity
informatization through actual investigation and
survey on the key processes of tourism
commodities. As a result, a web-based traceability
system was designed using two-dimensional code,
RFID, active server page (ASP), net module
development, and so on.
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3 Resource-Based View Model and
VRIN Analysis
Resource-Based View (RBV) model theory aims
to explain competitive advantage and in turn
integrated performance among firms. It sets as of
the highest priority the relationship that occurs
between the customers’ value with the competitive
advantage and the superior performance of the
business. As a result, the business that aims to
achieve competitive advantage focuses on
increasing its guests’ satisfaction and consequently
its profitability, by providing more quality services
compared to its competitors. Moreover, in the RBV
model, the company utilizes its key resources and
capabilities, in order to meet its customers’ needs
and improve its ability to obtain sustained
competitive advantage [8]. “Adapting a resource-
based approach towards management requires a
shift from focusing on products and product
development to concentrating on resources and
resource development.” [1]. This means that the
company that aims to achieve competitive
advantage by creating the highest possible value for
its customers should follow strategies that are
mainly based on its resources.
All the above can be summarized into the phrase
that the RBV can describe and represent each
company as a unique combination of resources.
However, this does not necessarily mean that all of a
company’s key resources participate equally in
providing with sustained competitive advantage.
They should be carefully assessed and those
resources which can enclose specific characteristics
like value, rareness, inimitability and non-
substitutability, should only be considered for
creating advantage to the company [3]. Thus, there
is need to identify the factors that can be used in
order for the business to have a clearer picture of
how its customers value business key resources as
well as to decide which of them can enhance the
value of customer experience from the offered
service.
In the RBV model of strategic management,
competitive advantage is closely related to
company’s internal characteristics, to its resources.
Since business resources are mobile and
heterogeneous in long term, in order company’s
resources to be considered as strategically important
for the achievement of competitive advantage, they
should also combine the characteristics of value,
rareness, inimitability and non-substitutability too
(VRIN), [20].
In the case that all the above characteristics occur
simultaneously in a resource, then, this resource can
be used to ensure company’s sustained competitive
advantage. This type of analysis is called VRIN
analysis and it can be used in supporting way with
the RBV model in order to explain which of the
resources are suitable for the company’s sustained
competitive advantage [3]; [20].
Hotels need to organize and develop their
strategies in an effort of achieving superior
performance against their competitors. To measure
the performance of the hotel businesses, there are
traditional measurement tools, such as financial
statements that evaluate the efficiency of tangible
assets of the business, like their beneficial flows to
the company, as well as the accurate determination
of historical costs. Although these balanced
performance measurement systems can provide a
clear enough picture of the business position,
nevertheless, it is demanded more attention to be
paid towards approaches that can also assess the
intangible aspects of a firm’s performance. To this
point of view, only through focusing on its key
resources and capabilities, as concentrated in the
VRIN analysis attributes, a hotel will enhance its
performance and gain sustained competitive
advantage, as it has been supported by the theory of
RBV model.
4 RESEARCH QUESTIONS AND
HYPOTHESES
The value chain analysis enriched with the RBV
model, are used in order to answer the research
questions (RQs) presented below. The first Research
Question argues on whether the use of IoT
innovative technology applications can be regarded
as an effective factor for a hotel to enhance the
quality of provided services. This question is closely
related to the value chain theory, where it is
supported that, from the customers’ perspective,
business value chain enhanced activities can lead to
value-added products and services. Thus,
recognizing high rated products and services could
result in higher guests’ satisfaction levels and
consequently, enhancement of company’s
competitiveness by establishing a competitive
advantage. Additionally, by setting the second
Research Question, it is investigated whether,
guests-wise, specific IoT applications are equally
valuable for the hotel’s various functionalities.
Businesses, such as hotels, operating in hyper
competitive industries, focus on achieving the
highest customer satisfaction possible, investing in
customer trust and loyalty, simply because highly
satisfied guests are more likely to revisit. To do so,
managers need to understand guests’ intentions and
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preferences towards hotels that use IoT applications
and whether they can be in favor of loyalty and
trust. On the whole, the third Research Question
focuses on whether guests are expected to express a
preference on hotels offering IoT applications or
not. The fourth Research Question, while taking into
consideration the previous three research questions,
is based on value chain and the RBV theory and
aims at providing a more generalized inference on
whether using IoT applications can be efficient way
for a hotel to gain competitive advantage.
RQ1: Can the use of IoT applications be
regarded an effective factor for a hotel to
enhance provided services quality?
RQ2: Guests-wise, are IoT applications
equally valuable for the hotel’s various
functionalities?
RQ3: Do guests express their preference
towards hotels that use IoT applications?
RQ4: Is the use of IoT applications an
efficient way for a hotel to gain competitive
advantage?
Four hypotheses, stemming directly from Research
Questions, serving as requisites of them, were
formed. The 1st Hypothesis refers the development
of a hotel’s own IoT applications for delivering IoT
technology mediated services. The 2nd hypothesis
investigates whether guests evaluate some hotel IoT
applications as more important and efficient than
others. The 3rd Hypothesis investigates whether IoT
applications positively impact guests’
accommodation experience and satisfaction. The 4th
Hypothesis assumes that, additionally to customer
satisfaction, IoT applications will increase the
possibility of returning guests.
H1: There is the need for hotels to design “smart”
applications for their services.
H2: Guests evaluate some of the hotel IoT
applications as more valuable than others.
H3: The use of IoT applications in a hotel positively
affects guests’ accommodation experience. (guests’
satisfaction)
H4: Guests prefer to return back to hotels that
employ IoT applications for service provision.
(guests’ intention for repeated visit)
The current research predicates its Research
Questions and Hypotheses on some assumptions.
More specifically, the IoT applications of a hotel’s
functionalities are indicative, as they are applied in
the upper primary representative activities of a
hotel’s value chain. These applications are
connected only to the hotel’s operations and
marketing components, since, customer-wise, they
can add value to the integrated services provision, as
guest involvement is direct and the consumption of
them is immediate. No IoT applications were
proposed for hotel’s support activities, because of
their far below significance levels of direct value
creation for guests. As a result, since the purpose of
the current research is to investigate the influence of
IoT applications in hotels from a guest’s point of
view, the chosen IoT applications which hotels can
obtain to develop a ‘smart’ environment were front-
desk and reception, billing, in-room, F&B, guest
relations, conferences and group, as well as
marketing services like surveys, where guest
participation is significant, Table 1.
Table 1. Hotel’s IoT applications
4 METHODOLOGY
Considering previous research investigating the IoT
relationship with hospitality and tourism industry
[12]; [21], methodology was designed based on a
model congruent to our general objectives, which
enclose mainly the achievement of high-quality
services and increased levels of guests’ satisfaction
for gaining competitive advantage. Resourced-
Based View model was chosen, because it
concentrates on how the key resources of a hotel,
create value for its customers and how this value
creates a value proposition enhancing the hotel’s
competitive advantage. A method of descriptive
statistical analysis was adopted, where frequencies,
means, variance, standard deviation and
independency tests were calculated. Variable
Check in & Check out services
Mobile Check-in/out
through hotel's mobile application
Smartphone as Room Key
e.g. after checking in, guests can approach their smartphone to the
door of the room, where a sensor "recognizes" the specific guest and
the door opens automatically
Billing services
Auto Billing
e.g. whatever guests can purchase at the hotel is added to their bill
through their smartphone
Bill Check
e.g. guests will be able to control their bill, during their stay, through
hotel' s mobile application
In-Room facilities and services
"Smart" Rooms
e.g. guests can enjoy the preferable conditions of the room
environment, such as temperature, humidity, lighting, music, as
"smart" devices in the room can be programmed according to guests’
standards throughout their stay
"Smart" Mini-Bars
e.g. sensors in mini bars can inform hotel staff about the appropriate
and timely refilling according to guests' consumption and needs
Food & Beverage (F&B) services
Auto Restaurant Reservations
e.g. guests can make restaurant reservations at the hotel through their
smartphone
Auto Tracking and Ordering
e.g. through their smartphones, when guests are around hotel's bars
or restaurants area, they can be informed about any available seats
and if so, they can be sent the menu and order online
Target Promotions
e.g. special offers and promotions can be sent to guests through the
hotel's smartphone application according to their consumption history
Guest Relations services
Auto Last-Minute Booking
e.g. if guests want to book a room in hotel the last moment they arrive
at specific place, they can search about any availability and proceed
with the booking through hotel's mobile application
Late Arrival services
e.g. if guests arrive late at night at hotel, the hotel can be informed
through the application
Daily Activities Schedule
e.g. every day guests can be informed about possible activities that
are going to take place at the hotel or around it, regarding their
interests
Social Networks
e.g. guests can rate and share their stay experience of the hotel and its
services, while possible new guests can find out what to expect
Events, Meetings, Conferences services
Event Alarm and Location
e.g. alarm is sent to speakers and attendants of a conference, meeting
or event, reminding of the specific time and directions to place they
should attend
Group Tracking
e.g. everyone in a group is warned about the schedule of activities or
events they have to participate
Guest sur veys
Service surveys
e.g. surveys are sent through hotel's smartphone application allowing
guests to rate a service provided by hotel staff
Overall Survey
e.g. survey sent through hotel's smartphone application allowing
guest to rate their overall stay at the hotel
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correlations were reported and the level of a hotel’s
IoT applications influence on guests’ opinions
towards their accommodation experience was
examined through a regression model. R i386 3.4.3
program (version 3.4.3, 2017) was used for
statistical computing.
4.1 Survey Instrument and Sample
The questionnaire consisted of 26 questions grouped
two sections (Appendix) and an introduction, which
briefly presented research’s goal. In one section,
nominal scales were used to collect respondents’
demographics which were thought to reveal
significant information about the survey sample
population. The other included questions about
guests’ opinion towards a hotel’s IoT applications,
with a five point Likert scale ranging from
“Strongly Disagree” to “Strongly Agree”. The
original English questionnaire was reviewed for
content validity by a university member of
International Hellenic University, Greece,
specializing in marketing research, according to the
recommendations of [11]. Last, the reviewed
questionnaire was back and forth translated into
Greek to ensure translation equivalence [5].
An intercept method was followed in Greek public
locations that are considered to be busy (e.g.
shopping malls, bus and train stations, public parks).
The respondents were chosen based on a sampling
schedule. Multiple timescales were created to secure
random selection, and sampling hours were adjusted
different for working days and weekends.
Timescales, places, and individuals were randomly
selected. The sample population for this research
was past guests and potential future ones of a 5-star
hotel, where IoT technology is possible to be
applied. Individuals were first asked whether they
had been guests in a 5 start hotel in the past and only
those who declared “yes” could participate the
survey. Finally, the self-administered instrument
was delivered to 500 individuals, who satisfied all
parameters, from 01st August 2021 to 30th
September 2021 and a total of 315 completed
questionnaires were collected, from which 286 were
usable.
5. RESULTS
5.1 Respondents Profile
55.10% of the respondents were female and 44.90%
were male. 53.06 % were in the age group of 26 to
45 years old, 15.65 % younger than 25 years old,
19.05% were from 46 to 60 years old and 12.24%
are 61 years old or older. Concerning their travel
preferences, those who prefer to travel with friends
and family share almost equal percentage and
together they count for approximately 84% of total
responses (41.50% and 43.54% separately). The
remaining 14.96% is divided to 7.48% who travel
with business or work colleagues, 2.72% who prefer
to travel alone and 4.76% to other. The vast
majority of respondents (78.91%) choose to visit a
five-star hotel for their holidays and relaxation,
16.33% for business and 5.06% for educational or
other purpose, e.g. a conference.
5.2 Research Questions and Hypotheses
validation
According to frequency distributions, respondents’
overwhelming majority (84.35%) possess, as
expected, a smartphone or tablet and use
smartphone applications (81.63%). The high rate of
smartphone ownership and use of applications
validates the contention of H1 about the necessity of
a hotel to develop smartphone applications for
applying IoT’s proposed functionalities.
Question 3, was divided into subcategories,
illustrating each IoT application type, regarding the
hotel’s most common activities. Respondents were
asked to indicate their level of agreement towards
the proposed hotel’s IoT applications. Applications
mean values were used to underpin their
importance. Applications’ mean value over 3 is
considered positive, as it is above the neutral point.
For those whose mean value exceeds the value of
3.75, representing a value of over 75% in the 1 to 5
scale used, is of the highest significance for guests.
Along with mean values, variance and standard
deviation, as measures for revealing possible gaps
and differences in values occurring between sample
responses of a variable, were calculated. Minor
difference variations translate to narrower values of
variance and standard deviation; hence, the mean
values are by far more representative in describing
sample responses.
Figure 1 shows that all suggested IoT applications
exhibit mean scores between 3,748 and 4,061
(>3.75), meaning that they are all above the neutral
point of agreement (>3). The ones with the highest
mean values along with standard deviation and
variance less than 1 were auto restaurant
reservation, daily activities, smart rooms and auto
last-minute booking, thus the answer for H2 is
provided, by indicating which IoT applications
potential guests tend to consider as more valuable
and therefore must be prioritized by hotel managers
for resource development.
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Figure 1. IoT applications’ Means, variances &
standard deviations
Question 4 examined guests’ intention to choose a
hotel that incorporates IoT applications.
Respondents, when planning their upcoming
holidays, were asked to express their preference
between “Hotel X” - a 5* category hotel in the
resort area A, where an average double room rate is
at 150€/night in all-inclusive base – and “Hotel Y” -
also a 5* category hotel in the same resort area A,
where an average double room rate is at 160€/night
in all-inclusive base, with the difference that time it
offers all the IoT’s functionalities proposed. Table 1
shows that 72.11% of respondents prefer Hotel Y,
while only 27.89% chose Hotel X, supporting H3
and H4.
Table 1. Frequency distributions between Hotel X
and Hotel Y
Seeking for a connection among an IoT enabled
hotel’s guests’ choice and use of smartphones and
apps, Table 2 and Figure 2 reveal that the segment
choosing to stay in an IoT enabled hotel is
composed by the significant percentage of 69.40%
for total population of smartphone owners and of
66.00% of the smartphone apps users.
Table 2. Contingencies between Hotel preference
with smartphone ownership and use of applications
Figure 2. Contingency percentages between hotel
preference with smartphone ownership and use of
applications
H3 and H4 were additionally examined through
questionnaire’s question 5, by investigating
prospective guests’ agreement level, as to whether
they consider an IoT enabled hotel as value for
money, as well as how possible it is to choose the
same IoT enabled hotel for a future visit. According
to Table 3, for value for money dimension, the mean
value is 3.796, the variance is 0.684 and standard
deviation is 0.827, exhibiting that the mean response
of the sample is above the neutral point and even
higher than the level of 3.75. In combination with
low variance and standard deviation (<1) values, the
mean value represents the average opinion of over
75% of sample population, signifying that the
average respondent considers a hotel’s IoT
applications as value of money. As far the
dimension of returning guests, the mean value of
3.571, along with a variance of 0.630 and a standard
deviation of 0.79 (Table 3), indicates the existence
of prospect future visits.
Table 3. Statistic Summaries of guests’ attitude
toward an IoT enabled hotel
To reveal possible dependencies among the
research’s variables, independency t-test was
conducted. Table 4 illustrates the independency
cases occurring among variables and more
specifically the ones dependent to the mean values
of guest perception about the value of the IoT
enabled hotel (q5a) and the probability of returning
to the same IoT enabled hotel (q5b).
Table 4. t-test independency between the
smartphone ownership and value for money/
probability of returning to the same hotel-enabled
hotel (q5a) and the probability of returning to the
same IoT enabled hotel (q5b).
Variable
n (=315)
f (=1)
f% (=100)
q4 (hotel_with_iot)
315
1.0000
100.00
Hotel X
Hotel Y
106
209
0.2789
0.7211
27.89
72.11
Hotel with_IoT
smart_phone
smart_app
Yes
No
total
Yes
No
Total
Hotel X
15,00%
12,90%
27,90%
15,60%
12,20%
27,80%
Hotel Y
69,40%
2,70%
72,10%
66,00%
6,20%
72,20%
Total
84,40%
15,60%
100,00%
81,60%
18,40%
100,00%
Variable (5-point Likert scale)
mean
variance
sd
q5. (guest_opinion)
a. iot_value_of_money
3,795918
0,6840928
0,8270990
b. guest_return_to_ioT_hotel
3,571429
0,6301370
0,7938117
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When examining whether mean value of value for
money/probability of returning to the same hotel is
independent to smartphone ownership responses, a
95% probability of statistical significance arises,
where smartphone ownership and value for money
are not independent, as p-value is less than 5%.
Thus, it is accepted that value for money depends on
smartphone ownership, meaning that smartphones
owners perceive the IoT enabled hotel more
valuable than those who are not. Accordingly,
smartphone ownership seems to affect the mean
value of a potential revisit, as p-value t-test is less
than 0.05, meaning that smartphones owners exhibit
a more positive attitude towards revisiting the same
IoT enabled hotel. Table 5 shows that means of both
value for money and probability of returning to the
same IoT enabled hotel depend on smartphone
ownership, use of applications and guests’ intention
to choose an IoT enabled hotel, whereas gender is
independent.
Table 5. t-test independency between smartphone
ownership and value for money/ probability of
returning to the same IoT enabled hotel
The assessment of proposed IoT applications
influence on value for money and probability of
returning to the same IoT enabled hotel, was tested
through a correlation matrix. Table 6 shows
correlation (about 0.6) between value for money and
probability of returning to the same IoT enabled
hotel with some of the IoT applications. The two
regression models examining the relation of IoT
applications towards value of money (model 1) and
guest probability of revisiting the same IoT enabled
hotel (model 2) respectively, were found significant.
Table 6. Correlation matrixes between value for
money/ probability of returning to the same hotel to
IoT functionalities
F-ratio was calculated to prove the relative
importance of the regression models (if p-value <=
0.05. then the regression is appropriate). Tables 7
and 8 reveal that both models are significantly
appropriate.
Table 7. Regression model_1
ioT_value_of_money by smartphone
mean
Yes
No
3.959677
2.913043
t.test independency of ioT_value_of_money by hotel_with_iot
t = -7.3808, df = 36.598
p-value = 0.000000009469
(with 95 percent confidence interval)
guest_return_to_ioT_hotel by smartphone
mean
Yes
No
3.750000
2.608696
t.test independency of ioT_value_of_money by hotel_with_iot
t = -8.3541, df = 34.657
p-value = 8.1e-10
(with 95 percent confidence interval)
ioT _value_of_money by smart_app
mean
Yes
No
3.966667
3.037037
t.test independency of ioT_value_of_money by hotel_with_iot
t = -6.1009, df = 40.519
p-value = 0.0000003248
(with 95 percent confidence interval)
guest_return_to_ioT _hotel by smart_app
mean
Yes
No
3.783333
2.629630
t.test independency of ioT_value_of_money by hotel_with_iot
t = -7.9531, df = 37.196
p-value = 0.000000001527
(with 95 percent confidence interval)
t-test independency between variables q5 with q6
ioT _value_of_money by gender
mean
Female
Male
3.802469
3.787879
t.test independency of ioT_value_of_money by hotel_with_iot
t = 0.10424, df = 127.75
p-value = 0.9171
(with 95 percent confidence interval)
guest_return_to_ioT _hotel by gender
mean
Female
Male
3.592593
3.545455
t.test independency of ioT_value_of_money by hotel_with_iot
t = 0.3561, df = 137.61
p-value = 0.7223
(with 95 percent confidence interval)
t-test independency between variables q5 with q4
ioT _value_of_money by hotel_with_iot
mean
Hotel X
Hotel Y
3.097561
4.066038
t.test independency of ioT_value_of_money by hotel_with_iot
t = -6.7092, df = 59.984
p-value = 0.000000007829
(with 95 percent confidence interval)
guest_return_to_ioT _hotel by hotel_with_iot
mean
Hotel X
Hotel Y
2.926829
3.820755
t.test independency of ioT_value_of_money by hotel_with_iot
t = -7.0876, df = 72.976
p-value = 7.174e-10
(with 95 percent confidence interval)
IoT's hot el functionalities cor relation with iot _value_of_money
check_in_out
smart_key
iot_value_ofmoney
check_in_out
1.0000000
0.6197180
0.6337635
smart_key
0.6197180
1.0000000
0.5758698
iot_value_of money
0.6337635
0.5758698
1.0000000
auto_bill
bill_check
iot_value_of money
auto_bill
1.0000000
0.7575045
0.4689535
bill_check
0.7575045
1.0000000
0.4677493
iot_value_of money
0.4689535
0.4677493
1.0000000
smart_room
smart_mini_bar
s
iot_value_of money
smart_room
1.0000000
0.6758951
0.4271974
smart_mini_bars
0.6758951
1.0000000
0.3981523
iot_value_of money
0.4271974
0.3981523
1.0000000
auto_restaurant_
reserv
auto_track_
order
target_promotion
iot_value_of money
auto_restaurant_reserv
1.0000000
0.7296890
0.5496936
0.6342308
auto_track_order
0.7296890
1.0000000
0.4937029
0.5939884
target_promotion
0.5496936
0.4937029
1.0000000
0.4307182
iot_value_ofmoney
0.6342308
0.5939884
0.4307182
1.0000000
auto_last_min_book
ing
late_arrival
daily_activities
social_networks
iot_value_of
money
auto_last_min_Booking
1.0000000
0.7040911
0.4421996
0.4996844
0.5695064
late_arrival
0.7040911
1.0000000
0.5167424
0.4555281
0.3961273
daily_activities
0.4421996
0.5167424
1.0000000
0.5111804
0.4221680
social_networks
0.4996844
0.4555281
0.5111804
1.0000000
0.5356185
iot_value_of money
0.5695064
0.3961273
0.4221680
0.5356185
1.0000000
event_alarm
group_tracking
iot_value_of money
event_alarm
1.0000000
0.8175788
0.3528610
group_tracking
0.8175788
1.0000000
0.4620313
iot_value_of money
0.3528610
0.4620313
1.0000000
service_surveys
overall_surveys
iot_value_of money
service_surveys
1.0000000
0.9223621
0.3639167
overall_surveys
0.9223621
1.0000000
0.3546741
iot_value_of money
0.3639167
0.3546741
1.0000000
IoT's hot el functionalities cor relation with guest_r eturn_to_iot _hot el
check_in_out
smart_key
guest_return_
to_iot_hotel
check_in_out
1.0000000
0.6197180
0.5713188
smart_key
0.6197180
1.000000
0.4236702
guest_return_to_iot_hotel
0.5713188
0.4236702
1.0000000
auto_bill
bill_check
guest_return_to_iot_
hotel
auto_bill
1.0000000
0.7575045
0.5059591
bill_check
0.7575045
1.0000000
0.5599847
guest_return_to_iot_hotel
0.5059591
0.5599847
1.0000000
smart_room
smart_mini_bar
s
guest_return_to_iot_
hotel
smart_room
1.0000000
0.6758951
0.3944933
smart_mini_bars
0.6758951
1.0000000
0.2436550
guest_return_to_iot_hotel
0.3944933
0.2436550
1.0000000
auto_restaurant_rese
rv
auto_track_orde
r
target_promotion
guest_return_to_iot_
hotel
auto_restaurant_reserv
1.0000000
0.7296890
0.5496936
0.6334171
auto_track_order
0.7296890
1.0000000
0.4937029
0.5233866
target_promotion
0.5496936
0.4937029
1.0000000
0.4199379
guest_return_to_iot_hotel
0.6334171
0.5233866
0.4199379
1.0000000
auto_last_min_book
ing
late_arrival
daily_activities
social_networks
guest_return_to
_iot_hotel
auto_last_min_booking
1.0000000
0.7040911
0.4421996
0.4996844
0.5742648
late_arrival
0.7040911
1.0000000
0.5167424
0.4555281
0.5411735
daily_activities
0.4421996
0.5167424
1.0000000
0.5111804
0.4536601
social_networks
0.4996844
0.4555281
0.5111804
1.0000000
0.5449054
guest_return_to_iot_hotel
0.5742648
0.5411735
0.4536601
0.5449054
1.0000000
event_alarm
group_tracking
guest_return_to_iot_
hotel
event_alarm
1.0000000
0.8175788
0.3379192
group_tracking
0.8175788
1.0000000
0.4175025
guest_return_to_iot_hotel
0.3379192
0.4175025
1.0000000
service_surveys
overall_surveys
guest_return_to_iot_
hotel
service_surveys
1.0000000
0.9223621
0.3884253
overall_surveys
0.9223621
1.0000000
0.3860937
guest_return_to_iot_hotel
0.3884253
0.3860937
1.0000000
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Table 8. Regression model_2
Contingency tables were created by combining the
findings of the demographical characteristics with
the respondent preference of a hotel with IoT
functionalities or not, Table 9. From the respondents
with a preference to Hotel X there is an almost equal
distribution among man and woman. From the
respondents with a preference to Hotel Y women
tend to achieve greater rates than man. Concerning
age, young people (up to 45 years old) exhibit a
much more positive attitude towards an IoT enabled
hotel (Hotel Y) than older ones. Also, irrespective of
travel preferences and purpose of staying in five-star
hotel, majority of respondents choose Hotel Y over
Hotel X, in general.
Table 9. Contingency Tables
Table 10 shows the two regression models, where
Model 1 shows that the most statistically significant
IoT functionalities concerning the perception of
potential guests about value of IoT in hotels are auto
check in and out and auto last-minute booking.
Model 2 shows that the most statistically significant
IoT functionalities that loaded more in relation to
revisiting guests were auto check-in and out, smart
room and smart mini-bars (their estimates Pr(>|t|)
<= 0.05). That is, even though proposed IoT
functionalities are perceived as significant from
potential guests they posse different importance
weightiness.
Table 10. Regression models
Regression Model 1
Regression Model 2
6. DISCUSSION
The current research investigated the influence of
IoT in hotel industry, from guest’s perspective. H1-
need of smart phones and smart apps, in
combination with the H2- guest evaluation of the
IoT functionalities in hotels’, were developed to
examine the contention of Research Question 1,
about whether the use of IoT is an effective factor
for a hotel to enhance the quality of the provided
services. Research Question 2 was formed to invest
with Research Question 1, aiming to provide a more
lucid and balanced picture potential guests’
perception towards IoT enabled hotels, through
testing the H2.
H1 and H2 were supported, identifying all IoT
functionalities as means for enhancing the hotel
services that guests can enjoy, though some IoT
functionalities were found to be more valuable for
guests than others. Considering the high percentages
of smartphone owners and users of applications,
along with the recognized value of IoT applications
in provided services. hotels could concentrate on
developing smartphone applications that primarily
focus on IoT functionalities rated as more valuable
from potential guests.
Research Question 3 is connected with H3, where
the results clearly showed the preference of
potential guests to choose an IoT enabled hotel with
iot_value_of_money - regression
Residual standard error: 0.5599 on 129 degrees of freedom
Multiple R-squared: 0.5604. Adjusted R-squared: 0.5025
F-statistic: 9.674 on 17 and 129 DF. p-value: 5.769e-16
guest_return_to_ioT _hotel – regression
Residual standard error: 0.5531 on 129 degrees of freedom
Multiple R-squared: 0.6048. Adjusted R-squared: 0.5528
F-statistic: 11.61 on 17 and 129 DF. p-value: < 2.2e-16
Two-way tables between guests’ intention to choose an IoT enabled hotel with
smartphone ownership and use of applications
hotel
with
_iot
smart_phone
smart_app
Yes
No
Total
Yes
No
Total
Hotel X
15.00%
12.90%
27.90%
15.60%
12.20%
27.80%
Hotel Y
69.40%
2.70%
72.10%
66.00%
6.20%
72.20%
Total
84.40%
15.60%
100.00%
81.60%
18.40%
100.00%
Two-way tables between guests’ intention to choose an IoT enabled hotel with gender and age
Hotel
with
_iot
gender
age
F
M
Total
Up to
25
26-45
46-60
61 or
ol der
Total
Hotel
X
13.60
%
14.30
%
27.90%
2.10%
9.50%
7.50%
8.80%
27.90%
Hotel
Y
41.50
%
30.60
%
72.10%
13.60
%
43.50
%
11.60
%
3.40%
72.10%
Total
55.10
%
44.90
%
100.00
%
15.70
%
53.00
%
19.10
%
12.20
%
100.00%
Two-way tables between guests’ intention to choose an IoT enabled hotel with travel preference
Hotel
with iot
travel_preference
alone
with
friends
with
familly
with business
/work
col legues
othe
r
Total
Hotel X
0.70
%
6.10%
15.60%
2.00%
3.50
%
27.90%
Hotel Y
2.00
%
35.40%
27.90%
5.40%
1.40
%
72.10%
Total
2.70
%
41.50%
43.50%
7.40%
4.90
%
100.00%
Two-way tables between guests’ intention to choose an IoT enabled hotel with travel purpose
Hotel
with_iot
travel_purpose
for hol idays/
relaxation
for business/
work
for education al
purpose
other
Total
Hotel X
23.80%
2.00%
1.40%
0.70%
27.90%
Hotel Y
55.10%
14.30%
2.00%
0.70%
72.10%
Total
78.90%
16.30%
3.40%
1.40%
100.00%
iot_value_of_money - regression
Estimate
Std.
Error
t value
Pr(>|t|)
(Intercept)
0.39141
0.31248
1.253
0.21261
check_in_out
0.21933
0.07151
3.067
0.00263
**
smart_key
0.07400
0.05848
1.265
0.20803
auto_bill
0.04059
0.07821
0.519
0.60468
bill_check
-0.11837
0.08981
-1.318
0.18981
smart_room
0.13522
0.08444
1.601
0.11173
smart_mini_bars
0.05988
0.08778
0.682
0.49636
auto_restaurant_reservation
0.04841
0.09980
0.485
0.62848
auto_track_order
0.11983
0.07241
1.655
0.10039
target_promotion
0.04231
0.06359
0.665
0.50699
auto_last_min_booking
0.25663
0.09034
2.841
0.00523
**
late_arrival
-0.14188
0.07450
-1.904
0.05909
-
daily_activities
-0.08278
0.08002
-1.035
0.30284
social_networks
0.08440
0.07784
1.084
0.28027
event_alarm
-0.05610
0.11840
-0.474
0.63643
group_tracking
0.15846
0.12219
1.297
0.19697
service_surveys
-0.04999
0.15393
-0.325
0.74590
overal_surveys
0.10829
0.14119
0.767
0.44449
Residual standard error: 0.5599 on 129 degrees of freedom
Multiple R-squared: 0.5604. Adjusted R-squared: 0.5025
F-statistic: 9.674 on 17 and 129 DF. p-value: 5.769e-16
guest_return_to_ioT _hotel – regr ession
Estimate
Std.
Error
t value
Pr(>|t|)
(Intercept)
0.54419
0.31631
1.720
0.08775
check_in_out
0.17217
0.07239
2.378
0.01885
*
smart_key
-0.10568
0.05920
-1.785
0.07659
auto_bill
0.00800
0.07917
0.101
0.91967
bill_check
0.04152
0.09091
0.457
0.64861
smart_room
0.22500
0.08547
2.632
0.00951
**
smart_mini_bars
-0.19020
0.08886
-2.141
0.03419
*
auto_restaurant_reservation
0.12434
0.10102
1.231
0.22062
auto_track_order
0.07591
0.07330
1.036
0.30231
target_promotion
0.03855
0.06437
0.599
0.55036
auto_last_min_booking
0.10961
0.09144
1.199
0.23285
late_arrival
0.14364
0.07542
1.905
0.05905
-
daily_activities
-0.02926
0.08100
-0.361
0.71847
social_networks
0.04863
0.07880
0.617
0.53818
event_alarm
-0.12641
0.11985
-1.055
0.29348
group_tracking
0.16488
0.12368
1.333
0.18485
service_surveys
-0.08339
0.15581
-0.535
0.59346
overal_surveys
0.16215
0.14292
1.135
0.25866
Residual standard error: 0.5531 on 129 degrees of freedom
Multiple R-squared: 0.6048. Adjusted R-squared: 0.5528
F-statistic: 11.61 on 17 and 129 DF. p-value: < 2.2e-16
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Volume 21, 2022
IoT, supporting H3. That result entails that the use
of IoT in a hotel positively affects guests’
experience and it was used as background
knowledge Research Question 4 about raising the
prospect gaining competitive advantage through
hotel IoT applications integration. As proposed in
H4, embedding efficient IoT in a hotel’s provided
services adds value to potential guests, resulting to
increased customer satisfaction and loyalty. H3 and
H4 are supported, demonstrating potential guests’
recognition of value for money and probability of
revisiting an IoT enabled hotel.
The variables dependencies reveal that gender-
neutral, potential guests’ perception of value for
money from IoT functionalities and likelihood of
revisiting the same IoT enabled hotel depends on
smartphone ownership, use of smartphone
applications and preference of an IoT enabled hotel.
By investigating the influence of IoT in hotel
services, correlation between the proposed IoT
functionalities and value for money/probability of
revisiting the same IoT enable hotel were examined,
resulting in a statistically acceptable correlation.
Regression models were significantly appropriate;
however, data adjustment was not ideal. as multiple
R-squared was quite low, since the relation between
perspective guests’ value for money recognition of
IoT functionalities and the probability of guests’
revisit were not ideally linear.
It total, after answering all Research questions there
is the need to combine results with RBV theory.
Since IoT functionalities have been found effective
and valuable in hotels’ providing services (RQ1.
RQ2), they can contribute in enhancing the quality
of customer service and consequently add value to
potential customer. IoT enabled hotels could adjust
their customer-based value chain to their guests’
needs and preferences, achieving higher satisfaction
levels and developing customer loyalty. These two
distinct capabilities could in turn lead to the creation
of competitive advantage, as suggested in RQ3 and
RQ4.
Following the RBV model approach, the most
attractive IoT functionalities should be received as
top priority for the purpose of providing the highest
possible value to customers, mainly based on key
resources. Special attention must be paid on the
resources that can be developed to add value to
hotel’s value chain and lead to competitive
advantage. Auto check-in and out, auto restaurant
reservation, auto track and order and auto last-
minute booking should be technologically elevated
to improve hotel’s key resources and capabilities,
such as enhanced quality of services, higher levels
of customer trust, extensive and accurate knowledge
about customer needs and preferences.
7. LIMITATIONS AND
RECOMMENDATIONS FOR
FURTHER RESEARCH
Current research’s first limitation is its geographical
constraint. The survey included only Greek citizens
without considering any differences posed by other
cultures. Also, the IoT functionalities proposed in
the current research focus on hotel’s primary
activities and operations. They are designed as
operational and marketing activities of the hotel’s
value chain, such as reception (check-in and check-
out), billing, room (smart room facilities), food and
beverage (reservations in hotel’s restaurants and
bars), guest relation (late check-in/out, last minute
booking, recommendations for daily activities,
social media), marketing and sales (target
promotions. special offers), evaluating (specific and
overall surveys) and convention (alarm for the
speakers, group tracking) services. These services
are selected as indicative and they are not the only
ones that a five-star hotel could provide.
Future studies could expand the results of the
current research to a wider geographical area, for
instance Balkan countries, or throughout Europe,
even in a global level as the hotel sector enables the
term of globalization. Moreover, additional research
for examining IoT functionalities that can occur in
supporting activities of value chain could be
implemented, for instance proposing ways in which
IoT can be used to enhance the structural,
technological, financial and human assets of a hotel.
Acknowledgments
The authors wish to acknowledge
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WSEAS TRANSACTIONS on COMMUNICATIONS
DOI: 10.37394/23204.2022.21.21
Grammatikopoulou Georgia, Tsourela Maria
E-ISSN: 2224-2864
180
Volume 21, 2022
