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Citation: Gaji´c, T.; Petrovi´c, M.D.;
Peši´c, A.M.; Coni´c, M.; Gligorijevi´c, N.
Innovative Approaches in Hotel
Management: Integrating Artificial
Intelligence (AI) and the Internet of
Things (IoT) to Enhance Operational
Efficiency and Sustainability.
Sustainability 2024,16, 7279.
https://doi.org/10.3390/su16177279
Academic Editor: Jun (Justin) Li
Received: 24 July 2024
Revised: 20 August 2024
Accepted: 21 August 2024
Published: 24 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Innovative Approaches in Hotel Management: Integrating
Artificial Intelligence (AI) and the Internet of Things (IoT) to
Enhance Operational Efficiency and Sustainability
Tamara Gaji´c
1,2,3,
* , Marko D. Petrovi´c
1,4
, Ana Milanovi´c Peši´c
1
, Momˇcilo Coni´c
5
and Nemanja Gligorijevi´c
5
1Geographical Institute “Jovan Cviji´c”, Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia;
m.petrovic@gi.sanu.ac.rs (M.D.P.); a.milanovic@gi.sanu.ac.rs (A.M.P.)
2Institute of Environmental Engineering, Peoples’ Friendship University of Russia (RUDN University),
Moscow 117198, Russia
3Faculty of Hotel Management and Tourism, University of Kragujevac, 36210 Vrnjaˇcka Banja, Serbia
4
Department of Regional Economics and Geography, Faculty of Economics, Peoples’ Friendship University of
Russia (RUDN University), Moscow 117198, Russia
5Department of Leskovac Vocational College, Academy of Vocational Studies Southern Serbia,
16000 Leskovac, Serbia; momcilo.conic@gmail.com (M.C.); gligorijevicnemanja@gmail.com (N.G.)
*Correspondence: tamara.gajic.1977@gmail.com
Abstract: The integration of artificial intelligence (AI) and the internet of things (IoT) is bringing
revolutionary changes to the hospitality industry, enabling the advancement of sustainable practices.
This research, conducted using a quantitative methodology through surveys of hotel managers in the
Republic of Serbia, examines the perceived contribution of AI and IoT technologies to operational
efficiency and business sustainability. Data analysis using structural equation modeling (SEM) has
determined that AI and IoT significantly improve operational efficiency, which positively impacts
sustainable practices. The results indicate that the integration of these technologies not only optimizes
resource management but also contributes to achieving global sustainability goals, including reducing
the carbon footprint and preserving the environment. This study provides empirical evidence of
the synergistic effects of AI and IoT on hotel sustainability, offering practical recommendations for
managers and proposing an innovative framework for enhancing sustainability. It also highlights
the need for future research to focus on the long-term impacts of these technologies and address
challenges related to data privacy and implementation costs.
Keywords: artificial intelligence; internet of things; sustainability; hotel business
1. Introduction
In modern hospitality, sustainability has become crucial, not only as an environmen-
tal goal but also as an economic and social obligation. The use of artificial intelligence
(AI) and the internet of things (IoT) brings new opportunities for enhancing sustainable
practices in the hotel industry [
1
–
3
]. However, despite the obvious advantages, there is a
significant lack of research focusing on the integration of these technologies in the context
of sustainable hotel operations. Shani et al. [
4
], in their systematic and critical review
of IoT in contemporary hospitality, confirm this gap. They emphasize that, despite the
progress, there is a shortage of studies providing a detailed review of IoT applications in the
hotel industry.
Artificial intelligence (AI) is becoming increasingly prevalent in hospitality, allowing
hotels to optimize their operations, improve customer experience, and enhance sustainabil-
ity. AI is used to analyze guest data to provide personalized services and recommendations,
resulting in increased guest satisfaction [
5
–
7
]. On the other hand, the term internet of
things (IoT) was first used by Kevin Ashton in 2009 [
8
] while working on a research project
for the company Procter & Gamble (P&G). Ashton used this term to describe a system of
Sustainability 2024,16, 7279. https://doi.org/10.3390/su16177279 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 7279 2 of 24
connected devices that communicate over the internet, enabling data exchange between
physical objects and computer systems without the need for human intervention. His idea
was that RFID technology (radio frequency identification) could be used to connect physical
objects to the internet, allowing for real-time tracking and management. Since then, the
term IoT has become widely accepted and encompasses a broad range of technologies
and applications that enable the connection and communication between various devices
and systems via the internet. Additionally, IoT can be seen as part of a broader trend that
includes machine learning, where both concepts enable the connection and processing
of large amounts of data from various sources. While IoT facilitates the collection and
exchange of data between devices, machine learning utilizes this data for analysis and in-
telligent decision making, further enhancing the functionality of IoT systems. This synergy
between IoT and machine learning enables advanced applications that not only automate
processes but also predict future events, thus optimizing operations in various industries,
including hospitality [9].
Eskerod et al. [
10
] explored how the application of AI and IoT technologies can
contribute to environmental sustainability in the hotel sector, with a particular focus on
energy management and reducing carbon emissions. Their research demonstrated that
these technologies have significant potential to optimize energy consumption, which not
only reduces operational costs but also significantly contributes to reducing the negative
impact of hotels on the environment. The authors emphasize that the implementation
of these technologies enables hotels to use resources more efficiently, reducing the need
for fossil fuels and increasing the use of renewable energy sources. Moreover, it has been
shown that hotels that successfully integrate AI and IoT technologies not only achieve
environmental goals but also realize long-term economic benefits through reduced energy
costs and improved guest satisfaction.
Hossain [
11
] examines the broad application of AI and IoT technologies for sustainable
applications across various sectors, including hospitality. This study emphasizes how the
combination of these technologies improves operational efficiency and reduces the ecologi-
cal footprint through better resource management. Arana-Landín et al. [
12
] explore how AI
can enhance environmental sustainability, with a particular focus on energy management.
Their study reveals that AI analytics enable the prediction of energy needs and the adjust-
ment of HVAC (heating, ventilation, and air conditioning) systems, which not only reduces
energy consumption but also improves guest comfort. In addition to the environmental
benefits, these authors expand the topic by investigating how the application of AI and
IoT technologies can enhance sustainability in a broader sense, encompassing social and
economic aspects. They also emphasize that the integration of these technologies allows
hotels to reduce resource consumption while significantly improving working conditions
for employees, which has a direct impact on social sustainability. Increased work efficiency
reduces stress and burden on employees, while the automation of routine tasks allows
staff to focus on providing higher-quality service to guests. Furthermore, the same authors
highlight that the application of AI and IoT technologies can significantly contribute to the
long-term economic sustainability of hotels through more efficient resource use, reduction
in operational costs, and increased guest satisfaction. These factors together not only
improve the profitability of hotels but also strengthen their image as socially responsible
and environmentally conscious entities.
Similar findings were reported by other authors, highlighting that AI tools allow hotels
to efficiently forecast energy needs [
13
,
14
]. Additionally, AI enables predictive maintenance,
where equipment failures are anticipated before they occur, thereby reducing the need for
emergency repairs and extending the lifespan of the equipment [15].
The integration of AI into hotel operations facilitates the management of carbon foot-
prints and waste reduction in facilities [
16
], while IoT systems integrate various hotel
operations into a single platform [
17
,
18
]. Fraga-Lamas et al. [
19
] emphasize the impor-
tance of green IoT and Edge AI technologies in a sustainable digital transition. Sensors
integrated with AI algorithms allow for real-time monitoring of energy usage, leading to a
Sustainability 2024,16, 7279 3 of 24
decrease in carbon emissions and a reduction in operational expenses. He further argues
that the integration of renewable energy sources further enhances energy management
strategies in hotels. The adoption of these technologies is influenced by factors such as
hotel classification, target market, and geographical location [
20
]. Applications of AI and
IoT reach far beyond the hospitality industry, impacting smart cities, energy systems, and
supply chains while also playing a significant role in advancing several UN sustainable
development goals [21].
In the broader context of sustainable architecture, AI and IoT play a crucial role in
improving building performance and energy efficiency [
22
]. IoT applications in hotels
include intelligent robots and guest control systems, enabling contactless services and
enhancing operational efficiency [
23
,
24
]. The integration of artificial intelligence has led to
significant improvements in metrics such as productivity, quality, and customer satisfaction
across various sectors [
25
]. These technologies have also revolutionized food procurement
practices and performance in luxury hotels [
26
,
27
]. Henri and Journeault [
26
] argue that
IoT technologies enhance operations and contribute to sustainability by enabling precise
inventory management. Sensors powered by IoT can notify staff of low stock levels
or approaching expiration dates, helping to reduce excess inventory and costs while
also minimizing the hotel’s environmental impact. They cite examples such as smart
locks providing occupancy data and facilitating the efficient planning of maintenance and
room services.
Kamruzzaman [
28
] investigates the application of AI and IoT technologies in educa-
tional systems during the COVID-19 pandemic, but the findings have significant impli-
cations for the hotel industry as well. The study indicates that the combination of these
technologies enables more efficient resource management and optimization of operational
processes, contributing to the economic and environmental sustainability of hotels.
Recent research indicates that while AI and IoT technologies offer numerous advan-
tages for the hotel industry, significant challenges and limitations in their application must
be considered [
29
–
31
]. The implementation of modern technology can optimize operations,
enhance guest experiences, and improve sustainability [
32
]. However, there are ongoing
concerns regarding data privacy, job displacement, and ethical deployment [
33
,
34
]. The
hotel industry is increasingly adopting AI applications such as service robots, chatbots, and
booking engines [
35
,
36
]. Nonetheless, in recent years, there has been a growing emphasis
on addressing potentially unethical issues in the application of AI and IoT, leading to
the proposal of frameworks such as FAST (Fairness, Accountability, Sustainability, and
Transparency) [
37
]. While AI offers potential benefits, its implementation requires careful
consideration of sustainability and ethical implications [38].
One of the key challenges is data security, as IoT devices collect vast amounts of
data that can be vulnerable to hacking and unauthorized access [
39
]. Additionally, the
high initial cost of implementing and maintaining IoT infrastructure can be a barrier for
many hotels, especially smaller establishments with limited budgets [
40
,
41
]. Lee et al. [
42
]
highlight that IoT can complicate business models, particularly when it comes to inte-
gration with existing systems. They also point out that a detailed analysis is necessary
to ensure that the implementation of IoT technologies brings real value to hotels. Shin
et al. [
43
] explore the application of digital technologies in hospitality and emphasize that
a lack of expertise and staff training presents a significant challenge. Without adequate
training, staff may struggle to use new technologies, leading to operational problems and
reduced efficiency.
Buonincontri and Micera [
44
] discuss the experience of creating smart tourist destina-
tions and highlight that over-reliance on technology can compromise the authenticity of
guest experiences. They argue that it is important to find a balance between technological
innovations and preserving traditional hospitality values. Abomhara and Geir [
45
], in
their study on cybersecurity in the IoT context, emphasize that IoT devices often have
vulnerabilities that can be exploited for cyber-attacks. They suggest that robust security
protocols and continuous software updates are necessary to minimize risks.
Sustainability 2024,16, 7279 4 of 24
Although previous literature has covered various aspects of the application of AI and
IoT technologies, there is a need for a deeper analysis of their contribution to sustainability.
Special attention should be paid to the environmental, economic, and social dimensions of
sustainability that these technologies can enhance. AI and IoT can significantly contribute to
reducing energy consumption, optimizing resource use, and improving working conditions,
which are key aspects for long-term sustainability in the hospitality industry.
The aim of this research is to examine how AI and IoT technologies can enhance
sustainability in the hotel industry by improving energy efficiency, waste management,
sustainable supply, implementation of green building practices, and employee educa-
tion. However, despite the apparent advantages, there is a significant lack of research
addressing the integration of these technologies in the context of sustainable hotel op-
erations. Specifically, there is a shortage of empirical evidence on the synergistic effects
of AI and IoT on operational efficiency and sustainability in the hotel industry. This re-
search aims to fill this gap by providing empirical evidence on the contribution of these
technologies to sustainable practices in the hotel industry in the Republic of Serbia. Our
study employs a quantitative methodology and structural equation modeling (SEM) to
analyze hotel managers’ perceptions of the impact of AI and IoT on operational efficiency
and sustainability.
In a world where innovation is often highlighted as a key driver of progress, this study
transcends conventional boundaries by providing a detailed analysis of how cutting-edge
technologies such as artificial intelligence (AI) and the internet of things (IoT) fundamentally
reshape sustainability practices in the hotel industry. Our findings highlight how AI
and IoT can transform resource management, reduce the carbon footprint, and improve
overall sustainability, directly contributing to global sustainability goals. By providing a
detailed analysis of the role of these technologies in optimizing resource use and enhancing
environmental sustainability, this study offers a robust model for future research and
practical application in various sectors.
2. Methodology
The research was conducted through four interrelated studies addressing different
aspects of the impact of artificial intelligence (AI) and the internet of things (IoT) on the
operational efficiency and sustainability of hotel operations. Figure 1shows the conceptual
model used in the research, illustrating the relationships between artificial intelligence
(AI), the internet of things (IoT), operational efficiency (OE), and sustainable hotel business
(SHB). The model also includes the mediating effect of operational efficiency, as well as the
moderating effect of the integration of AI and IoT with sustainability (IAIIoTWS). Based on
previous reviews of the literature and similar research, research hypotheses and a model
were set (Figure 1):
H1. Artificial intelligence (AI) positively impacts the operational efficiency of hotels.
H2. The internet of things (IoT) positively impacts the operational efficiency of hotels.
H3. Operational efficiency positively impacts the sustainable business practices of hotels.
H3a. Operational efficiency positively mediates the relationship between artificial intelligence and
the sustainability of hotels.
H3b. Operational efficiency positively mediates the relationship between the internet of things and
the sustainability of hotels.
H4. The integration of AI and IoT with sustainability positively impacts the sustainable business
practices of hotels.
Sustainability 2024,16, 7279 5 of 24
H4a. The integration of AI and IoT with sustainability positively moderates the relationship
between operational efficiency and the sustainable business practices of hotels.
Sustainability 2024, 16, x FOR PEER REVIEW 5 of 24
H4. The integration of AI and IoT with sustainability positively impacts the sustainable business
practices of hotels.
H4a. The integration of AI and IoT with sustainability positively moderates the relationship be-
tween operational eciency and the sustainable business practices of hotels.
Figure 1. Proposed research model.
Study 1. The impact of articial intelligence (AI) on hotel operational eciency.
Study 1 focuses on analyzing the impact of AI technology implementation on the
operational eciency of hotels. This study explores how the implementation of AI can
optimize various operational processes in the hospitality industry, potentially reducing
costs and improving service eciency. The research questions posed in this context are as
follows:
R.Q.1. How does the implementation of articial intelligence aect the optimization
of operational processes in hotels?
R.Q.2. In what ways does AI contribute to the reduction in operational costs in the
hotel industry?
R.Q.3. To what extent do AI technologies inuence guest satisfaction through per-
sonalized services?
Study 2. The impact of the internet of things (IoT) on hotel operational eciency.
H3, H3a, H3b
H1
H2
Study 2
H4,H4a
Study 4
Study 3
Study 1
Artificial
intelligence—AI
Internet of things—
IoT
Operational
efficiency
Sustainable hotel
business
Integrating AI and IoT
with sustainability
Figure 1. Proposed research model.
Study 1. The impact of artificial intelligence (AI) on hotel operational efficiency.
Study 1 focuses on analyzing the impact of AI technology implementation on the
operational efficiency of hotels. This study explores how the implementation of AI can
optimize various operational processes in the hospitality industry, potentially reducing
costs and improving service efficiency. The research questions posed in this context are
as follows:
R.Q.1. How does the implementation of artificial intelligence affect the optimization
of operational processes in hotels?
R.Q.2. In what ways does AI contribute to the reduction in operational costs in the
hotel industry?
R.Q.3. To what extent do AI technologies influence guest satisfaction through person-
alized services?
Study 2. The impact of the internet of things (IoT) on hotel operational efficiency.
Study 2 investigates the impact of IoT technologies on the operational efficiency
of hotels. The goal of this study is to analyze how IoT devices can contribute to the
improvement in operational processes in hotels through better resource management,
automation of routine tasks, and optimization of energy efficiency. This study aims to
answer the following questions:
R.Q.4. How do IoT technologies enable real-time data collection and analysis to
improve operational efficiency?
R.Q.5. In what ways do IoT devices contribute to the optimization of energy and
resource consumption in hotels?
R.Q.6. How much do IoT technologies enhance guest safety and comfort?
Study 3. The relationship between operational efficiency and sustainable hotel business.
Sustainability 2024,16, 7279 6 of 24
Study 3 focuses on exploring the relationship between hotel operational efficiency and
sustainable business practices. This study investigates how high operational efficiency, sup-
ported by AI and IoT technologies, can mediate the implementation of sustainable business
practices in the hospitality industry, including reducing the environmental footprint and
better resource management. The research questions covered in this study are as follows:
R.Q.7. How does high operational efficiency, supported by AI and IoT technologies,
mediate the implementation of sustainable business practices in hotels?
R.Q.8. In what ways does operational efficiency affect the reduction in the hotel’s
environmental footprint?
R.Q.9. How much does the integration of AI and IoT contribute to the implementation
of sustainable practices in the hotel sector?
Study 4. Moderation effects of AI and IoT technology integration.
Study 4 analyzes the moderating effects of AI and IoT technology integration on hotel
operational efficiency and sustainability. This study explores how the combination of AI
and IoT can have a synergistic effect on enhancing the operational efficiency of hotels,
thereby supporting business sustainability through more efficient resource use and better
management of hotel operations. The research questions this study seeks to answer are
as follows:
R.Q.10. How does the combination of AI and IoT technologies synergistically affect
the enhancement of hotel operational efficiency?
R.Q.11. In what ways does the integration of AI and IoT contribute to the comprehen-
sive sustainability of hotel operations?
R.Q.12. What are the challenges and limitations in integrating AI and IoT technologies
with sustainable practices?
2.1. Participants and Settings
This research involved 220 hotel managers from various hotels across the Republic of
Serbia. Participants were selected using random sampling to ensure the representativeness
of the sample and minimize bias. The demographic profile of participants included man-
agers with varying levels of experience and positions within the hotel industry. Of the total
number of participants, 60% were men (132 managers) and 40% were women (88 managers).
Participants held various positions within the hotels, including general managers (25%),
operations managers (30%), front desk managers (20%), marketing managers (15%), and
maintenance managers (10%). All participants had completed higher education, ensuring
a high level of expertise and relevance in their responses. The years of work experience
among managers varied, with 20% of participants having less than 5 years of experience,
40% between 5 and 10 years, and 40% more than 10 years of experience in the hotel industry.
This diversity in experience allowed for a comprehensive overview of different perspectives
and levels of knowledge about the application of AI and IoT technologies.
The sampling included hotels of various sizes, star ratings, and types of services, such
as luxury hotels, business hotels, family hotels, and hotels with specialized facilities such
as spa and wellness centers. Although data on average room rates were not available, other
key factors were considered, such as the hotel’s location and the level of technological
infrastructure. All hotels were located in urban areas, which allowed them easier access
to advanced technological resources. Most hotels are situated in large cities and popular
tourist centers, close to technological hubs, which further facilitated the implementation of
AI and IoT technologies. Hotels were located in Belgrade, Novi Sad, Kragujevac, Zlatibor,
and Kopaonik.
In terms of technological infrastructure, the hotels are generally well-equipped, consid-
ering that they are located in urban areas with access to high-speed internet and advanced
IT systems. This favorable context enabled more efficient integration of new technologies,
which is an important factor in understanding the impact of AI and IoT technologies on
the operational efficiency of hotels.
Sustainability 2024,16, 7279 7 of 24
In total, 30% of hotels had fewer than 50 rooms, 40% had between 50 and 150 rooms,
and 30% had more than 150 rooms. Regarding the classification of hotels, they were
divided based on their star rating: 35% were 4-star hotels, 40% were 3-star hotels, 15% were
2-star hotels, and 10% were 5-star hotels. The sampling method involved identifying and
contacting hotels via phone and email, after which managers were invited to participate in
the research. The survey was conducted in person, ensuring a high response rate and the
ability to clarify questions directly with respondents. The data-collection process lasted
from March to June 2024. All participants were informed in advance about the purpose
of the research, and their consent was sought before starting the questionnaire. Data
confidentiality was ensured, and the identity of participants was protected.
To determine the adequate sample size for this study, G*Power software 3.1.9.7 was
used. Based on parameters, including a medium effect size (f
2
= 0.15), significance level
(
α
= 0.05), statistical power (1
−β
= 0.80), and the number of predictors (4), G*Power
analysis indicated that a sample size of 129 participants was required. This number
ensures sufficient statistical power to detect the effects present in the model, ensuring the
validity and reliability of the research results. Research ethics were strictly adhered to, with
informed consent and data confidentiality guarantees. Participants were informed that
their participation in the research would not negatively impact their position or relationship
with the hotel. Sample limitations include the possibility of bias due to the self-selection of
participants, as well as limited geographical representation of hotels, which may affect the
generalization of results.
2.2. Questionnaire Design
The questionnaire was designed to assess the impact of artificial intelligence (AI) and
internet of things (IoT) technologies on the operational efficiency and sustainability of hotel
operations. Its purpose is to identify hotel managers’ perceptions of how these technologies
contribute to improving operational processes and sustainable practices.
It must be emphasized that operational efficiency in this study was measured through
the opinions and perceptions of hotel managers who participated in the survey. In this
phase of the research, quantitative measurements, such as specific data on operational costs
or processing times, were not used, which represents a limitation of the study. Although the
measurements based on opinions are subjective, several factors contribute to the reliability
of this approach. First and foremost, the respondents were directly involved in managing
hotel operations, meaning they had a deep understanding and insight into the hotel’s
performance before and after the implementation of the technologies. Additionally, the
survey was structured in a way that minimizes respondent bias, using validated scales and
multiple indicators for each aspect of operational efficiency.
This approach allows the collection of rich qualitative data that reflects the real experi-
ence of managers with new technologies, which is essential for understanding the impact
of AI and IoT on operational efficiency. Although quantitative data were not included
in this phase of the research, the results provide valuable insights and form a basis for
future studies that could include objective indicators to further strengthen the validity of
the findings.
The questionnaire consists of various types of questions, including multiple-choice
questions, Likert scales, and open-ended questions. The Likert scale is used to assess agree-
ment with statements, ranging from 1 (strongly disagree) to 5 (strongly agree). Questions
were adapted from existing research on the application of AI and IoT technologies and
modified to fit the specific context of this study. The questionnaire is divided into five main
sections: artificial intelligence (AI) (includes 15 statements), internet of things (IoT) (15
statements), operational efficiency (OE) (12 statements), sustainable hotel business (SHB)
(14 statements), and integration of AI and IoT with sustainable hotel business (IAIIoTWS)
(8 statements).
Questions are arranged in a logical order to ensure a natural flow of responses and to
maintain respondent engagement. Before conducting the main research, the questionnaire
Sustainability 2024,16, 7279 8 of 24
was tested on a smaller group of respondents similar to the target population to identify
and correct any potential ambiguities. The testing indicated that the questions were clear
and relevant, covering all aspects of the study well. Clear instructions were provided for
completing the questionnaire, including an explanation of the research purpose, how to
respond to different types of questions, and other relevant information.
During the conduct of this research, all ethical procedures were strictly adhered to.
Participants were informed in advance about the research objectives and were given the
opportunity to participate voluntarily. Their anonymity was guaranteed, and the data were
used solely for the purposes of this study. Additionally, special attention was paid to the
issue of moral hazard to avoid any participant behavior that could be influenced by the
knowledge that their responses would not have consequences for their employment or
business. In this way, we ensured that participants provided honest and accurate responses
without feeling pressured to answer in a way that might be perceived as more favorable
to them.
The data analysis plan was defined in advance, including methods for coding and
categorizing responses, as well as statistical analyses to be used for interpreting the results.
The questionnaire was reviewed and revised multiple times based on feedback from pre-
testing and pilot studies to ensure clarity and relevance of the questions.
2.3. Data Analysis
Data analysis in this research was conducted using the SPSS 23.00 (Statistical Pack-
age for the Social Sciences) and SmartPLS 3 (Partial Least Squares Structural Equation
Modeling) software packages. Before detailed analysis, the data were cleaned to ensure
accuracy and completeness. Incomplete responses and incorrect data were removed to
reduce the risk of bias. After cleaning, descriptive statistics were created to summarize
the basic characteristics of the data, including means, standard deviations, and frequency
distributions for the main variables. The reliability of the scales used in the questionnaire
was assessed using Cronbach’s alpha coefficient (
α
). The Cronbach’s alpha values for
all scales exceeded the recommended threshold of 0.7, indicating a high level of internal
consistency and reliability of the questionnaire [
46
,
47
]. Exploratory factor analysis (EFA)
was conducted to identify the underlying structure of the data [
48
,
49
]. The Kaiser–Meyer–
Olkin (KMO) measure was 0.704, and Bartlett’s test of sphericity was significant (
χ2
= 2.835,
p< 0.001), indicating that the data were suitable for factor analysis [
50
,
51
]. Promax rotation
was used for the interpretation of the obtained factors.
Structural equation modeling (SEM) was conducted using SmartPLS 3 software to
test the hypotheses and research models [
52
,
53
]. We also performed a sensitivity analysis
to assess the impact of changes in key assumptions on the model results. By varying
input parameters within a reasonable range, we observed that the model results remained
stable across different scenarios, demonstrating high robustness. Additionally, we used
cross-validation techniques to further confirm the predictive power of the model. The
dataset was split into training and test sets using k-fold cross-validation (k = 10), where the
data were divided into ten subsets [
49
]. The model was trained on nine subsets and tested
on the remaining one, repeating this process ten times so that each subset served as the test
set. This allowed us to evaluate the model’s performance across different segments of the
data, confirming its generalizability.
Fit indices included the standardized root mean square residual (
SRMR = 0.025
), com-
parative fit index (CFI = 0.97), root mean square error of approximation (
RMSEA = 0.05
),
and Tucker–Lewis index (TLI = 0.96), indicating good model fit [
54
]. The R
2
values were
0.299, indicating an adequate explanation of the variance of the dependent variables [
55
].
The Heterotrait–Monotrait (HTMT) ratio was used to assess discriminant validity [
55
].
HTMT values were below the recommended threshold of 0.85, indicating adequate discrim-
inant validity of the constructs. Convergent validity was assessed by analyzing loadings
and the average variance extracted (AVE). All loading values were above 0.70, while AVE
values exceeded the threshold of 0.50, confirming convergent validity [56,57].
Sustainability 2024,16, 7279 9 of 24
Multicollinearity was tested using the variance inflation factor (VIF). All VIF values
were below 5, indicating no multicollinearity issues among the predictor variables [
58
,
59
].
Regression analysis was conducted to examine the relationships between dependent and
independent variables [
60
–
62
]. The regression coefficients and their statistical significance
were interpreted in the context of the research hypotheses, confirming the significant
effects of AI and IoT technologies on the operational efficiency and sustainability of hotels.
Mediation and moderation analyses were conducted using the bootstrapping method,
allowing for the assessment of indirect and interaction effects in the model.
3. Results
3.1. Descriptive and Factor Analysis Results
Tables 1and 2provide a detailed overview of the descriptive statistics, factor loadings,
and construct validation used in the study, encompassing artificial intelligence (AI), the
internet of things (IoT), operational efficiency (OE), sustainable hotel business (SHB), and
the integration of AI and IoT with sustainability (IAIIoTWS). The average value for the
artificial intelligence (AI) construct is 3.73, indicating a positive perception among hotel
managers regarding the contribution of AI technologies. The reliability of the construct is
confirmed by a Cronbach’s alpha coefficient of 0.739, which is above the acceptable thresh-
old of 0.7. The percentage of variance explained by this construct is 11.028%, indicating a
significant contribution of AI technologies to the variability in the perception of operational
efficiency and sustainability.
These results suggest that hotel managers recognize AI as a key factor for improving
business efficiency, which is reflected in increased guest satisfaction through personalized
services and resource optimization. The positive attitude toward AI technologies indicates
that hotels that successfully integrate these technologies can achieve significant benefits in
terms of operational efficiency and sustainable practices.
A similar positive perception exists for the internet of things (IoT) construct, with an
average value of 3.33. The reliability of the construct is confirmed by a Cronbach’s alpha
coefficient of 0.773, while the IoT factor explains 9.531% of the variance, further emphasizing
its importance in the hospitality industry. This implies that IoT technologies, such as
smart thermostats and energy monitoring sensors, contribute to more efficient resource
management and reduced operational costs, which directly impact hotel sustainability.
These results not only confirm the reliability of the measures used but also highlight
their significance in a practical context, suggesting that hotels adopting these technologies
can significantly improve their operations and contribute to sustainability in line with
global goals.
Operational efficiency (OE) has an average value of 3.56, indicating that hotel managers
recognize the significant contribution of AI and IoT technologies in improving operational
processes. The high reliability of this construct is confirmed by a Cronbach’s alpha coef-
ficient of 0.900, which emphasizes the consistency of responses among the respondents.
The OE factor explains 7.943% of the variance, further highlighting its key role in business
efficiency. These data imply that the implementation of AI and IoT technologies enables
hotels to optimize workflows, reduce costs, and increase employee productivity, which is
crucial for maintaining competitiveness in the market.
The integration of AI and IoT with sustainability (IAIIoTWS) shows an average value
of 3.24, with high construct reliability confirmed by a Cronbach’s alpha coefficient of 0.901.
This factor explains 5.422% of the variance, emphasizing the importance of integrating
technology with sustainable practices. These results indicate that hotel managers recognize
that the combination of these technologies not only improves operational efficiency but
also contributes to business sustainability, which is essential for long-term success and
alignment with global sustainable development goals.
Sustainability 2024,16, 7279 10 of 24
Table 1. Descriptive statistics of the main constructs, factor loadings, and construct validation.
Factor Statement m sd αFA
Artificial intelligence
(AI)
m = 3.73
sd = 1.533
α= 0.739
% variance—11.028
CR—0.745
AVE—0.692
AI tools reduce time spent on administrative tasks. 2.16 1.278 0.798 0.770
AI optimizes operational processes and increases
staff efficiency. 2.81 1.442 0.793 0.743
AI reduces hotel management costs. 2.33 1.418 0.797 0.713
AI personalized recommendations to improve the
guest experience. 2.85 1.375 0.736 0.703
AI chatbots provide real-time information
and support. 2.32 1.401 0.766 0.685
AI analyzes guest feedback and resolves
requests faster. 2.19 1.379 0.716 0.633
AI analytics help make decisions about
pricing strategies. 2.25 1.448 0.745 0.795
AI predicts capacity occupancy and adjusts services.
2.66 1.609 0.794 0.869
AI provides insights into market trends and
competitor behavior. 2.18 1.502 0.795 0.848
AI automation reduces manual work in operations. 4.13 2.301 0.809 0.847
AI automatically updates guest and
reservation data. 3.64 0.072 0.723 0.725
AI systems facilitate inventory and
resource management. 3.42 0.123 0.791 0.850
AI helps tailor offers to guest preferences. 2.98 1.918 0.777 0.852
AI enables personalized marketing campaigns. 3.28 0.016 0.793 0.709
AI enables the creation of personalized itineraries
and guest services. 3.77 0.230 0.761 0.756
Internet of things
(IoT)
m = 3.33
sd = 1.389
α= 0.773
% variance—9.531
CR—0.709
AVE—0.671
IoT surveillance improves hotel security. 3.08 1.967 0.794 0.742
Smart locks and security systems increase
guest safety. 3.60 0.061 0.734 0.827
IoT sensors detect and prevent fires
and emergencies. 3.30 1.785 0.791 0.662
IoT sensors reduce energy consumption by
controlling light and temperature. 3.02 1.878 0.774 0.743
Smart thermostats maintain the optimal temperature
in the rooms. 2.90 1.532 0.799 0.777
IoT monitors and optimizes the use of water
and resources. 2.45 1.405 0.798 0.804
IoT devices track inventory in real time. 2.40 1.472 0.800 0.777
IoT reduces the risk of shortages or overstocks. 2.38 1.461 0.766 0.685
IoT sensors manage food and beverage supplies,
reducing waste. 3.13 1.962 0.801 0.834
Smart rooms with IoT devices increase
guest comfort. 2.83 1.892 0.866 0.739
IoT technologies enable faster and more
efficient service. 3.64 0.333 0.843 0.743
Sustainability 2024,16, 7279 11 of 24
Table 1. Cont.
Factor Statement m sd αFA
IoT devices personalize services for guests. 3.63 0.125 0.804 0.821
IoT sensors monitor the infrastructure and enable
timely maintenance. 3.33 0.098 0.809 0.759
IoT technologies detect failures and reduce
maintenance costs. 3.45 0.109 0.811 0.695
IoT devices facilitate the management of
HVAC systems. 2.78 1.690 0.838 0.699
Note: m—arithmetic mean, sd—standard deviation,
α
—Cronbach alpha, FA—factor loading, CR—composite
reliability, AVE—average variance extracted.
Table 2. Descriptive statistics of the main constructs, factor loadings, and construct validation.
Factor Statement m sd αFA
Operational efficiency
(OE)
m = 3.56
sd = 0.933
α= 0.900
% variance—7.943
CR—0.887
AVE—0.720
The integration of AI and IoT improves the flow of
information among employees. 2.79 1.698 0.799 0.689
AI and IoT tools improve
departmental coordination. 2.71 1.611 0.708 0.704
Managers manage tasks and resources better thanks
to AI and IoT. 2.62 1.575 0.727 0.763
AI and IoT technologies reduce total operating costs.
2.75 1.878 0.755 0.766
Operations have become more economically
efficient after implementing AI and IoT. 2.78 1.745 0.763 0.648
AI and IoT technologies reduce maintenance and
repair costs. 3.72 2.108 0.792 0.639
Automating tasks through AI and IoT increases
employee productivity. 3.28 1.968 0.706 0.708
AI and IoT optimize work processes and shorten
task times. 2.90 1.433 0.777 0.823
Hotel processes are more efficient thanks to AI
and IoT. 2.11 1.246 0.795 0.818
AI and IoT improve resource management. 2.08 1.167 0.802 0.657
Resource optimization is achieved through real-time
monitoring and predictive analytics. 2.69 1.588 0.794 0615
AI and IoT technologies reduce resource wastage
and increase efficiency. 1.78 1.297 0.749 0.750
Integrating AI and IoT
with sustainability
(IAIIoTWS)
m = 3.24
sd = 0.128
α= 0.901
% variance −5.422
CR—0.751
AVE—0.694
AI algorithms optimize energy, reducing costs and
environmental footprint. 4.21 2.207 0.802 0.803
AI-based smart energy management systems
contribute to sustainable business. 3.02 2.121 0.794 0.870
AI helps identify and implement renewable
energy sources. 3.16 2.237 0.792 0.667
IoT sensors monitor waste and optimize disposal,
reducing the environmental footprint. 2.60 1.502 .705 0.645
IoT technologies enable more efficient waste
management and increase recycling. 2.63 1.425 0.793 0.733
IoT devices identify areas to reduce waste,
improving business sustainability. 3.49 2.294 0.773 0.696
Sustainability 2024,16, 7279 12 of 24
Table 2. Cont.
Factor Statement m sd αFA
AI tools select suppliers that meet
sustainable standards. 2.96 1.829 0.715 0.601
AI optimizes supply logistics. 3.86 2.097 0.788 0.804
Sustainable hotel
business
(SHB)
m = 2.92
sd = 0.318
α= 0.715
% variance—4.536
CR—0.780
AVE—0.604
Energy-efficient technologies reduce operating costs.
3.25 2.062 0.703 0.786
Automated systems reduce energy consumption. 2.88 1.645 0.792 0.770
Local products reduce the carbon footprint. 2.23 1.292 0.758 0.767
Renewable energy sources preserve
the environment. 2.23 1.315 0.744 0.732
Recycling programs reduce waste in landfills. 2.44 1.523 0.782 0.708
Waste-reduction strategies contribute to
business sustainability. 2.20 1.688 0.802 0.624
Educating employees about sustainable practices
increases awareness. 3.64 2.243 0.804 0.639
Environmental initiatives of employees contribute to
sustainable business. 3.22 2.210 0.795 0.837
Technological improvements improve the hotel’s
operational efficiency. 3.25 2.062 0.703 0.650
Employing sustainable practices attracts
environmentally conscious guests. 2.85 1.798 0.791 0.680
Energy efficiency increases the attractiveness of
the hotel. 2.77 1.661 0.707 0.699
Environmental initiatives in hotels contribute to
sustainability and guest satisfaction. 3.26 2.084 0.732 0.703
Advanced technologies reduce the negative impact
on the environment. 3.12 1.965 0.729 0.725
Technological innovations enable more efficient use
of resources. 3.60 2.056 0.775 0.677
Note: m—arithmetic mean, sd—standard deviation,
α
—Cronbach alpha, FA—factor loading, CR—composite
reliability, AVE—average variance extracted.
Sustainable hotel business (SHB) has an average value of 2.92, suggesting that ho-
tel managers are aware of the moderate benefits brought by sustainable practices. The
reliability of this construct, measured by Cronbach’s alpha coefficient, is 0.715, while the
percentage of explained variance for SHB is 4.536%. These data imply that, although
managers recognize the importance of sustainable practices, there is room for further
improvement and advancement in this area. Hoteliers should focus on further imple-
menting sustainable strategies that will enhance their competitiveness and contribute to
environmental preservation.
3.2. Results of the SEM Analysis
The results indicate a high level of reliability and validity for the constructs used in
the study (Table 3). All constructs, including artificial intelligence (AI), the integration of AI
and IoT with sustainability (IAIIoTWS), the internet of things (IoT), operational efficiency
(OE), and sustainable hotel business (SHB), show satisfactory values for Cronbach’s alpha
coefficient, composite reliability, and average variance extracted (AVE). Cronbach’s alpha
for all constructs exceeds the recommended threshold of 0.7, indicating a high level of
internal consistency. These values confirm that the measures used are reliable and accurately
reflect the concepts they are intended to measure.
Sustainability 2024,16, 7279 13 of 24
Table 3. Construct reliability and validity.
Cronbach’s Alpha rho_A Composite
Reliability
Average Variance
Extracted (AVE)
Artificial intelligence (AI) 0.857 0.823 0.893 0.603
Integrating AI and IoT with sustainability (IAIIoTWS) 0.863 0.917 0.901 0.663
Internet of thing (IoT) 0.712 0.850 0.832 0.790
Moderating effect: IAIIoTWS →OE →SHB 0.907 0.733 0.846 0.688
Operational efficiency 0.870 0.747 0.757 0.642
Sustainable hotel business (SHB) 0.820 0.892 0.796 0.651
The high values of composite reliability for all constructs further confirm that the
measures used are consistent and provide stable results. AVE values exceeding 0.5 for all
constructs indicate satisfactory convergent validity, meaning that the items within each
construct are correlated and adequately measure the same concept.
These findings imply that the constructs used in the study are adequately defined
and provide reliable data for analysis. This high level of reliability and validity is crucial
for the credibility of the study’s results, as it ensures that the conclusions drawn from the
data reflect the actual relationships and effects being investigated. In practice, these results
confirm that hotels can trust the implementation of AI and IoT technologies as tools for
improving operational efficiency and sustainability, with the assurance that these tools
consistently deliver positive outcomes.
The results of the Heterotrait–Monotrait ratio (HTMT) analysis show low values
between different constructs, indicating good discriminant validity within the study
(Figure 2). The low HTMT values suggest that constructs such as artificial intelligence (AI),
the internet of things (IoT), operational efficiency (OE), sustainable hotel business (SHB),
and the integration of AI and IoT with sustainability (IAIIoTWS) are adequately distinct
from each other. The highest HTMT values are below the recommended thresholds of 0.85
or 0.90, confirming that each construct measures different concepts and that there is no
significant overlap between them.
These results are crucial because they confirm that the constructs used are adequately
separated and address specific, unique aspects within the study. This ensures that the
measurements are not too similar, which could obscure the differences between various
concepts. For example, although AI and IoT both represent technological innovations,
the HTMT analyses show that these constructs are perceived as separate entities with
different roles in enhancing operational efficiency and hotel sustainability. The colors
in the HTMT ratio matrix indicate the strength of discriminant validity between con-
structs. Darker shades suggest higher HTMT values, while lighter shades indicate stronger
discriminant validity.
Figure 3shows the variance inflation factor (VIF) values for all variables in the model.
VIF values measure the degree of multicollinearity among the variables. Generally, VIF val-
ues below 5 are considered acceptable and indicate that there is no serious multicollinearity
among the predictors. In this case, all VIF values are below 2, indicating low multicollinear-
ity among the variables in the model. This means that the estimates of regression coefficients
can be considered reliable and that multicollinearity will not significantly affect the stability
and interpretation of the model.
These results further confirm the reliability of the model, as low multicollinearity
allows for more precise and stable estimates of the relationships between variables. In
practice, this means that hotels implementing AI and IoT technologies can expect clear and
consistent benefits from these technologies without the risk of effects being canceled out
due to excessive correlation between variables.
Sustainability 2024,16, 7279 14 of 24
Sustainability 2024, 16, x FOR PEER REVIEW 13 of 24
(Figure 2). The low HTMT values suggest that constructs such as artificial intelligence
(AI), the internet of things (IoT), operational efficiency (OE), sustainable hotel business
(SHB), and the integration of AI and IoT with sustainability (IAIIoTWS) are adequately
distinct from each other. The highest HTMT values are below the recommended thresh-
olds of 0.85 or 0.90, confirming that each construct measures different concepts and that
there is no significant overlap between them.
Figure 2. HTMT ratio.
These results are crucial because they confirm that the constructs used are ade-
quately separated and address specific, unique aspects within the study. This ensures
that the measurements are not too similar, which could obscure the differences between
various concepts. For example, although AI and IoT both represent technological inno-
vations, the HTMT analyses show that these constructs are perceived as separate entities
with different roles in enhancing operational efficiency and hotel sustainability. The col-
ors in the HTMT ratio matrix indicate the strength of discriminant validity between con-
structs. Darker shades suggest higher HTMT values, while lighter shades indicate
stronger discriminant validity.
Figure 3 shows the variance inflation factor (VIF) values for all variables in the
model. VIF values measure the degree of multicollinearity among the variables. Gener-
ally, VIF values below 5 are considered acceptable and indicate that there is no serious
multicollinearity among the predictors. In this case, all VIF values are below 2, indicating
low multicollinearity among the variables in the model. This means that the estimates of
regression coefficients can be considered reliable and that multicollinearity will not sig-
nificantly affect the stability and interpretation of the model.
These results further confirm the reliability of the model, as low multicollinearity
allows for more precise and stable estimates of the relationships between variables. In
practice, this means that hotels implementing AI and IoT technologies can expect clear
and consistent benefits from these technologies without the risk of effects being canceled
out due to excessive correlation between variables.
Figure 2. HTMT ratio.
Table 4presents the model selection criteria, including AIC, AICu, AICc, BIC, HQ,
and HQc for operational efficiency and sustainable hotel business. Lower values of these
criteria indicate a better fit of the model to the data. The results show that the model
for sustainable hotel business has lower values for all criteria compared to the model for
operational efficiency, suggesting that the model for sustainable hotel business better fits
the data.
Table 4. Model selection criteria.
AIC (Akaike’s
Information
Criterion)
AICu
(Unbiased
Akaikes
Information
Criterion)
AICc
(Corrected
Akaikes
Information
Criterion)
BIC (Bayesian
Information
Criteria)
HQ (Hannan
Quinn
Criterion)
HQc (Corrected
Hannan–
Quinn
Criterion)
Operational
efficiency −73.119 −70.099 149.067 −62.938 −69.008 −68.773
Sustainable
hotel business −76.463 −72.426 145.818 −62.888 −70.981 −70.603
This means that the model for sustainable hotel business provides more accurate
results and better describes the relationships between variables compared to the model for
operational efficiency. Lower values of criteria such as AIC and BIC indicate that the model
successfully balances between accuracy and simplicity, which is important for properly
understanding how AI and IoT technologies impact hotel sustainability. These results show
that the model is adequately constructed and accurately describes the real relationships in
the data, enabling reliable recommendations for the implementation of sustainable practices
in hotels.
Table 5presents the results of the effects analysis and hypothesis confirmation in the
context of hotel business sustainability. This study included the impact of artificial intelli-
gence (AI) and the internet of things (IoT) on operational efficiency (OE) and sustainable
Sustainability 2024,16, 7279 15 of 24
hotel business (SHB), as well as the mediating and moderating effects of the integration of
AI and IoT with sustainability (IAIIoTWS).
Sustainability 2024, 16, x FOR PEER REVIEW 14 of 24
Figure 3. VIF values.
Table 4 presents the model selection criteria, including AIC, AICu, AICc, BIC, HQ,
and HQc for operational efficiency and sustainable hotel business. Lower values of these
criteria indicate a beer fit of the model to the data. The results show that the model for
sustainable hotel business has lower values for all criteria compared to the model for
operational efficiency, suggesting that the model for sustainable hotel business beer fits
the data.
Table 4. Model selection criteria.
AIC (Akaike’s
Information
Criterion)
AICu (Unbiased
Akaikes
Information
Criterion)
AICc
(Corrected
Akaikes
Information
Criterion)
BIC (Bayesian
Information
Criteria)
HQ (Hannan
Quinn
Criterion)
HQc (Corrected
Hannan–Quinn
Criterion)
Operational efficiency −73.119 −70.099 149.06
7
−62.938 −69.008 −68.773
Sustainable hotel
b
usiness −76.463 −72.426 145.818 −62.888 −70.981 −70.603
Figure 3. VIF values.
The results show that artificial intelligence (AI) has a significant positive impact on
the operational efficiency of hotels, with an effect coefficient of 0.175 and a statistical
significance of 0.005, which confirms the initial assumptions regarding Hypothesis H1. This
Sustainability 2024,16, 7279 16 of 24
indicates that the application of AI technologies in hotel operations contributes to more
efficient functioning.
Similarly, the internet of things (IoT) significantly improves operational efficiency,
with an effect coefficient of 0.479 and a p-value of 0.022, confirming Hypothesis H2. This
means that the introduction of IoT technologies, such as smart devices and sensors, enables
better organization and resource management in hotels. Operational efficiency (OE) has
been shown to be a key factor for hotel sustainability, with an effect coefficient of 0.215
and a p-value of 0.018, which is consistent with Hypothesis H3. These results suggest
that more efficient hotel operations directly contribute to reducing the ecological footprint
and improving sustainable practices. Additionally, AI technologies have been found to
indirectly contribute to hotel sustainability by increasing operational efficiency, with an
indirect effect of 0.038, supporting Hypothesis H3a. This finding indicates that AI not only
directly improves efficiency but also indirectly contributes to the long-term sustainability
of hotels.
Table 5. Hypothesis confirmation.
Effect m sd t pIndirect Effects
Confirmation
AI →OE 0.175 0.099 0.171 2.027 0.005 - H1 √
IoT →OE 0.479 0.311 0.392 3.224 0.022 - H2 √
OE →SHB 0.215 0.164 0.090 2.383 0.018 - H3 √
MedE: AI →OE →SHB - - - - - 0.038 H3a √
MedE: IoT →OE →SHB - - - - - 0.103 H3b √
IAIIoTWS →SHB 0.299 0.187 0.129 2.324 0.021 - H4 √
ModE: IAIIoTWS * OE →SHB 0.317 0.070 0.388 5.817 0.014 - H4a √
Note: AI—artificial intelligence, OE—operational efficiency, SHB—sustainable hotel business, IoT—internet of
things, IAIIoTWS—integrating AI and IoT with sustainability, MedE—mediating effect, ModE—moderating effect,
√—confirmed, *—indicate moderation that is different from mediation.
As for IoT, its impact on hotel sustainability through operational efficiency has also
been confirmed, with an indirect effect of 0.103, supporting Hypothesis H3b. This shows
that IoT technologies play a key role in improving sustainable practices through the op-
timization of operations. Furthermore, the integration of AI and IoT technologies with
sustainability positively affects hotel sustainability, with an effect coefficient of 0.299 and
ap-value of 0.021, consistent with Hypothesis H4. The combined application of these
technologies creates a synergistic effect that further enhances sustainable operations. The
moderating effect of the integration of AI and IoT technologies with operational efficiency
was found to be significant, with an effect coefficient of 0.317 and a p-value of 0.014, sup-
porting Hypothesis H4a. This finding indicates that the joint application of AI and IoT
technologies, in combination with operational efficiency, significantly contributes to the
overall sustainability of hotels.
Figure 4visually illustrates the structural model path, highlighting how AI and IoT
technologies, when integrated into hotel operations, significantly enhance operational
efficiency. This, in turn, fosters sustainable business practices within the hotel industry.
The model clearly shows that the synergy between AI and IoT not only optimizes routine
processes and resource management but also contributes to broader sustainability goals.
Additionally, the model indicates that the integration of AI and IoT with sustainability
efforts strengthens these impacts, creating a robust framework that supports long-term sus-
tainability. The visual representation emphasizes the interconnectedness of these variables
and the cumulative benefits of combining AI and IoT to achieve sustainable outcomes in
hotel management.
Sustainability 2024,16, 7279 17 of 24
Sustainability 2024, 16, x FOR PEER REVIEW 16 of 24
moderating effect of the integration of AI and IoT technologies with operational effi-
ciency was found to be significant, with an effect coefficient of 0.317 and a p-value of
0.014, supporting Hypothesis H4a. This finding indicates that the joint application of AI
and IoT technologies, in combination with operational efficiency, significantly contrib-
utes to the overall sustainability of hotels.
Figure 4 visually illustrates the structural model path, highlighting how AI and IoT
technologies, when integrated into hotel operations, significantly enhance operational
efficiency. This, in turn, fosters sustainable business practices within the hotel industry.
The model clearly shows that the synergy between AI and IoT not only optimizes routine
processes and resource management but also contributes to broader sustainability goals.
Additionally, the model indicates that the integration of AI and IoT with sustainability
efforts strengthens these impacts, creating a robust framework that supports long-term
sustainability. The visual representation emphasizes the interconnectedness of these
variables and the cumulative benefits of combining AI and IoT to achieve sustainable
outcomes in hotel management.
Figure 4. Graphical illustration of the structural equation model.
Figure 4. Graphical illustration of the structural equation model.
4. Discussion
This research was conducted with the aim of examining how the integration of artificial
intelligence (AI) and the internet of things (IoT) can enhance the sustainability of hotel
operations. A particular focus was placed on operational efficiency and how it can mediate
the impacts of AI and IoT technologies on hotel sustainability, as well as the moderating
effects of the integration of these technologies.
The results of the first study show that AI significantly improves the operational effi-
ciency of hotels. Our findings are consistent with previous research demonstrating that AI
has a substantial impact on operational efficiency across various industries. The application
of AI allows for the optimization of operational processes, reduction in operational costs,
and increased guest satisfaction through personalized services. Specifically, AI is used to
automate administrative tasks, perform predictive analytics, and enhance decision-making
processes, resulting in increased efficiency and reduced time required for routine tasks.
These results confirm the findings of other studies that show similar benefits of AI tech-
nologies in various sectors. For example, Bruno [
63
] and Pchelincev et al. [
64
] highlighted
that AI significantly impacts the optimization of operational processes through automation
Sustainability 2024,16, 7279 18 of 24
and predictive analytics. In the healthcare sector, Ambay et al. [
65
] demonstrated that AI
reduces patient waiting times and increases equipment utilization, while Al-witwit and
Ibrahim [
66
] found that AI achieved an accuracy of 95.25% in the personalization of policies
in government operations, leading to significant efficiency improvements. Tariq et al. [
67
]
and Agarwall et al. [
68
] emphasized that the adoption of AI technologies in business opera-
tions results in increased operational efficiency, reduced operational costs, and improved
revenues for enterprises. Our findings further confirm that AI technologies contribute
to reducing operational costs through automation and predictive analytics, allowing for
better resource management and the reduction in unnecessary expenses. Additionally,
personalized services based on guest data analysis increase guest satisfaction, reflecting
an overall positive experience during their stay in the hotel. Based on the study results,
we can conclude that the implementation of AI significantly optimizes operational pro-
cesses (R.Q.1), reducing the time required for administrative tasks and increasing employee
efficiency. Moreover, AI technologies contribute to reducing operational costs through
automation and predictive analytics (R.Q.2), enabling better resource management and
reducing unnecessary expenses. Finally, personalized services based on guest data analysis
increase guest satisfaction (R.Q.3), reflecting an overall positive experience during their
stay in the hotel.
The results of the second study indicate that IoT technologies significantly contribute
to the operational efficiency of hotels. Our findings confirm previous research showing that
IoT enables real-time data collection, analysis, and informed decision making, leading to
increased business efficiency [
69
]. In logistics, IoT acts as an intermediary between strategic
management and operational performance, improving efficiency through proactive deci-
sions and resource connectivity [
70
]. IoT applications in operations management focus on
digitization, monitoring, and smart systems [
71
]. The technology allows for high levels
of efficiency in energy and infrastructure management [
72
]. The implementation of IoT
improves sales, marketing, resource management, and profitability [
73
]. In the oil and
gas industry, IoT enhances operational efficiency and asset management and reduces HSE
risks [
74
]. However, challenges such as cybersecurity and technological readiness must
be addressed for successful IoT implementation [
75
]. Based on the study results, we can
conclude that IoT technologies enable real-time data collection, allowing managers to make
informed decisions and quickly respond to changes in operational conditions (R.Q.4). IoT
devices, such as smart thermostats and light sensors, contribute to optimizing energy and
water consumption, reducing the overall operational costs of hotels (R.Q.5). Improved
guest security and comfort are achieved through the implementation of IoT devices, such
as smart locks and surveillance systems, which increase guest satisfaction and loyalty to
the hotel (R.Q.6).
The results of the third study show that high operational efficiency, supported by
AI and IoT technologies, mediates the implementation of sustainable business practices
in hotels. Our findings align with previous research highlighting the strong connection
between operational efficiency and sustainable practices across various sectors. For in-
stance, lean manufacturing practices positively affect both ecological and operational
performance [
76
,
77
]. Similarly, sustainable supply chain management practices improve
firm performance based on market, operational, and accounting metrics [
78
]. Additionally,
our findings confirm that knowledge management processes in the public sector positively
influence operational efficiency and sustainable development [
79
]. Agile capabilities have
been shown to be essential for maximizing sustainable supply chain performance [
80
]. Eco-
efficiency in manufacturing firms is associated with various managerial and operational
practices, including ecological strategic planning and product redesign [
81
]. Moreover,
the integration of lean and green practices leads to improved ecological and operational
performance [
82
]. These studies emphasize the synergistic relationship between operational
efficiency and sustainable practices across different sectors and organizational levels, which
is consistent with our findings on the positive impact of AI and IoT technologies on the
sustainability of hotel operations [
83
]. Based on the study results, we can conclude that high
Sustainability 2024,16, 7279 19 of 24
operational efficiency, supported by AI and IoT technologies, enables the implementation
of sustainable practices, such as waste reduction and efficient resource use, contributing
to hotel sustainability (R.Q.7). Operational efficiency reduces the hotel’s environmental
footprint through energy consumption optimization and the introduction of green practices,
such as recycling and the use of renewable energy sources (R.Q.8). The integration of AI
and IoT technologies contributes to the implementation of sustainable practices in the hotel
sector, allowing for better real-time monitoring and resource management (R.Q.9).
The results of the fourth study show how the combination of AI and IoT can have
a synergistic effect on improving the operational efficiency and sustainability of hotels.
The integration of these technologies not only enhances individual operations but also
creates additional value through their interaction. Our findings are consistent with previ-
ous research showing that synergy between AI and IoT increases system efficiency and
improves user experience [
84
–
86
]. AI can effectively process large volumes of data gen-
erated by IoT, enabling intelligent interactions and autonomous decision making [
87
,
88
].
However, challenges such as data privacy, security, and technology integration need to be
addressed [
89
]. The integration of AI and IoT can lead to improved operational efficiency,
cost reduction, and new IoT applications and services [
90
,
91
]. However, internal threats
of IoT can moderate the relationship between AI and smart decision making, potentially
weakening positive outcomes when threats are high [
92
]. Based on the study results, we
can conclude that the combination of AI and IoT technologies creates a synergistic effect,
significantly enhancing the operational efficiency of hotels through automation, predictive
analytics, and resource optimization (R.Q.10). The integration of AI and IoT contributes to
the overall sustainability of hotel operations, enabling better coordination of operational
processes and reducing the environmental footprint (R.Q.11). The main challenges in
integrating AI and IoT technologies include data privacy issues, system security, and high
initial implementation costs. However, the long-term benefits of these technologies can
significantly outweigh the initial challenges (R.Q.12).
5. Conclusions
This research provided a comprehensive analysis of the impact of artificial intelligence
(AI) and the internet of things (IoT) on the operational efficiency and sustainability of
hotels in the Republic of Serbia. Using a quantitative methodology through surveys of
hotel managers and structural equation modeling (SEM), it was found that AI and IoT
technologies significantly contribute to operational efficiency, which in turn positively
affects sustainable business practices. These findings confirm that the integration of AI
and IoT not only optimizes resource management but also contributes to achieving global
sustainability goals.
5.1. Theoretical Implications
The theoretical implications of this research emphasize the significance of the synergy
between AI and IoT technologies in enhancing the operational efficiency and sustainabil-
ity of hotels. The findings extend existing theoretical frameworks on the integration of
technologies and sustainable practices, confirming that high operational efficiency me-
diates the achievement of sustainability goals. Additionally, this research contributes to
the literature on mediation and moderation effects, providing evidence of the importance
of these technologies across various industries. This study reveals how the integration
of AI and IoT technologies can be used not only to improve operational efficiency but
also to achieve broader ecological and economic objectives. This discovery highlights the
need for new theoretical models that encompass the interaction between technologies and
sustainable practices in the hospitality industry. This research also uncovers the complex
dynamics between AI and IoT technologies and their impact on different aspects of business
operations, contributing to a deeper understanding of their combined functionality and
synergistic effects. Empirical evidence on the mediation and moderation effects of these
technologies aids in a more precise understanding of their impact on sustainability, which
Sustainability 2024,16, 7279 20 of 24
is useful for developing new theoretical models. This study underscores the importance
of contextual factors, such as hotel size, geographic location, and type of guests, which
can moderate the impact of technologies on operational efficiency and sustainability. This
indicates the need to adapt theoretical frameworks to the specific contexts in which these
technologies are applied. The research calls for further theoretical studies to examine the
long-term impacts of AI and IoT technologies, as well as the challenges associated with
their implementation, such as costs, data privacy, and security. These issues are crucial for
a comprehensive understanding of the potential and limitations of these technologies in
achieving sustainable goals in the hospitality industry and beyond.
5.2. Practical Implications
The practical implications of the research are particularly significant for hotel man-
agers, indicating that the implementation of AI and IoT technologies can significantly
enhance operational efficiency and sustainability. Hotel managers should focus on au-
tomating administrative tasks, predictive analytics, and resource optimization to reduce
costs, increase guest satisfaction, and improve sustainable practices. The implementation of
these technologies can bring long-term economic and ecological benefits. Hotel managers
and industry leaders can leverage the insights provided to effectively implement AI and
IoT technologies, thereby gaining a competitive advantage. This study offers specific rec-
ommendations for improving operational processes, reducing costs, and increasing guest
satisfaction through personalized services enabled by these technologies. These practical
guidelines are crucial for decision makers who aim to adopt sustainable practices without
compromising on efficiency or guest experience.
5.3. Recommendations for Future Research
Future research should include a larger sample of hotels from various geographical
areas to improve the generalization of the findings. It is also necessary to explore the long-
term impacts of AI and IoT technologies on sustainability, as well as specific challenges such
as data privacy, system security, and high implementation costs. Additionally, research
could be expanded to other hospitality sectors to examine the broader impact of these
technologies on sustainability. Given the rapid development of AI and IoT, our study also
lays the groundwork for future research that will investigate long-term impacts and address
emerging challenges such as data privacy, cybersecurity, and implementation costs. By
identifying these areas, we not only contribute to current knowledge but also set a strategic
direction for subsequent studies that will build upon our work. The innovative approach
and significant findings of this study offer a fresh and comprehensive perspective on the
role of AI and IoT in promoting sustainability in the hotel sector. Future research could
include quantitative measurements of operational efficiency to provide a more detailed and
objective analysis of the impact of AI and IoT technologies on hotel operations. Comparing
results before and after the implementation of these technologies, as well as comparing with
hotels that have not adopted these innovations, could offer deeper insights into the actual
impact of these technologies on operational performance. Additionally, further research
could explore the long-term effects of applying AI and IoT technologies, including their
impact on business sustainability and environmental aspects in the hospitality industry.
5.4. Research Limitations
This study was conducted in the Republic of Serbia, which may limit the generaliz-
ability of the results globally. Including hotels from various geographical areas in future
research would enhance the validity of the findings. Methodologically, the research relied
on quantitative methods and surveys of hotel managers, which may limit a deeper qualita-
tive understanding of the impact of AI and IoT technologies. The sample may be biased
due to self-selection, as managers interested in the topic were more likely to participate,
potentially affecting the reliability of the results. The short-term focus of the research limits
consideration of the long-term effects of AI and IoT technologies. Long-term studies would
Sustainability 2024,16, 7279 21 of 24
provide deeper insights into the enduring benefits and challenges. This study included
hotels of different sizes, types, and locations but did not cover all variations within the
industry, which could influence the findings. Data privacy and system security present
key challenges in implementing IoT technologies, requiring further investigation. High
initial implementation costs can be a barrier for many hotels, particularly smaller ones with
limited budgets. A lack of adequate training and expertise among staff can hinder the effec-
tive implementation of AI and IoT technologies, potentially leading to operational issues.
Over-reliance on technology may compromise the authenticity of the guest experience and
traditional hospitality values. Achieving a balance between technological innovation and
preserving traditional values poses a challenge for hotel managers. Another limitation of
this study is the lack of quantitative measurements of operational efficiency. Although
the results, based on the opinions and perceptions of hotel managers, provided valuable
insights, the absence of objective, quantitative data, such as specific operational costs,
processing times, or other key metrics, may limit the comprehensiveness of the analysis.
This approach relies on subjective opinions, which, although relevant, can be susceptible to
bias or variations in interpretation.
Author Contributions: Conceptualization, T.G. and M.D.P.; methodology, A.M.P.; software, M.C.;
validation, N.G., T.G. and M.D.P.; formal analysis, M.C.; investigation, A.M.P.; resources, M.C.;
data curation, N.G.; writing—original draft preparation, T.G.; writing—review and editing, M.D.P.;
visualization, N.G.; supervision, T.G. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding author.
Acknowledgments: This research was supported by the Ministry of Science, Technological Develop-
ment and Innovation of the Republic of Serbia (Contract No. 451-03-66/2024-03/200172) and by the
RUDN University (Grant No. 060509-0-000).
Conflicts of Interest: The authors declare no conflicts of interest.
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