Innovative Approaches in Hotel Management: Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to Enhance Operational Efficiency and Sustainability

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.

1. Introduction

In modern hospitality, sustainability has become crucial, not only as an environmental 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 [1,2,3]. 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 sustainability. AI is used to analyze guest data to provide personalized services and recommendations, resulting in increased guest satisfaction [5,6,7]. 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 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 intelligent 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 ecological 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 adjustment 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 footprints 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 importance 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 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 educational systems during the COVID-19 pandemic, but the findings have significant implications 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 advantages for the hotel industry, significant challenges and limitations in their application must be considered [29,30,31]. 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 integration 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 destinations 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.

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 education. However, despite the apparent advantages, there is a significant lack of research addressing the integration of these technologies in the context of sustainable hotel operations. 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 research 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 1 shows 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.

H4a. 

The integration of AI and IoT with sustainability positively moderates the relationship between operational efficiency and the sustainable business practices of hotels.

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 personalized 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.

Study 3 focuses on exploring the relationship between hotel operational efficiency and sustainable business practices. This study investigates how high operational efficiency, supported 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 comprehensive 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 managers 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, considering 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.

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 (f² = 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 experience 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 agreement 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 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 Package 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), comparative 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 R² 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 discriminant 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].

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 [60,61,62]. 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

Table 1. Descriptive statistics of the main constructs, factor loadings, and construct validation.

Factor	Statement	m	sd	α	FA
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
	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.

and

Table 2. Descriptive statistics of the main constructs, factor loadings, and construct validation.

Factor	Statement	m	sd	α	FA
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
	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.

provide 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 threshold 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 coefficient 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.

Sustainable hotel business (SHB) has an average value of 2.92, suggesting that hotel 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 implementing 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. 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

). 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.

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 constructs. 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. Generally, 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 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.

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

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 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.

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 5. Hypothesis confirmation.

	Effect	m	sd	t	p	Indirect 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.

presents the results of the effects analysis and hypothesis confirmation in the context of hotel business sustainability. This study included the impact of artificial intelligence (AI) and the internet of things (IoT) on operational efficiency (OE) and sustainable hotel business (SHB), as well as the mediating and moderating effects of the integration of AI and IoT with sustainability (IAIIoTWS).

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 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.

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 optimization of operations. Furthermore, the integration of AI and IoT technologies with sustainability positively affects hotel sustainability, with an effect coefficient of 0.299 and a p-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, supporting 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 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.

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 efficiency 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 technologies in various sectors. For example, Bruno [63] and Pchelincev et al. [64] highlighted that AI significantly impacts the optimization of operational processes through automation 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 operations 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 processes (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 decisions 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 instance, 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 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 previous research showing that synergy between AI and IoT increases system efficiency and improves user experience [84,85,86]. AI can effectively process large volumes of data generated 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 sustainability of hotels. The findings extend existing theoretical frameworks on the integration of technologies and sustainable practices, confirming that high operational efficiency mediates 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 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 managers, indicating that the implementation of AI and IoT technologies can significantly enhance operational efficiency and sustainability. Hotel managers should focus on automating 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 recommendations 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 generalizability 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 qualitative 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 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 effective 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.