Smart Tourism Technologies and Tourist Experience: The Role of Digital Safety and Convenience in Shaping Satisfaction and Behavioral Intentions

1. Introduction

Smart tourism is increasingly changing the way tourists plan, experience, and remember their trips. The development of mobile applications, artificial intelligence, the Internet of Things, and digital platforms has enabled tourists to access information more easily, make decisions more quickly, and move more efficiently within destinations. These technologies are no longer just an addition to the tourism offer but are becoming an important part of the overall tourist experience (Zhang et al., 2022; Ionescu & Sârbu, 2024).

Previous research shows that smart technologies can increase tourist satisfaction, encourage revisit intentions, and influence recommendations to other users (Torabi et al., 2023; Gopinath & Jyotsna, 2025). However, most of these studies focus on the usefulness and functionality of technology, while less attention is given to how tourists perceive safety and comfort when using digital systems within destinations. Safety and convenience are becoming just as important as the availability of technology itself. Tourists use digital systems for navigation, real-time information tracking, communication, and activity planning, which can increase their sense of control but also raise concerns related to privacy and data security (Tiwari et al., 2024; Omar et al., 2025). At the same time, the comfort and ease of use of technology can significantly influence overall satisfaction with the experience. That is why there is a need for research that simultaneously examines perceived safety, comfort and convenience, satisfaction with the tourist experience, and the intention to use smart digital services. Existing studies rarely examine perceived safety, comfort and convenience together within a single integrated model, particularly in the context of smart tourism destinations. Most existing models are based on technology acceptance theories, but they rarely include these dimensions together in the context of tourism.

The aim of this study is to examine the role of smart digital systems in shaping tourists’ perceptions of safety, comfort, and satisfaction, as well as their relationship with future intention to use such technologies. Special attention is given to understanding how experience with digital technologies and gender influence perceptions of safety and comfort in smart tourism destinations.

2. Literature Review

Smart tourism has become one of the central research areas in contemporary tourism due to the rapid development of digital platforms, mobile applications, artificial intelligence, the Internet of Things, real-time information systems, contactless services, and intelligent destination infrastructure. Contemporary literature increasingly views smart tourism technologies not only as operational tools but also as systems that shape the tourist experience, perceived value, satisfaction, safety, convenience, intention to use, and destination loyalty. Therefore, the literature review is structured into two thematic sections: smart tourism technologies and technology acceptance, and digital safety, comfort, and tourist experience in smart destinations.

2.1. Smart Tourism Technologies and Technology Acceptance

Smart tourism technologies can be understood as digital systems that enhance the interaction between tourists, service providers, destinations, and technological infrastructure. They support tourists in all phases of travel, including information search, planning, navigation, on-site experience, decision-making, and post-visit behavior. Research shows that smart tourism technologies improve tourist experience and satisfaction through perceived value, where accessibility, informativeness, interactivity, and personalization are often highlighted as key technology attributes (Zhang et al., 2022; Torabi et al., 2022; Torabi et al., 2023; Yang & Zhang, 2022; Chang, 2022).

A growing number of studies confirm that smart tourism technologies contribute to the creation of memorable tourist experiences, which subsequently influence satisfaction, revisit intention, recommendations, and willingness to pay a higher price for enhanced services (Zhang et al., 2022; Torabi et al., 2022; Torabi et al., 2023; Yang & Zhang, 2022; Shin et al., 2023). Similarly, Elshaer and Marzouk (2024) show that smart tourism technologies contribute to memorable experiences through hotel innovations, while Chang (2022) emphasizes that smart technologies and perceived value positively influence memorable experience and destination image. Goo et al. (2022) further show that smart tourism technologies can simultaneously stimulate a sense of novelty and reduce tourist anxiety, thereby increasing travel satisfaction. Also, social influence and perceived ease of use significantly enhance the perceived usefulness of gamification, which in turn shapes tourists’ attitudes and strongly drives their intention to use gamified technologies, while hedonic behaviour primarily contributes indirectly through improving ease of use (Šostar et al., 2026a).

Smart tourism is increasingly conceptualized in recent literature as a digital ecosystem. Bibliometric and systematic reviews emphasize that smart destinations connect tourists, infrastructure, service providers, and information and communication technologies in order to improve experience, competitiveness, and sustainable destination management (Ercan, 2023; Alsharif et al., 2024; Liu et al., 2024; Law et al., 2022). Garanti (2023) further emphasizes the importance of value co-creation in smart destinations, while Chuang (2023) shows that smart tourism platforms integrate various digital services and enable an active role of tourists in shaping their own experience. Díaz-Parra et al. (2023), Ionescu and Sârbu (2024), and Shafiee et al. (2023) highlight that the integration of smart technologies with infrastructure, services, and attractions can improve destination management, service quality, and strategic planning of tourism development.

Artificial intelligence, chatbots, the metaverse, mobile applications, and location-based services play a particularly important role in recent research. Orden-Mejía and Huertas (2022) show that informativeness, empathy, and interactivity of destination chatbots significantly increase tourist satisfaction. Koo et al. (2025) emphasize that ethical AI practices and personalization stimulate the adoption of smart tourism technologies, where trust plays a key mediating role. Florido-Benítez and del Alcázar Martínez (2024) highlight that artificial intelligence enhances tourism marketing and experience through personalization and process optimization, but at the same time raises issues related to data security, privacy, and reduced human contact. Liu and Park (2024) show that metaverse experiences, through the sense of presence, perceived usefulness, and ease of use, can stimulate the intention of actual destination visits, while Xiong and Zhang (2024) emphasize the importance of digital literacy, ease of use, and perceived autonomy in shaping tourist engagement and loyalty when using location-based applications. Solomovich and Abraham (2026) further confirm that trust in ChatGPT chatbots and the perception of their usefulness and ease of use increase the intention to use them for travel planning.

In the context of technology acceptance, research most often relies on models such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, but increasingly complements them with constructs such as trust, experience, emotions, social influence, hedonic motivation, and technological readiness. Sujood et al. (2024) show that the intention to use smart technologies in tourism and hospitality is best explained by a combination of perceived usefulness, ease of use, attitudes, social influence, and trust. Khoshroo and Soltani (2025) confirm that the acceptance of digital technologies in tourism in the context of Industry 5.0 depends on expected benefits, ease of use, social influence, facilitating conditions, hedonic motivation, price value, and habits. Similarly, Dyussekeyeva et al. (2025), Hidayat and Faturohman (2024), Ahmad and Rasheed (2025), and Li et al. (2024) emphasize that perceived usefulness and ease of use are among the most important predictors of the intention to adopt and use tourism digital technologies. On the other side, Šostar et al. (2026b) indicate that word-of-mouth is the most influential form of tourism promotion, having the highest priority and a significantly stronger impact on tourists’ decision-making compared to digital and traditional communication channels.

Many research increasingly points to the importance of attitudes, emotional factors, and user characteristics. Zheng et al. (2024) show that the use of smart technologies increases tourist satisfaction, and satisfaction then directly affects revisit intention. Nafees and Sujood (2025) emphasize that the intention to use interactive technologies arises from a combination of technological and emotional factors, while Sharma et al. (2025) show that even attitudes toward digital-free tourism can be explained by expected benefits, perceived effort, social influence, and cultural dimensions. The results of Šostar et al. (2025) show that the internet has a stronger influence on consumer purchasing decisions in product categories such as travel, technology, and clothing, where higher levels of trust in online information and greater information search are required, while younger users demonstrate significantly higher engagement and reliance on digital content in the decision-making process.

Tuomi et al. (2023) further show that older adults adopt digital cultural tourism services to a limited extent, where technical difficulties, lack of interaction, and insufficient awareness act as barriers. Campayo-Sánchez et al. (2025) also highlight that hedonic motives encourage the use of smart tourism technologies, while concerns about security and privacy, as well as higher age, can reduce their usage. Novianti et al. (2022) further confirm that smart tourism technologies influence tourist behavior both directly and indirectly by shaping attitudes, social influences, and perceived control. Despite these advances, existing studies often focus on isolated technological or behavioural factors, while fewer studies adopt an integrated perspective that simultaneously considers experiential and contextual dimensions.

2.2. Digital Safety, Comfort, and Tourist Experience in Smart Destinations

In smart destinations, tourist safety and comfort are increasingly shaped by digital systems such as IoT technologies, artificial intelligence, mobile connectivity, real-time information, contactless services, wearable technologies, crowd monitoring systems, and intelligent surveillance. Such systems can increase the sense of control, reduce uncertainty, facilitate movement in space, and improve the quality of the tourist experience. At the same time, literature warns that digital safety is not only a matter of technical protection, but also of trust, privacy, perceived risk, and technology acceptance.

Research on data security, privacy, and trust shows that these factors play a key role in the adoption of smart tourism technologies. Tiwari et al. (2024) show that trust, shaped by the perception of security and privacy risks, mediates between perceived usefulness, ease of use, attitudes, and the intention to adopt IoT technologies in smart tourism destinations. Omar et al. (2025) confirm that expected benefits, ease of use, and facilitating conditions encourage the intention to use smart technologies, while concerns about privacy and security weaken this effect. Wang et al. (2026) show that the intention to support artificial intelligence in tourism is primarily driven by perceived benefits and positive emotions, while privacy concerns have a negative effect, with digital literacy mitigating this negative impact. Law et al. (2026) emphasize that tourists’ privacy concerns are still fragmented in research, although they significantly influence technology adoption in tourism and hospitality. Romani et al. (2023) confirm that perceived safety and trust strongly determine the propensity to use technologies in smart cities, while Issakov et al. (2025) show that digital technologies shape the perception of safety and thereby contribute to destination sustainability through strengthening trust, positive image, and stable tourism development.

Digital safety is particularly important in the context of smart cities, tourism destinations, and intelligent monitoring systems. Walczak et al. (2025) show that the adoption of IoT technologies in smart cities differs by gender: women express higher trust, stronger perception of safety, and greater support for ecological IoT applications, while men base adoption more strongly on perceived usefulness. Fu (2026) shows that intelligent systems combining IoT sensors, mobile data, and machine learning algorithms can improve hazard prediction, reduce false alarms, and accelerate response in risky tourism environments. Couto et al. (2026) emphasize that mobile connectivity, internet, Wi-Fi, and safety networks are key factors of tourist experience in remote destinations, as the lack of safety infrastructure can influence tourists’ decision to abandon activities. In the field of real-time information, Akhla et al. (2023) show that such information positively affects tourist experience by reducing waiting time, optimizing routes, and increasing the sense of control, while Chang and Lee (2023) show that real-time crowding information influences tourist decisions and enables schedule adjustments for a more pleasant experience.

Contactless and wearable technologies further confirm the link between safety, comfort, and intention to use. Burkett and Virto (2025) show that contactless tourism services increase perceived safety, usefulness, ease of use, user experience, and perceived value, which then shape a positive attitude and higher intention to use. Burkett and Recuero Virto (2025) also confirm that perceived safety in the adoption of contactless innovations in air transport and hospitality positively affects user experience and tourists’ willingness to pay for additional safety services. Guebel et al. (2025) show that safety and trust are key determinants of the intention to use wearable technologies in tourism, while usefulness, ease of use, and socio-symbolic elements primarily act through building trust. A similar broader logic is confirmed by Chowdhury (2023), who shows that perceived convenience and service quality significantly shape attitude and intention to use digital services, although perceived safety in that specific context did not have a significant effect.

Comfort and convenience of digital technologies emerge as important determinants of satisfaction and user behavior. Nur et al. (2025) show that smart tourism infrastructure, quality communication, perception of safety and hygiene, and accessibility significantly increase tourist satisfaction, and satisfaction then encourages revisit intention. Lasisi et al. (2025) emphasize that perceived convenience plays an important role in tourist mobility decisions, while Zeqiri et al. (2023) show that perceived convenience and value stimulate repurchase intention of online services through trust and recommendations. Gopinath and Jyotsna (2025), in a meta-analysis, show that tourist satisfaction with smart technologies is primarily determined by expectation confirmation, usefulness, informativeness, and the quality of information and service. Lee and Jan (2022) develop and validate a smart tourism experience scale and show that smart technologies enrich tourist experience through useful information, easier planning, and more diverse activities, leading to higher satisfaction and loyalty. Diaz et al. (2026) further confirm that availability, enjoyment, safety, and interactivity of smart technologies positively shape satisfaction with technology and travel and encourage recommendations and future behavioral intentions.

Some studies also consider the perspective of employees, destinations, and broader service management. Cimbaljević et al. (2024) show that technological readiness of employees in tourism positively influences their attitudes and intention to use technologies, partly through perceived ease of use and usefulness. Tavitiyaman et al. (2026) confirm that personalized and functional smart technologies in hotels increase perceived usefulness among employees, which then leads to better evaluation of service experience and improved business performance. Ma (2024) shows that artificial intelligence in tourism increases satisfaction through personalized recommendations, virtual assistants, resource optimization, and enhanced safety. Le Anh and Thanh (2026) show in the context of smart destinations that perceived safety through smart technologies, innovation, destination attractiveness, digital interpretation, and involvement in the local context can increase the perception of integration of smart tourism services and demand for tourism activities. Nieves-Pavón et al. (2025) further show that tourist motivations, perceived value, and education stimulate smartphone use in destinations, which then increases online recommendations and willingness to pay for additional services.

The literature review shows that smart tourism technologies shape the tourist experience through interconnected dimensions of usefulness, ease of use, informativeness, interactivity, personalization, safety, trust, comfort, and satisfaction. Previous research confirms that technology affects not only functional aspects of travel but also emotional, experiential, and behavioral outcomes. In this context, smart digital systems can increase perceived safety, facilitate movement, reduce uncertainty, and improve the overall tourist experience, which is reflected in satisfaction and future intention to use digital services. Based on the above, this study develops a conceptual model that integrates perceived safety, perceived comfort and convenience, satisfaction with tourist experience, perceived usefulness of smart technologies, experience with digital technologies, and gender as relevant factors in the context of smart digital tourism services.

The theoretical framework of this study is based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). These models are widely used to explain how users accept and use new technologies. According to TAM, perceived usefulness and ease of use influence users’ attitudes and behavioural intention, while UTAUT highlights the importance of experience and facilitating conditions in shaping technology adoption. In this study, perceived comfort and convenience (COMF) corresponds to ease-of-use-related constructs, while perceived usefulness (SIZE) and behavioural intention (BI) reflect key elements of TAM and UTAUT. Satisfaction with the tourist experience (SAT) extends these models by including experiential outcomes relevant to tourism. Perceived safety (SEC) is added as a context-specific construct, reflecting the importance of digital security and trust in smart tourism environments. Based on this framework, the research hypotheses are presented in the following section.

3. Materials and Methods

3.1. Research Design and Sample

This study employed a quantitative research design based on a structured questionnaire. The aim of the research was to examine the role of smart digital systems in tourists perceived safety, comfort, satisfaction, and future behavioural intention to use smart digital services. Prior to the main data collection, the questionnaire was reviewed to ensure clarity and comprehensibility of the items.

The survey was conducted on a sample of 104 respondents. All measurement items were assessed using a five-point Likert scale, where 1 indicated the lowest level of agreement and 5 indicated the highest level of agreement. Data was collected using an online survey distributed via social media platforms and personal networks, following a convenience sampling approach. The survey link was shared through channels such as Facebook and email, targeting individuals with prior travel experience. The data collection was conducted over the period of May 2026, and participation in the study was voluntary and anonymous. Respondents were informed about the purpose of the research and provided informed consent before completing the questionnaire. Given the non-probability sampling method and relatively small and non-random sample, the results should be interpreted with caution in terms of generalizability.

Ethical approval for this research was granted by the Ethics Committee of the Faculty of Tourism and Rural Development. The study was conducted in accordance with the ethical standards of the institution and the principles of voluntary participation, anonymity, and confidentiality. No personally identifiable data was collected from the respondents.

3.2. Measurement Instrument

The measurement instrument was developed based on previously established scales in the fields of technology acceptance and smart tourism, drawing on the theoretical foundations of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). These frameworks provide a basis for understanding user behaviour, particularly in terms of perceived usefulness and behavioural intention, which are adapted and extended in this study to the context of smart tourism.

The questionnaire included six main latent constructs: perceived safety related to smart digital systems (SEC), perceived comfort and convenience (COMF), satisfaction with the tourist experience (SAT), perceived usefulness of smart technologies in destinations of different sizes (SIZE), behavioural intention to use smart digital services (BI), and experience with digital technologies (EXP). Gender (G1) was included as a sociodemographic variable for testing group differences.

Perceived comfort and convenience (COMF) in this study conceptually corresponds to ease-of-use-related dimensions identified in TAM and UTAUT, while perceived usefulness (SIZE) and behavioural intention (BI) directly align with core constructs of these models. Satisfaction with the tourist experience (SAT) extends traditional technology acceptance models by incorporating experiential outcomes relevant in tourism contexts. Perceived safety (SEC) is treated as a context-specific extension particularly relevant in smart tourism environments, where digital surveillance, real-time information, and intelligent systems play a critical role in shaping user perceptions.

All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The measurement items were adapted to reflect the context of smart tourism and digital destination services.

An overview of the constructs, their corresponding codes, number of items, and example statements is presented in Table 1.

3.3. Research Hypotheses

Based on the proposed hypotheses, a conceptual research model was developed to illustrate the relationships among the examined constructs (Figure 1). The model shows the proposed effects of perceived safety, comfort and convenience, satisfaction, perceived usefulness, experience with digital technologies, and gender on the examined outcomes.

Table 2. Research hypotheses.

Hypothesis	Relationship	Hypothesis statement	Expected direction
H1	SEC → SAT	Higher perceived safety related to smart digital systems is positively associated with satisfaction with the tourist experience.	Positive
H2	COMF → SAT	Higher perceived comfort and convenience of smart systems is positively associated with satisfaction with the tourist experience.	Positive
H3	SAT → BI	Higher satisfaction with the tourist experience supported by smart systems is positively associated with the intention to use smart digital services in the future.	Positive
H4	SIZE → BI	Higher perceived usefulness of smart technologies in larger destinations is positively associated with the intention to use smart digital services.	Positive
H5	COMF → BI	Higher perceived comfort and convenience of smart systems is positively associated with the intention to use smart digital services.	Positive
H6a	EXP → SEC	Greater experience with digital technologies is positively associated with perceived safety related to smart digital systems.	Positive
H6b	EXP → COMF	Greater experience with digital technologies is positively associated with perceived comfort and convenience of smart digital systems.	Positive
H7	G1 → SEC	There is a statistically significant difference in perceived safety related to smart digital systems according to tourists’ gender.	Group difference

Table 2. Research hypotheses.

Hypothesis	Relationship	Hypothesis statement	Expected direction
H1	SEC → SAT	Higher perceived safety related to smart digital systems is positively associated with satisfaction with the tourist experience.	Positive
H2	COMF → SAT	Higher perceived comfort and convenience of smart systems is positively associated with satisfaction with the tourist experience.	Positive
H3	SAT → BI	Higher satisfaction with the tourist experience supported by smart systems is positively associated with the intention to use smart digital services in the future.	Positive
H4	SIZE → BI	Higher perceived usefulness of smart technologies in larger destinations is positively associated with the intention to use smart digital services.	Positive
H5	COMF → BI	Higher perceived comfort and convenience of smart systems is positively associated with the intention to use smart digital services.	Positive
H6a	EXP → SEC	Greater experience with digital technologies is positively associated with perceived safety related to smart digital systems.	Positive
H6b	EXP → COMF	Greater experience with digital technologies is positively associated with perceived comfort and convenience of smart digital systems.	Positive
H7	G1 → SEC	There is a statistically significant difference in perceived safety related to smart digital systems according to tourists’ gender.	Group difference

3.4. Conceptual Model

Based on the proposed hypotheses, a conceptual research model was developed (Figure 1) to illustrate the relationships among the examined constructs. The model integrates key dimensions of smart tourism technologies, including perceived safety, perceived comfort and convenience, satisfaction, and behavioural intention. Perceived safety (SEC) and perceived comfort and convenience (COMF) are conceptualized as primary antecedents of satisfaction with the tourist experience (SAT). Satisfaction is further expected to influence behavioural intention to use smart digital services (BI). Additionally, perceived usefulness of smart technologies in larger destinations (SIZE) and perceived comfort and convenience are hypothesized to directly affect behavioural intention. Experience with digital technologies (EXP) is assumed to positively influence both perceived safety and perceived comfort and convenience. Finally, gender (G1) is included as a grouping variable to examine potential differences in perceived safety.

3.5. Data Analysis

The data were analysed using descriptive and inferential statistical methods. Frequencies and percentages were used to describe the sample structure. Descriptive statistics for the constructs were presented using minimum values, first quartile, median, third quartile, and maximum values.

The internal consistency of the measurement scales was assessed using Cronbach’s alpha coefficient. Normality was tested using the Kolmogorov-Smirnov test. Since all constructs significantly deviated from normal distribution, Spearman’s rank correlation coefficient was used to test the relationships between constructs. Gender differences in perceived safety were tested using the Mann-Whitney U test. All statistical analyses were conducted using IBM SPSS Statistics (version 25). The significance level was set at p < 0.05. Given the exploratory nature of the study and the relatively small sample size, correlation analysis was considered an appropriate method for examining the relationships between constructs.

4. Results

4.1. Sample Characteristics

The final sample consisted of 104 respondents. Women accounted for 67.31% of the sample, while men accounted for 32.69%. The sample was predominantly young, with 85.58% of respondents belonging to the 18–30 age group. Regarding travel frequency, the largest share of respondents travelled two to three times per year.

Table 3. Sociodemographic characteristics of respondents.

Characteristic	Category	N	%
Gender	Male	34	32.69
Gender	Female	70	67.31
Age	18–30	89	85.58
Age	31–45	6	5.77
Age	46–60	4	3.85
Age	60+	5	4.81
Travel frequency	Up to once per year	38	36.54
Travel frequency	2–3 times per year	51	49.04
Travel frequency	4–6 times per year	11	10.58
Travel frequency	More than 6 times per year	4	3.85

Table 3. Sociodemographic characteristics of respondents.

Characteristic	Category	N	%
Gender	Male	34	32.69
Gender	Female	70	67.31
Age	18–30	89	85.58
Age	31–45	6	5.77
Age	46–60	4	3.85
Age	60+	5	4.81
Travel frequency	Up to once per year	38	36.54
Travel frequency	2–3 times per year	51	49.04
Travel frequency	4–6 times per year	11	10.58
Travel frequency	More than 6 times per year	4	3.85

4.2. Descriptive Statistics and Reliability Analysis

Descriptive statistics indicate that respondents generally evaluated smart digital systems in tourism positively. Median values ranged from 3.50 to 4.50, suggesting moderate to high levels of agreement with the measured statements.

The reliability analysis showed high internal consistency for most constructs. Cronbach’s alpha values were 0.887 for SEC, 0.940 for COMF, 0.944 for SAT, 0.868 for SIZE, and 0.938 for EXP. The initial reliability of the BI scale was lower, with Cronbach’s alpha of 0.661. After excluding the reverse-coded item BI2, the reliability of the BI scale increased to 0.891. Therefore, the purified BI scale was used in further interpretation.

Table 4. Descriptive statistics and reliability of constructs.

Construct	Min	Q1	Median	Q3	Max	Cronbach’s α	Reliability assessment
SEC	1.00	3.00	4.00	4.56	5.00	0.887	High
COMF	1.00	3.69	4.50	5.00	5.00	0.940	Very high
SAT	1.00	3.40	4.00	4.80	5.00	0.944	Very high
SIZE	1.00	2.69	3.50	4.31	5.00	0.868	High
BI	1.75	3.25	4.00	4.69	5.00	0.661	Questionable
BI (refined)	1.00	3.33	4.17	5.00	5.00	0.891	High
EXP	1.00	3.33	4.33	5.00	5.00	0.938	Very high

Table 4. Descriptive statistics and reliability of constructs.

Construct	Min	Q1	Median	Q3	Max	Cronbach’s α	Reliability assessment
SEC	1.00	3.00	4.00	4.56	5.00	0.887	High
COMF	1.00	3.69	4.50	5.00	5.00	0.940	Very high
SAT	1.00	3.40	4.00	4.80	5.00	0.944	Very high
SIZE	1.00	2.69	3.50	4.31	5.00	0.868	High
BI	1.75	3.25	4.00	4.69	5.00	0.661	Questionable
BI (refined)	1.00	3.33	4.17	5.00	5.00	0.891	High
EXP	1.00	3.33	4.33	5.00	5.00	0.938	Very high

4.3. Normality Testing

The Kolmogorov-Smirnov test was used to examine the normality of the construct distributions. The results showed statistically significant deviations from normal distribution for all constructs. Consequently, non-parametric statistical procedures were used in the hypothesis testing.

Table 5. Kolmogorov-Smirnov test of normality.

Construct	Statistic	df	p-value
SEC	0.133	104	<0.001
COMF	0.217	104	<0.001
SAT	0.152	104	<0.001
SIZE	0.090	104	0.037
BI	0.201	104	<0.001
EXP	0.213	104	<0.001

Table 5. Kolmogorov-Smirnov test of normality.

Construct	Statistic	df	p-value
SEC	0.133	104	<0.001
COMF	0.217	104	<0.001
SAT	0.152	104	<0.001
SIZE	0.090	104	0.037
BI	0.201	104	<0.001
EXP	0.213	104	<0.001

4.4. Correlation Analysis

Since the assumption of normality was not met, Spearman’s rank correlation coefficient was used to examine the relationships between the constructs. The results indicate that all the constructs examined were positively and statistically significantly correlated.

The strongest correlation was found between perceived comfort and convenience and satisfaction with the tourist experience (COMF–SAT, ρ = 0.756, p < 0.001). Strong positive associations were also found between experience with digital technologies and perceived comfort and convenience (EXP–COMF, ρ = 0.717, p < 0.001), as well as between perceived usefulness in larger destinations and behavioural intention (SIZE–BI, ρ = 0.696, p < 0.001). The strength of correlations was interpreted according to commonly accepted thresholds (weak, moderate, strong relationships).

Table 6. Spearman correlation matrix.

Construct	SEC	COMF	SAT	SIZE	BI	EXP
SEC	1.000
COMF	0.712**	1.000
SAT	0.574**	0.756**	1.000
SIZE	0.436**	0.460**	0.627**	1.000
BI	0.687**	0.685**	0.677**	0.696**	1.000
EXP	0.597**	0.717**	0.684**	0.510**	0.708**	1.000

Note: Spearman’s rank correlation coefficients (ρ); **p < 0.01.

Table 6. Spearman correlation matrix.

Construct	SEC	COMF	SAT	SIZE	BI	EXP
SEC	1.000
COMF	0.712**	1.000
SAT	0.574**	0.756**	1.000
SIZE	0.436**	0.460**	0.627**	1.000
BI	0.687**	0.685**	0.677**	0.696**	1.000
EXP	0.597**	0.717**	0.684**	0.510**	0.708**	1.000

Note: Spearman’s rank correlation coefficients (ρ); **p < 0.01.

All observed correlations were positive and statistically significant, indicating a consistent pattern of relationships among the constructs.

4.4. Hypothesis Testing

The results support all correlation-based hypotheses from H1 to H6. Perceived safety was positively associated with satisfaction, supporting H1. Perceived comfort and convenience showed the strongest relationship with satisfaction, supporting H2. Satisfaction, perceived usefulness of smart technologies in larger destinations, and perceived comfort and convenience were all positively associated with behavioural intention, supporting H3, H4, and H5. Finally, experience with digital technologies was positively associated with both perceived safety and perceived comfort and convenience, supporting H6a and H6b.

Table 7. Results of hypothesis testing for H1–H6 (Spearman’s rank correlation).

Hypothesis	Relationship	Coefficient	p-value	Decision
H1	SEC → SAT	0.574	<0.001	Supported
H2	COMF → SAT	0.756	<0.001	Supported
H3	SAT → BI	0.677	<0.001	Supported
H4	SIZE → BI	0.696	<0.001	Supported
H5	COMF → BI	0.685	<0.001	Supported
H6a	EXP → SEC	0.597	<0.001	Supported
H6b	EXP → COMF	0.717	<0.001	Supported

Table 7. Results of hypothesis testing for H1–H6 (Spearman’s rank correlation).

Hypothesis	Relationship	Coefficient	p-value	Decision
H1	SEC → SAT	0.574	<0.001	Supported
H2	COMF → SAT	0.756	<0.001	Supported
H3	SAT → BI	0.677	<0.001	Supported
H4	SIZE → BI	0.696	<0.001	Supported
H5	COMF → BI	0.685	<0.001	Supported
H6a	EXP → SEC	0.597	<0.001	Supported
H6b	EXP → COMF	0.717	<0.001	Supported

The Mann-Whitney U test was used to examine whether perceived safety related to smart digital systems differed according to gender. Although women had a slightly higher mean rank than men, the difference was not statistically significant. Therefore, H7 was not supported.

The results presented in Table 8 show no statistically significant difference in perceived safety between male and female respondents (U = 1133.00; Z = 0.399; p = 0.690). Accordingly, H7 is not supported.

Table 9. Results of hypothesis testing for H7 (Statistical test Mann-Whitney U).

Hypothesis	Relationship	Statistic	p-value	Decision
H7	Gender differences in SEC	Z = 0.399	0.690	Not supported

Table 9. Results of hypothesis testing for H7 (Statistical test Mann-Whitney U).

Hypothesis	Relationship	Statistic	p-value	Decision
H7	Gender differences in SEC	Z = 0.399	0.690	Not supported

These results provide strong support for the proposed model, confirming all hypothesized relationships except for the gender-based difference in perceived safety.

5. Discussion

The results of this study confirm that smart digital systems play an important role in shaping the tourist experience and user behavior and the obtained relationships also indicate certain deviations from dominant theoretical assumptions. The most important finding relates to the strong impact of perceived comfort and convenience on satisfaction with the tourist experience. This result suggests that tourists primarily evaluate the value of smart technologies through their functionality, ease of use, and ability to reduce effort during travel. Such a finding is consistent with studies that highlight the importance of convenience and ease of use as key determinants of satisfaction (Torabi et al., 2023; Gopinath & Jyotsna, 2025), but at the same time points to a broader shift toward an experience-oriented evaluation of technology, where users do not evaluate the technology itself, but rather its contribution to the overall experience.

It is also important to conclude that perceived safety, although statistically significant, shows a weaker impact on satisfaction compared to comfort and convenience. This is partly contrary to part of the literature that positions safety and trust as key determinants of technology acceptance (Tiwari et al., 2024; Omar et al., 2025). This result can be explained by the fact that tourists increasingly perceive safety as a basic standard rather than a differentiating factor. In other words, safety is necessary, but not sufficient on its own to create a positive experience. A similar argument is found in Buhalis & Leung (2018), who emphasize that in smart destinations, basic technological functions become “hygiene factors”, while competitive advantage is created by elements that enhance the experience.

The results also show that satisfaction, perceived usefulness, and comfort have a significant positive impact on the intention to use smart digital services. This confirms the fundamental assumptions of technology acceptance models, according to which perceived usefulness and positive experience shape behavioural intentions (Li et al., 2024; Sujood et al., 2024). These findings also suggest that traditional technology acceptance models such as TAM and UTAUT may not fully capture the experiential and context-specific nature of technology use in tourism environments. The strong role of comfort and convenience indicates that user experience may be equally or even more important than purely functional perceptions such as usefulness. The fact that comfort and convenience also have a direct impact on behavioural intention suggests that traditional models such as TAM and UTAUT may not sufficiently emphasize the experiential dimension of technology use in tourism. The results of this study support an approach that assure us that the integration of functional and experiential factors in explaining user behavior, which is also confirmed by previous research (Ukpabi & Karjaluoto, 2017) highlights the importance of context and user experience in digital tourism. Also, the results confirm that experience with digital technologies significantly influences the perception of safety and comfort. Tourists with greater experience show higher levels of trust and more easily accept digital systems, which is consistent with research on digital literacy and technological readiness (Cimbaljević et al., 2024; Xiong & Zhang, 2024). This finding may also indicate potential digital inequality among tourists, where less experienced users may encounter difficulties in using smart systems, which can negatively affect their experience. Similar conclusions are seen by Neuhofer et al. (2015), who emphasize that technology can simultaneously enhance and limit the experience, depending on the level of user competencies. Opposite of previous research, the results of this study do not confirm statistically significant differences in perceived safety between men and women. Although women showed slightly higher levels of perceived safety, the difference was not significant. This finding deviates from studies that indicate more pronounced gender differences in the perception of technology and risk, particularly in earlier stages of digital transformation. One possible explanation is that digital technologies in tourism have become widely accepted and standardized, thereby reducing differences among users. The homogeneity of the sample and the level of digital literacy of respondents may further contribute to the absence of differences.

6. Conclusions

The results of the study confirm that smart digital systems play a significant role in shaping the tourist experience and user behavior. The strong impact of perceived comfort and convenience on satisfaction stands out, suggesting that tourists perceive the greatest value of technology in its functionality, ease of use, and ability to facilitate their stay in a destination. Satisfaction, together with perceived usefulness and comfort, has been shown to be an important predictor of the intention to use smart digital services, thereby confirming the fundamental assumptions of technology acceptance models.

While perceived safety is statistically significant, its impact is weaker compared to comfort and convenience, suggesting that tourists increasingly perceive safety as a standard rather than a key differentiating factor. Also, experience with digital technologies has proven to be an important factor that positively influences the perception of safety and comfort, while gender differences were not statistically significant. From a practical perspective, the results indicate the need for destination managers and service providers to focus the development of smart solutions on simplicity, intuitiveness, and real usefulness for tourists. Technologies that reduce waiting time, facilitate movement, and provide relevant real-time information have the greatest potential to increase satisfaction and encourage future use. It is important to maintain a high level of security and transparency in data management to preserve user trust. From a theoretical perspective, this study contributes to the existing literature by extending traditional technology acceptance models with context-specific constructs such as perceived safety and perceived comfort and convenience in smart tourism environments.

Despite the significant findings, the study has certain limitations. The sample is relatively small and was collected using a convenience sampling method, which limits the generalizability of the results. Also, the use of correlation-based methods does not allow for establishing causal relationships between variables. The observed issues with the reliability of the initial behavioural intention scale indicate the need for more careful design of measurement instruments. Future research should include larger and more diverse samples and apply more advanced analytical approaches to further validate and extend the findings.