The impact of smart tourism technologies on tourists’ interest in the hotel sector

This era of digitalization and technological advancements has strengthened the relationship among customers, employees and organizations in the marketplace (Megdadi et al., 2023). As technology becomes more accessible, more individuals connect to the cyber world to meet their wants and aspirations (Alharafsheh et al., 2023). The rapid advancement of the digital economy in this century is one of the notable phenomena (Rakhimov, 2022). The brisk proliferation and acceptance of smart technologies can potentially improve the customer experience, as people’s experiences depend on efficient services provided by organizations (Rahi et al., 2020). As a result, customers expect modified, improved, fast and effective services from organizations and retailers (Halim et al., 2023).

According to a Deloitte (2020) report, investment in smart technology in retail was anticipated to be $14bn in 2015, with a 35% annual rise to $53bn by 2021. Both researchers and practitioners stress the importance of improving the customer experience in a rapidly changing context. Incorporating various distribution and communication channels, primarily based on smart technologies, becomes very important for organizations and consumers (AlMutairi and Yen, 2022). The digital economy has become a prominent factor, affecting almost all aspects of socio-economic life (Al-Okaily, 2025a; Habes et al., 2023). Specifically, technology adoption has primarily benefitted the tourism industry concerning its growth and competition (Alghizzawi et al., 2018).

The use of smart technologies is critical in the tourism industry (Alhadidi et al., 2025). The concept of smart tourism describes the recent changes and modifications in the tourism sector due to technology adoption, which uplifted the tourism industry to enter an era of digital systems and improved governance. Mainly described as advancement in e-tourism, smart tourism comprises the digitization of tourism-related systems (Gajdošík, 2018). The term smart tourism ensures better opportunities for the tourism industry, guaranteeing improved socio-economic outcomes. Tourism is not a big industry, but rapid technological modification will lead to rapid booming and improved financial results (Ercan, 2019). The definition of smart tourism is focused on the services offered by organizations and the needs of tourists through the integration of smart tourism technologies (STTs) (Wang, 2016).

Smart tourism, together with the technologies associated with this context, has become an appropriate term to describe the synchronization, coherence and interconnection of the different travel technologies and their impact on tourists and their choices (Yoo et al., 2017). The concept of smart tourism has become a global phenomenon of great importance and concern in all areas of the economy, politics and society, covering education, governance, business, market share, labor, productivity, culture, trade and consumers (Ibrahim et al., 2023; Wang, 2016). However, to our knowledge, no studies have analyzed the effect of tourists’ experience with the use of smart technologies by hotel establishments on a series of consequences related to their attitude and behavior. Therefore, it is essential to understand the perceived experience of tourists using smart technologies when they visit a given hotel establishment. Thus, this research addresses the following question: Do STTs play an essential role in behavioral aspects concerning hotel establishment and tourist destinations? The answer to this question requires an investigation of the repercussions of the experience with STTs on service experience satisfaction (SES), travel experience satisfaction (TES), destination image, word of mouth (WOM) and revisit intention (Alghizzawi et al., 2018).

Therefore, the company aims to meet the needs and achieve tourists’ satisfaction by improving the quality of the hotel services including smart services in order to provide unique and high-quality hotel services to satisfy the tourists of hotels in the view of local and international competitiveness (Palos-Sanchez et al., 2021). The need of STTs in supporting tourism and tourist decisions (Jawabreh, 2020) and enhancing their marketing effectiveness by means of suitable technologies in the hotel sector (Ngoma et al., 2020; Morrison, 2023) suggest the need of further development of tourism services and marketing of destinations effectively and adaptably to different developments on an ongoing basis – hence, their relevance. Most of the studies shown above indicate that the satisfaction of the service experience, the experience of travel, the image of knowledge and the emotional image from the consumer’s point of view and support of their decisions influence the use of STTs in hotels (Qian et al., 2023). Still, hotels have not been well researched.

Accordingly, the issue of this research is to answer the following questions: Do STTs significantly help to improve hotel travel decision support? The response to this question calls for a thorough pilot study to identify, process use and apply STTs to hotels in addition to analyzing not just the use of STTs but also their activities and preferences by tourists. It also calls for an analysis of the effects of the STT experience on the satisfaction of the service experience, satisfaction with the travel experience, the influence of the experience of the STT systems on the tourist satisfaction point of view at hotels and the impact of the satisfaction of the service experience and positive WOM on supporting travel decisions in hotels. Consequently, the findings of this research can give practitioners and scholars some concepts regarding the relevance of smart services in marketing tourism.

The use of smart technologies has drawn much attention due to the growing pervasiveness of technology across sectors. Smart technologies, in particular the integration of the offline and online worlds, have opened up new markets for commercial prospects in various industries (Alsafran et al., 2025a). Smart technologies have been depicted in these situations as proper instruments with specific features that provide value in various ways. For example, using quick-response codes in retail might benefit customers (Neuhofer et al., 2015). A smart city is constructed using a combination of sensors, tags, radio-frequency identification, semantics and cloud computing (Petrolo et al., 2017).

Throughout the literature, academics and practitioners have shown a strong interest in finding the factors that make tourists behave positively (e.g. satisfaction, loyalty, revisit or WOM) towards a company or tourist destination. Some authors have determined that using STTs affects overall travel satisfaction and the tourist experience (Huang et al., 2017). According to Lim et al. (2017), experience through smart technologies can indirectly affect TES through tourists’ perceived value (emotional and social value). Social value significantly impacts the satisfaction of the tourists’ experience and contributes to tourism decision-making and behavior (Huang et al., 2017). The use of technology, especially mobile devices, has become an integral part of the tourism industry. These devices make the tourists’ experience more pleasant, facilitate their ease of access to different facilities and satisfy them regarding their overall tourism experiences.

Remarkably, the tourism industry adopts and integrates smart technologies that satisfy customers’ needs, personalization and revenue (Mehraliyev et al., 2020). The STTs employed by tourism service providers within the destination provide a consumer service that facilitates decision-making and makes the tourism experience more effective (Fotis et al., 2013). STTs provide rapid access to and search for information. In addition, communication and a more remarkable ability to share information among users are generated, facilitating interaction among the users of these technologies. These elements create a satisfactory experience for tourists (Buhalis et al., 2019; Buhalis and Amaranggana, 2015) and affect their perception (Miguéns et al., 2008). In this way, the following hypothesis is proposed:

Consumer satisfaction is defined as a consumer’s appreciation of a characteristic of a product or service that provides a positive outcome from consumption (Jin et al., 2015). Within technology satisfaction, organizations have recognized that the consumer’s experience with smart technologies creates higher satisfaction levels (Gaffney and Robertson, 2018; Jin et al., 2015). Smart technologies enable interactions between the product and service provider and the consumer to be enhanced with greater engagement, ease of use and responsiveness to consumer needs. In addition, real feedback and time monitoring are generated (Pantano and Priporas, 2016). Within the tourism field, many smart cities and tourism service providers aim to increase consumers’ satisfaction by creating smart destinations. Thus, cities must ensure that visitors are delighted with the services provided in the destinations and that smart tourism is fully equipped with the attributes of smart tourism technology. Smart tourism uses online tourism channels and information, including social media and smartphone applications, to share information and create purchasing decisions (Gretzel et al., 2015). In this way, the following hypothesis is proposed:

The most general definition defines the concept as the psychological representation in individuals of ideas, knowledge, beliefs, emotions and general impressions of a particular destination (Huang et al., 2017). Based on this definition, some studies have attempted to create a comprehensive model that includes the impact of smart technologies on destination image, visitor loyalty to destinations and satisfaction (Ramseook-Munhurrun et al., 2015). STTs consist of tools or applications that allow tourists to effectively exchange experiences with other users and improve the destination’s image (Hernández-Méndez et al., 2015). These include virtual reality tools, interactive screens, blogs, artificial intelligence, discussion forums and social networks (Wang et al., 2012). In this way, the following hypothesis is proposed:

SES has been defined as a psychological structure and subjective response resulting from tourists’ contact with tourism service providers and the exchange of experiences (Corrêa and Gosling, 2021). Maunier and Camelis (2013) demonstrated that technologies provided by tourism service providers, such as hotels, provide service satisfaction to the consumer. There are different ways to reveal SES by consumers. One of the main instruments to express their satisfaction is WOM communication. It has been shown throughout the literature that WOM is the most effective way of influencing people’s behavior compared to other market-controlled sources (Roy et al., 2017) and can exert a strong influence on consumer choice (Alazmi and Alemtairy, 2024). The importance of WOM is greater in the context of service due to the “intangible” nature of services. New, innovative and unique products are likely to attract interest, which could lead to greater WOM (Hua et al., 2017). As a result, the following hypothesis is proposed:

The destination image is an individual’s mental representation of the knowledge (beliefs), feelings and general perception of a particular destination (Alsafran et al., 2025a). Image affects tourists through their decisions to choose tourist destinations and their behavior, such as satisfaction, loyalty, revisit intention or WOM (Tanford et al., 2020). The destination image affects the user’s intention to take an essential role in their travel decisions. Today, through the development of social media and smart technologies, destination image contributes to promoting tourists’ travel experiences through WOM (Huang et al., 2017). Based on the above argument, the following hypothesis is proposed:

Satisfaction can affect consumer purchasing behavior and urge their revisit intention, highlighting the importance of satisfaction in the service experience (Liu et al., 2017). SES can influence a tourist’s perceptions and their decision to choose a service provider or travel to a destination. Tourists’ satisfaction with the destination is linked to intense satisfaction with the overall trip and the service received by the tourist (Constantoglou, 2020). As Chen et al. (2020) noted, the digital revolution and the rise of smart technology in tourism have facilitated visitors’ access and intention to revisit. Tourists can enjoy better services with ease of access and ease of use with the improved services. Overall, within the current context of smart technologies, it can be predicted that an adequate experience with the use of smart technologies by tourism service providers or in a specific environment (such as a destination), as well as TES, can lead to a revisit intention by the consumer. Finally, the image of the destination has direct effects on behavioral intentions (Kim and Kim, 2017). Thus, it is established that the destination image will influence the revisit intention of the destination. Following this assertion, the following hypotheses are proposed:

The significance of WOM for service and tourism businesses has been well recognized (Ahmad et al., 2019). Some studies suggest that a positive WOM plays a key role in improving tourists’ decisions to choose service providers and destinations (Amblee, 2015). Furthermore, most people who express WOM communication are delighted with their experience (Liu et al., 2022). In addition, a tourist who is extremely satisfied with a service is likely to recommend the specific features of the destination to others and return to the destination in the future (Liu and Lee, 2016). Thus, it can be suggested that positive WOM communication of tourists regarding the smart technologies used by tourism service providers or a particular environment (such as a tourist destination) can influence revisit intention. Thus, the following hypothesis is proposed:

This study includes the moderating variable geographical context to analyze differences between perceived smart tourism technology experience (PSTTE) and some consequences on tourists (SES, TES and destination image). The fact that the geographical scope analyzed in terms of the culture of each tourism destination considered moderates the effect of the PSTTE on several user consequences (SES, TES and destination image) would provide a good basis for examining the different decision-making mechanisms in other geographical areas and cultures. As an illustration, Bazazo and Alananzeh (2016) examine the impacts of perceived destination attributes on visiting behavior among Jordanian tourists. The researcher used the survey method and selected a sample of tourists from other countries. Results indicated that destination attributes and perceived usefulness, including expenditures, are highly associated with the tourist’s behavior. For most participants, expenditure is essential to making any relevant decision. Therefore, based on the literature, the following hypotheses are established:

Thus, this research proposes the following sub-hypotheses: (H11a) the geographical context analyzed moderates the relationship between PSTTE and SES; (H11b) the geographical context analyzed mediates the relationship between PSTTE and TES; (H11c) the geographical context analyzed moderates the relationship between PSTTE and destination image; (H11d) the geographical context analyzed moderates the relationship between service experience satisfaction and WOM; (H11e) the geographical context analyzed moderates the relationship between TES and WOM; (H11f) the geographical context analyzed moderates the relationship between destination image and WOM; (H11g) the geographical context analyzed moderates the relationship between service experience satisfaction and revisit intention; (H11h) the geographical context analyzed moderates the relationship between TES and revisit intention; (H11i) the geographical context analyzed moderates the relationship between destination image and revisit intention and (H11j) the geographical context analyzed moderates the relationship between WOM and revisit intention. To conclude and simplify the above relationships, Figure 1 represents the model proposed on the influence of PSTTE on a series of consumer consequences in the tourism context with the hypotheses formulated.

Intending to contrast the objectives and hypotheses set out previously, quantitative research was carried out to analyze the influence of the PSTTE with the use of smart technologies employed by Marriott Hotels on a series of consequences for tourists: SES, TES, destination image, WOM and revisit intentions. The PSTTE variable will be composed of a series of dimensions (informativeness, accessibility, personalization and interactivity). In addition, the moderating effect of the geographical context variable will be analyzed.

This study used a convenient sampling approach online survey to contact travelers who had visited some of the leading smart cities. The respondents answered the questions in the survey according to their experiences in the Marriott Hotels in the smart cities considered. The smart cities were selected following the cities in Motion Index (Al-Okaily and Al-Okaily, 2025). By representing the Middle East and Europe, where advanced ICTs were embedded in the city, the Middle Eastern cities considered were occupied Palestine (70th position) and Dubai (92nd position). The smart European cities incorporated in the sample were Madrid (25th position) and Barcelona (26th position). Before launching the surveys, this study conducted a series of preliminary tests, including a pretest and expert reviews of the survey instrument to enhance its clarity and reliability. The pretest sampled 20 users of hospitality services in Petra who helped refine the wording and readability of the instrument. After reviewing the questionnaire, the survey was carried out on tourists who visited the Marriott Hotels in the smart cities considered. Participation in the study was voluntary. The respondents were instructed to respond to the questionnaire based on their most recent experience with smart technologies at the Marriott Hotel in selected cities.

In this sense, the sample set for the model of this research came from the Middle East and European sites. This study carried out a series of preliminary tests, including a pretest and expert reviews of the survey instrument to improve its clarity and dependability before starting the polls. In total, 20 Petra hotel service consumers who assisted in improving the instrument’s readability and wordings were pretested. Following a review of the questionnaire, the survey was conducted among visitors of the Marriott Hotels located in the smart cities under consideration. The study participants participated voluntarily. The respondents were directed to answer the questionnaire depending on their most recent encounter with smart technologies at the Marriott Hotel in the chosen cities. These two geographical regions – Europe and the Middle East – have been selected because of their cultural variations to enable a comparison of the acquired outcomes.

Convenience sampling was used to receive a reasonable number of participants. To determine the sample size, G*power software analysis was employed, and the outcomes demonstrate that if a medium effect size of 0.15 is assumed with 0.95 power in the previous power analysis, then a sample of 172 respondents is mandated for inferential analysis (Alazemi et al., 2025) as can be seen in Appendix 1. The sample size of the current research is 355, which is considered appropriate for using structural equation modeling (SEM).

Participants were asked to complete the questionnaire upon agreeing to take part. The poll included clear instructions for possible responders to answer depending on their most recent encounter with the smart technologies in Marriott Hotels. Based on industry reports (Perry, 2017) and current literature (Pantano and Priporas, 2016; Willems et al., 2019). We developed a shared knowledge of smart technologies addressing customers.

This study’s structured questionnaire split into four pieces forms the tool utilized to gauge the variables. The first portion relates to the destination or smart city chosen as well as some questions connected to the trip taken to the city visited (duration of trip, purpose, travel party, sources of travel information or smart gadgets utilized in the destination). The second part examines how well the elements of the PSTTE fit travel companies – particularly, the scores on informativeness, accessibility, customization and interactivity. Examining the effects of the supposed STT experience in the destination takes up the final part. Particularly, the variables are TES, destination image, WOM intentions, revisit intention and SES. Finally, questions of a socio-demographic type are included to define the basic consumers.

The characteristics of the quantitative design presented in the research and the approach adopted have determined the most appropriate information analysis techniques for determining the proposed objectives. The first set of analyses for the study consists of univariate and bivariate analyses to obtain descriptive results on the samples. In this case, the SPSS program is used to develop these analyses. In addition, the modeling of structural equations has been used to verify the hypotheses raised in the theoretical models and the proposed multi-group models. For this, Smart PLS.

Finally, univariate descriptive methods (frequencies, percentages and averages) of the variables used to define the sample acquired in the study will be applied in order to attain the exposed aims earlier. Second, two particular variables are tested for an association or not using bivariate analysis. The contingency tables are underlined as the most often used method to examine the relationship between nominal variables. Analysis of the relationship between two non-metric variables mostly depends on this method. Their aim is to confirm whether the behavior of the outcomes falling into the one variable category corresponds with the categories of another (Alsafran et al., 2025b). Using the chi-square independence test (H2) helps one verify whether the frequency variations are statistically significant.

Ultimately, the comparison of means is a descriptive process that lets us get descriptive statistics of the several groups and subgroups specified by one or more independent variables. Using the means of two random samples taken from those populations, the present study employs the t-test for two independent samples to confirm that the means for two independent populations are equal. The first analysis of the Levine test helps one to investigate the relevance of the variations in means between groups and the appropriate examination of the t-test. This test investigates variances’ equality to provide the interpretation in every situation. These deviations cannot be regarded as equal if their significance is minor (p < 10).

Using SEM, the causal link between the independent and dependent variables were examined. The computational researcher’s two-stage technique for SEM includes measurement models and structural models (Henseler et al., 2015).

The reliability of the measurement scales for the constructs estimated in Mode A is carefully analyzed, as suggested by Hair et al. (2019). In this regard, service experience satisfaction, travel experience satisfaction, destination image, WOM and revisit are carefully measured. In general, a high level of internal consistency was observed. In this case, all the loadings for the respondents were greater than 0.708, except SES1, TES1 and DES8. As the loadings are higher than 0.4, if there is no problem, they are maintained (Hair et al., 2019). Therefore, it is necessary to verify the result in the rest of the measurement indices for the constructs of these items (Hair et al., 2019). The individual reliability of each construct was verified using three indicators: Cronbach’s alpha (CA), composite reliability (CR) and Dijkstra–Henseler’s rho (ρА) (Hair et al., 2019).

As summarized in Table 1 below, the CR of all the variables successfully surpasses the threshold value of 0.7, indicating a high internal consistency among the constructs (Sarstedt et al., 2019). The CA values range from 0.902 to 0.816, indicating all the CA values surpass the designated threshold value of 0.70. Also, they are higher than the threshold value (0.70). Similarly, the ρА also exceeds the threshold value of 0.7, confirming that reliability is successfully established (Dijkstra and Henseler, 2015). The average variance extracted (AVE) values surpass the threshold value of 0.5, ranging from 0.791 to 0.677, indicating that the convergent validity is strongly established among the study variables. Finally, the researcher determined the significance of loads by bootstrapping the sampling procedure to obtain the t-values. Notably, a 95.0% confidence level is generally considered the minimum criterion to affirm the significance. All the indicators are significant at 99.9% (Hair et al., 2019).

To investigate the discriminant validity, the researchers used the Fornell–Larcker criterion to differentiate the values of discriminant validity. Fornell and Larcker (1981) proposed the traditional metric and suggested that each construct’s AVE should be compared to the squared inter-construct correlation (as a measure of shared variance) of that same construct and all other reflectively measured constructs in the structural model. The shared variance for all model constructs should not be larger than their AVEs (Hair et al., 2019). Thus, in the current research, latent variables in the research model did not exceed this value (see Tables 2 and 3), so it is found that the measures in this study are sufficiently witnessing reliability, convergent and discriminant validity.

Finally, the multidimensional construct variables (informativeness, accessibility, interactivity and personalization – Mode B) are examined. In this case, Bagozzi and Yi (2012) suggested that instead of evaluating each construct element, the conventional techniques of measuring reliability and validity are inapplicable. In such a situation, the researcher examined the collinearity of the indicators to identify any absence of correlation among some items, along with the magnitude of their importance and weights. Appendix 2 shows that all the variance of inflation factor (VIF) values are lower than the threshold value of 3.3, indicating that there is no collinearity, and all weights are significant at 99.9% of variance along with all the VIF values lower than 3.3, indicating that there is no indication of multicollinearity (Hair et al., 2019).

The next step in the partial least squares (PLS) analysis was to evaluate the structural model (as indicated in Table 4). According to Hair et al. (2019), if the collinearity does not occur, the next step regarding the structural model assessment is to conduct the R² analysis of all the endogenous variables. Thus, in the current research, we did not find any collinearity; it is possible to perform the R² assessment. According to Hair et al. (2019), R² ranging from 0.25 to 0.75 is plausible and is considered substantial. If the R² values are as low as 0.10, they will be regarded as satisfactory. Thus, in the current research, the explanatory power of the structural model is examined by calculating the R² values. The structural model explains 54.5% of the variance in destination image, 64.5% of the variation in the revisit, 62.5% of the variance in SES, 59.5% of the variance in travel service experiences and 69.8% of the variance in WOM (see Table 5).

With regard to effect size, the effect size of destination on WOM is large at f² = 0.437, the effect size of perceived smart tourism technology on destination image is moderate at f² = 0.199, the effect size of WOM on revisit is moderate at f² = 0.182% and the effect size of travel service experiences on revisit is null at the f² = 0.017. Besides, the effect size of services experience satisfaction on revisit and travel service satisfaction on revisit remained null (0.007 and 0.001) (see Table 5).

Lastly, the importance of performance map analysis (IPMA) in the current research shows that destination image attained the lowest score with a value of 75.14; nevertheless, the total effect’s values reported the most significant effect at 0.472. Moreover, regarding the highest performance, the results revealed that TES had attained the highest score of 84.805 with a value of the total effects of 0.182. Appendix 3 illustrates the IPMA scores and results.

Sarstedt et al. (2019) and Henseler et al. (2016) greatly emphasized measurement invariance assessment (MICOM) between two or more groups before conducting the multi-group analysis in partial least squares structural equation modeling (PLS-SEM). MICOM comprises three steps as follows: (1) configurable invariance analysis, (2) establishing compositional invariance analysis and (3) analyzing the variances and equal means (Henseler et al., 2016). Thus, this research also involves MICOM as a primary requirement for conducting, comparing and describing the multi-group analysis (Sarstedt et al., 2019). This finding raises concerns because measurement invariance is a prerequisite for multi-group analysis (see Table 6).

Today, technology in smart hotels is greatly leveraged to enhance guest experiences and also to streamline the hotel management system for the administrators and the staff. According to Buhalis et al. (2019), the hospitality industry provides several opportunities to utilize automation solutions to improve hotel room services. As a result, guests enjoy comfort and convenience and hotel management, employees and owners benefit from cost savings, strong efficiency and guest satisfaction (Al-Okaily, 2025b). According to Giuliodori et al. (2023), the use of technology in smart hotels has the potential to greatly enhance the guest experience by providing more convenience, security, personalization and communication. For instance, the primary goal of the hotel industry is to provide their guests with strong control over the environment. When individuals stay in a smart room, they can control and automate thermostats, lights, door locks and others (Pantano and Gandini, 2017). Upgrading traditional hotel rooms with smart technology is trending all over the world. Creators are also developing and improving technology in the hotel industry (Sujata et al., 2019).

Similarly, this research also examined the impacts of smart tourism technology on the tourists’ experiences. Notably, the variable “perceived smart tourism technology” was operationalized into four sub-constructs: informativeness, accessibility, interactivity and personalization (Madar, 2017). As noted by do Valle and Assaker (2016), the greater availability and acceptance of smart technologies in the hotel industry indicate the role and importance of technology in the hospitality sector. Notably, as observed in current research, this technology offers enhanced services as a dynamic factor to attract tourists. For example, mobile apps can provide real-time information about local safety hazards, such as natural disasters or political unrest. Smart tourism technology can also help tourists locate emergency services and communicate with authorities in case of an emergency (Liu et al., 2017).

This study also witnesses the impact of smart technologies on the hospitality industry in the Middle Eastern and European regions (Neuhofer et al., 2015). As noted by Aldebi et al. (2017), the hospitality industry is significantly linked with tourism aimed at providing tourists with improved experiences through smart technology. After the development and broader adoption of technology in tourism and hospitality, tourists remain focused before, during and after the tourists’ departure. This study also witnessed the use of smart tourism technology as a crucial factor behind the emerging concept of smart hotels and destinations.

The first theory holds that “perceived smart tourism technology experience will have a positive impact on SES.” Especially, services improved by technology have tremendously helped the hotel sector. Improved services that can also be tailored depending on the needs of the visitors help to meet their expectations regarding the services given to them (Yoo et al., 2017). The results confirmed this notion even more since it was discovered that PSTTE improves SES. The second theory holds that “travel experience satisfaction will benefit from PSTTE.” Smart technology, as Spiro (2019) pointed out, has fundamentally changed travel as the hotel sector uses technology to guarantee guest delight. Furthermore, the results from PSTTE showed a favorable effect on TES.

In addition, the third study hypothesis, “Perceived smart tourism technology experience will have a positive impact on destination image,” was validated since it was predicated on the idea that visitors having a good experience resulting from smart technology would probably have a positive view of the destination image. Al Jayyousi et al. (2023) points out that technology in travel and hospitality gives visitors a fundamental concept of the quality of the services and features of the chosen place. Once the visitors get to the place and find it to satisfy their previous expectations. Also important is the fourth study hypothesis, “SES will have a positive impact on WOM.” The pertinent theories revealed their applicability in the arguments presented by Alazmi (2025). Technology lets visitors choose a fit hotel by knowing their needs and demands. They turn to technology for this aim since it enables them to choose the best services and benefit from the thoughts and remarks left by guests (WOM) who have already been at the hotel.

Assuming the effects of travel experience on the visitors, the fifth study hypothesis, “Travel experience satisfaction will have a positive effect on WOM,” Ercan (2019) claims that among the important elements of the hotel and travel sectors are happy guests. Once the visitors have good experiences and their expectations are met, they are more likely to share their thoughts with the other visitors searching for travel possibilities. The study participants disagreed with the effect of travel experience pleasure on WOM, so the findings did not match Ercan’s expectations. Similarly, the sixth hypothesis – that “destination image will have a positive impact on WOM” – remains unchanged since most respondents disagreed that the travel service expectations have a positive effect on WOM. As a client-centered industry, tourism welcomes and is eager to adopt creativity, dependability and consumer loyalty.

However, before witnessing a service, visitors cannot have either a good or unfavorable view (Technology, 2019). Assuming “service experience satisfaction will have a positive impact on revisit intention,” the researcher suggested in the seventh hypothesis a positive influence of this factor on the revisit intention. Results confirmed this theory since the respondents agreed that they are likely to return to the same location if they feel happy with the services they have had. As Lee et al. (2009) pointed out, revisiting intentions is really closely correlated with positive experiences. Important elements that raise visitors’ pleasure are, for example, better travel, greater hospitality and technology, allowing personalization of services. As such, the same visitors wish to go through these adventures once more.

With relation to the eighth hypothesis, “travel experience satisfaction will have a positive effect on revisit intention,” the results once more revealed the hypothesis as relevant and accepted. The respondents mostly agreed that positive travel experiences influenced revisit intention according to tourist expectations and helped to define it. According to Wetzel and Barten (2016), enough service experience might also lead to reconsidering intentions using the modern ways paired with smart technologies.

Talking about the study variables, the selection of all the variables was based on the empirical stance, as the existing literature supported the assumptions. In this regard, it can also be concluded that the study variables include PSTTE, TES, and service experience satisfaction. It also indicated the relevance of study concepts with the existing literature, witnessing the role and importance of technology in enhancing the customers’ experiences, particularly in the tourism and hospitality industries. Today, quality of service and travel experiences plays an important role in customer satisfaction. Notably, in the tourism sector, these two factors need special attention, especially when tourists rely on technology and expect ease of use and valuable results. For López-Sanz et al. (2021), service experiences and travel experiences are part of a core strategy that further helps measure tourist perceptions, behavioral intention and technology dependence. Satisfaction with the service experience can also be relevant in investigations of cognitive psychology, behavior and intentions, where satisfaction can be further operationalized and linked to “motivation” to take specific actions. Tourist services and restaurants can lead to positive talk about the destination’s image.

According to Miličević et al. (2017), speaking specifically about intended image and WOM, it is necessary to note that a positive destination image after a satisfying experience leads to positivity. It is noteworthy that the results of multiple groups conducted for tourists from Africa and the Middle East in smart cities in Europe and the Middle East show that there are significant differences for SES WOM. For the rest, no differences were found. Here, we conclude that the current findings provide the Jordanian tourism industry with a valuable and in-depth focus on service experiences. These findings also provide a broader understanding of the dimensions of catering. In addition, this study provides deeper insights into existing methods of PLS-SEM (PLS-PM and PLS-SEM), which is also accompanied by an IPMA that provides implications for decision-makers in the tourism and hotel industry. It also highlights the strongest relationships in the model. In this case, the H1 relationship is the strongest.

For the researchers, students and additional professional researchers, this study has numerous main intellectual consequences. Especially, the present studies follow a self-proposed model developed and backed by the pertinent theories and factors. These ideas and variables can also be used by future studies to investigate the elements influencing the use of technology for travel needs. Studies on the use of technology for tourism and hospitality goals are carried out nowadays with few dimensions and variables. The body of current research on STTs originates from all around the world. Consequently, the generalizability of results in another geographical location is the accepted view of these investigations. Future academics will be able to grasp the function and relevance of smart technologies in improving tourism by appreciating the need for technology in the travel and hospitality sector. Moreover, researchers can also apply other experiences – a component of travel and service experiences – to investigate the effects of technology in modernizing and substituting the traditional methods applied by the travel and hospitality sectors.

Regarding the conceptual model of the present work, most hypotheses stayed confirmed. The confirmation of the conceptual model gives even more opportunities for future academics to investigate technology and its effects on the hotel and tourism sector. To investigate the function of STTs, other motivating and influencing elements might therefore substitute the variables including TES, service experience satisfaction and destination image. For instance, the way smart technologies arrange food and accommodation is also regarded as crucial in influencing the experiences of the visitors. Apart from that, this study offers another crucial factor: smart tourism technology, services and electronic WOM, which can enable the future studies to investigate the other crucial elements about the success or failure of tourism and hospitality.

The relevance of the findings and research challenge, this paper also offers some pragmatic consequences for practitioners. First, the study emphasizes the significance and part technology plays in the hotel and travel sectors. This technology use and integration in the traditional hotel reservation system not only increases the relevance of technology but also offers a possible chance for the tourism and hotel stakeholders to take technology usage and updates under consideration to keep pace with the modern criteria of satisfying the customer’s needs. One should keep in mind that patterns in the hotel and tourism sectors are always shifting. Adopting all the newest trends and ideas helps one to keep the visitors delighted and content. Their changing needs today make these tendencies even more consumer-centric.

In this sense, some trends – check-in automation, smart rooms, website optimization for voice search, sustainability, automation, personalized experiences, blockchain solutions, virtual reality, hyper-targeted advertising, data-driven decision and many others – are exploding now. Blockchain technology, for example, is mostly recognized as the source of power behind cryptocurrencies – that is, Ethereum, Bitcoin and others. In the travel and hospitality sector, on the other hand, this technology can be applied to guarantee visitor personal data security while offering smart services. By means of data-by-data decentralization, blockchain also aids in data protection so as to guarantee system non-offering. Blockchain technology guards the website against cyberattacks as well. Blockchain integration in the travel sector will give visitors a safe transaction mechanism and foster their trust, therefore guiding their choice of a relevant location.

As in other studies, the current study has some limitations that should be considered in future research. One of these research limitations is that the empirical data were collected from Marriott Hotel tourists by using the non-probability techniques with a convenience sampling method; thus, empirical data may not be generalizable. Accordingly, further research should consider probability sampling techniques by involving tourists from different countries visiting different destinations worldwide that are required for generalizable results. Finally, this study lacks focus on technology that may further enhance the tourists’ experiences. From online recommendation and support to service delivery, artificial intelligence is one of the leading technological approaches. However, further researchers are recommended to conduct studies regarding the adoption, integration and use of artificial intelligence as a part of intelligent tourism technology, further improving their experiences.

The supplementary material for this article can be found online.