Smart Destination Selection Process: Research on Generation Y Tourists

Mobile technology, which started to develop in the early 1980s, and the Internet, which started to gain its current importance in the early 1990s, are among the most influential technological innovations that affect the course of the global world and individual lifestyles (Akdağ & Akmaz, 2019; French & Shim, 2016). In 1991, the World Wide Web (WWW) was developed by Tim Berners-Lee and thus the Internet gained its current utility. The ever-evolving concept of the Internet has transformed into the Internet of services (e.g. e-commerce), the Internet of people (e.g. social media), and the Internet of things (IoT). With the use of the Internet in mobile technology, the concept of smart has emerged. The concept, which was first used for smartphone-like devices, has gained a new dimension with the development of IoT technology and has turned into an approach that provides effective resource consumption and offers solutions to global and environmental problems (Akmaz, 2022; Akmaz & Kale, 2020; Liu et al., 2015).

Smartness is emerging as an important force in shaping demand, so the tourism industry needs to be sensitive about smartness and try to provide appropriate provisions. In the tourism scope, smart cities present new challenges while providing new service and business opportunities. To be able to better adopt the smart structure in cities where the tourism sector is the economic driving force, so-called intelligent destinations have been dubbed smart tourism destinations. The smart city principle has become the common denominator of urban discourse, which has been enthusiastically received in the media, institutional, and academic fields. However, this ideal city brings with it significant challenges (Giffinger & Gudrun, 2010; Sigalat-Signes et al., 2020).

Many personal, social, cultural, demographic, and economic factors can be decisive in the destination selection process of tourists. However, smart products and applications that entered the lives of consumers in the 1990s have become at least as influential in the purchasing processes of tourists as other factors (Ünal & Bayar, 2020). In other words, the rapid development of information and communication technologies and their adoption in the context of travel and tourism have significantly affected the functioning of the industry and the attitudes and behaviours of tourists. The demands of many tourists who travel in the age of smart technologies differ from those who travelled in the pre-Internet years (Ghaderi et al., 2018). When the literature on the subject was examined, it was revealed that smart tourism technologies create unforgettable tourism experiences and enhance tourists’ enjoyment (Jeong & Shin, 2020; Lee et al., 2018).

With the increasing impact of smart technologies on the tourism industry, more research has been done on smart tourism. However, most of the studies on smart tourism focus on destinations, hotels, restaurants, entertainment, and traffic. In contrast, much less attention has been paid to touristic places. Moreover, because many smart tourism studies emphasise the deployment of complex technological platforms, in-depth research on smart tourism from a tourist’s point of view is rather limited (Wang et al., 2016). In other words, there are very few studies that explore the smart destination selection process of tourists (Ghaderi et al., 2018; Ünal & Bayar, 2020). The main focus of this research is the thoughts of millennials, who use technology effectively, on this subject. In this context, the aim of the study is to reveal the smart destination selection process of millennial tourists. This focus on the age group that uses technology the most makes this research effort different from others. This research, focused on Generation Y, offers more realistic results thanks to people with high technology literacy.

Smart destinations are inspired by the ‘smart city’ discourses, and a useful destination management approach has been adopted to confront the profound effects of digitalisation processes on tourism. Buhalis (2000) summarised six key components of a smart tourist destination: attractions (natural, artificial, or cultural attractions), accessibility (transportation systems, available routes, airport terminals, and public transportation), amenities (accommodation, restaurants, and leisure activities), available packages (available services offered by intermediaries), activities (to enhance tourists’ visitation experiences), and ancillary services (banks and hospitals).

Smart destinations do not yet have a comprehensive set of indicators that consider the performance of destinations in the different areas in which they are expected to make efforts, such as connectivity, big data treatment, technology deployment, and their interrelationship with the sustainability and accessibility of destinations (Ivars-Baidal et al., 2021). Beyond the limited previous studies, there is limited research to date that has investigated the smart destination selection process of millennials. The primary purpose of offering tourists a tourist destination is to provide them with an unforgettable visit. To this end, it is necessary to reinvent the destination by making it attractive and exciting. The tourist does not choose a destination only because of the monuments, idyllic scenery, culture, and gastronomy that the destination offers. There are other important issues. For example, today’s tourists want to be surprised by the novelty of the destination; they want up-to-date information on what to do, where to visit, and how to reach a destination with the help of technology. Tourists also want to know if there is Internet and Wi-Fi access at a destination and whether mobile devices and other communication channels are used frequently. For this reason, a tourism destination should have certain characteristics such as being surrounded by modern and convenient communication facilities, equipped with exciting and innovative elements that can arouse the curiosity of tourists, full of desired and expected experiences for tourists, and easily accessible (Da Costa Liberato et al., 2018).

Femenia-Serra et al. (2019) wrote that younger tourists are also starting to use smart technologies in the hospitality context in different dimensions of their experiences, and the smartphone seems to be the preferred tool to do so: 20% of millennials have already checked into a hotel through a mobile device, 12% have used it to order room service, and 55% state they want to be able to connect their smartphones to the in-room media to enjoy their personal entertainment.

Tavitiyaman et al. (2021) investigated tourist perceptions of smart tourism application features that could influence their perceived image of a destination and improve their future behavioural intentions toward the destination. Their research showed that smart information systems, smart travel, e-commerce systems, and smart forecasting features have a positive effect on the perceived destination image for tourists. In addition, the perceived destination image among tourists positively affected their behavioural intentions. In a similar study, Xia et al. (2018) showed that website activity can positively increase perceived usefulness, ease of use, online experience, and destination image. In the work of Da Costa Liberato et al. (2018), tourists also referred to the importance of Internet access as a factor influencing their intention to return to a destination. In addition, available information technologies (Internet, smartphones, or other mobile devices and applications) at the destination are very important in explaining the experiences of the tourists. Therefore, these studies support the idea that smart applications affect the destination image and behavioural intention of tourists.

One reason tourism service providers are promoting smart tourism is to improve tourist experiences. In summary, tourists in the smart age have shown some different needs and behavioural patterns from their counterparts in the pre-Internet/social media era. They have become more dependent on information technology, self-service, and personal booking tools. They value easier access to information technology, better use of their money and time, and greater diversity, flexibility, personalization, and security. Such changes in tourists’ needs and behaviours have also brought challenges to the tourism industry and prompted the development of ‘smarter tourist attractions. In addition to the demands from these new tourists in the smart age, other factors such as environmental impacts and technology development have made smart technologies necessary for traditional tourist attractions (Wang et al., 2016).

Smart tourism is in many ways a logical evolutionary development of traditional tourism and e-tourism, where the groundwork for technology-driven innovation is laid. Inspired by the idea of smart cities, smart tourism destinations are innovative tourism destinations built on a modern technology infrastructure that supports the sustainable and accessible development of tourist areas designed to provide better tourism experiences and a higher quality of life for residents (Shafiee et al., 2019). The concept of smart tourism has recently gained importance as a strategic tool for the development of tourism and studies in different countries are expanding (Gretzel et al., 2015). Although still in its infancy (for many reasons), smart tourism is expected to grow quickly in world tourism destinations. Although smart tourism has great potential to serve tourists better, the use of this technology has not been adequately addressed (Gretzel et al., 2016).

“There are slightly different views regarding the range of years in which individuals considered millennials were born. One perspective set the years from 1980 to the mid-1990s (Cavagnaro et al., 2018). Similarly, a report by the Organisation for Economic Co-operation and Development (OECD, 2018) defines millennials as those born in the early 1980s to the mid-1990s. A report by KPMG (2017) marks the years as 1980 to 1995.” (Ketter, 2019, p. 192). Generation Y, which is called the digital generation, has adopted developing and changing technology into their lives from the moment they were born (Nusair et al., 2011). Generation Y consists largely of educated individuals who are more egocentric than other generations, have high self-confidence and expectations, are realistic, and do not like authority; on the contrary, they prioritise their freedom. They care about brands, entertainment, and friendship, are technology enthusiasts, and prefer more informal communication channels (Akşit Aşık, 2020; Behrstock-Sherratt & Coggshall, 2010; Bolton et al., 2013). Generation Y tourists are the main source of the postmodern tourism paradigm in which advanced technology and individualisation are experienced. As its first members were born in the 1980s, it is currently an important generation that is currently engaging in tourism behaviour and will potential tourists long into the future. In other words, Generation Y tourists, whose expectations and demands differ from those of previous generations, are environmentally sensitive, lifelong learners. They are looking for unique experiences, and they constitute an important market that will continue to be important in the future (Perçin & Mahmutoğulları, 2018). These millennials use technology intensively. At this point, the importance of the smart destination in their destination selection (Cohen et al., 2014; Hoang et al., 2016; Rahman et al., 2017) has attracted the attention of tourism marketers. Although a lot of research has been done on service quality, tourist perception, destination image, loyalty, intention, and destination selection, very little research has been done thus far on the dimensions of smart destinations. Considering studies on destination selection, the common conclusion that smart technologies are important is reflected in research findings. Rahman (2012) proposed and tested a framework regarding the perceptions of tourists in destination selection. Researchers have argued that the perceived behaviour of tourists in destination choice is largely dependent on Internet adoption and subsequent customer satisfaction.

The theory of planned behaviour (TPB) proposed by Ajzen (1985, 1991) is a commonly used analytical framework in social science disciplines, including tourism. It predicts the occurrence of specific behaviours that individuals aim to perform based on personal and social factors such as relative attitude toward the behaviour, subjective norms, and perceived behavioural control. Although the TPB it actually based on social psychology, the theory, which is widely used to explain behaviours that fall under the field of many different disciplines, was designed to explain and predict human behaviour that takes place in a certain environment. If a behaviour is perceived as positive (personal attitude), that behaviour is more likely to be performed. According to the same theory, if the attitude (subjective norm) of people whom the individual considers important to a behaviour is positive, this attitude will push the individual to perform that behaviour. Finally, if the individual perceptions are that the person has control over the behaviour (perceived behavioural control), this will further encourage the behaviour to be performed (Nunkoo & Ramkissoon, 2010, p. 529). In other words, according to the TPB, all behaviours occur for certain reasons. The results of the behaviours are calculated in advance, the decision is made to achieve any of the results, and the decision turns into action. Applications of the TPB can thus provide valuable insight into tourist decision-making processes (Ghaderi et al. 2018).

Considering the profound changes in consumer preferences and the characteristics of tourists, it is important to analyse whether destinations adapt to the changes as previously discussed. In addition, despite the acknowledged impact that technology has on tourism and tourist experiences, only a few studies have addressed the impact of smart destination strategy. However, millennials, who use technology the most, have been neglected in the research. At this point, the importance of doing research with the millennials and smart destination concepts is easily understood. This study aims to expand the conceptualisation of smart destinations and thus establish more relevant marketing theories. For this reason, it is focused on the demand side of smart destinations, which is a special case of smart cities.

The research hypotheses to be used in this study have emerged based on the theoretical structure of the study. In light of these evaluations, the following hypotheses will be tested.

This section gives the findings related to some demographic characteristics of the participants and the results of the analysis. Accordingly, information on the demographic variables of the participants is given in Table 6.

The frequency averages and standard deviations of the participants’ answers to the statements in the scale were analysed in the SPSS package program, and the table averages and standard deviation values obtained are shown separately in Table 7.

In the study, the value for the expression ‘New facilities like smartphones, websites, etc., have made travelling easier than before’, which has the highest average of participation levels of the millennial tourists in the smart destination selection process, is 4.08, as seen in Table 7. Among the other expressions, the second highest value was for ‘New facilities like smartphones, websites, etc., have enabled me to better plan my trip’, with an average of 4.06. The third highest value was for the expression, ‘It is important for me to easily locate my cell phone and inform friends and relatives where I am’, with an average value of 3.95.

These responses show that millennial tourists generally agree with the questions about the smart destination selection process.

4.1

SEM Findings Regarding the Variables in the Research Model

SEM is the general name of statistical methods that allow the causal and reciprocal relationships between latent variables to be examined through measurable observed variables (Jöreskog & Sörbom, 1993; Raykov & Marcoulides, 2000). In this research, a path analysis was made regarding the model established with the structural equation model. The hypotheses of the research were tested by path analysis. As can be seen in Figure 2, there are significant relationships between all the variables in the study. Therefore, significant effects can be predicted between the variables. In a continuation of the meaningful results, path analysis was made within the framework of the structural equation model, and the effects between the variables and the goodness-of-fit values of the model were examined. The analysis findings of the structural equation model are shown in Table 8.

Figure 2:

Table 8:

Model goodness-of-fit values

Variable	χ2	df	χ2/df	GFI	CFI	NFI	SRMR	RMSEA
Criterion			≤ 5	≥ 0.90	≥ 0.90	≥ 0.90	≤ 0.08	≤ 0.08
Modal	103.65	51	2.032	0.927	0.962	0.935	0.0468	0.076

Note: GFI = goodness-of-fit index; CFI = Comparative Fit Index; NFI = normed fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of approximation.

When the fit indexes of the model are examined in Table 8; the goodness-of-fit index (GFI) value is 0.92, the CFI value is 0.96, the normed fit index (NFI) is 0.93, standardised root mean square residual error (SRMR) value of 0.04 and root mean square error of approximation (RMSEA) value was determined as 0.07. For a model to be accepted, its RMSEA value must be below 0.08. (Şimşek, 2007). Within the scope of these values, it can be said that the structural equation model created is acceptable because the data give good values of fit and values close to the standard (Jöreskog & Sörbom, 1993; Kline, 1998).

As a result of the analysis of the structural equation model (Table 9), it was found that attitude and behaviour affect travel intention positively and significantly. As a result of the analysis, H₁, ‘(The attitudes of millennial tourists toward smart products significantly affect their travel intentions), and H₂, (The behaviour of millennials regarding smart products significantly affects their travel intention’), were supported.

Table 9:

Structural model regression weights

Analysis path			B	β	SE	C.R.	P
Travel Intention	<---	Attitude	0.474	0.457	0.091	5.209	0.000
Travel Intention	<---	Behaviour	0.790	0.684	0.127	6.201	0.000

Increasing urban populations with the acceleration of migration from rural to urban areas by globalisation have revealed important problems in resource allocation. In this context, the importance of innovation practices in solving growing management problems has increased (Bedi et al. 2019, p. 491; Khan et al. 2017, p. 3). Especially in the 21st century, where limited resources and investments exist, the use of technology is important in terms of ensuring optimum resource management. It is thought that the quality of visitor experience will change as a result of the reflection of smart tourism features on the field through these models. The improvement in the quality of experience provided by all these smart technologies has drawn visitors to the destination and the quality of experience is measured by integrating it with their routine. To achieve this integration, the routine actions of the person are understood with the information obtained from the person about their daily life process and adapted to the travel process. On the other hand, this change in experience has also occurred in the form of consumers playing an active role in producing their own experiences and technology playing a role in experience production through social media (Özekici, 2020). At this point, the importance of smart tourism is easily understood. In this context, this study focuses on millennials, who use technology the most, and reveals the smart destination selection process of millennial tourists. In other words, the lack of a deep examination of the relationship between tourists and technology in the context of smart tourism has been addressed in the research, responding to the need identified by various researchers. In the research, a three-factor structure emerged using attitude, behaviour, and intention to travel. It was observed that the expression, ‘New facilities like smartphones, websites, etc., have made traveling easier than before’, had the highest average response. Based on this result, smart applications seem to make travelling easier. However, the expression with the second highest average response is, ‘New facilities like smartphones, websites, etc., have enabled me to better plan my trip’. These results show that millennials prefer smart products both in the travel planning process and during their travel. This result is supported the work by Ünal and Bayar (2020) and Ghaderi et al. (2018).

Our results revealed that the attitudes of Generation Y tourists toward smart products significantly affect their travel intentions. Another remarkable result of the research is that the behaviours of millennial tourists regarding smart products significantly affect their travel intentions. Therefore, the conclusion that attitudes and behaviours toward smart products influence the intention to travel has been confirmed in this research.

There is no significant difference between the status of Generation Y tourists regarding smart products and gender variables. This result shows that the gender variable is not effective. At the same time there is a significant difference between the age status of the Generation Y tourists and their travel intentions. This finding means that the intention to travel is statistically significantly higher in the 30 to 35 age group. This result is quite remarkable. In Turkish society, those in the 30 to 35 age group travel more than those in other age groups, so this situation is effective in the results of the research. Similarly, there is a significant difference between the education status of Generation Y tourists and their travel intentions.

Although these results provide important outputs, similar studies should be conducted in different cultures because attitude, behaviour, and intention to travel might differ according to sociocultural variables. In addition, comparisons can be made with studies conducted in different cultures, and the results of the research can be expanded by including different variables. This research is valuable for both academics and practitioners. In the long term, the development of smart tourism destinations will revolutionise the tourism industry and offer a better future for tourism-based economies and sustainable tourism. Due to the growth of new information technologies, smart features are becoming more and more important for tourism destinations. The wider tourism industry can be further developed if the necessary technological, social, and economic infrastructure for smart tourism is created.

The positive effects of smart tourism should be used for regional sustainable development. The participation of local residents as well as tourists is important for the future of these practices.

The purpose of this research was to reveal the smart destination selection process of Generation Y tourists. We determined that the attitudes and behaviours of Generation Y tourists toward smart products significantly affect their travel intentions. Considering that technology is the main feature that keeps Generation Y superior to other generations, this research has proven that technology also has an effect on travel behaviour. Therefore, the use of current marketing approaches such as social media marketing, e-marketing, and mobile marketing will be more effective for this target market. The development of technology at a dizzying pace has provided the opportunity for structural changes in the tourism sector, as it has in all other sectors. It is more important for airline companies, travel agencies, tour operators, and hotels to follow technology closely to survive in an atmosphere of intense competition. At a time when technology has an important effect on travel behaviour, tourism businesses that respond to the needs and wishes of the consumer in a timely manner will gain a competitive advantage.