Visiting Tourism Destination: Is It Influenced by Smart Tourism Technology?

The development of technology has changed various sectors in life to be smart, including tourism. This study aims to analyze the effect of smart tourism technology attributes on visit intention and visiting tourist destinations. This study used a sample of 324 tourists in West Java Province, Indonesia. Partial Least Square is applied to test the relationship between variables. The results of the study revealed that smart tourism technology attributes such as smart information systems, smart tourism management, smart sightseeing, e-commerce systems, smart safety, smart traffic, and virtual tourism objects affect visit intention. The study also revealed the effect of visiting intentions on visits to tourist destinations. The findings of this study provide the basis for formulating strategies for implementing smart tourism technology that is appropriate in attracting tourist visits.


INTRODUCTION *
The term smart tourism has been widely used throughout all stages of tourist travel. Tourists use smartphones to arrange travel plans and interact with other travelers to share in-depth reviews of their visiting experiences [1]. Tourism destinations can be smart because they use technology to gain a deeper tourism experience about the characteristics and meaning of human mobility [2]. The rapid development and adoption of Information Communication Technology (ICT) in the context of travel and tourism has significantly influenced tourism activities, attitudes, and behavior of tourists [3]. However, the existence of tangible technology is not enough to make a smart tourist destination. Thus, further research is needed to highlight the main elements of smart tourism technology. Therefore, this study identifies factors in technological attributes while clarifying the concept of smart tourism.
The increasing use of smart technology in the tourism industry is encouraging a lot of research on smart tourism. Most of the previous studies have discussed smart tourism that focuses on the experience of tourists [1], smart tourism city [4], and human mobility [2]. The discussion also found about tourist destinations in the context of health tourism [3], social media influences [5], and national culture [6]. However, there is still little attention to the importance of smart tourism technology in influencing tourist * Correspondence address: Eko Susanto Email : eko.susanto@polban.ac.id Address : Jl. Gegerkalong Hilir, Bandung Regency,40559 intentions and behavior. In particular, Smart tourism research from the perspective of smart tourism technology needs to be analyzed to understand tourist preferences about smart tourism.
This research focused on tourists in the Greater Bandung tourist destination. It is a tourist destination in Indonesia that is experiencing technological developments in the tourism sector. Based on data from the Indonesian government, the number of foreign tourist arrivals in 2018 was 15.81 million, with an upward trend of 12.58% from the previous year. Nationally, the tourism sector can produce a Gross Domestic Product of USD 19.29 billion, equivalent to 4.8% of Indonesia's total GDP [7]. To increase the quantity and quality of tourist visits in the future, Indonesian tourism stakeholders increasingly pay attention to the development of tourist attractions by adjusting to the concept of smart technology. It implies that the study of smart tourism technology will be useful for understanding the needs of tourists for smart technology. Smart tourism technology is crucial to be analyzed because smart tourism is developing in this country.
This research develops a structured and systematic approach to get a better understanding of smart tourism technology that is needed by tourists. The attributes of intelligent tourism technology are analyzed for their influence on visiting intentions and decisions to visit tourism destinations. This research is essential for the smart tourism industry to identify, set standards, and evaluate smart tourism technology that can improve the quality Smart Tourism Technology and Tourist Behavior (Susanto, et al.) J. Ind. Tour. Dev. Std., Vol.8, No.3, September 2020 and quantity of tourist visits. The study was divided into several sections. The next part is a literature review, including some background information about technology in tourism, followed by a research framework and hypothesis development. The following parts are methods, data analysis, discussion, conclusions, implications, and limitations of research.

Smart Tourism
The era of information technology development has also influenced the development of the tourism industry. There are several studies analyzing information technology factors in the tourism sector, such as the evaluation of website quality [8], hotel ranking [9], smart city, and technology in the context of tourism [2]. Since the advent of the Internet of Things (IoT), travel and tourism have become a sector that is undergoing frequent changes [10]. The impact of technology in tourism has become a concern recently, and tourists are looking for suitable facilities and services to help and enhance the travel experience [3].
Several new trends in tourist behavior are driven by the development of communication and information technology, such as accessing more information via the internet, requesting better services, wanting more specific offers, becoming more knowledgeable, mobile, critical, and price-sensitive [11]. The focus has shifted from electronic tourism centered on the web site to smartphones and sensors that play a role before, during, and after the tour [12]. It shows that the development of technology has made changes in conventional tourism into smart tourism.
The concept of smart tourism has become an important issue in recent years, both theoretically and practically, that has emerged from the development of smart cities [4,12]. The true meaning of smart tourism is to focus on the needs of tourists by combining ICT [13]. Travelers use online information provided by social media, blogs, applications, and so on, rather than using non-online information in receiving and sharing travel information and even making reservations and using payment services [14]. Smart tourism has built a smart tourism ecosystem (STE), which is a system that collects, regulates, and implements tourism-related information and provides maximum service and experience value for tourists through smart technology and shares a large amount of information and values [4]. As technology develops, traditional tourism attractions are challenged to be smarter in responding to requests from new tourists, environmental impacts, and technological development [15]. Tourists are becoming more dependent on information technology, selfservice, and personal reservations when traveling. They like easier access, flexibility, personalization, and more excellent safety of a destination. Changes in tourist needs and behavior also bring challenges for the tourism industry to make tourist attractions and destinations smarter.

Tourism Destination and Visit Intention
The development of smart tourism is more than the application of information and communication technology in tourism destinations. The basis of this initiative is a cocreated value transformation, a change in destination-marketing strategy (destination relationship management), and different views on destination competitiveness (resources, big data) [16]. Smartness helps policymakers and managers make better decisions and organizations to function more efficiently; this is partly due to the large volume of data (known as big data) that can be collected and analyzed to improve planning and policy [2]. Efforts to optimize destination values through ICT systems that connect tourists with information sources, enable access to information, provide a face to face communication, and adjust information needed in a smart way [17]. Besides, ICTs and the environment with the availability of the internet at the destination are very important to maximize the tourism experience [16]. Smart destinations must-have technological resources deemed necessary to increase visit intentions.
Some elements of tourism destinations require specific services, such as attractions, facilities, accessibility, and human resources [4]. Accommodation, convenience, attractiveness, access, and additional services are components of smart tourism destination cities [4]. Buhalis and Amaranggana [12] describe four core dimensions of smart technology that can be embedded in a purpose: information, access, interaction, and personalization. For tourism destinations, it is essential to provide significant opportunities to use open data to develop cultural, transportation, marketing, and environmental scenes [18].
The influence of the internet has significantly changed the tourism industry in various ways. This phenomenon has become one of the most

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Smart Tourism Technology and Tourist Behavior (Susanto, et al.) J. Ind. Tour. Dev. Std., Vol.8, No.3, September 2020 efficient ways to reach new tourism markets and encourage repeat visits to tourism destinations [19]. Various important factors that can influence selecting tourism destinations are accommodation services, food, traffic attractions, activities, and special events or festivals [4]. In the context of smart tourism, previous research showed the influence of travel intentions on the selection of smart destinations [3]. If prospective travelers have a high desire to visit a destination, they will try to visit it [20].

Smart Tourism Technology
The reason for tourism service providers to develop smart tourism is to enhance the tourist experience. Therefore, it is imperative to understand new tourists and their needs in the smart era to get a better understanding of tourist preferences on smart tourism attractions [15]. Smart tourism can maximize the use of tourism resources and also manage tourism cities, maintain tourism attractions, and improve the quality of life and communication between tourists and host residents [17]. Then, technologies such as smart devices, tourismrelated platforms, and ICTs can influence tourism experiences from the planning stage to after the tour [12]. Therefore, an intelligent system encourages visitors to explore the city and improve their travel service experience through direct feedback from the smart tourism technology system [20]. Based on this information, tourists can find exotic locations near where they are and can get information about monuments and roads in the areas they visit [21]. Specifically, tourism sites with a welldeveloped smart environment directly affect travel experiences using smart technology compared to those who do not [22].
Developments in the tourism industry require the originality, uniqueness, and intact resources of tourist attractions [23]. In tourist attraction recommendations, three important aspects must be considered: tourist preferences, tourist attraction themes, and sentiments on tourist attraction themes [24]. There are three components to tourist attractions, such as tourists, sites to see, and an image that makes an attraction different [25]. A smart tourism attraction must have a smart internet of things, data warehouses, and cloud computing. This technology allows tourism attractions to be smart in terms of generating real-time intelligence about the needs and desires of tourists while having the ability to respond.
Another opinion mentioned four forms of information and communication technology that are very important for setting up intelligent tourism systems: cloud computing, the internet of things, cellular communication, and artificial intelligence technology [26]. Tourism service providers maximize the experience of tourists by using virtual reality (VR), augmented reality (AR), beacons, or near-field communication (NFC), combined with tourist smartphones or other devices [27]. Tourist preferences of smart tourism attractions were identifed by smart information systems, intelligent tourism management, smart sightseeing, e-commerce systems, smart safety, intelligent traffic, smart forecasts, and virtual tourist attractions that should exist in a smart tourism attraction (STA) [15]. In this study, we identified the factors in the STA to analyze their effects on visit intentions. Tourism technology can influence future behavioral intentions [28].

MATERIAL AND METHOD
In this study, we try to understand how smart tourism technology influences travel intention, which then affects visiting tourism destinations. The measurement items were adopted from previous literature and modified for this study. Based on the consideration, the research hypothesis has been determined as follows.
The research framework is presented in Figure 1. Twenty-seven statement items of eight variables in smart tourism technology, three items measure travel intention, and three items measure visiting tourism destinations. The smart tourism technology attribute was adapted from Wang et al. [15] in measuring the tourist preferences of the smart tourism technology. Then the attributes of visit intention and visiting tourism destinations are adapted from Ghaderi et al. [3]. We use multi-measurement items to prevent measurement errors. All items of this study were measured on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The survey was written in English and then translated into the Indonesia language by researchers who are fluent in both languages. The questionnaire is shown in Table 2.
After completing the questionnaire, we surveyed Indonesians who had traveled to a tourist destination that uses smart tourism attributes before to get survey responses based on their experiences of smart tourism. We ask people who visit two or more tourism destinations to respond to a questionnaire about Smart Tourism Technology and Tourist Behavior (Susanto, et al Descriptive statistical analysis was used to analyze the demographic characteristics of the respondents. Based on the results of the distribution of questionnaires to respondents, 324 questionnaires can be used for data analysis. Forty percent of respondents were men, and 60% of respondents were women. Then, based on the level of education, it is known: 28% are high school graduates, 37% have a bachelor's degree, and 35% have a master/doctoral degree. Based on income per month known: 37% respondents have income < IDR 2,000,000, 32% respondents have an income > IDR 2,000,000.-5,000,000.-then, 31% income > IDR 5,000 .000, -. Based on the type of work, 31% of respondents are students, 44% are civil servants, 10% are private company employees, and only 5% are entrepreneurs. Then, as much as 1% are state company employees, and 9% rest is outside the specified group. These results illustrate that the respondents in this study had a distribution of the amount spread across each group of characteristics, and none were dominant in certain groups.  Table 2 illustrates 27 items of smart tourism attraction produced from eight focus groups. The dimensions of smart tourism attraction were assessed using exploratory factor analysis with the extraction of the main components. A varimax rotation with Kaiser normalization was applied, revealing eight factors that accounted for 73.57% of the variance. The first factor, smart information system, had six items representing the tourism attraction website, free wifi, online information access, dan mobile application. The next factor, intelligent tourism management, consisted of five items reflecting smart card (band), electronic-entrance guard system, tourist-flow monitoring, crowd handling, and smart education. Smart sightseeing was the third factor consisted of three items reflecting personal-itinerary design, e-tourism recommendation system, and e-tour map.

Smart Tourism Technology Dimensions
The fourth factor, e-commerce system, included three items related to mobile payment, online coupons, and online booking. The fifth factor, smart safety, had three items representing intelligent-environment monitoring, travel-safety protection, and smart emergencyresponse system. The next factor, intelligent traffic, consisted of two items reflecting smart vehicle-scheduling and real-time traffic broadcast. The seventh factor, smart forecast, had three items representing tourist-flow forecast, queuing-time forecast, and weather

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Smart Tourism Technology and Tourist Behavior (Susanto, et al.) J. Ind. Tour. Dev. Std., Vol.8, No.3, September 2020 forecast. The last factor, virtual tourist attraction, had two items representing virtual tourism experience and virtual travel community.
All measurement items had loading values of more than 0.4. Thus, these items can be used for further analysis. Five dimensions extracted from the exploratory analysis have Cronbach's Alphas above 0.80, indicating its reliability [29]. The Bartlett's test of sphericity (p <0.01) and the Kaiser-Meyer-Olkin test (0.928) confirm the adequacy and reliability of the sampling used.

Measurement Model
Partial least square analysis (PLS) was chosen in this study, along with the bootstrap technique. The Bootstrap technique creates a large number of samples by randomly pulling cases from original sample research [30]. Compared to the structural equation modeling (SEM) method, PLS analysis is suitable for small sample sizes and can handle highly predictive models. Widely known, PLS is the right tool for analyzing existing theories and constructing formative constructs of structural models. Besides, a Confirmatory Factor Analysis was carried out to check the suitability of the model, the reliability and validity of the model, and the general method bias. The research hypothesis was tested with the structural equation model-partial least square (SEM-PLS) [29].
Confirmatory factor analysis was first carried out to assess the measurement model. In evaluating the measurement model, convergent and discriminant validity tests were performed. According to Chin [31], convergent validity is examined by using loading factors, composite reliability, and average variance extracted (AVE).
As shown in Table 3, the loading factor of all items is above 0.7, so it is accepted [31].
Composite reliability values for all constructs range from 0.898 to 0.965, which meet the requirements [32]. Then, to ensure reliability, Alpha Cronbach's score is higher than 0.7, ranging from 0.829 to 0.946, which meets the standard criteria of 0.7 [32]. Therefore, the convergent validity and reliability of each construct and item can be accepted. To demonstrate discriminant validity, the square root of each AVE construct must be higher than the construct correlation with other latent variables [33]. Besides, cross-loading of all items was tested, and the results showed that each loading of items in construction was higher in the construction measured than in cross loading in other items. It shows the discriminant validity of the measurement model is acceptable [31]. Table 4 shows the results of the discriminant validity analysis. It is recommended that the square root scores of all AVE constructs are the highest among correlated constructs [33]. In this study, the square root score has a greater correlation between each construct, which shows the accepted discriminant validity.

Structure Model
In analyzing the structural model (inner model), there are two criteria recommended for the significance of the path coefficient and the value of R 2 that was applied [29]. R 2 sizes are 0.75, 0.50, and 0.25 for all endogenous structures, which are substantial, moderate, and weak. The results of the study of R 2 in the intention to visit variable is 0.849, which is in the substantial criteria. The next step is to test the direct effect between the variables in table 5. The proposed hypothesis is analyzed with SEM, adapting the bootstrap technique.
However, the results of hypothesis 7 testing show that smart forecast does not significantly influence visit intention (H7; β = 0,481, t = 6,875). The results also indicate that visit intention significantly influences visiting tourism destinations, supporting the H9 hypothesis (H9: β = 0,671, = 25,290). Figure 2 summarizes  [152] Smart Tourism Technology and Tourist Behavior (Susanto, et al.)   We examine the attributes of the smart tourism technology that affect visit intention and then the effect of visit intention on visiting smart destinations. This study shows important attributes of smart tourism technology that can affect travel intention, such as smart information systems, intelligent tourism management, smart sightseeing, e-commerce systems, smart safety, intelligent traffic, and virtual tourist attractions. This study complements some previous research on technological factors that influence visit intention, such as mobile tour information systems [34] and smart tourism technology [28]. Smart Tourism Technology and Tourist Behavior (Susanto, et al.) J. Ind. Tour. Dev. Std., Vol.8, No.3, September 2020 More specifically, we have identified the attributes of smart tourism technology that have the most significant influence on travel intention, namely the smart information system. The availability of facilities such as tourist attraction home page, free Wi-Fi, online information access, mobile applications, quick-response codes, and electronic touch screens can provide the most significant contribution to visit intention.
The results also prove that a significant influence on visit intention of visiting tourist destinations. These results validate previous studies on the effect of visit intention on visiting smart destinations [3]. Visit intention refers to the willingness of potential tourists to visit destinations. Thus, this study reveals the importance of emotional assessment that is the desire of tourists in visiting tourist destinations. This study also reports that in the desire to travel, tourists choose and visit tourist destinations because of the availability of smart facilities, safety and security, and more experience and pleasure gained at smart destinations.

CONCLUSION
Based on the explanation of the results of the study, smart tourism technology attributes were found to have a significant direct effect on travel intention. The model construction has successfully confirmed the direct influence of exogenous variables in this study. Moreover, the constructed model in this study is a model that is suitable for explaining the smart tourism technology attributes that underlie the behavior of tourists to travel to tourism destinations.
The model in this study proves that there are attributes in smart tourism technology that do not significantly affect smart tourism technology, namely smart forecast. We assume that tourists intend to travel more because of the facilities available at these tourist destinations, not because of tourist forecast, queuing-time forecasts, and weather forecasts that are not essential factors in traveling. Apart from that, this research has successfully proven the important attributes of smart tourism technology that can be a reference for stakeholders in increasing tourist visit intention that affects visiting tourism destinations.