1. Introduction
For the past three decades, research on tourism has endeavored to describe sustainability in the field. Through this effort, researchers were able to formulate the concept of sustainable tourism and have it show positive results by establishing a theoretical foundation and expanding the base of quantitative studies [
1,
2,
3,
4,
5]. Furthermore, because mobile technology has become an indispensable commodity in daily life, its relationships with tourism, along its role, are key topics to be addressed in this field. This naturally leads to how to conceptualize mobile technology as a driver of change within a sustainable tourism framework and to establish a related theoretical foundation. In 2015, Gretzel et al. and Li et al. emphasized the role of mobile technology in tourism and attempted to conceptualize smart tourism, which further highlights the importance of the topic [
6,
7,
8,
9].
To formulate theoretical foundation and directions for empirical research on smart tourism based on mobile technology, it is essential to understand in advance the goals of tourism and how sustainability in tourism led to these goals. The theme of sustainability in tourism emerged as a regulatory interest to resolve environmental concerns and global inequality in the 1960s and 1970s, when global tourism exhibited growth [
10], and this theme was primarily based on the observation of the negative impacts of tourism that exceed its the benefits. These negative impacts included inflation, housing price increase, temporary influx of strangers and the consequent increases in the crime rate, and undesirable influences on local children [
1]. Sustainable development focused on the goal of minimizing negative impacts and maximizing social, environmental, and economic impacts by proposing moral and ethical goals to stakeholders, such as tourists, residents, and firms [
10,
11,
12].
During this period, researchers explored the relationship between the competitiveness and sustainability of tourism desetinations [
13], government interventions, regulations, and partnerships in sustainable tourism development [
3,
4], the creation of indicators to evaluate tourism sustainability [
14], measurements of sustainability in tourism [
15,
16,
17,
18], and sustainable strategies in tourism [
2,
19,
20,
21]. However, the discussion on the role of technology in sustainable tourism was limited and was not a key aspect of the sustainable tourism conversation.
From 2007, with release of iPhone driving the adoption of the smartphone and the deployment of LTE and Wi-Fi mobile networks [
22,
23], mobile technology became an essential determinant of quality of life and the direction of an industry [
24]. The diffusion of mobile technology in daily life and industries has surpassed expectations, and tourism is no exception [
6,
25]. The importance of ICT in tourism was acknowledged [
26,
27], and smart city was explored as the most commonly used label to represent sustainable city [
28].
New technology changes the structure and process of an industry and brings opportunities and threats to stakeholders [
29]. Through mobile technology, we can expect that tourism acquires sustainable competencies and contributes to economic development [
6,
9]. Researchers in the field of tourism should formulate the direction of empirical research and establish a theoretical foundation and conceptualization using the basic components of tourism, sustainability, and technology. By consideration, the technology axis should include mobile technology as an independent axis.
Gretzel et al. (2015) stated that smart tourism is tourism that collects and consolidates data on destinations, provides rich onsite experiences to users with the support of mobile technology, and creates value from a business perspective. Sources of data include the physical infrastructure, social connections, government/organizations, and human bodies/minds, and these integrated efforts are assumed to focus on efficiency and sustainability [
6,
8,
30,
31].
Mobile technology is expected to resolve challenges that have persisted. These challenges include precise and continuous assessment of the impact of tourism, analyzing mutual interactions between tourists and environments, monitoring and analyzing mutual interactions among other activities at the destination, and supporting tourism planning and tourism development by identifying a tourist’s needs and expectations [
1,
32].
Researchers are endeavoring to formulate the conceptual framework of smart tourism and expand the base of research by setting predictable relationships and responses among technology, tourists, the industry, residents, and the community [
6,
7,
9,
31,
33]. The outputs of the effort include theoretical and conceptual systematization, a general description of technology, and consumers’ adoption of mobile technology in tourism [
34,
35,
36]. All of these studies are essential to determining relationships among new technology, consumers, related players, and industries. However, at this point, we also need to adopt a comprehensive view of the current research landscape regarding the theme of mobile technology and tourism in both practice and academia.
The purpose of this research is to identify the role of mobile technology in achieving sustainable and smart tourism from the technology and customer perspectives, and to suggest future research and technology directions for academia and managers in practice. In the past, discussions on relationships between information technology and tourism occurred in both academia and practice under the theme of e-tourism [
29,
37]. We intend to formulate relationships between mobile technology and tourism and to clarify concepts using real field data by considering the findings and discussions on e-tourism with a particular focus on the uniqueness of mobile environments. Three research questions naturally arise from this purpose.
RQ1. What are the mobile technologies that is at its core in tourism and the relationship between technologies?
The first question also covers the similarities and differences between technologies in PC-based e-tourism and mobile tourism.
RQ2. What are the issues and variables related to mobile technology in tourism?
The second research questions covers the context and circumstance related to the stakeholders of a tour (e.g., tourists) under which mobile technology is utilized.
RQ3. What are the differences between the current and the past in consumer attitudes and satisfaction with mobile technology in tourism?
Answers to this question will provide implications for future directions of mobile technology.
This research is divided into two parts. The first part is a literature review that provides definitions of mobile technology in tourism from the existing literature, the role of mobile technology from the sustainability perspective, and the present role of mobile technology in tourism. Furthermore, the literature review identifies how existing studies were expanded from the perspectives of technology, customer and sustainability. The second part collects and analyses the data generated in the practical field to compare with the topics and perspectives identified in the literature review ultimately to deduce implications. This part uses real data to formulate the relationship between mobile technology and smart tourism while exploring future directions.
The second part consists of three studies. The first study identifies past and present mobile technology by analyzing the relationships between patent’s IPC codes. The mobile technology in tourism discussed in the literature review is a topic raised by researchers, and the practical patent data sets complement the studies that are required to have a holistic overview. Patents are viewed as implementations of technical ideology that can be consolidated in a technology map for a holistic overview of a field [
38,
39].
The second study analyzes recent trends in mobile technology and related issues by analyzing patent data, research papers, and news data related to mobile technology and tourism, using text mining. After a delay of 1 to 1.5 years, patents are publicized after submission, which means that a limitation exists in deducing the present state of the technology. Furthermore, patents emphasize the technological aspect, and the rights and novelty of a technology. Therefore, they are not appropriate for use in deducing how a technology is used by a user. Patent and research paper complement each other and allow for an analysis to find unexpected perspectives. News data is good material for identifying consumers’ interests and enterprises’ foci regarding the theme of mobile technology and tourism [
40,
41,
42].
The third study considers the attitude of the actual consumer to cover the consumer aspect, which is one of the most important variables affecting mobile technology. The study selects mobile app services in tourism to explore consumer attitudes, preferences, and satisfaction.
This research is expected to contribute to the existing literature in three ways. First, this research provides the present status of mobile technology using field data from multiple sources to complement the conceptualization of smart tourism in the existing literature and to provide guidance on future research directions. The comprehensive analysis on patents, the academic literature, and news data identifies gaps in the research.
Second, whereas the existing literature focused on one or two mobile technologies in tourism as along with consumer attitudes, adoption [
34], and satisfaction [
43], this research explores technologies that should be addressed in the academia and that are attracting the interest of the practical field and consumers.
Lastly, this research traces the dynamic changes in consumer attitudes and satisfaction regarding mobile technology by reviewing mobile applications from different points in time. Consumer response can change because of new technology, and tracing such changes over time is meaningful. However, tracing the changes through surveys and observation requires significant resources. The alternative method employed by this study is to select two mobile apps that are different in both chronology and initial startup, and to identify differences in consumer attitudes (For example, TripAdvisor is a review-based service that started in the PC-based social networking and virtual communities, whereas Google Trips is a service launched relatively recently and started as a mobile-based service.).
5. Study 3
5.1. Data and Background
In study 3, we explore the relationship between mobile technology and customers by analyzing user reviews from the TripAdvisor mobile apps and the Google Trips mobile app. From TripAdvisor, 3464 reviews from September 2015 to April 2017 were analyzed, as were 834 Google Trips reviews from September 2016 (launch of the service) to April 2017. The reviews were crawled on the basis of Google Play.
TripAdvisor is a forerunner of recommendation services based on user review data [
7]. TripAdvisor started out as a web service and expanded to a mobile service (recently provided in the form of a mobile app). It is also a mobile app that started during the initial stages of e-tourism and has the highest number of queries in Google Trend among all tour apps. The core functionality of TripAdvisor is enabling tourists to share reviews of hotels and restaurants and connecting the users to sites that can make reservations for hotels and restaurants. TripAdvisor also provides information on tourist attractions and historic places, along with maps and routes. Because TripAdvisor started out as review site, it is the web and mobile service with highest number of reviews. However, the mobile app is considered less convenient than the web site in searching and accessing information.
On the other hand, Google Trips is a pure mobile tourism app service that has attracted the media’s attention. Google Trips is a travel app linked to Google Maps and Gmail, and provides route functionality. It recommends tourist attractions with “things to do” and enables users to share reviews. The “day plans” functionality plans the trip for the user using a destination set on the basis of the user’s past search history from the Google search engine. Google Trips exploits all of Google’s web and mobile resources, including recommendations for food, drinks, restaurants, phone numbers, ratings, general information, routes, and need to know tips. However, Google Trips does not enable making direct reservations.
The most significant difference between TripAdvisor and Google Trips is that TripAdvisor started out as a web service and expanded to the mobile platform, whereas Google Trips was launched as a mobile service from the start. In terms of the chronology, TripAdvisor is a service launched in the era of e-tourism, whereas Google Trips was launched very recently (2016). Third, Google Trips is strong in scheduling, and providing links to Gmail and the convenience of using Google Maps, whereas TripAdvisor provides highest number of reviews and researvation functionality.
TripAdvisor was selected because it is the top tour app in terms of queries, as found through Google Trends. Google Trips was selected because it is the most recently launched global travel app and it provides comprehensive functionalities through its linkage with Google and Google Maps. In addition, Google Trips includes most of the functionality of mobile technology in tourism, as deduced from the patents, academic papers, and news data in study 1 and study 2.
5.2. Method
We analyze the review text using content analysis based on text mining. The first utilizes computer-based content analysis using Leximancer, which overcomes the potential bias and error of researchers of qualitative analysis while ensuring reliability and reproducibility of the results. Leximancer is based on corpus linguistics observations and uses a statistical algorithm. It generates information such as concept and theme using unsupervised learning on the basis of inputs, including frequency and pattern of co-occurrence. The concepts deduced by Leximancer indicate keyword groups that co-occur most frequently within the text, and the themes indicate the group of concepts that are clustered on the concept map. The name of the theme takes after the name of the most prominent concept [
40,
172]. Connectivity indicates the relative importance of a theme and hits indicates the number of text excerpts that matches each concept [
123,
135].
Leximancer uses co-occurrence information as T-LAB, but uses a different algorithm when extracting. Leximancer uses two different algorithms serially (semantic extraction and relational extraction). In the first stage, a naive Bayesian co-occurrence metric (not only how frequently two words co-occur but also how often they occur apart) is used. In the second stage, text segments are classified using learned semantic classifiers. Leximancer deduces the relative co-occurrence frequency, because general co-occurrence information generates asymmetric information if the frequency of concepts is considered. Taking into account that consumer reviews are in the form of free talking by users, it is necessary to control the counts of frequently occurring words and to avoid interpretation of incidental interactions between concepts as real interactions. Minimizing errors, therefore, requires using relative co-occurrence that considers the frequency of concepts, which is the reason that study 3 uses Leximancer instead of T-LAB.
First, deduced concepts and themes are mapped to the timeframe of the review through the correspondence analysis provided by Leximancer. Because three years of TripAdvisor data and one year of Google Trips data are considered, the relative trends were analyzed by dividing both into seven time sub-frames (TripAdvisor in quarters and Google Trips in months).
Second, we reviewed the expressions related to evaluation and the keywords related to mobile app service functions. By identifying the keywords related to functions, we referred to the existing literature and the role of mobile technology as deduced in study 1 and study 2 [
173]. We deduced attributes of functions related to the information sought by the user, functionalities that the user wants when using the service with his or her device, and the legacy systems that need to be interconnected with the new mobile technology. Words that express evaluation were selected from the results of Kang et al. (2014) [
174], and we identified keywords that express a positive review and those that express a negative review. To ensure reliability, we compared words selected by both authors. Keywords that were not agreed on were included in the list only if confirmed in the source text and both authors agreed on including them [
129,
175,
176]. Finally, a third-party expert who plans and develops mobile apps confirmed the list of words.
Third, we used DICTION, a dictionary-based computerized content analysis program, and SPSS to statistically evaluate whether the evaluation of the service provided is different for the two services. DICTION is based on linguistic theory and uses 33 different diction libraries to text mining. DICTION calculates scores of preset variables using statical weighting procedures that relate the same words and words that have related meanings but that are different in form.
DICTION is a useful tool comparing the characteristics of two or more groups when expressed in texts. Among the 40 variables calculated by DICTION, we used scores of praise, blame, and satisfaction variables as they were considered to be related to evaluation. The scores of the praise, blame and satisfaction variables were compared using ANCOVA. ANCOVA was used because the length of each review was different. The total words analyzed, which indicates the length of the review, was set as a covariate.
5.3. Result
5.3.1. Theme and Concept in TripAdvisor and Google Trips User Reviews
In the TripAdvisor user reviews, concepts related to the theme of hotels have high hits and connectivity with other words as keywords. This is followed by the theme of need, which contains search, city, and information as related concepts. The third theme describes food, and the fourth theme indicates evaluation of the app with its name as helpful. Overall, information (e.g., hotel, city and food) and evaluation (helpful) have high connectivity and frequency for TripAdvisor (
Table 11).
In contrast, the case of Google Trips indicates that reservations (representing app functionality) and useful (representing evaluation) have high hits and connectivity (
Table 12). This result is distinct from the TripAdvisor app in that, in the reviews, users of TripAdvisor focus on food, hotel, and evaluating reviews on the basis of being helpful.
For Google Trips, the themes of reservation and useful require further investigation. This is because Google Trips does not directly offer reservation functionality to its users; therefore, further investigation is needed on how the theme was deduced. The representative quotes that determine reservation as a theme are noted as follows and consist of users’ desire for the functionality fully provided in the future and complaints on linkages with Gmail.
“Option to add reservations manually will make it awesome.”
“Would love to use but cannot App looks like it would be very useful; unfortunately, it isn’t pulling any of my reservations from my Gmail and there is no way to manually enter them.”
“ok but cant edit the reservations??? good thing is that it will consolidate all bookings from emails however, I cannot edit the reservations?”
The texts that determined useful as a theme expressed either the usefulness of linkage with Google Calendar and Google map or the complaints on errors in the linkage with Gmail. Whether the reviews are positive or negative, these are in the context of the experience provided by Google services.
“Thank God everything was also scheduled in Google Calendar, so I use that a lot with cab drivers.”
“Very useful, tailored on my Google experience It’s cool, I just would add the map of the city in order to quickly find streets or shops, without downloading Maps offline. Thx Google;).”
Figure 6 is derived from matching the concepts deduced from review texts with reviews periods using correspondence analysis. Dividing the reviews of each app into seven time points results in seven quarters that have elapsed since the launch of TripAdvisor in July 2015 and seven months that have elapsed since the launch of Google Trips in September 2016.
Figure 6 shows how themes and concepts have evolved over time. TripAdvisor was linked to the themes of photo and update until the first quarter after the launch, and was more strongly linked with themes related to evaluation, such as love and easy, in the second and third quarter. In the fourth quarter, the trend continued and TripAdvisor was more strongly linked with themes related to evaluation, such as helpful and nice. This was followed by themes of food and photo in the fifth quarter, and the theme of food in the sixth quarter. Finally, TripAdvisor was more linked to themes related to offline and corresponding concepts, such as phone, map, and download, in the seventh quarter. Given that Google Trips was recently launched, its time points were in the interval of months rather than quarters. In the first month, Google Trips was more strongly linked with the concept of useful and the theme of nice, followed by the theme of better in the second month and the themes of nice and basic in the third month. In the fourth month, Google Trips was more linked with keywords related to evaluation, such as helpful, wrong, and limited. In the fifth month, Google Trips was more linked to the theme of reservation. In the sixth month, Google Trips was more strongly linked to keywords related to a negative review, such as useless. Finally, the seventh month is more strongly linked to Google. The overall results indicate that Google Trips reviews exhibited more frequent occurrences of words related to evaluation, such as useful, helpful, nice, better, useless, word, and limited.
Because the keywords that describe the functionalities and the evaluation of the two apps differ, we must further consider the differences as shown in the following
Section 5.3.2.
5.3.2. Keywords for Function and Evaluation Appraisal
Keywords for function express the functionalities that the app services need to support, those that devices need to support. A common tendency is to want information on hotels, flights, restaurants, cities and attractions, with emphasis on transportation (e.g., airline, car, bus, and train) for Google Trips. Mandatory main functionalities include reservation, recommend, map, and share. Functionalities for manipulation of services and devices include download, display, compare, detect, and option. Specifically for Google Trips, keywords that stand out are combine, integrate, and merge. There are many indications to link the service with an e-mail service and both the usefulness and the complaints related to linking to Gmail are highlighted for Google Trips (
Table 13).
A tendency exists for TripAdvisor to have greater variety of keywords because it was launched before Google Trips. Keywords targeted to devices such as desktop, phone, button, and battery are unique to TripAdvisor.
Keywords related to an evaluation range from positive to negative, with positive comprising the relative majority. It is interesting to observe that a variety of words expressing positive (e.g., perfect, useful, brilliant, essential, user-friendly, smart, top) and negative (e.g., annoy, frustrate, clunky, fabulous, glitch, hard, hate, rubbish, slow, stupid, suck) occur. In addition, evaluation keywords indicating failure of a specific functionality (e.g., bug, fail, lack and problem) are observed.
It should be noted that consideration is required to interpret words such as good and comfortable, because they can be listed serially with negative words (e.g., not good). The data processing process should ensure that such errors do not arise in sentiment analysis.
5.3.3. Comparative DICTION Analysis of Reviews of the Two Apps
TripAdvisor and Google Trips were not launched on the same date. TripAdvisor’s mobile app was released in July 2015 and Google Trips was released in September 2016. In the previous chapters, we qualitatively compared the reviews of the two apps using content analysis, which means that the findings cannot be evaluated in terms of statistical significance. In this chapter, we use the dictionary-based DICTION software tool to attempt to quantify evaluation indices of each app and to attempt to compare the two apps to identify statistically significant differences. A comparison of the two apps needs to include how these apps with different timeframes can be compared. We may compare the reviews of the apps in a same timeframe for both, however, doing so indicates a difference in maturity for the apps and users’ familiarity with the app, which need to be considered in the analysis. Alternatively, we may set the same time interval after the launch. However, doing so means that consumers from 2015 will be compared with consumers from 2016, and these consumers will have different familiarity and experiences in IT. This research resolves these issues by conducting the analysis in both ways and comparing the results.
First, we compare the reviews within the timeframe from September 2016 to April 2017. In this case, one must consider that the ages of the apps are differ. Second, we compare the reviews from the apps’ launch to seven months after the launch. For Google Trips, this timeframe is from September 2016 to seven months after, and for TripAdvisor it is from July 2015 to seven months after. Although it is possible to qualitatively analyze the reviews, we use scores deduced from DICTION’s analysis of the texts. We selected praise, blame, and satisfaction variables among the 40 variables deduced by DICTION because they are related to an evaluation of the apps’ reviews [
177]. The first analysis that compared the same timeframe of September 2016 to April 2017 indicated that we may not support the hypothesis that the evaluation of the two apps are different. This is because the blame and satisfaction scores did not meet the assumption of homogeneity of variance, whereas the praise score met the assumption, but its
p-value was 0.333, indicating that there is no statistically significant difference in the reviews of the two apps (
Table 14).
The second analysis that compared the seven months after the launch included praise and blame because they met the assumption of homogeneity of variance. For blame, the
p-value was 0.436, which did not reject the null hypothesis. However, for praise, the
p-value was 0.032, and we concluded that statistically significant difference exists between the praise score means of TripAdvisor and Google Trips (
Table 15). Taking into account that consumers’ mobile experience was in the initial stage for TripAdvisor and consumers had accumulated a more enriched mobile experience for Google Trips in the timeframe concerned, it is natural to see that TripAdvisor has a higher praise score.
Because the praise scores of the two apps show a statistically significant difference, we conducted a correlation analysis between the praise score and the frequency of the keywords related to evaluation as deduced from the previous parts of study 3. We also included satisfaction scores that were excluded because they did not meet the assumption of homogeneity of variance. The keywords that are positively correlated with a
p-value of less than 0.05 are listed in
Table 16. TripAdvisor had greater variety of keywords with a positive correlation. It is also worthwhile to note that there is no keyword that is positively correlated with both praise and satisfaction, despite the fact that the two signify similar meaning. It should be highlighted that easy is positively correlated with praise, and amaze, awesome and fun is positively correlated with satisfaction.
Mixing the scores of the variables deduced by DICTION and other quantitative indices (e.g., evaluation keyword) to conduct statistical analysis has already been attempted in other studies, and this research ensured that the analysis is in line with that of these studies [
178,
179,
180].
6. Discussion and Conclusions
6.1. Theoretical Implications for Sustainability and Smart Tourism
One of the purposes of our research is to understand smart tourism from the perspective of sustainability. As identified in study 2, the keywords environment and sustainability indicate that scholars are considering smart tourism in relation to sustainable tourism. Although the tendency is to research mobile technology in tourism from the perspective of user (i.e., tourist) and his or her satisfaction and intention to use, it cannot be denied that mobile technology in tourism is basically related to sustainable tourism. The development of destinations considering environmental effects and cultural heritage [
181,
182,
183], and the role of ICT in connecting services to smart city communities [
28] are also discussed in the realm of sustainable tourism.
It is difficult to acknowledge the contribution of mobile technology (as identified in study 1) to sustainability in tourism without precise measurements or assessments. However, this contribution can be deduced qualitatively from various frameworks in the existing literature. First, we can use the concept of QoL to determine whether mobile technology in tourism enhances QoL or brings increases in the various types of capitals that contribute to QoL. Second, we review the relationship between mobile technology and indicators and the concepts of sustainability introduced in the literature review. Third, we apply a new mobility paradigm as proposed by Creswell (2010) and Moscardo (2013). Based on studies 1, 2 and 3, we subsequently describe the role of mobile technology in a hypothetical situation, which allows for an evaluation of the impacts on sustainability.
“Mobile technology provides various information to the user (i.e., tourist) and assists with real-time decision making. Consumers use mobile devices and map related systems to acquire sufficient information, to set routes to destinations, and to modify routes if necessary. By using mobile technology, consumers can capitalize on opportunities and create value. Destinations can use data gathering technology and big data processing technology based on sensors to measure and respond beforehand to potential negative effects on the environment. Firms, as suppliers, can create economic value by providing information optimized for individuals that is processed from context aware data gathered on the basis of users’ lifestyle and location.”
First, from the perspective of QoL and capital, frequent and long tourist visits to a destination can increase GDP growth (economic development) and residents’ financial capital. Sensor, big data analysis, and data processing technology qualitatively enhance both environmental systems (natural capital) and transport systems (built capital). Use of mobile Internet and travel mobile apps, given the nature of social media, enhances the tourist-tourist relationship and, in turn, increases social capital through reciprocity and cooperation. Real-time communication enables transactors to share value and increase cultural capital. Sharing knowledge reduces asymmetric information and increases human capital. The evolution of mobile technology further reduces asymmetric information in other fields and develops participatory democracy that increases political capital. Increases in these various types of capitals leads to growth in QoL and brings positive impacts on sustainability.
Second, the responses of the destination and the supplier reinforce responses and measurements of state, as described in the DPSIR model. This results in improvements in real-time measurements of the environmental impacts of system quality, enhances management, and fosters growth in the competency of policy formulation. Furthermore, energy efficiency, early warnings, and environmental management improve as the sustainability indicators and concepts.
This discussion assumes that tourists have homogeneous characteristics and objectives. The previous discussion can be altered depending on the length of stay at a destination, the number of visits to a destination, the objectives for visiting a destination, and interactions with residents. In the event that many tourists are attracted to a destination because of promotions and/or advertisements through mobile technology, both positive and negative impacts coexist in economic, environmental, and social dimensions. If tourists that aim for employment at the destination (indicating that they may stay for a long period) are included, employment relationship at the destination can become uncertain. Therefore, the contribution of mobile technology depends on the nature of the tourist, the forms of engagement with residents, and the industry/employment structure of the destination.
Third, we can infer the relationship between mobile technology and mobility by utilizing Moscardo’s framework that considers a tourist’s duration, frequency, and engagement in the context of six dimensions of mobilities. Whereas the first part of the discussion focused on the direct effect of QoL and capital on mobile technology, this third part focuses on the sequence of effects through which mobile technology affects mobility and mobility affects capital. The combination of mobility variables can create a diversity of phenomenon for mobility in tourism.
- (i)
In terms of motivation, why do tourists move? Is it enforced or voluntary? Although many factors affect motivation, the classification by Moscardo et al. (2013) of archetypal tourists—whose objective is leisure and joy—is voluntary in motivation, whereas movement caused by economic necessity is enforced movement. If the role of mobile technology is described in the narrow scope of the archetypal tourist, data gathering and process technologies generate information relevant to the personal context and provide information to users. In turn, this motivates users to tour and encourages overall financial and social benefits.
- (ii)
In terms of speed, it is difficult to identify mobile technology factors that affect the velocity of movements. The existing literature indicates that slower speeds lead to positive impacts. Therefore, if mobile technology causes tourists to move slowly, a positive impact would exist in terms of sustainability. However, it is difficult to conclude with certainty whether or not mobile technology allows tourists to move slowly.
- (iii)
In terms of rhythm, it is important to determine whether or not tourists moves in predictable patterns. Although it may differ for types of tourists, systematic accumulation of data enables making predictions and/or forecasts for these tourists.
- (iv)
It is difficult to determine the effects of mobile technology on routes (even when types of tourist are considered). If tourists have adequate access to the required information to make decision, they make appropriate decisions that suit their objectives and can move within or out of the region when new information is obtained [
47]. If integrated information systems across regions are available, such movement can be further stimulated. According to Moscardo, a wider range of movements within regions brings positive effects to capital. However, the positive effect of movements across regions, is still unconfirmed in the literature. If mobile technology can resolve the negative effects from movements across regions, the relationship between mobility and sustainability may contrast with that in the existing literature. Therefore, additional data and empirical research is essential.
- (v)
The longer and more frequent a tourist’s visit is and the deeper his or her engagement, the more positive the impact that tourism may bring. Because mobile services in tourism resemble social networks in nature, they are likely to deepen the engagement with the community of tourist’s destination. Therefore, we cautiously infer that mobility may positively affect sustainability.
Because the effects of mobility on sustainability depend on various factors, such as a destination’s tourist type, demographics, and geographics, it is difficult to estimate the serial relationship from mobile technology to mobility to sustainability. Relating to the QoL and capital that contribute to sustainability, types of tourists and destinations can change the effect on capital [
47]. For example, various dimensions, such as the objective of the tour, the length of the stay, and employment of tourists at destinations, may have different impacts on sustainability. The tourist may have visited for leisure (short holiday), to relax after retirement, or for a job to earn money (e.g., younger generations). Further complicating the issue is that the objective of a tour can change during the tour.
Destinations can also differ socially, economically and physically. The destination can be a sparse region with little population or can be an agricultural region. In general, smart tourism that is defined on the basis of the role and functionality of mobile technology tends to not consider tourist type and destination characteristics. Because the definition of new concepts in the initial stages requires clarification of the concept, it is a positive that the concept of the smart city focuses on few variables based on rational assumptions. However, after the conceptual framework has been consolidated, it should be expanded to various studies that consider the type of tourist and the characteristics of destination-related smart tourism.
Advances in mobile technology can provide bountiful and appropriate information to tourists, enabling tourists to make decisions that maximize their own well-being. Destinations and firms can use refined and accumulated data to make accurate predictions and to respond effectively. However, trends and consumer preferences (e.g., slow food and the slow culture movement) can bring about different directions of changes to the mobile industry regardless of the contribution of mobile technology. In this case, the choice is up to consumers, whereas accumulation of data and responses to changes are up to the firm and the destination.
Theoretically, it is also meaningful to combine the role of mobile technology in smart tourism with the theory discussed in existing literature. The effect of mobile technology on consumers’ decision making can be inferred by applying the TTF theory of adoption. With the assistance of mobile technology, consumers can recognize various data (G06K) and are provided with image with a sufficient amount of high quality data (H04N). Such information is not only provided before the tour but also during the tour considering a user’s location (H04W-004/02); thus it is optimized for personal context. Accumulated information includes individuals’ lifestyle information (G06F-017/30), which is easily understandable and accessible through the support of visualization technology (G06K-017/00). The quality of personal decision making improves, and increases in the TTF of a tour drives the adoption of mobile tour services based on mobile technology [
119].
This paper is theoretically significant in that it extends beyond simple analysis of mobile technology as a factor of tourism and considers relationship among tourism, mobile technology and sustainability. For this, this paper considers sustainability related concepts such as QoL, six facets of mobility proposed by Cresswell (2010) and the sustainability indicator to identify the relationship between sustainability and mobile technology. This ultimately provides an outlook to analyze the smart tourism concept from the perspective of sustainability. Moreover, while existing literature conceptualizes smart tourism by systematically devising frameworks based on existing literature and research results, this paper uses real field data from multiple sources to provide realistic direction for smart tourism conceptualization and theory.
Furthermore, we also identified from analysis of real field data that smart tourism can be understood from the sustainability perspective related to markets, environments, residents and related firms, in addition to traditional view of technology and consumer perspective.
6.2. Research Limitations
Comprehensive analysis based on multiple data sources to depict the overall landscape is indispensable because the conceptualization of smart tourism is in the initial stages and the need exists to establish a theoretical foundation and literature base. However, this research still faces the following challenges, which future studies need to overcome.
First, study 1 focused only on IPC for patent analysis. Some studies used citation/inventor or the CPC to analyze patents. Although the CPC has a more granular classification than the IPC has, this study chose the IPC because not all patents are classified according to the CPC. Furthermore, the citation was not selected because it tends to place greater importance on a patent as time passes (i.e., older patents are more likely to be considered important). In addition, the objective of study 1 was to provide an overall image of technology, making the IPC sufficient. The relative weakness in details for IPC analysis have been partially complemented by the centrality analysis of the keyword matrix in study 2.
Second, although the text mining methodology based on computer-generated coding primarily filters researchers’ prejudices and errors, a manual analysis of the text is required to interpret specific keywords/clusters with multiple meanings. Therefore, a precise analysis requires more time.
Third, analysis of consumer reviews of mobile apps aims to identify the functionalities desired by consumers and to explore the variables related to consumer emotions and evaluations. However, we observe that the number of reviews is not as vast as conventional text data and that the reviews of two apps do not meet the assumption of having homogeneity of variance. Therefore, we were limited in analyzing differences in praise scores from the analysis of the reviews within the same timeframe or the differences in praise and blame scores from the analysis of reviews created within seven months after the app launched. This limitation may have resulted from the fact that the amount of review data is not sufficient for statistical analysis, data were not cleansed sufficiently, or the dictionary for the content analysis did not sufficiently include variables related to evaluation and emotion. To overcome these limitations, future studies must ensure that big data are collected and that a dictionary including words related to evaluation and emotion is used.
Furthermore, the risk exists of including fake reviews when analyzing consumer reviews. When there are many fake reviews, the validity and accuracy of the research is at stake. We believe that our criterion of crawling reviews that are indicated as helpful by users would have minimized such errors.
Lastly, TripAdvisor and Google Trips reviews were common in that they were evaluations of app use but were not consistent regarding the point in time during which the app was used. There was no distinction among whether the review was done before the actual tour, during the tour, or after the tour. Because consumer behavior may differ depending on context-aware information during those different points in time, future studies should consider the point in time during which the reviews were created, if possible.
6.3. Future Research Direction
6.3.1. Empirical Research on the Relationship between Mobile Technology and Sustainability
We hypothetically proposed a relationship between mobile technology and sustainability on the basis of results of studies 1–3, and conceptual frameworks such as QoL, type of capital, new mobility paradigm and sustainability indicator. This proposition is not empirical research based on practical data, and thus, requires validation by future researches. Furthermore, researchers must decide on whether or not to consider the type of tourists, the breadth and depth of movement, and various characteristics (demographics, economic positioning, industry structure, and infrastructure) of a destination. Furthermore, empirical considerations and discussions are needed on whether such types of destinations should be taken into account or whether such segmentation is unnecessary. These issues can be addressed by either case studies or analysis of big data using individual context data (profile and behavior).
6.3.2. Network Environments and Consumer Costs
When understanding mobile technology, it is also essential to consider factors related to the cost burden of users and the level of technology. If a tourist seeks access to information during a tour, the network environment must be able to provide the needed coverage and speed, and the cost of being always connected must be affordable to users. If either the destination’s network environment is unfavorable or the data consumption costs exceed the tourist’s willingness to pay, the user must prepare by downloading the minimum required data to his or her device before departing [
79]. Although it is important to consider the design and implementation of mobile technology and/or service that is to be adopted to smart tourism, considering the technology’s level needed to support its objective and its cost is indispensable because these factors may change consumer behavior.
E-tourism expanded under the assumption of consumers’ use of PC-based fixed Internet, however, smart tourism leverages the mobile Internet that serves as a channel for information and mobile technology. In addition, future research should address which of fixed Internet and mobile Internet is selected by consumers depending on the type of trip, the decision process, environments, and consumer costs.
6.3.3. The Role of Mobile Application and Text Mining in Tourism
In this research, the mobile app was considered a window of contact between mobile technology and consumers. The mobile app is a tool for consumers to collect tourism-related information, a tool for planning a tour, and a window for purchasing tour packages. Tourists interact with other tourists, firms that supply tour services, and other systems related to tours through mobile apps. Therefore, the mobile app is a strategic point of contact for the supplier to develop because it serves as an information channel for consumers. Therefore, it is crucial to consider the new types of information channels that may emerge in the future [
6,
34,
43,
184] and differences in consumer behavior that use the tools relative to the traditional information channel. Tourism-related suppliers must prepare beforehand responses to these new situations. The mobile app bears significance for firms and consumers as a strong practical tool and for academic researchers as a future research topic.
The mobile app can be a useful tool for researchers to develop and complement the existing literature. In the past, tourism research used surveys on the relationships among quality, usefulness, satisfaction, and intention to visit a destination [
171] to identify consumer behavior. However, we can elaborate on existing methodologies based on data collection using surveys by considering consumer behavior through studies on mobile app reviews. For example, this complementary measure can help researchers identify relevant variables before applying the TAM and service quality and satisfaction theories. Taking the results of the Google Trips app as an example, the “useful” theme deduced from the analysis may infer that perceived usefulness affects intention to use. The results of the analysis on the two apps indicate that expressions for evaluation can be potential variables to explain adoption and satisfaction. Study 3 was an attempt in this context, however, researchers must ensure the validity of the research even when unstructured data are used. In this research, we manually checked the text to validate the interpretation of the keywords deduced using text mining. This is an indispensable task for text mining research but also a limitation of the methodology, which cannot automatize all processes. This task of confirming the contexts of deduced keywords/themes also applies to other fields using big data. A complete automatic process is expected to be developed through the development and enhancement of artificial intelligence (especially natural language processing). Therefore, it is necessary for the academic field and the business field to collaborate to develop the necessary algorithms and to share knowledge.
6.3.4. Proposal of Research Direction for the Innovation Perspective
The results of studies 1–3 imply that the role of mobile technology in tourism can be evaluated on the basis of users and is likely to operate to enhance value to a user. Therefore, these results suggest the importance of research on users and imply a shift from past research on destination. From the consumer perspective, we need to consider users’ potential tour scenarios and mobile technology’s response to support these scenarios.
However, the mobile technology in tourism based on the responses of end users is focused on enhancing these users’ unmet needs expressed by their words. Therefore, these incremental improvement [
185] will consequently indicate that the response will be minimal. To bring breakthrough innovation rather than incremental innovation to tourism [
186], observing a tourist’s actions and an understanding of stakeholders’ interests must be taken into account in response [
187,
188]. We hope that sensor technology and big data contribute into breakthrough innovation and we believe that approaches that reflect the future-oriented opinions of experts are necessary in future research.
6.3.5. Data Monopoly by Mobile Technology Platform Player and Its Governance
One factor that was not considered in this research is the monopoly by the first mover given the first-mover advantage latent in mobile technology [
189,
190]. When mobile technology is deployed as an infrastructure based on a specific standard/dominant design, other technology cannot enter into the market and the fate of tourism is also likely to rely on the dominant technology [
191,
192,
193]. This hinders the sustainable growth of firms in other industries and new entrant firms. Although the discussion may be out of the scope of this research as a theme of industry structure and monopoly/oligopoly, future studies must focus on it because of the possibility that the platform player dominates the mobile technology to determine the governance of tourism planning and the competency of tourism destination.
6.3.6. Tourism Ecosystem and Digital Twin
In consideration of smart tourism, for which consumers are important, the relationship between tourists and other entities such as destination, firms, systems, related industries, and other tourists must be considered. We also introduced the research by Grezel et al., which uses the concept of a smart tourism ecosystem to describe players and related systems of tourism. Understanding smart tourism from the perspective of a business ecosystem can increase the interest of managers and researchers in participants of smart tourism. These findings, which indicate smart tourism is a business ecosystem, are still valid from the perspective of sustainability and are meaningful as a catalyst to stimulate diversity in research. Specifically, mobile technology makes service and data moveable, which makes ecosystems more open and enables coordination and collaboration among entities of smart tourism ecosystem, including government agencies and stakeholders from other industries. New technologies that comprise smart tourism support new forms of collaboration and foster innovation that creates value [
6].
From the perspective of tourism ecosystem, we believe that digital twin technology—a technology that forecasts outcomes for complex relationships among entities through simulation—will play an important role in tourism. Digital twin technology is one of the top 10 strategic technologies selected by Gartner and is a concept proposed by General Electric (GE). This technology creates a twin in the digital world within a computer and forecasts the outcomes using computer simulation [
194,
195]. This technology was first applied to manufacturing [
196,
197], the military [
198,
199], and buildings [
200]. It can be applied to cities using computer simulation to create a digital twin of a city and to forecast issues related to lifestyle and disasters. For tourism, we can create a digital twin of a tourism destination as a set of participants (tourist, supplier, resident consumer, government, and other industry supplier) and collect and/or analyze data related to smart tourism. Furthermore, using indicators that can measure various types of data, we will be able to assess from the perspective of sustainability the positive and negative impacts on tourism participants and resources. This assessment will enable a priori evaluation of business viability [
1] and robust tourism development. For this purpose, the systematic accumulation of relevant data and optimized integration of other systems must precede through the required contribution from mobile technology.
We have been observing the commoditization of mobile Internet, smartphone and mobile apps, along with the rapid adoption of social media through fixed Internet and mobile Internet. In the mobile connected society, consumers directly access data/information and systems share/integrate data saved in databases [
29]. The development of mobile technology creates innovative experiences for consumers, fosters a sustainable competitive advantage for suppliers related to tourism destinations, and brings sustainable competency to tourism [
7]. Ultimately, mobile technology will provide new opportunities for value creation [
201]. We hope that this research provides guidance on research directions for mobile technology to enhance value creation in the future.