The Role of Augmented Reality for Experience-Influenced Environments: The Case of Cultural Heritage Tourism in Korea

Abstract

In cultural heritage sites around the globe, augmented reality (AR) is being utilized as a tool to provide visitors with better experiences while preserving the integrity of the sites. However, little research has examined the impact of AR on customers’ attitudes and behavioral intentions toward the sites. By integrating the post-acceptance model of information systems (IS) continuance, balance theory, and the theory of reasoned action (TRA), we investigate the causal mechanism underlying consumers’ beliefs about AR (perceived advantage, aesthetic experience, and perceived enjoyment) and AR satisfaction in conjunction with the attitudes and behavioral intentions toward the destination. The results show that the perceived advantage and aesthetics of AR influence AR satisfaction. In turn, AR satisfaction affects behavioral intentions toward the heritage destination, indirectly via the attitude toward the destination through AR. Based on these findings, we present theoretical and practical implications as well as suggestions for future research.

Keywords

cultural heritage augmented reality (AR)balance theory smart tourist post-acceptance model of IS continuance

Introduction

Cultural heritage sites are among the most popular tourist destinations around the world. Their rich historical, cultural, and architectural components attract tourists, but such richness also requires a great deal of information and knowledge for visitors to fully appreciate the cultural heritage (Kerstetter, Confer, and Graefe 2001). Often, the original physical structure is not intact, and the past lives of the people there are no longer tangible (Caton and Santos 2007). Moreover, these sites suffer from natural degradation, which can be aggravated further by influxes of tourists (Paquet and Viktor 2005). Therefore, many heritage sites limit access to certain areas for preservation or restoration reasons. In turn, the quality of the experience perceived by tourists can decline further.

Recent developments in information technology (IT) has enabled visitors to more fully explore and appreciate cultural heritage sites, moving beyond time, space, and language barriers. In particular, augmented reality (AR), a visualization technique that superimposes digitized virtual information on top of the real-world view of a location (Kounavis, Kasimati, and Zamani 2012), is a popular method for enhancing users’ cognitive capability to appreciate the surroundings in real time (Azuma et al. 2001; Haugstvedt and Krogstie 2012; Bujak et al. 2013). AR offers opportunities to transform how travelers travel.

Because of the mobile nature of tourists, mobile technology has naturally been utilized actively in new services in tourism (Gretzel, Werthner, Koo, and Lamsfus, 2015). Cultural heritage tourism is one of the most important areas served by the mobile application of AR (Adhani and Rambli 2012; Tutunea 2013) because AR can enhance tourists’ historical and geological knowledge and social awareness (Jung, Chung, and Leue 2015). According to a private research institution, the AR market is expected to grow at a compounded annual growth rate of 79.6% between 2015 and 2020, with the tourism sector and the logistics sector having a high growth rate (Business Wire 2015).

In Korea, initiated by government organizations, AR applications have been developed promptly and applied to cultural heritage sites and museums. More specifically, Deoksugung Palace, one of the royal palaces in Korea, launched a mobile application called “Deoksugung, in My Hands,” which contains 1,634 items of pictures, videos, and 3D images related to the palace and nearby points of interest through augmented reality (Korea Tourism Organization 2013).

This new information technology gives travelers a novel way of enjoying the tourism experience at specified destinations. Accordingly, the ultimate goal of developing AR is to provide enhanced experiences at cultural heritage sites and therefore potentially valuable relationship that may increase the revisit and recommendation intentions to the focal site (Jung et al. 2015), rather than to achieve favorable perceptions of AR itself. However, there has been little research on the link between AR and the travelers’ perceptions toward the tourism destination. Thus, it is worth investigating the impact of AR satisfaction on the behavioral intention toward a specified destination. Traveler–technology relationships have been studied in many contexts both theoretically and practically (e.g., Chung, Han, and Joun 2015; Chung, Koo, and Kim 2014; Han et al. 2015). Travelers are looking for AR hardware (e.g., headsets or mobile phones) and software (e.g., AR applications) that enable them to overlay virtual elements onto real places (McKone, Haslehurst, and Steingoltz 2016). The successful incorporation of AR into the tourism model will contribute to understanding the rapidly evolving technologies travelers will face in the future.

For a relationship between the tourism destination and AR to truly exist, the incorporation of AR and a place must collectively affect a traveler. Theoretically, this relationship between augmented reality and a real place can be supported by balance theory (Hummon and Doreian 2003), which asserts that if an element a (traveler in this context) is connected to b (AR) with strong ties and a (traveler) interacts with c (destination) intensively, then b (AR) and c (destination) also interact with each other (Meyerson 1991). Little empirical research has investigated this relationship in a tourism context.

In addition, we identify other underexplored areas regarding the critical features of AR. Specifically, AR has inherent synchronicity of the virtual and real worlds (Liang and Roast 2014). For traditional travel-related online media, the impacts on the ex ante intentions (Kaplanidou and Vogt 2006) or perceived risk (Lepp, Gibson, and Lane 2011) toward the destination have gained considerable scholarly attention. In contrast, for the application of AR, which is utilized meaningfully only at the specified site, ex post evaluations of the application and the intentions (i.e., intentions to revisit and recommend the site after the experience) appear to be important. Consequently, we consider the post-acceptance model of IS continuance (Bhattacherjee 2001) as another relevant theoretical framework.

Related to the synchronicity, another feature of the AR application is the importance of aesthetics (Papagiannis 2014). Aesthetic issues include perception, interpretation, and visualization and reality, as opposed to “seeing like machines” (Sterling 2012). Aesthetic characteristics are particularly highlighted because the virtual content should blend well with the real environment. The real world is represented through AR with visual images and illusions, which can aesthetically personalize an experience in the tourism sector. In the end, it is critical for AR to provide an aesthetic experience, which can be defined as “indulged in environment” (Oh, Fiore, and Jeoung 2007, 121). Although the aesthetic features of the AR application have been examined in previous studies, the extant research has investigated only the direct impact on overall tourist satisfaction (K. Lee, Lee, and Ham 2014) or the overall experience (Jung et al. 2016). The detailed mechanisms, for example, the antecedents and the cognitive/affective consequences of the perceived aesthetic characteristics have not been integrated.

Regarding these points, we propose the corresponding research questions:

Research question 1: Is there a causal relationship between experiential satisfaction with AR and the behavioral intentions toward a specified destination? This question is related to whether AR applications give extra value to travelers’ perceptions regarding a real place and naturally extends the destination connection to the traveler.

Research question 2: Is the post-acceptance model of IS continuance valid for AR applications in cultural heritage tourism? In other words, does the post-usage confirmation affect consumers’ beliefs about this novel information technology?

Research question 3: What are the roles played by the aesthetic experience of AR applications? Previous literature suggests that the aesthetic experience is affected by the post-usage confirmation and it affects ex-post beliefs about and satisfaction with AR.

In this way, this study fills the current gap in the literature by developing a research model that extends the post-use model of IS continuance and integrates balance theory. More specifically, utilitarian (i.e., perceived advantage) and hedonic components (i.e., aesthetic experience and perceived enjoyment) of the application of AR are investigated along with satisfaction in the post-acceptance framework of IS continuance. With this step, we intend to find out which perceived factors of AR influence the satisfaction with AR for cultural heritage sites. Then, we refer to balance theory (Festinger 1957) to uncover whether satisfaction with using AR forms positive attitudes and revisit/recommendation intentions toward the destination.

Therefore, the aim of this paper is twofold: (1) to identify whether satisfaction with AR influences the attitude toward and intention to visit tourism sites and (2) to empirically identify the impact of expectation-confirmation on positive beliefs (e.g., perceived advantage and perceived enjoyment) and aesthetic experience, which are predictors of AR satisfaction. With the results of the present study, we provide both theoretical and practical implications for cultural heritage marketers and AR developers.

Literature Review

Augmented Reality and Prior Studies

Research on augmented reality in the areas of tourism and information systems has only just begun. The existing research, which is scant, has investigated the usability of augmented reality in a tourism site (Alzua-Sorzabal, Linaza, and Abad 2007), visitors’ requirements for a mobile AR tourism application in urban heritage sites (Han, Jung, and Gibson 2013), mobile game features in the context of tourism AR (Linaza, Gutierrez, and García 2013; Weber 2014), satisfaction with AR with respect to the attributes of the AR (Jung, Chung, and Leue 2015), and users’ perceptions about AR based on their cultural backgrounds (Eastern culture vs. Western culture) (H. Lee, Chung, and Jung 2015) (Table 1). Jung et al. (2016) examined the impact of both AR and the VR (virtual reality) applications on the overall tour experience and subsequently on revisit intentions toward the destination.

Table 1.

Brief Description of the Previous Studies in Relation to Augmented Reality.

Scholar	Contents and Results
Alzua-Sorzabal, Linaza, and Abad (2007)	- Conducted a usability study assessing a new prototype of AR technology (PRISMA) used at a tourist site, which was focused on the development of a personalized AR service. - Found that tourists who used AR in the tourist site agree that AR is useful and strengthens the interactive experience between them and the tourist content, and they are willing to pay for the experience (up to three euros).
Han, Jung, and Gibson (2013)	- Investigated what tourists require for the development of mobile AR apps by adopting in-depth interviews. - Classified the requirements into several sections: local context, reviews/social network, personalization and user empowerment, map and easy navigation, multi-lingual and internationality, up-to-date information and speed
Weber (2014)	- Developed the design of the conceptual research development framework for location-based AR games used by tourists by adopting game design theory and HCI theory - Asserted the importance of developing user-centered game design to enhance tourists’ experience.
Jung et al. (2015)	- Examined the impacts of AR quality (content, system, and personalized service) on AR satisfaction and intention to recommend and the moderating effects of personal innovativeness in the relationships between AR qualities and satisfaction. - Showed the significant effects of 3 AR qualities on AR satisfaction. - Indicated that the impacts of content quality were weakened, while system quality was strengthened in high personal innovativeness group
Lee, Chung, and Jung (2015)	- Assessed the impacts of aesthetics of AR on perceived attributes (e.g., perceived usefulness, perceived ease of use and perceived enjoyment) and investigated the influence of cultural differences (South Korea, representative of Eastern culture, vs. Ireland, representative of Western culture) on these causal sequences - Indicated that aesthetic experience is an important factor in both cultures - Showed that individuals from Eastern cultures (high power distance, high uncertainty avoidance, and collectivism) consider usability, enjoyment, and social influence as important whereas individuals from Western cultures (low power distance, low uncertainty avoidance, and individualism) consider ease of use as important
Jung et al. (2016)	- Investigated the impacts of the social presence of mixed experience of AR and VR on 4 realms of the experience economy, tour experience, and intention to revisit the attraction. - Found that social presence was a strong predictor of four realms of the experience economy, and education, entertainment, and escape experience influenced the tour experience and intention to revisit the attraction.

Still, several aspects of AR application have not yet been explored. First, although one of the most conspicuous features of cultural heritage AR lies in the fact that it is blended with real experiences, only a small number of previous studies have examined AR as a part of aesthetic travel experiences (e.g., Jung et al. 2016; H. Lee, Chung, and Jung 2015; K. Lee, Lee, and Ham 2014). Therefore, the detailed procedure from predictors of aesthetic experience to its cognitive and affective consequences remains unidentified. Moreover, previous studies investigated antecedents of intention to use AR, while employing participants who had already experienced AR. However, after the initial use of AR, it is reasonable to consider the concept of users’ ex post behavioral intentions (Bhattacherjee 2001). Therefore, this study regards the expectation-confirmation variable as the antecedent of aesthetic experiences and investigated the impacts of it.

Second, while Jung et al. (2016) expanded the boundary of AR research by relating the overall tour experience to customers’ ex post behavioral intentions toward the tourism site, the mechanism underlying AR satisfaction, the attitudes toward the heritage destination, and the behavioral intentions toward the destination is still unclear. Thus, by incorporating balance theory, we test the causal links more rigorously.

Balance Theory

Our study is related to balance theory (Heider 1958), which postulates that if an element a is connected to b with strong ties and a interacts with c intensively, then b and c also interact with each other (Meyerson 1991). The triangular relationship is also supported by the cognitive dissonance theory of Festinger (1957) in the context of the transitivity argument. According to cognitive dissonance theory, individuals who feel dissonance among opinions, beliefs, knowledge about the environment, and knowledge about one’s own action and feelings will experience discomfort and pressure to reduce or eliminate the dissonance by changing their attitudes toward the target (Festinger 1957). In other words, a person tends to change his or her attitude “in the direction of increased congruity within the subject’s cognitive schema” (Dean 2002, 79).

With regard to balance theory, Heider (1946, 1958) explained the process mechanisms in the minds of social actors. He introduced the concept of the POX triple, where P is a focal person, O is another factor (attitude object) and X is an object (which may be a third person). Figure 1 presents the POX triple (Hummon and Doreian 2003).

Figure 1.

Balanced and imbalanced triadic configurations (Hummon and Doreian, 2003).

As shown in Figure 1, triples in the top row are defined as balanced, and those in the bottom row are defined as imbalanced. In the first triple in the top row, P has a positive attitude toward O, and O has a positive attitude toward X; consequently, P also has a positive attitude toward X. In contrast, as shown in the first triple on the bottom row, although P has a positive attitude toward O, and O also has a positive attitude toward X, if P has a negative attitude toward X, then P will experience dissonance and pressure to reduce or eliminate it. Consequently, P will change his attitude toward O or X to create consonance.

In the tourism context, it is possible, for example, for a tourist who is satisfied with a specific tourism destination to buy some souvenir representing the destination (first triple in the top row). In contrast, if a visitor is dissatisfied with the site, he or she will not be inclined to buy a souvenir even if he or she likes the souvenir per se, because the consumer will negatively change his or her belief to reduce or eliminate the dissonance (third triple in the top row). In the consumer behavior literature, balance theory has a long and rich history. However, there have been few studies in tourism research in spite of the relevance of the theory to the field (Table 2 provides an overview).

Table 2.

Brief Description of the Previous Studies in Relation to Balance theory.

Scholar	Contents and Results
Niininen, Szivas, and Riley (2004)	- Experimented with destination loyalty and repeat behavior using optimum stimulation level (OSL), which is based on balance theory and cognitive dissonance theory - Demonstrated that tourists with a high need for variety will display a varied pattern in their vacation destination selection
Woodside et al. (2007)	- Using brand netnography*, investigated how visitors of Italy report specific Italian cities as unique brand icons and illustrated the story using a mapping application considering the balance and imbalance relationships among the concepts implied by the story
Hsu et al. (2009)	- Using brand netnography*, investigated how visitors of China interpret the place, people, and situations to resolve paradoxes in their minds by unbalanced states, such as inciting incidents, and illustrated the story using a mapping application of balance theory
Su et al. (2011)	- Experimented with the ways in which Korean TV dramas affect Taiwanese consumers’ attitudes toward the locations where the dramas are filmed (onscreen locations) - Demonstrated that consumers’ parasocial relationships with a character and consumers’ attitudes toward the character are related to their attitudes toward the location (but this relationship is significant only for those viewers with high perceived cultural proximity between Taiwan and Korea)

Netnography: a method of assessing how visitors’ attitude are changed by “analyzing first-person on-line stories consumers tell that include discussions of their product and brand use” (Woodside, Cruickshank, and Dehuang 2007, 162).

As stated above, balance theory postulates that individuals tend to change their “attitude” toward product/service to maintain balance and avoid cognitive dissonance. This theory will be very useful in explaining how AR affects travelers’ attitudes toward the destination. If a traveler likes the experience from AR and is satisfied, AR satisfaction will lead to revisit intentions toward the destination. Reciprocally, the traveler will tend to like the destination further to maintain a psychological balance.

The theory of reasoned action (TRA) posits that attitudes toward behavior affect intentions to perform the behavior (Fishbein and Ajzen 1975). Combining TRA and balance theory, this article extends the territory of balance theory to include the “behavioral intention to revisit and recommend the site” as a consequence of the attitudes toward the destination.

Post-Acceptance Model IS Continuance

Because this research regards consumers’ beliefs after the initial use of AR and their impacts on the attitudes and intentions toward the destination, the post-acceptance model of IS (Information Systems) continuance (Bhattacherjee 2001) has theoretical relevance. Whereas the traditional expectation confirmation theory (ECT) (Oliver 1993) emphasizes expectation in the pre-usage stage as an antecedent of confirmation, the post-acceptance model of IS continuance highlights the causal link from confirmation to post-use expectation in the post-usage stage. After the use of the system, consumers are likely to update their expectations, which is postulated to serve as a strong predictor of IS continuance. In terms of consumers’ continued usage, compared to the initial acceptance of IS, the post-acceptance model of IS continuance has been regarded as a more useful tool with stronger explanatory power than previous IS acceptance models. Bhattacherjee (2001) conceptualized the post-use expectation by perceived usefulness.

The post-use confirmation model of IS continuance has been integrated with other research models such as the technology acceptance model (TAM) (Davis 1985), theory of planned behavior (TPB) (Ajzen 1985), and unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, and Davis 2003). For example, integrating the post-use confirmation model of IS continuance and UTAUT, Shin et al. (2011) proposed and showed that confirmation affects satisfaction directly and through perceived usefulness and ease of use, which then affect users’ intention to continually use smartphones as a ubiquitous learning tool. While a considerable number of studies have examined IS in the context of tourism, little research has incorporated AR technology usage at a post-use stage of IS continuance.

Motivational Theory

Whereas Bhattacherjee’s model of IS continuance includes perceived usefulness as a single construct of consumer beliefs about the performance of the information system, we note that beliefs are multifaceted (Trafimow and Sheeran 1998). Motivational theory (Deci 1975) posits that user acceptance of a product or service is explained by extrinsic and intrinsic motivations. In the context of IT use, the extrinsic motivation is driven by the utilitarian purpose of the IS usage, that is, expecting rewards or benefits by analyzing the functionality of the system rationally, whereas the intrinsic motivation is driven by the hedonic purpose, that is, expecting benefits from the interaction with the system itself (Van der Heijden 2004). Thus, it is plausible that after the initial use of IS, consumers will evaluate the experience and formulate updated cognitive and affective beliefs in relation to the product performance related to these motivations.

For the extrinsic motivation, performance expectancy, which is rooted in perceived usefulness of technology acceptance model (TAM) (Davis 1989), has been conceptualized in UTAUT (Venkatesh et al. 2003). Formally, performance expectancy is defined as “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al. 2003, 447). According to expectation confirmation theory, when users’ expectation is confirmed in the actual experience, they will have more favorable beliefs about the performance of the system, which in turn will affect their satisfaction with the IS (Bhattacherjee 2001; I. Lee et al. 2007).

Since the publication of UTAUT, performance expectancy has been applied to tourism research as well. In the context of online purchase of rural tourism (San Martín and Herrero 2012) and app-based mobile tour guides (Lai 2013), performance expectancy was found to be the strongest predictor of the intention to use information systems.

Perceived enjoyment, which is also drawn from motivational theory, is another facet of customer beliefs. Perceived enjoyment is defined as “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davis, Bagozzi, and Warshaw 1992, 1113). Theoretical and empirical research has validated the idea that perceived enjoyment is an important factor for successful IT usage (e.g., Carroll and Thomas 1988; Igbaria, Parasuraman, and Baroudi 1996; Venkatesh and Brown 2001). In particular, consumers of IS with a hedonic purpose are more likely to be emotionally driven in pursuing fun, enjoyment, and sensory stimulation (Chun, Lee, and Kim 2012). It has been found that perceived enjoyment is significant and even more important than perceived usefulness as a determinant of the hedonic use of IT (Van der Heijden 2004).

Consequently, perceived enjoyment has been incorporated into the TAM, particularly for the hedonic information system (Davis et al. 1992). In empirical research on tourism, Ayeh, Au, and Law (2013) demonstrated that when travelers use consumer-generated media for travel planning, perceived enjoyment positively influences both satisfaction and behavioral intentions toward such media. M. J. Kim et al. (2013) also demonstrated that perceived enjoyment has a significant effect on satisfaction in mobile tourism shopping.

Aesthetic Experience

Pine and Gilmore (1998) suggested a paradigm shift from the product or service itself to the experience of consuming product and service. They also highlighted the importance of staged experience and proposed four realms of the experience economy: entertainment, education, escapism, and aesthetics. According to Oh, Fiore, and Jeoung (2007), all four realms of the experience economy are strongly related to the tourism industry. However, among them, aesthetic experience is related to AR tour experience because it features “becoming physically or virtually a part of the experience itself” (Pine and Gilmore 1998, 31).

Aesthetic concerns have been recognized as a major issue in any visualization that is integrated with a larger environment (Skog, Ljungblad, and Holmquist 2003). Thus, as for the AR applications, which augment the users’ tour experience in the combination of real and virtual contents, aesthetic appeals are also critical (Jung et al. 2015b; H. Lee, Chung, and Jung 2015; Weber 2014).

While the term aesthetics has evolved with various meanings for different schools of thought, it is commonly defined as “an artistically beautiful or pleasing appearance” (Tractinsky 2004). In this context, the aesthetic property in the information system is realized by the visual elements, such as color, photographs, shapes, and font (Cyr, Head, and Ivanov 2006). The aesthetics have significance in AR applications, particularly on mobile devices, which have the limitations of a smaller display with a lower resolution than traditional devices such as desktop computers (Sadeh 2003). Consumers’ cognitive processes have been found to depend on the device type (e.g., smartphone and computer) (Koo, Wati, and Chung 2013). Thus, it is important to take into account the mobile space when designing a mobile application or website (K. C. Lee and Chung 2009) instead of simply converting a traditional computer-based website to a mobile format (Fling 2009; Han and Kim 2013).

To the users of AR, the virtual and real objects coexist in the same space (Azuma 1997). Thus, the aesthetic features of AR in the context of tourism in particular should contribute to a sense of harmony and sensory pleasure (Hosany and Witham 2010). This means that the aesthetic issues of AR eventually boil down to whether the AR system successfully provides users with an “aesthetic experience,” which can be defined as “indulged in environment” (Oh, Fiore, and Jeoung 2007, 121). The experience economy literature has postulated that in the aesthetic experience, tourists passively participate in destination activities and are immersed in them, “becoming physically or virtually a part of the experience itself” (Pine and Gilmore 1998, 31).

Several studies thus far have investigated the role of aesthetic experience in AR usage. For example, immersion in AR was found to be related to satisfaction with AR (W. Y. Z. Lee, Cheung, and Chan 2014). Visual appeals of AR were found to affect both the perceived usefulness and ease of use (Chung, Han, and Joun 2015). The aesthetic quality of the interface design was found to affect trust, which in turn influences customer satisfaction with mobile banking (Delone and McLean 1992; DeLone 2003).

Conceptual Model and Hypotheses

To investigate the role of AR on the attitudes and behavioral intentions toward cultural heritage sites, we propose the conceptual model shown in Figure 2 based on the extant literature. In this section, we propose hypotheses for the causal links among the constructs and present the theoretical background.

Figure 2.

Proposed research model.

Confirmation and Beliefs

The beginning point of our conceptual model is post-use expectation confirmation. Referring to Bhattacherjee (2001), we conceptualize expectation confirmation as visitors’ perception of the congruence between the expectations of AR use and its actual performance in a cultural heritage tour. It has been proposed that in the context of IS continuance after the initial adoption, customers are likely to update their beliefs about the core attributes of the system (Bhattacherjee 2001). In the context of AR applications, if the performance of AR exceeds the ex-ante expectation, a high degree of confirmation will lead users to update their beliefs about the product and service more favorably.

The extant literature, for example, motivational theory (Deci 1975), suggests that beliefs are multifaceted and can be analyzed in terms of utilitarian (extrinsic) and hedonic (intrinsic) motivations. Based on this premise, we examine the beliefs in terms of the performance, aesthetics, and enjoyment of using AR applications, namely, the perceived advantage, aesthetic experience, and perceived enjoyment.

As in the existing IS literature, regarding innovative technology in particular (e.g., Eriksson, Kerem, and Nilsson 2008), we conceptualized performance expectancy as visitors’ perception of the advantages of AR use in heritage tourism because the advantages of using the innovation such as AR applications, relative to existing solution, are important for the continued use of the IS. In UTAUT, performance expectancy was used as a logical surrogate for perceived usefulness, which is postulated to be affected by confirmation (Bhattacherjee 2001).

Furthermore, referring to Davis et al. (1992), perceived enjoyment was conceptualized as visitors’ perception of enjoyment of AR use. Thong, Hong, and Tam (2006) demonstrated that confirmation of e-government service has a significant influence on both perceived usefulness and perceived enjoyment, which contribute to satisfaction and continued usage intention.

Considering the unique characteristics of the application of AR for cultural heritage tourism, we included aesthetic experience for the third dimension of customer beliefs toward the IS. Following Oh, Fiore, and Jeoung (2007), aesthetic experience was conceptualized as visitors’ indulgence in augmented reality. There has been little research regarding the relationship between confirmation and aesthetic experience. However, theory suggests that aesthetic experiences are likely to be an important motivation for travel (Pine and Gilmore 1998; Oh, Fiore, and Jeoung 2007). As such, after the initial use of the AR application, which is particularly designed to provide richer appreciation of the cultural heritage, it is likely that consumers will evaluate the aesthetic aspect of its usage. We propose that consumers’ perception of aesthetics is also an important dimension of their belief in the use of AR, which is affected by confirmation. Thus, we formulated the following hypotheses:

Hypothesis 1: Expectation confirmation has a positive impact on the perceived advantage of AR.

Hypothesis 2: Expectation confirmation has a positive impact on the aesthetic experience of AR.

Hypothesis 3: Expectation confirmation has a positive impact on the perceived enjoyment of AR.

Aesthetic Experience, Perceived Advantage, and Perceived Enjoyment

As explained earlier, we conceptualized aesthetic experience as visitors’ indulgence in augmented reality (Oh, Fiore, and Jeoung 2007). For aesthetic experience to be materialized, it is critical to present the AR application with design aesthetics, namely, the beauty that can be expressed through elements such as color, photographs, font style, and layout. Design aesthetics is also regarded as one of the most important communication routes between mobile-vendors and customers (Li and Yeh 2010). Aesthetics have a strong halo effect (Al Sokkar and Law 2013), that is, a tendency through which initial outstanding impressions or characteristics affect overall judgments even after contradictory evidence is exposed (Rosenzweig 2009). Thus, initial impressions derived from aesthetic aspects can induce users to judge the usefulness or joyfulness of the product.

Because mobile devices have a greater limitation in the size and resolution of their displays than traditional devices such as desktop computers (Sadeh 2003), it is important to take the mobile space into account when designing mobile applications (K. C. Lee and Chung 2009) instead of simply converting a traditional computer-based website to a mobile format (Fling 2009). Li and Yeh (2010) demonstrated that the design aesthetics of m-commerce websites have a positive effect on usefulness, which in turn, influences loyalty. Cyr, Head, and Ivanov (2006) showed that the design aesthetics of mobile commerce significantly influence both perceived usefulness and enjoyment in performing information retrieval tasks on the Lonely Planet website.

Thus, aesthetic characteristics of mobile AR application, which are expected to lead to consumers’ aesthetic experience, may enhance cognitive ability regarding the attributes of mobile applications. Based on these premises, we postulate that the aesthetic experience affects the cognitive and affective characteristics of smartphone-based AR applications. Thus, the following hypotheses are proposed:

Hypothesis 4: The aesthetic experience of AR has a positive impact on the perceived advantage of AR.

Hypothesis 5: The aesthetic experience of AR has a positive impact on the perceived enjoyment of AR.

Beliefs and Satisfaction

The users’ positive beliefs toward a specific information system are crucial internal and external drivers for IS acceptance. Users of an AR application who have positive beliefs about its attributes are more likely to feel satisfied with the AR application. Thus, consistent with the theory of reasoned action (TRA) and the theory of planned behavior (TPB), IS adoption literature also asserts that satisfaction with IS determined by users’ beliefs (Thong, Hong, and Tam 2006). In TAM, beliefs about the system are postulated to influence attitudes toward using the system, which is a parallel concept to customer satisfaction. These theories suggest that positive cognitive and affective beliefs about product/service are likely to induce satisfaction (Thong, Hong, and Tam 2006; Van der Heijden 2004).

As stated above, our conceptual model includes three belief constructs, namely, perceived advantage, aesthetic experience, and perceived enjoyment. Several studies have demonstrated a close relationship between belief and satisfaction toward information systems (Al-Gahtani and King 1999; Nelson, Todd, and Todd 2005). Al-Gahtani and King (1999) proposed that the ease of use, the enjoyment, and the relative advantage affect end-user satisfaction with the computing system, and they found that only the relative advantage has a significant result. Nelson, Todd, and Wixom (2005) reported that beliefs about IS significantly influence satisfaction with information and system. Thus, the following hypotheses are proposed:

Hypothesis 6: The perceived advantage of AR has a positive impact on satisfaction with AR.

Hypothesis 7: The aesthetic experience of AR has a positive impact on satisfaction with AR.

Hypothesis 8: The perceived enjoyment of AR has a positive impact on satisfaction with AR.

AR Satisfaction, and Attitudes and Behavioral Intentions toward a Destination

Balance theory (Heider 1946), which examines relationships in triads (Basil and Herr 2006), has been widely applied to marketing and management research. For example, Manrai et al. (1997) investigated the relationship between advertising slogans for ecologically harmless products in connection with consumer attitudes toward nature. Basil and Herr (2006) examined the consumer response to cause-related marketing, which involves the pairing of a firm and a charity organization, in relation to the preexisting attitude toward the firm.

There has been little research on tourism utilizing balance theory; however, several studies dealing with balance theory have used netnography (Hsu, Dehuang, and Woodside 2009; Woodside, Cruickshank, and Dehuang 2007), which is a method of assessing how visitors’ attitudes change based on the stories that consumers tell online (Woodside, Cruickshank, and Dehuang 2007, 162). Su et al. (2011) examined the impact of TV dramas on consumers’ attitudes toward the location featured in the drama. The results showed that consumers’ attitudes toward the characters of the drama are related to their attitudes toward the locations for viewers with high perceived proximity to the cultures featured in the dramas. Heider (1946) posited that individuals change their attitudes toward product/service to maintain balance and avoid cognitive dissonance.

In our conceptual model, the three entities in the relationship system include the consumer, the AR application, and the cultural heritage site. Oliver (1980) posited that satisfaction influences post-purchase attitudes toward the product and future behavioral intentions. Combining this proposition with balance theory, it is likely that consumer satisfaction with the application of AR has a positive impact on the attitudes and behavioral intentions toward the cultural heritage site featured in the AR application.

In other words, when tourists have positive attitudes toward a destination, it induces them to have the behavioral intention to visit or revisit the site. Thus, we postulate that attitudes toward cultural heritage sites that are formulated through the use of AR applications will influence the intention to visit the heritage sites. Thus, the following hypotheses are suggested:

Hypothesis 9: Satisfaction with AR has a positive impact on the attitude toward the destination.

Hypothesis 10: Satisfaction with AR has a positive impact on the behavioral intentions toward the destination.

Hypothesis 11: Attitude toward a destination has a positive impact on the behavioral intentions toward the destination.

Research Method

Measures

From previous literature, we adopted the following measurement items: expectation confirmation (Bhattacherjee 2001), perceived advantage (Venkatesh et al. 2003), aesthetic experience (Oh, Fiore, and Jeoung 2007), perceived enjoyment (Agarwal and Karahanna 2000; H.W. Kim, Chan, and Gupta 2007), AR satisfaction (self-developed), attitudes toward a destination (Oh, Fiore, and Jeoung 2007), and behavioral intentions toward a destination (Pavlou and Gefen 2004).

This procedure yielded 34 measurement items. All 34 measurement items are summarized in Table 4 by each construct: expectation confirmation (three items), perceived advantage (four items), aesthetic experience (five items), perceived enjoyment (four items), satisfaction (four items), attitude toward the destination through AR (ten items), and intention to revisit the destination (four items). All items were measured on a seven-point Likert scale ranging from strongly disagree (1) to strongly agree (7).

As for the attitudes toward the destination, following Kaplanidou and Vogt (2007), who integrated the cognitive and affective images for a destination, we included items that measure the cognitive image of destination, such as “educating,” and the affective image, such as “enjoyable,” “captivating,” and “exciting.” In particular, considering the educational aspect of cultural and historical tourism, we judge that it should not be a problem to introduce educational attitudes with more conventional items of attitudes toward the destination.

The survey questionnaire was developed in English first and then translated into Korean by individuals who were proficient in both languages. Then, researchers who were fluent in English and Korean, with academic specializations in the area under study, compared the translated version with the original version. No material discrepancies were found.

Data Collection

To validate the proposed research model, a survey was conducted at Deoksugung Palace on November 16, 2013. Deoksugung Palace is one of the royal palaces in Korea and has more than one million visitors annually (Seoul Metropolitan Government Statistics 2013). In 2013, the palace launched a mobile application called “Deoksugung, in My Hands” to provide visitors, both overseas and domestic, with high-quality historical and point-of-interest information. The application provides an augmented reality containing 1,634 photos and videos, as well as 3D images related to Deoksugung Palace, which serve as a sophisticated tour guide with multilingual capabilities (Korean, English, Japanese, and Chinese). The application also allows users to view 3D images of currently nonexisting palace buildings that are projected on the existing palace building to “augment” the sense of reality (“Seoul Palace,” Korea Tourism Organization 2013) (see Figure 3).

Figure 3.

Snapshot of augmented reality application in Deoksugung Palace.

A pilot test was conducted to assess the content validity of the survey instrument. A total number of 132 college students participated in the pilot test and assessed the length, readability, and clarity of the measurement items. The processes in this pretest stage showed that all of those questions were valid and reliable.

Considering that most visitors are not aware of AR applications, we presented video and print materials that explain AR in general and how to use “Deoksugung, in My Hands” to participants prior to the survey. Specifically, we provided the material with two purposes: (1) to allow the visitors to familiarize themselves with the application so that they can evaluate the AR more accurately and (2) to allow the visitors to form an ex-ante expectation of the AR application. Then, using a convenience sampling process, selected visitors were asked to use “Deoksugung, in My Hands” in front of the three major palace buildings (e.g., Junghwajeon, Hamnyungjeon, and Seogeodang) for approximately 30 minutes and participate in the survey.

All respondents received a gift certificate worth KRW5,000 (about USD5) as a reward for participation. Out of the 162 questionnaires collected, 17 incomplete questionnaires were eliminated. Therefore, a total of 145 questionnaires were used for this study (89.51%). Table 3 summarizes the characteristics of the respondents. Out of the 145 total respondents, 94 (64.8 %) were female and 51 (35.2%) were male. Approximately half of the respondents are between 20 and 29 years old (46.2%) or students (60.0%). Although the respondents of this survey were young and highly educated, only 48 (33. 1%) of them had ever used AR previously.

Table 3.

Sample Description.

Profile Category	Frequency	Percentage (%)
Gender
Male	51	35.2
Female	94	64.8
Age
≤20	35	24.1
20–29	67	46.2
30–39	23	15.9
40–49	16	11.0
50–59	4	2.8
≥60	0	0.0
Education
High school	37	25.5
In university/college	45	31.0
University/college	47	32.4
Graduate school	16	11.0
Job
Student	87	60.0
Office management	20	13.8
Sales service	3	2.1
Technical post	3	2.1
Specialist	12	8.3
Public servant	8	5.5
Housewife	8	5.5
Other	4	2.8
Have you ever used AR in the past?
Yes	48	33.1
No	97	66.9

Analysis and Results

To test the proposed research model, we used a partial least squares (PLS) analysis. PLS-Graph version 3.0 PLS analysis offers several advantages, including a small sample size, and has few assumptions about the measurement scale and normal distribution (Ahuja and Thatcher 2005). According to Barlett, Kotrlik, and Higgins (2001), the sample size for a PLS estimation is required to exceed 10 times the number of measurement items of the most complex construct, which is 10 in this study (attitudes toward a destination). Therefore, 100 questionnaires are needed for analysis. Because we collected 145 responses, the requirement was satisfied.

Using PLS-Graph, the measurement model and structural model were estimated. Before conducting any analyses, we first calculated the constructs’ skewness and kurtosis (see Table 5) to check their normality (Tabachnick and Fidell 2007). Skewness and kurtosis values ranged from –1.249 to –0.293 and from –0.526 to 1.835, respectively. Considering that the items were approximately normally distributed, we estimated the measurement and structural model.

Measurement Model

To validate our measurement model, we assessed content, discriminant, and convergent validity. The content validity of our survey was examined from the existing prior literature, and our measurements were developed by adopting constructs validated by other researchers. Discriminant validity was assessed by comparing the average variance extracted (AVE) associated with each construct, with the correlations among constructs (Fornell and Larcker 1981). To claim discriminant validity, the square root of the AVE associated with a particular construct must be greater than its correlations with other constructs. According to the estimates presented in Tables 4 and 5, each construct sufficiently differed from the other constructs, and, therefore, the measures represented discriminant validity. Convergent validity was assessed by the composite reliability (CR) and Cronbach’s α. Each of the reliability measures exceeded the recommended 0.70 threshold (Fornell and Larcker 1981) (Tables 4 and 5). Combined with the strong evidence for discriminant and convergent validity, the measurement model appears to be acceptable.

Table 4.

Reliability and Cross-Loadings.

Construct	Measurement Items	Loading	t Value	α	CR	AVE
Expectation confirmation	My experience of using AR was better than what I expected.	0.844	12.194	0.829	0.884	0.718
	The service level provided by AR was better than what I expected.	0.854	12.307
	Overall, most of my expectations from using AR were confirmed.	0.844	28.300
Perceived advantage	I find the AR app useful for visiting Deoksugung.	0.887	26.869	0.895	0.928	0.765
	Using the AR app I can visit Deoksugung more effectively.	0.925	51.940
	The app motivated me to revisit Deoksugung.	0.789	20.667
	I feel I will be able to get more information about Deoksugung by using AR.	0.892	41.760
Aesthetic experience	When I used AR at Deoksugung, I felt a real sense of harmony.	0.868	31.632	0.924	0.943	0.768
	When I used AR at Deoksugung, just being there was very pleasant.	0.907	48.085
	When I used AR at Deoksugung, the setting was not bland.	0.901	48.805
	The setting really showed attention to design detail.	0.836	27.446
	The setting provided pleasure to my senses.	0.867	35.290
Perceived enjoyment	I had fun using AR.	0.911	47.085	0.925	0.947	0.818
	Using AR provided me with a lot of enjoyment.	0.910	47.494
	I enjoyed using AR.	0.924	54.455
	Using AR did not bore me.	0.871	31.667
AR satisfaction	I am satisfied with the quality of information provided by the AR.	0.904	56.024	0.912	0.941	0.800
	I am satisfied with the system stability and speed of the AR.	0.847	20.086
	I am satisfied with the visual interface design (such as graphic) of the AR.	0.882	27.844
	Overall, I am satisfied with the AR.	0.943	83.658
Attitudes toward a destination (cognitive and affective)	The experience of using the AR app has made me more knowledgeable about Deoksugung	0.822	19.065	0.953	0.961	0.710
	I learned a lot by using the AR app at Deoksugung	0.867	31.758
	Using the AR app at Deoksugung has stimulated my curiosity to learn new things.	0.856	24.214
	Using the AR app at Deoksugung was a real learning experience.	0.885	33.470
	Experiencing Deoksugung through the AR app has been very educational.	0.897	48.549
	Experiencing Deoksugung through the AR app was enjoyable.	0.859	18.140
	Experiencing Deoksugung through the AR app was captivating.	0.850	28.022
	I enjoyed Deoksugung through the AR app.	0.870	32.571
	Experiencing Deoksugung through the AR app excited me.	0.839	24.640
	Deoksugung was very entertaining through the AR app.	0.661	9.440
Behavioral Intention toward a destination	I think I will visit the Deoksugung again after experiencing the AR app.	0.903	36.324	0.883	0.918	0.737
	I intend to visit Deoksugung frequently after experiencing the AR app.	0.811	19.418
	I will continue to visit Deoksugung in the future after experiencing the AR app.	0.880	23.254
	I want to recommend Deoksugung to others after experiencing the AR app.	0.836	24.176

Note: CR = composite reliability; b. AVE = average variance extracted.

Table 5.

Correlation and Discriminant Validity.

Construct	Correlation of Constructs							Mean	SD	Skewness	Kurtosis
Construct	(1)	(2)	(3)	(4)	(5)	(6)	(7)	Mean	SD	Skewness	Kurtosis
(1) Expectation confirmation	0.847							4.726	1.377	–0.293	–0.526
(2) Perceived advantage	0.419**	0.875						5.490	1.195	–1.249	1.643
(3) Aesthetic experience	0.467**	0.584**	0.876					5.508	1.183	–1.049	1.204
(4) Perceived enjoyment	0.465**	0.641**	0.657**	0.904				5.372	1.202	–0.754	0.740
(5) AR Satisfaction	0.534**	0.682**	0.549**	0.564**	0.894			5.112	1.286	–0.745	0.318
(6) Attitude toward a destination	0.562**	0.687**	0.717**	0.835**	0.677**	0.843		5.352	1.125	–0.964	1.835
(7) Behavioral intention	0.371**	0.319**	0.406**	0.368**	0.313**	0.406**	0.858	5.859	0.986	–0.904	0.600

Note: Diagonal elements (in bold) in the “correlation of constructs” matrix are the square root of the average variance extracted (AVE). For adequate discriminant validity, the diagonal elements should be greater than the corresponding off-diagonal elements.

p < 0.05, ^**p < 0.01.

Structural Model

Figure 4 displays the outcome of the structural model. The structural models were examined for their explanatory power and path significance using a bootstrapping technique. The size of the bootstrapping sample that was used in the PLS analyses was 500. Table 6 shows the results of the hypothesis tests. Hypotheses 1 through 3 postulate a positive relationship between expectation confirmation and perceived advantage, aesthetics, and perceived enjoyment, respectively. Supporting these hypotheses, expectation confirmation is found to have a positive effect on perceived advantage (β = 0.273, t = 3.794), aesthetics (β = 0.520, t = 8.817), and perceived enjoyment (β = 0.240, t = 3.583). In addition, tests for hypotheses 4 and 5 indicate that aesthetics significantly influence perceived advantage (β = 0.442, t = 5.036) and perceived enjoyment (β = 0.533, t = 6.415). Thus, both hypotheses 4 and 5 are supported. Hypotheses 6 through 8 postulate a positive relationship between perceived advantage, aesthetics, perceived enjoyment, and satisfaction. Satisfaction is found to be significantly influenced by perceived advantage (β = 0.505, t = 6.888) and aesthetics (β = 0.178, t = 2.083). However, the path from perceived enjoyment to satisfaction is not significant (β = 0.133, t = 1.517). Thus, hypotheses 6 and 7 are supported but hypothesis 8 is not.

Figure 4.

Overall model: path estimates by PLS analysis.

Table 6.

Standardized Structural Estimates and Tests of the Hypotheses.

Hypotheses	Path			Estimates	t Value	Results
Hypothesis 1	Expectation confirmation	→	Perceived advantage	0.273	3.794	Supported
Hypothesis 2	Expectation confirmation	→	Aesthetic experience	0.520	8.817	Supported
Hypothesis 3	Expectation confirmation	→	Perceived enjoyment	0.240	3.583	Supported
Hypothesis 4	Aesthetic experience	→	Perceived advantage	0.442	5.036	Supported
Hypothesis 5	Aesthetic experience	→	Perceived enjoyment	0.533	6.415	Supported
Hypothesis 6	Perceived advantage	→	AR satisfaction	0.505	6.888	Supported
Hypothesis 7	Aesthetic experience	→	AR satisfaction	0.178	2.083	Supported
Hypothesis 8	Perceived enjoyment	→	AR satisfaction	0.133	1.517	Rejected
Hypothesis 9	AR satisfaction	→	Attitude toward a destination	0.688	11.951	Supported
Hypothesis 10	AR satisfaction	→	Behavioral intentions	0.086	0.764	Rejected
Hypothesis 11	Attitude toward a destination	→	Behavioral intentions	0.372	3.415	Supported
R²
Perceived advantage			0.396
Aesthetics			0.271
Perceived enjoyment			0.474
Satisfaction			0.527
Attitude toward a destination			0.473
Behavioral intentions			0.190

In addition, hypotheses 9 and 10 postulate that if a visitor is satisfied with using AR at cultural heritage sites, he or she will have positive attitude toward the destination and in turn will have greater intentions to revisit or recommend the site. AR satisfaction is found to have a significant impact on attitudes toward a destination (β = 0.688, t = 11.951). However, no significant relationship is found between AR satisfaction and behavioral intentions toward the cultural heritage site (β = 0.086, t = 0.764). Thus, whereas hypothesis 9 is supported, hypothesis 10 is not. Finally, attitudes toward a destination through AR has an impact on intentions to revisit the destination (β = 0.372, t = 3.415). Thus, hypothesis 11 is supported.

Discussion

The results show that AR satisfaction influences behavioral intentions toward the focal heritage site. However, the path was significant only through the attitude toward the destination through AR. Thus, if an AR user is satisfied with the application, he or she will have a positive attitude toward a destination through AR and, in turn, have an intention to revisit the destination, which is suitably well explained by balance theory in terms of the transfer of positive attitudes from toward AR to toward a destination. Once again, we assert that our study supports balance theory’s (Heider 1958) triadic relationships (i.e., satisfaction–attitude–destination). This result is similar to the previous studies that demonstrated that the websites of museums (Pallud and Straub 2014) and destination management organizations (Chung et al. 2015) are able to induce website visitors to have an intention to visit a real destination.

The post-acceptance model of IS continuance is also validated since the results show the positive links between the expectation confirmation and beliefs (perceived advantage, aesthetic experience, and perceived enjoyment). The results also indicate that aesthetic experience is a significant predictor of both utilitarian (e.g., perceived advantage) and hedonic (e.g., perceived enjoyment) attributes of AR application and experienced satisfaction with AR. Post-usage confirmation about the application of AR was found to provide tourists with an aesthetic experience that features tourists’ passive participation and immersion in the destination environment.

Furthermore, the results are consistent with previous studies in that the design components of a mobile application are important in mobile space (K. C. Lee and Chung 2009) and the aesthetic experience of AR induces a positive perception about AR (H. Lee, Chung, and Jung 2015).

Implications and Limitations

We recognized three limitations of previous AR studies: (1) limited research on balance theory in the tourism context, (2) limited applications of the post-usage confirmation framework in the context of AR usage, and (3) a lack of research on the detailed mechanism of the aesthetic experience of AR and its role. The present study tried to overcome these limitations by extending and integrating balance theory in the framework of the post-acceptance IS continuance model. Consequently, this study provides the theoretical and practical implications as follows.

Previously, only a few studies have brought balance theory to the area of tourism. Originally, balance theory postulated that individuals have a tendency to change their “attitudes” toward a product/service by leveraging a third thing, such as celebrity, to maintain balance and eliminate cognitive dissonance. Noting that the use of information systems such as AR applications at a cultural heritage site will transform not only the attitudes but also the likelihood of going to the destination, we extended the territory of balance theory from the change of “attitudes” to the change of “behavioral intention.”

This novel approach of employing and extending balance theory reveals useful insights about the role of technology in cultural tourism. Consistent with balance theory, the results show that satisfaction with the AR application at cultural heritage sites affects attitudes toward a destination, which, in turn, has an effect on the intention to revisit cultural heritage sites. Surprisingly, satisfaction with AR was not found to directly influence intention to revisit cultural heritage sites.

The findings of this study shed light on the detailed mechanism of the contribution of technology to cultural heritage tourism. In spite of the insignificant direct link between AR satisfaction and behavioral intentions, the contribution of AR to the destination is confirmed since satisfaction with AR still affects the behavioral intentions toward the site through consumers’ attitudes toward the destination. We also demonstrated that the aesthetic experience of AR enables its users to perceive positive utilitarian (e.g., perceived advantage) and hedonic (e.g., perceived enjoyment) attributes and satisfaction with AR. Most of the previous AR studies focused only on the roles of utilitarian attributes of AR (e.g., perceived usefulness and perceived ease of use). The results show that aesthetic experience is important as an antecedent of both perceived utilitarian and hedonic attributes in assessing the performance of AR.

As a practical implication, cultural heritage marketers and system developers can refer to this study in the design and actual operations of AR applications. In order to allow the users to form positive attitudes toward a destination and thereby keep them attracted, the design and development focus must be placed on users’ perceived advantage and aesthetic experience because the results show that these attributes are closely related to satisfaction with AR, while perceived enjoyment is not. The insignificant role of perceived enjoyment on AR satisfaction may be due to the characteristics of cultural heritage tourism, where fun and entertainment attributes are less spotlighted. To be more specific, the users of the AR applications in historical places tend to focus on the information contents from the application, which may improve their historical knowledge and current information.

Further, the way the information is presented was also found to be critical. That is, superimposed digitalized information needs to be harmonious, taking into account the fact that the mobile format is an information distribution channel, instead of simply converting the traditional computer-based website to a smaller screen. Therefore, cultural heritage marketers and system developers should enhance the information contents and aesthetic quality of information provided by AR applications.

The present study has some limitations. First, because AR applications have not yet been commercialized enough to be known to many tourists, we had to produce and provide a manual about how to use the AR applications before distributing the survey. Thus, our study was not able to assess tourists who spontaneously used AR applications at a cultural heritage site. That is, only relatively young and highly interested people who are willing to accept AR applications participated in the survey, which makes it difficult to say that the subjects of this study represent a truly random sample. Second, the AR applications used in tourism have been a mix of virtual reality and video. Thus, the users of the AR applications might misunderstand or misperceive the factors of the AR. It would be useful to investigate tourists who have actually and spontaneously used AR applications so that the relationship among beliefs, attitudes and behavioral intentions can be more accurately assessed.

AR in tourism has wide avenues for future research. For example, downloading activities or the feeling of mastering an AR application at tourism sites may add additional value to the overall tourism experience. We believe that future research will verify these points.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2925146).

Author Biographies

Namho Chung is a professor in the College of Hotel & Tourism Management and the director of Smart Tourism Research Center at Kyung Hee University in Seoul, Republic of Korea. His research interests include travel behavior, information search and decision making, destination marketing, knowledge management and the development of information systems for destination management organizations. His research work has been published in Information & Management, Computers in Human Behavior, Behavior and Information Technology, Electronic Commerce Research and Applications, Journal of Travel Research, Tourism Management, International Journal of Tourism Research, and others.

Hyunae Lee is a doctoral degree student majoring in hotel management at Kyung Hee University, Seoul, Republic of Korea. Her research interests focus on the role of information systems on pre-decision, decision and post-purchase evaluation of tourists. Her research work has been published in International Journal of Contemporary Hospitality Management, Technological Forecasting and Social Change, Knowledge Management Research, and others.

Jin-Young Kim is an assistant professor in the College of Hotel & Tourism Management at Kyung Hee University, Seoul, Republic of Korea. Her research interests include corporate and consumer behavior in the context of hospitality service. Her work has been published in International Entrepreneurship and Management Journal, Cornell Hospitality Quarterly, Computers in Human Behavior, and others.

Chulmo Koo is an associate professor in College of Hotel & Tourism Management, Kyung Hee University, South Korea. He is the editor-in-chief of the Journal of Korea Service Management and a director of Smart Tourism Research Center at Kyung Hee University. Since 2015, he has managed the special issue of “Smart Tourism” in Electronic Markets, Computers in Human Behavior, International Journal of Information Management, International Journal of Tourism Cities, Asia Pacific Journal of Information Systems, Information and Management, Sustainability, Tourism Review, Information Processing and Management, and Journal of Business Research. Dr. Koo is an author of more than one hundred refereed publications and, according to Google Scholar, has H-index 17 and around 1000 citations.

References

Adhani

N. I.

Rambli

D. R. A.

2012. “A Survey of Mobile Augmented Reality Applications.” Paper presented at: 1st International Conference on Future Trends in Computing and Communication Technologies, FTcom 2012, Malacca, Malaysia, December 13-15.

Agarwal

Karahanna

2000. “Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage.” MIS Quarterly 24 (4): 665–94.

Ahuja

M. K.

Thatcher

J. B.

2005. “Moving beyond Intentions and toward the Theory of Trying: Effects of Work Environments and Gender on Post-adoption Information Technology Use.” MIS Quarterly 29 (3): 427–59.

Ajzen

1985. “From Intentions to Actions: A Theory of Planned Behavior.” In Action Control, edited by Kuhl

Beckmann

, 11–39. Heidelberg: Springer.

Al-Gahtani

S. S.

King

1999. “Attitudes, Satisfaction and Usage: Factors Contributing to Each in the Acceptance of Information Technology.” Behaviour and Information Technology 18 (4): 277–97.

Al Sokkar

Law

2013. “Validating an Episodic UX Model on Online Shopping Decision Making: A Survey Study with B2C E-Commerce.” Paper presented at: 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, London, UK, June 24–27.

Alzua-Sorzabal

Linaza

M. T.

Abad

2007. “An Experimental Usability Study for Augmented Reality Technologies in the Tourist Sector.” Information and Communication Technologies in Tourism 2007, 231–42.

Ayeh

J. K.

Law

2013. “Predicting the Intention to Use Consumer-Generated Media for Travel Planning.” Tourism Management 35:132–43.

Azuma

Baillot

Behringer

Feiner

Julier

MacIntyre

2001. “Recent Advances in Augmented Reality.” Computer Graphics and Applications 21 (6): 34–47.

10.

Azuma

R. T.

1997. “A Survey of Augmented Reality.” Presence 6 (4): 355–85.

11.

Barlett

J. E.

Kotrlik

J. W.

Higgins

C. C

. 2001. “Organizational Research: Determining Appropriate Sample Size in Survey Research.” Information Technology, Learning, and Performance Journal 19 (1): 43–50.

12.

Basil

D. Z.

Herr

P. M.

2006. “Attitudinal Balance and Cause-Related Marketing: An Empirical Application of Balance Theory.” Journal of Consumer Psychology 16 (4): 391–403.

13.

Bhattacherjee

2001. “Understanding Information Systems Continuance: An Expectation-Confirmation Model.” MIS Quarterly 25 (3): 351–70.

14.

Bujak

K. R.

Radu

MacIntyre

Catrambone

Zheng

Golubski

2013. “A Psychological Perspective on Augmented Reality in the Mathematics Classroom.” Computers and Education 68:536–44.

15.

Business Wire. 2015. “Research and Markets: Augmented Reality Market 2015—Global Forecast to 2020—Tourism and Logistics Sector to Have a High Growth Rate in the Augmented Reality Market.” http://www.businesswire.com/news/home/20151124005495/en/Research-Markets-Augmented-Reality-Market-2015.

16.

Carroll

J. M.

Thomas

J. C.

1988. “Fun.” ACM SIGCHI Bulletin 19 (3): 21–24.

17.

Caton

Santos

C. A.

2007. “Heritage Tourism on Route 66: Deconstructing Nostalgia.” Journal of Travel Research 45 (4): 371–86.

18.

Chun

Lee

Kim

2012. “The Integrated Model of Smartphone Adoption: Hedonic and Utilitarian Value Perceptions of Smartphones among Korean College Students.” Cyberpsychology, Behavior, and Social Networking 15 (9): 473–79.

19.

Chung

Han

Joun

2015. “Tourists’ Intention to Visit Destination: Role of Augmented Reality Applications for Heritage Site.” Computers in Human Behavior 50:588–99.

20.

Chung

Koo

Kim

J. K.

2014. “Extrinsic and Intrinsic Motivation for Using a Booth Recommender System Service on Exhibition Attendees’ Unplanned Visit Behavior.” Computers in Human Behavior 30:59–68.

21.

Chung

Lee

S. J.

Koo

2015. “The Influence of Tourism Website on Tourists’ Behavior to Determine Destination Selection: A Case Study of Creative Economy in Korea.” Technological Forecasting and Social Change 96:130–43.

22.

Cyr

Head

Ivanov

2006. “Design Aesthetics Leading to M-Loyalty in Mobile Commerce.” Information and Management 43 (8): 950–63.

23.

Davis

F. D.

1985. “A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results.” PhD diss., Sloan School of Management, Massachusetts Institute of Technology, Cambridge.

24.

Davis

F. D.

1989. “Perceived Usefulness, Perceived Ease of use, and User Acceptance of Information Technology.” MIS Quarterly 13 (3): 319–40.

25.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

1992. “Extrinsic and Intrinsic Motivation to Use Computers in the Workplace.” Journal of Applied Social Psychology 22 (14): 1111–32.

26.

Dean

D. H.

2002. “Associating the Corporation with a Charitable Event through Sponsorship: Measuring the Effects on Corporate Community Relations.” Journal of Advertising 31 (4): 77–87.

27.

Deci

E. L.

1975. Intrinsic Motivation. New York: Plenum.

28.

DeLone

W. H.

2003. “The DeLone and McLean Model of Information Systems Success: A Ten-Year Update.” Journal of Management Information Systems 19 (4): 9–30.

29.

DeLone

W. H.

McLean

E. R.

1992. “Information Systems Success: The Quest for the Dependent Variable.” Information Systems Research 3 (1): 60–95.

30.

Eriksson

Kerem

Nilsson

2008. “The Adoption of Commercial Innovations in the Former Central and Eastern European Markets: The Case of Internet Banking in Estonia.” International Journal of Bank Marketing 26 (3): 154–69.

31.

Festinger

1957. A Theory of Cognitive Dissonance, 2 vols. Stanford: Stanford University Press.

32.

Fishbein

Ajzen

1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Boston, MA: Addison-Wesley.

33.

Fling

2009. Mobile Design and Development: Practical Concepts and Techniques for Creating Mobile Sites and Web Apps. Sebastopol, CA: O’Reilly Media.

34.

Fornell

Larcker

1981. “Evaluating Structural Equation Models with Unobservable Variables and Measurement Errors.” Journal of Marketing Research 18 (1): 39–50.

35.

Gretzel

Werthner

Koo

Lamsfus

2015. “Conceptual Foundations for Understanding Smart Tourism Ecosystems.” Computers in Human Behavior 50:558–63.

36.

Han

D. I.

Jung

Gibson

2013. “Dublin AR: Implementing Augmented Reality in Tourism.” Paper presented at: Information and Communication Technologies in Tourism 2014, Dublin, Ireland, January 21–24.

37.

Han

Chung

Koo

Lee

K. J.

2015. “Exhibition Attendees’ Smart Technology Actual Usage: A Case of Near Field Communications.” Paper presented at: Information and Communication Technologies in Tourism 2015, Lugano, Switzerland, February 3–6.

38.

Han

Kim

2013. “An Empirical Study on the Influencing Factors of Intention to Adoption of Mobile Government Service.” Asia Pacific Journal of Information Systems 23 (3): 77–104.

39.

Haugstvedt

A. C.

Krogstie

2012. “Mobile Augmented Reality for Cultural Heritage: A Technology Acceptance Study.” IEEE International Symposium on the Mixed and Augmented Reality (ISMAR). Georgia, Atlanta, November 5-8.

40.

Heider

1946. “Attitudes and Cognitive Organization.” Journal of Psychology 21 (1): 107–12.

41.

Heider

1958. The Psychology of Interpersonal Relations. New York: John Wiley.

42.

Hosany

Witham

2010. “Dimensions of Cruisers’ Experiences, Satisfaction, and Intention to Recommend.” Journal of Travel Research 49 (3): 351–64.

43.

Hsu

S. Y.

Dehuang

Woodside

A. G.

2009. “Storytelling Research of Consumers’ Self-Reports of Urban Tourism Experiences in China.” Journal of Business Research 62 (12): 1223–54.

44.

Hummon

N. P.

Doreian

2003. “Some Dynamics of Social Balance Processes: Bringing Heider Back into Balance Theory.” Social Networks 25 (1): 17–49.

45.

Igbaria

Parasuraman

Baroudi

J. J.

1996. “A Motivational Model of Microcomputer Usage.” Journal of Management Information Systems 13 (1): 127–43.

46.

Jung

Chung

Leue

M. C.

2015. “The Determinants of Recommendations to Use Augmented Reality Technologies: The Case of a Korean Theme Park.” Tourism Management 49:75–86.

47.

Jung

Tom Dieck

M. C.

Lee

Chung

2016. “Effects of Virtual Reality and Augmented Reality on Visitor Experiences in Museum.” Paper presented at: Information and Communication Technologies in Tourism 2016, Bilbao, Spain, February 2–5.

48.

Jung

Leue

M. C.

Lee

Chung

2015. “Examining Online to Offline Service Using Augmented Reality: Theory of Planned Behavior Perspective.” Paper presented at: 2015 World Hospitality and Tourism Forum, Seoul, South Korea, June 25–27, 137–57.

49.

Kaplanidou

Vogt

2006. “A Structural Analysis of Destination Travel Intentions as a Function of Web Site Features.” Journal of Travel Research 45 (2): 204–16.

50.

Kaplanidou

Vogt

2007. “The Interrelationship between Sport Event and Destination Image and Sport Tourists’ Behaviours.” Journal of Sport and Tourism, 12 (3-4): 183–206.

51.

Kerstetter

D. L.

Confer

J. J.

Graefe

A. R.

2001. “An Exploration of the Specialization Concept within the Context of Heritage Tourism.” Journal of Travel Research 39 (3): 267–74.

52.

Kim

H. W.

Chan

H. C.

Gupta

2007. “Value-Based Adoption of Mobile Internet: An Empirical Investigation.” Decision Support Systems 43 (1): 111–26.

53.

Kim

M. J.

Chung

Lee

C. K.

Preis

M. W.

2013. “Motivations and Use Context in Mobile Tourism Shopping: Applying Contingency and Task–Technology Fit Theories.” International Journal of Tourism Research 17 (1): 13–24.

54.

Koo

Wati

Chung

2013. “A Study of Mobile and Internet Banking Service: Applying for IS Success Model.” Asian Pacific Journal of Information Systems 23 (1): 65–86.

55.

Korea Tourism Organization. 2013. Seoul Palace Launched Augmented Reality Mobile App. 2013. “VisitKorea.” http://english.visitkorea.or.kr/enu/FU/FU_EN_15.jsp?cid=1786566.

56.

Kounavis

C. D.

Kasimati

A. E.

Zamani

E. D.

2012. “Enhancing the Tourism Experience through Mobile Augmented Reality: Challenges and Prospects.” International Journal of Engineering Business Management 4 (10): 1–6.

57.

Lai

I. K.

2013. “Traveler Acceptance of an App-Based Mobile Tour Guide.” Journal of Hospitality and Tourism Research 39 (3): 401–32.

58.

Lee

Chung

Jung

2015. “Examining the Cultural Differences in Acceptance of Mobile Augmented Reality: Comparison of South Korea and Ireland.” Paper presented at: Information and Communication Technologies in Tourism 2015, Lugano, Switzerland, February 3–6.

59.

Lee

Choi

Kim

Hong

S. J.

2007. “Culture-Technology Fit: Effects of Cultural Characteristics on the Post-adoption Beliefs of Mobile Internet Users.” International Journal of Electronic Commerce 11 (4): 11–51.

60.

Lee

H. R.

Ham

2014. “The Effects of Presence Induced by Smartphone Applications on Tourism: Application to Cultural Heritage Attractions.” Paper presented at: Information and Communication Technologies in Tourism 2014, Dublin, Ireland, January 21–24.

61.

Lee

K. C.

Chung

2009. “Understanding Factors Affecting Trust in and Satisfaction with Mobile Banking in Korea: A Modified DeLone and McLean’s Model Perspective.” Interacting with Computers 21 (5): 385–92.

62.

Lee

W. Y. Z.

Cheung

Chan

K. H. T.

2014. “Understanding the Development of Problematic Use of Massively Multiplayer Online Game.” Paper presented at: ICIS 2014. Auckland, New Zealand, December 14-17.

63.

Lepp

Gibson

Lane

2011. “Image and Perceived Risk: A Study of Uganda and Its Official Tourism Website.” Tourism Management 32 (3): 675–84.

64.

Y. M.

Yeh

Y. S.

2010. “Increasing Trust in Mobile Commerce through Design Aesthetics.” Computers in Human Behavior 26 (4): 673–84.

65.

Liang

Roast

2014. “Five Features for Modeling Augmented Reality.” Paper presented at: HCI International 2014, Heraklion, Crete, Greece, June 22–27.

66.

Linaza

M. T.

Gutierrez

García

2013. “Pervasive Augmented Reality Games to Experience Tourism Destinations.” Paper presented at: Information and Communication Technologies in Tourism 2014, Dublin, Ireland, January 21–24.

67.

Manrai

L. A.

Manrai

A. K.

Lascu

D. N.

Ryans

J. K.

1997. “How Green-Claim Strength and Country Disposition Affect Product Evaluation and Company Image.” Psychology and Marketing 14 (5): 511–37.

68.

McKone

Haslehurst

Steingoltz

M. V.

2016. “Virtual and Augmented Reality Will Reshape Retail,” Harvard Business Review, September 9. https://hbr.org/2016/09/virtual-and-augmented-reality-will-reshape-retail.

69.

Meyerson

E. M.

1991. Recruitment Procedures and Team Composition. Stockholm: Industrial Institute for Economic and Social Research.

70.

Nelson

R. R.

Todd

P. A.

Wixom

B. H.

2005. “Antecedents of Information and System Quality: An Empirical Examination within the Context of Data Warehousing.” Journal of Management Information Systems 21 (4): 199–235.

71.

Niininen

Szivas

Riley

2004. “Destination Loyalty and Repeat Behaviour: An Application of Optimum Stimulation Measurement.” International Journal of Tourism Research 6 (6): 439–47.

72.

Fiore

A. M.

Jeoung

2007. “Measuring Experience Economy Concepts: Tourism Applications.” Journal of Travel Research 46 (2): 119–32.

73.

Oliver

R. L.

1980. “A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions.” Journal of Marketing Research 17 (4): 460–69.

74.

Oliver

R. L.

1993. “Cognitive, Affective and Attributes of the Satisfaction Response.” Journal of Consumer Research 20 (2): 418–30.

75.

Pallud

Straub

D. W.

2014. “Effective Website Design for Experience-Influenced Environments: The Case of High Culture Museums.” Information and Management 51 (3): 359–73.

76.

Papagiannis

2014. “Working Towards Defining an Aesthetics of Augmented Reality: A Medium in Transition,” International Journal of Research into New Media Technologies 20 (1): 33–40.

77.

Paquet

Viktor

H. L.

2005. “Long-Term Preservation of 3-D Cultural Heritage Data Related to Architectural Sites.” Proceedings of the ISPRS 3D Virtual Reconstruction and Visualization of Complex Architectures, Mestre-Venice, Italy, August 22–24.

78.

Pavlou

P. A.

Gefen

2004. “Building Effective Online Marketplaces with Institution-Based Trust.” Information Systems Research 15 (1): 37–59.

79.

Pine

B. J.

Gilmore

J. H.

1998. “Welcome to the Experience Economy.” Harvard Business Review 76:97–105.

80.

Rosenzweig

2009. The Halo Effect and the Eight Other Business Delusions that Deceive Managers. New York: Simon & Schuster.

81.

Sadeh

2003. M-commerce: Technologies, Services, and Business Models. New York: John Wiley.

82.

San Martín

Herrero

Á.

2012. “Influence of the User’s Psychological Factors on the Online Purchase Intention in Rural Tourism: Integrating Innovativeness to the UTAUT Framework.” Tourism Management 33 (2): 341–50.

83.

Seoul Metropolitan Government Statistics. 2013. “Seoul Statistical Tables: Ancient Palaces.” http://115.84.165.91/jsp/WWS7/WWSDS7100.jsp.

84.

Shin

D. H.

Shin

Y. J.

Choo

Beom

2011. “Smartphones as Smart Pedagogical Tools: Implications for Smartphones as U-Learning Devices.” Computers in Human Behavior 27 (6): 2207–14.

85.

Skog

Ljungblad

Holmquist

L. E.

2003. “Between Aesthetics and Utility: Designing Ambient Information Visualizations.” In INFOVIS: IEEE Symposium on Information Visualization 2003 (IEEE Cat. No. 03TH8714), Seattle, WA, October 19–21, 2003: 233–40.

86.

Sterling

2012. “Helen Paragiannis, Google Glass, and a New Aesthetics of Augmented Reality.” https://www.wired.com/2012/05/helen-papagiannis-google-glass-and-a-new-aesthetics-of-augmented-reality/.

87.

H. J.

Huang

Y. A.

Brodowsky

Kim

H. J.

2011. “The Impact of Product Placement on TV-Induced Tourism: Korean TV Dramas and Taiwanese Viewers.” Tourism Management 32 (4): 805–14.

88.

Tabachnick

B. G.

Fidell

L. S.

2007. Using Multivariate Statistics, 5th ed. Boston, MA: Pearson.

89.

Thong

J. Y.

Hong

S. J.

Tam

K. Y.

2006. “The Effects of Post-Adoption Beliefs about the Expectation-Confirmation Model for Information Technology Continuance.” International Journal of Human-Computer Studies 64 (9): 799–810.

90.

Tractinsky

2004. “Toward the Study of Aesthetics in Information Technology.” Proceedings of ICIS 2004, Paper 62. http://aisel.aisnet.org/icis2004/62.

91.

Trafimow

Sheeran

1998. “Some Tests of the Distinction between Cognitive and Affective Beliefs.” Journal of Experimental Social Psychology 34 (4): 378–97.

92.

Tutunea

M. F. S.

2013. “Augmented Reality-State of Knowledge, Use, and Experimentation.” The USV Annals of Economics and Public Administration 13 (2): 215–27.

93.

Van der Heijden

. 2004. “User Acceptance of Hedonic Information Systems.” MIS Quarterly 28 (4): 695–704.

94.

Venkatesh

Brown

S. A.

2001. “A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges.” MIS Quarterly 25 (1): 71–102.

95.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

2003. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly 27 (3): 425–78.

96.

Weber

2014. “Augmented Reality Gaming: A New Paradigm for Tourist Experiences?” Paper presented at: Information and Communication Technologies in Tourism 2014, Dublin, Ireland, January 21–24.

97.

Woodside

A. G.

Cruickshank

B. F.

Dehuang

2007. “Stories Visitors Tell about Italian Cities as Destination Icons.” Tourism Management 28 (1): 162–74.