Determining the Key Drivers for the Acceptance and Usage of AR and VR in Cultural Heritage Monuments

Abstract

An increasing number of visitor attractions and monuments are designing and implementing augmented reality (AR) and virtual reality (VR) technologies and services to enhance tourists’ experiences and make their visit more attractive and enjoyable. It is essential to acquire an in-depth understanding of AR/VR applications and their influence on consumers’ experience. This paper set to investigate the key factors influencing tourists to adopt and use AR and VR applications by exploring the consumers’ perceptions within the cultural heritage settings, focusing on the use of technological tools during the on-site experience (core service experience). This study applied the Ntheoretical basis of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and suggested a research model of twelve hypotheses. The latter was empirically investigated within the Chinese context in two cultural heritage monuments. The findings indicated that AR/VR technology plays an important role in influencing the tourism experience. Performance expectancy, price value, habit, personal innovativeness, and involvement significantly positively affect behavioral intention. The most important drivers are price value and facilitating conditions. This study’s contribution is twofold: (i) theoretical, by elaborating an extended framework for digital technologies implemented in a challenging field of visitors’ experience, and (ii) practical, by formulating a set of suggestions for industry practitioners as well as designers and developers of AR/VR applications regarding the effective design and implementation of AR/VR technologies in cultural heritage monuments.

1. Introduction

Cultural tourism is an umbrella term encompassing “tangible (e.g., national and world heritage sites, monuments, historical places and buildings, cultural routes) and intangible heritage (e.g., crafts, gastronomy, traditional events and music festivals,” but also “contemporary culture (e.g., films, performing arts, design, fashion, new media).” [1]. In simple terms, cultural tourism is about discovering the unique and defining features, the culture of a country, seeing its art, experiencing its traditions, tasting its cuisine, and strolling through its history and monuments (World Tourism Organization, 1985) [2]. Regarding China, according to recent reports released by the China Tourism Academy (CTA) under the Ministry of Culture and Tourism, cultural tours boomed in China in 2019. They drove the development of China’s culture and tourism industries [3]. According to the CTA, over 80 per cent of tourists attended cultural activities and visited museums in 2019. More cultural venues and tourist attractions extended opening hours and engaged in more nighttime activities in 2019 to cater to visitors’ needs. Chinese consumers actively embrace domestic cultural tourism during the holidays, taking particular interest in sites with historical significance [3].

The consumers’ demand for cultural tourism is increasing; the cultural tourism industry chain also constantly expands and enhances. Cultural tourism shows a multi-field, multi-industry, and multi-region development momentum, forming a trend of a diversified cluster [4]. Following these developments and trends, China’s Ministry of Culture and Tourism recently released the 14th five-year plan for the industry’s growth for 2021–2025. The plan sets out the significant tasks of developing culture and tourism, including the value of cultural heritage and enhancing the modern tourism system by promoting new technologies and digitalization. The plan also underlines the development of internet-enhanced tourism and smart management of tourist attractions [4].

AR/VR technology is applied in today’s tourism industry because it has the characteristics of real-time interaction, tracking and positioning, and human–computer dialogue. It can act as a tour guide anytime and anywhere, make a private and personalized tour route for tourists to lead them to visit scenic spots, and also provide convenience to mark the direction and show the way for tourists. It can also give tourists a good understanding of the scenic spots and provide them with AR/VR technology. It can also combine virtual space and real space to meet the needs of tourists in the tourism process and increase the fun of tourism for tourists by using photo projection. The application of AR/VR technology in the tourism industry can not only make the attractions more fascinating but also promote the future development of tourism, creating a beautiful, convenient, and legendary journey environment for people who love tourism. Therefore, during the visit, tourists can appreciate the rich and far-reaching history and culture and penetrate the true meaning of life’s journey to obtain a better spiritual experience.

Against this background, academic research and business reports indicate that cultural attractions and heritage monuments face challenges resulting from consumer behavior and technological advances, mainly the integration of digital innovation. These visitor attractions must manage the tension of retaining and projecting local authenticity in an increasingly global and virtual world and tackle technological trends in the experience economy [5,6]. Therefore, the challenge is designing and offering cultural experiences amplified through the virtual, augmented, and immersive experiences. The appropriate balance between digital and conventional offerings must also be attained. This challenge requires an in-depth understanding of consumer behavior, specifically their perceptions about adopting and using digital technologies, such as augmented reality (AR) and virtual reality (VR). These digital technologies constitute two technological breakthroughs that stimulate reality perception; both have been applied in tourism settings to improve the consumer experience [7]. It is worth noting that AR superimposes computer-generated data while allowing users to enhance their perception of reality and the surroundings. At the same time, VR technology immerses the user in a synthetic environment where they cannot experience the natural world around them [8].

The topic of AR and VR and their applications and impacts on tourism has attracted scholarly research attention. The main developments in this tourism research field were synthesized by Correia Loureiro, Guerreiro, and Ali [9], Moro, Rita, Ramos, and Esmerado [7,8], and Wei [10]. These four review papers conducted an extensive and comprehensive analysis of studies on AR/VR in tourism. The main findings and suggestions of these papers are as follows: (i) marketing/promotion, tourism education, and tourism experience enhancement, as well as the dimensions and consequences of VR/AR-related user behavior experience, are the most common topics and contexts [2,9,10]; (ii) the most used theories in the studies are the technology acceptance model (TAM), stimulus–organism–response (SOR), theory of planned behavior (TPB), and flow theory [8,9]; (iii) most of the research using AR is based on mobile technology, and wearable devices still show few publications; there is a lack of research adopting other approaches based on secondary data [7]. These review papers also identified the research gaps. They indicated fruitful directions for tourism research on VR/AR applications, such as the lack of theory-based research in VR and AR and more empirical research in various tourism contexts/settings. This study attempts to address one of the knowledge gaps by adopting a consumer behavior perspective.

It is necessary to investigate the topic of AR/VR to acquire an in-depth understanding of the influence on consumers’ experience. The literature needs to sufficiently explore and analyze the adoption and usage of AR/VR technologies by tourists; studies on this issue and within cultural heritage tourism attractions are minimal. Hence, the research question is “What are the key drivers/factors influencing tourist consumers to accept and use AR and VR applications?”. This paper aims to address the gap by investigating the determining factors. This study explores the consumers’ perceptions about the acceptance and use of AR and VR within cultural heritage settings (attractions and monuments), focusing on using technological tools during the on-site experience (core service experience). Toward this aim, this study adopts the theoretical basis of the Unified Theory of Acceptance and Use of Technology (UTAUT) model [11,12]. Based on this model, it takes a consumer perspective to suggest a research model which consists of nine hypotheses. The latter is empirically investigated within the Chinese context of cultural heritage monuments.

This paper begins with a literature review and the theoretical basis adopted in this research field. The following section develops the research hypotheses and model based on the UTAUT2 model. The suggested research model is then tested through an exploratory study using a Supplementary Materials Survey Questionnaire; the main elements of this empirical study and findings are presented and discussed in Section 4. The article is completed by discussing the main conclusion, the study’s implications, and suggestions for future research avenues. The study’s originality/contribution is expected to be twofold: (i) theoretical, the elaboration of an extended UTAUT framework for digital technologies implemented in a challenging field of consumers/visitors’ experience, and (ii) practical/managerial, a set of suggestions and recommendations for industry practitioners as well as designers and developers of AR/VR applications regarding the effective design and implementation of AR/VR technologies in cultural heritage monuments.

2. Literature Review

2.1. Tourism Experience and AR/VR

Tourists are increasingly demanding personalized, emotional, and immersive experiences. AR/VR primarily influences consumer tourism decisions and experiences [13]. Museums use AR/VR technology to engage their visitors and improve their experiences [14]. For tourism suppliers and visitor attractions, the adoption and implementation of AR/VR technology create challenges, one of the main being the effect on the visitor experience [5]. Tourism suppliers need to know the factors and elements that determine consumers’/visitors’ acceptance and use of these digital technologies to address this challenge effectively.

A review of the extant literature indicated that there are two research streams, namely (i) the adoption and use of AR/VR applications and (ii) the AR/VR experience (consumer requirements, dimensions, effects, outcomes, and the factors affecting the tourists’ experience). The main findings of academic research are outlined in the following points.

2.1.1. Consumers’ Adoption and Use of AR/VR in the Tourism Context

A series of studies investigating the adoption and use of AR/VR applications is the focus of our study. Tom Dieck and Jung [15] argued that previous research proposed external dimensions influencing the use; however, scholars should have considered the context-specific dimensions. They suggested an AR acceptance model drawn on TAM in the context of urban heritage tourism. The same authors indicated seven elements that should be incorporated into AR acceptance research: information quality, system quality, costs of use, recommendations, personal innovativeness, risk, and facilitating conditions [15]. Regarding mobile online AR games, Han et al. [16] investigated the attributes of visitor adoption of augmented reality smart glasses (ARSG) in cultural tourism (in an art gallery setting). It was revealed that the main attributes were societal impact, perceived benefits, perceived attributes of innovation, and visitor resistance.

Hassan et al. [17] attempted to identify the determining factors of AR adoption by tourists. Innovativeness and user friendliness features appeared as the dominant reasons and positive elements of AR adoption by tourists. At the same time, availability and technological issues were seen as the negative factors of not adopting AR by tourists. The study by Jung et al. [18] explored the causal relationships among customers’ beliefs, evaluation, attitudes, perceived behavior control (time resources), subjective norms, and intentions to use wearable AR and visit a tourist attraction (an art gallery) using the theory of planned behavior (TPB). Their findings showed that time resources affected the intention to visit an art gallery, while the attitude toward wearable AR impacted the intention to use wearable AR. Subjective norms were found to predict intentions, and the intention to use wearable AR influenced the intention to visit an art gallery.

In the same research area, i.e., technology-supported environments and enhanced experiences, academic research was conducted in the context of AR/VR in tourism education and online shopping [19]. The study by Sun et al. [20] explored the effect of immersion and involvement in the Taiwanese context and found that involvement is a determining factor of the learning. In contrast, immersion of presence is not significant. Ali et al. [21] investigated computer-supported collaborative classrooms in tourism education based on the UTAUT2 model. They revealed that all seven examined factors (performance expectancy, effort expectancy, social influence, facilitating conditions, price value, hedonic motivation, and habit) significantly influenced students’ acceptance and usage of these experience-enhancing technologies/learning tools. Likewise, the acceptance of online virtual platforms as digital game-based learning within a Taiwanese university was examined by Chiao et al. [22] based on the UTAUT model. It was found that all examined factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and interaction) directly affected students’ behavioral intention to use and their behavioral use of the virtual platforms. The UTAUT2 model was also used by Escobar-Rodríguez and Carvajal-Trujillo [23] to explore the consumers’ acceptance and use of LCC e-commerce websites to purchase air tickets. They elaborated an extended UTAUT2 model to include the explanatory variables, the consumers’ innovativeness, and trust as the direct influence on their online purchase intention, and lastly, the adaptation of price value (i.e., price saving) and its effects on the website’s actual use. Their findings indicated that critical determinants of purchasing are trust, habit, cost saving, ease of use, performance and expended effort, hedonic motivation, and social factors, the most important being online purchase intentions, habit, and effort expectancy [23].

2.1.2. Experience and AR/VR: Consumer Requirements, Dimensions, Impact, Outcomes of, and Factors Influencing Tourism Experience

AR/VR provides new ways to improve the original attributes of the destination and attraction, contributing to attracting more visitors and improving the experience. Therefore, VR/AR will likely change how a person experiences the destination or attraction and promote more immersive, interactive, and diverse settings [24]. AR/VR technology enables consumers to understand and experience products and locations in novel ways. VR creates interactive sensorial experiences in cultural contexts based on the applications’ specific features of interaction and immersion [25]. That is why cultural attractions should make more pervasive and effective use of immersive VR. In the context of heritage tourism, AR is a potential tool to overcome the physical boundaries of heritage attractions. Since AR is using digital space to provide additional value, it is believed to support the sustainability of the heritage. Bekele [26] indicates that the purpose of most AR/VR applications is to provide enhancement, followed by exploration, and most AR/VR applications serve virtual museum followed by education and entertainment purposes. Along the same line, Tsai [27] suggested that the LBAR (location-based augmented reality) application forms an immersive experience, positively impacting the place satisfaction of heritage tourists. This impact is also magnified by user engagement and perceived authenticity in heritage tourism.

As for the consumer requirements, research that explores consumer behavior in VR tourism using the stimulus–organism–response (SOR) model identified that the brands and tourism organizations could affect the consumers’ opinions and decisions directly and effectively through this interactivity, experience, and immersion [28]. The roles of hedonic experience and emotional arousal were significant in determining the potential tourists’ behavioral intentions to visit a given destination and motivating them to become potential tourists [29]. Likewise, the navigation of the 3D environment was found to induce positive emotions, flow, and emotional involvement, which positively affect behavioral intentions, further extending the engagement and immersive experience that enhances the tourist’s needs. The study by Yin et al. [30] attempted to identify heritage tourists’ needs and involvement when developing mobile AR heritage applications using a grounded theory approach. This is an essential element in developing travel-oriented mobile AR heritage applications. Four themes emerged from this study, namely “the visitor as a key asset,” “reflecting visitor” “needs,” “visitor empowerment”, and “co-created tourism experiences”. The authors argue that these perspectives of respondents as heritage tourists could pave the way for the development of mobile AR heritage apps. Furthermore, visitor engagement and interaction between developers and visitors can transform visitors into active partners in creating future value through a value co-creation process.

Some valuable studies from a marketing perspective are outlined hereafter. Han, Yoon and Kwon [31] explored how the components of AR experiential value (i.e., visual appeal, entertainment, enjoyment, and escapism) affect supportive behavior through AR satisfaction and experiential authenticity in heritage tourism. They argue that the willingness to support cultural heritage tourism conservation can be an important dependent variable of tourists’ post-visit activities. Furthermore, it is necessary to improve tourists’ satisfaction with augmented reality experiences to increase their willingness to support cultural heritage conservation through authentic experiences. The paper by González-Rodríguez et al. [32] focused on how VR technologies influence tourists’ quality of experience while visiting a cultural heritage destination by using a tourist product based on a virtual tour. Regarding the virtual experience and its effects on tourists, Huang et al. [33] investigated the applicability of flow theory and the concept of involvement in understanding the impacts of virtual experiences of Second Life on consumers’ tourism intentions. Their findings indicated that three factors influence achieving an engaging and pleasant experience in Second Life: the skills available to tackle challenging tasks, the perceived interactivity, and the degree of presence sensation perceived by customers.

Furthermore, the findings indicated that flow experience mediated the association between involvement and consumers’ behavioral intentions. Jung et al. [34] assessed users’ satisfaction with and behavioral intention to recommend AR applications, within the context of a theme park in Jeju Island, South Korea, by applying process theory and using a quality model. Their findings revealed that content, personalized service, and system quality affect visitors’ satisfaction and intention to recommend AR applications. In addition, personal innovativeness was found to reinforce the relationships among content quality, personalized service quality, system quality, and satisfaction with AR. Tom Dieck et al. [35] investigated visitor engagement through AR at science festivals based on the experience economy dimensions framework.

Table 1. Summary of main studies: key influences and determining factors.

Papers/Studies	Context	Key Influences and Determining Factors
1. First research stream: consumers’ adoption and use of AR/VR in the tourism context
Tom Dieck and Jung [15]	Urban heritage tourism	Personal innovativeness, risk, and facilitating conditions
Han et al. [16]	Mobile online AR games in cultural tourism (in an art gallery setting)	Societal impact, perceived benefits, innovation
Jung et al. [18]	Tourist attraction (an art gallery)	Time resources, subjective norms, attitude toward wearable AR
Sun et al. [20]	Tourism education, Taiwan	Involvement
Ali et al. [21]	Computer-supported collaborative classrooms in tourism education	Performance expectancy, effort expectancy, social influence, facilitating conditions, price value, hedonic motivation, and habit
Chiao et al. [22]	Online virtual platforms as digital game-based learning	Performance expectancy, effort expectancy, social influence, facilitating conditions
Escobar-Rodríguez & Carvajal-Trujillo [23]	Low-cost carriers’ e-commerce websites to purchase air tickets	Habit, cost-saving, and effort expectancy
2. Second research stream: experience and AR/VR, the factors affecting the tourists’ experience
Yin et al. [30]	Mobile AR heritage applications	Visitor engagement and interaction
Han, Yoon, and Kwon [31]	AR satisfaction and experiential authenticity in heritage tourism	Components of AR experiential value: visual appeal, entertainment, enjoyment, and escapism
Jung et al. [34]	Theme park in Jeju Island, South Korea	Content, personalized service, system quality, personal innovativeness

depicts a summary of the main influences from the most important papers.

While some studies have been undertaken to identify acceptance factors of AR/VR, mainly mobile AR, research exploring the adoption of AR/VR applications implementing the UTAUT2 model is minimal. Therefore, limited research was conducted on the specific issue of acceptance and use of AR/VR technologies in the heritage tourism context. Moreover, most studies have examined the consumer’s intention to use, not the actual use of AR/VR environments/applications. This study attempts to fill this knowledge gap by adopting the UTAUT2 model as the theoretical foundation.

2.2. Theoretical Basis: An Expanded UTAUT2 Model

UTAUT (Unified Theory of Acceptance and Use of Technology) is an optimal synthesis based on the diffusion of innovation theory, theory of reasoned action (TRA), TPB, and TAM.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is an optimization of the TAM model based on the integration of IDT, TRA, and TPB by Venkatesh and Morris in 2003, which proposes four core variables, performance expectancy, effort expectancy, convenience, and community influence, and four moderators, age, gender, experience, and voluntariness. Among the four core variables of the UTAUT model, performance expectation (PE), effort expectation (EE), and social influence (SI) directly affect individuals’ intention to use and usage behavior. Facilitating conditions (FC) do not directly affect individuals’ intention to use but directly affect users’ usage behavior. PE is a concept similar to the perceived usefulness in the TAM model and refers to the degree to which individuals believe using a system will lead to improved performance. Effort expectancy (EE) refers to the effort individuals put into using a system, a concept similar to perceived ease of use in the TAM model. Social influence (SI) is a measure of social influence. SI is the degree of influence of the social environment on an individual’s use of a system, such as the subjective norm in TAM. FC is the degree of perceived infrastructure related to the use of a system, which can also be translated as "contributing factors". It is also translated as “enabling factors” and expressed as the TRA model’s perceived behavioral control.

The literature suggests that the model UTAUT has been widely utilized to explain the use of technology [36,37]. It was chosen to explore the adoption and usage of various technologies in different contexts [38] and explains 70 per cent of the technology use. In 2012, Venkatesh et al. added the variables hedonic motivation (HM), price value (PV), and habit (HT) to the UTAUT and removed the voluntary adjustment variables to form the UTAUT2 model. Scholars implemented the UTAUT2 with three types of extensions/fields. The first is the application into new environments, such as new technologies (online technologies), new user groups (such as consumers), and new cultural environments/contexts (e.g., China, India). The second type of extension adds new constructs to expand the endogenous theoretical mechanisms described in UTAUT2. The third is to introduce new elements to the UTAUT model and expand its theoretical boundary of UTAUT to enhance its interpretation potential and capabilities.

This study opted for the UTAUT2 as the theoretical basis. The improved UTAUT2 model posits that a consumer’s intention to use and actual usage of a technology is determined by seven factors, namely: PE, EE, FC, SI, HM, PV, and HT [21,39]. The model was adapted to the study’s context and purpose. An in-depth examination of UTAUT2 extensions revealed that one of the popular extensions is the construct of ‘personal innovativeness’ [36]. Moreover, the study incorporates the construct ‘involvement’ as an additional determining factor due to AR/VR-supported environments [13]. Consequently, the research model encompasses nine constructs, namely PE, EE, SI, FC, HM, PV, HB, personal innovativeness (PIN), involvement (INV), behavioral intention (BI), and use behavior (or actual use) of AR/VR applications. The nine constructs (latent variables) of the extended UTAUT2 model are hypothesized to have a significant role as either direct or indirect determinants of user acceptance and usage behavior. The inter-relationships among the constructs are analyzed and discussed in Section 2.3. The following hypotheses were developed and discussed, along with the adaptation of research constructs to this study’s aim and context.

2.3. Development of Research Hypotheses and Model

This study suggests two dependent variables, the behavioral intention to use and the behavior use (or actual use) of AR/VR. Behavioral intention (BI) refers to the likelihood that consumers will use a technological application, the degree to which a person has formulated prearranged plans to perform or not perform a specific behavior [39,40]. It directly determines users’ usage of new technologies and leads to real adoptions [41]. The intention is a factor evaluating how users/consumers are willing to conduct a particular behavior and make an effort to perform it [42]. BI was frequently measured as conative loyalty, an important goal in marketing [43]. Previous studies found positive relationships between the antecedents and the BI. This study investigates the factors influencing visitors’ acceptance and use of AR/VR in heritage monument settings. Regarding the construct BI, three criteria were selected to explore the consumers’ behavioral intentions: the willingness to use, the reuse intention/loyalty, and the positive word-of-mouth communications/recommendations.

Furthermore, evidence suggests that a strong relationship exists between intention to use and actual use; the BI plays a significant role in actual behaviors [18,19,23,40,44,45,46,47,48]. It is worth noticing that research assessing the effect of behavioral intention to use information systems on behavioral use is very limited in the tourism field, with only two studies [22,23] investigating this construct.

In sum, the study’s aim is twofold: (i) to explore the main factors influencing the intention to use AR/VR in cultural heritage monuments and (ii) to examine the impact of behavioral intention, habit, and facilitating conditions on the actual usage of AR/VR in cultural heritage settings. Therefore, it is suggested that the BI is affected by nine constructs/factors, namely PE, EE, SI, FC, HM, PV, HB, PIN, and INV and that the use behavior (actual use) is determined/influenced by BI, FC, and HB. Overall, we have nine independent factors and two dependent variables. There are few studies in the field/context of heritage monuments and cultural tourism in which the UTAUT2 model was adopted. In this study, the advanced hypotheses are supported by research conducted mainly in the other fields where the experiences are technology-enhanced by digital and virtual environments, such as tourism education and online shopping.

The first determinant factor is PE, which is how technology will benefit consumers performing certain digital activities [11,12]. It refers to an individual’s perception that technology facilitates completing a task [12]. It is the extent to which an individual believes a technological application will help them perform their jobs better [22]. Various attributes of technology, such as efficiency, speed, and accuracy, can develop individuals’ performance expectancy, influencing their technology usage intentions and behavior [21]. It is the most significant factor influencing consumers in the online context and in education. This research describes this construct as the level consumers consider that the AR/VR applications in a cultural heritage monument will satisfy their needs. A series of studies confirmed the positive influence and inter-relationship between PE and BI, mainly in digital shopping and education [22,23,40,46,49].

According to findings from previous studies, PE constitutes an antecedent determining the BI of visitors towards AR/VR. Hence, visitors’ PE could significantly influence their acceptance and usage of AR/VR implemented in heritage monuments. Therefore, the following research hypothesis was proposed:

Hypothesis 1 (H1). 

Performance expectancy positively influences the behavioral intention to use AR/VR applications in heritage monuments.

Effort expectancy (EE) is the second latent variable. EE is the degree of ease/effort associated with consumers’ use of a technology [11,12]. It is how easy an individual believes the system is to use. This study describes the factor as the level of affluence/effort linked to the usage of AR/VR applications in heritage monuments. Previous studies [21,22,23,40,46,49] demonstrated that EE positively influences consumers’ BI in a technology-enhanced environment and towards AR/VR. EE constitutes one of the critical determinants of purchasing flights from low-cost carrier (LCC) websites [23] and of accepting experience-enhancing technologies in education, such as computer-supported collaborative classrooms in tourism education [21] and virtual platforms for cultural tourism education [22]. Based on this argument, the following research hypothesis was postulated:

Hypothesis 2 (H2). 

Effort expectancy positively affects behavioral intention to use AR/VR applications in heritage monuments.

Social influence (SI) is described as the level at which a user/consumer is affected by other people (e.g., family, friends, and peers) around them to decide whether to use a particular technology [12]. It is the degree to which others around an individual influence their intention to use a specific technology [22]. The consumers perceive those essential others (e.g., family, peers, educators, and friends) and believe that should they accept and use AR/VR during their museum/monument visit, they will do also so. Previous studies have explored the influence of SI, mainly in online shopping and the digital/virtual education context. Escobar-Rodríguez and Carvajal-Trujillo’s [23] research revealed that this factor determines the behavioral intention and actual usage of online purchasing of tickets for low-cost carriers. Other studies confirmed this finding, e.g., Ali et al. [21], in students using computer-supported collaborative classrooms in tourism education, and Chiao et al. [22], in students using a virtual platform for cultural tourism education. Based on these suggestions and considering the importance of SI as a critical factor in forming visitors’ behavioral intentions in a heritage attraction technology-supported environment, the following hypothesis was advanced:

Hypothesis 3 (H3). 

Social influence positively affects the behavioral intention to use AR/VR applications in heritage monuments.

The fourth factor is facilitating conditions (FC), which refers to the degree to which consumers/users believe that there is an organizational and technical infrastructure to support the use of the system. It is the extent to which a consumer/user has the personal knowledge and organizational resources available to use the technology [12]. In the technology-supported shopping context, education, and VR/AR, consumers/users perceive the resources and support available to use AR/VR applications properly. The positive influence of this factor was found in previous studies, e.g., Escobar-Rodríguez and Carvajal-Trujillo [23] and Palau-Saumell et al. [48]. Likewise, the factor FC was found to be one of the drivers of intentions to use a virtual platform in cultural tourism education [22], technology-supported collaborative learning [21], and mobile restaurant applications [48]. Escobar-Rodríguez and Carvajal-Trujillo [23] also indicated that FC is one of the main predictors of online purchase intention, although it is the last in order of importance. Based on these suggestions, the following research hypotheses were formulated:

Hypothesis 4 (H4). 

Facilitating conditions perceived in AR/VR positively affect the behavioral intention to use AR/VR applications in heritage monuments.

Hypothesis 5 (H5). 

Facilitating conditions perceived in the use of AR/VR positively affect the use of AR/VR applications in heritage monuments.

Hedonic motivation (HM) is the motivation to do something due to internal satisfaction. HM is the perceived pleasure or enjoyment of using technology [12]. It is the satisfaction and happiness that consumers obtain when using technology. It is similar to perceived enjoyment and entertainment [50]. This antecedent concerns the fun and pleasure tourists experience when using digital platforms [51]. The literature has established perceived enjoyment’s role in intrinsic motivation to describe information system adoption [51,52]. The interactive nature of AR/VR is a source of entertainment for visitors. Therefore, this research defines HM as the desire to use AR/VR applications in heritage. Thus, in the case of monument visits, the perceived enjoyment will be the visitors’ enjoyable experience of using AR/VR to explore the site and its exhibits.

In previous studies, HM was endorsed as a determinant of BI within the context of digital-enhanced environments, such as the use of the 3D virtual world in tourism marketing [53], virtual learning experiences [54], technology-supported collaborative classrooms [21], and online travel agencies [55]. Previous studies explored the influence on and relationship of HM with BI and actual usage, e.g., [23,48], confirming that it is one of the main determinants. Therefore, considering the importance of perceived HM as a critical factor in influencing consumers’ behavioral intention in technology-enhanced environments, the following hypothesis was postulated:

Hypothesis 6 (H6). 

The perceived hedonic motivation positively affects the behavioral intention to use AR/VR applications in heritage monuments.

Price value (PV) is the consumers’ cognitive trade-off between the perceived benefits of the applications and the cost of using them [12,56]. The PV construct is often important in predicting consumer purchase behavior. Commonly, the definition of the price value is a trade-off between benefits and sacrifices [46]. The PV is positive when the benefits of using technology are more significant than the monetary costs. Some studies on online and mobile purchase contexts [46,51] and technology-supported education [21] endorsed the positive influence of perceived PV on the behavioral intentions of consumers and students. Similarly, [23,48] found similar constructs—’price saving’ and ‘price-saving orientation’—are two of the main predictors of digital purchase intention. Based on these suggestions, the following research hypothesis was advanced:

Hypothesis 7 (H7). 

The perceived price value positively affects the behavioral intention to use AR/VR applications in heritage monuments.

Habit (HT) is the seventh factor. It is defined as the extent/degree to which users/consumers use technology behaviors automatically because of learning [12,57]. Studies on habitual intentions and habitual usage behaviors have demonstrated that habit is a strong predictor of technology usage. For instance, Kim and Malhotra [58] also argued that ‘prior use/habit’ is relevant to technology acceptance and use. The habit construct encompasses past behavior, reflex behavior, and individual experience [59]. Findings from recent studies [21,23,48] indicated that habit is one of the key factors determining consumer acceptance and adoption of technology. The consumers’ habit of utilizing the platform was found to be the first driver in order of impact on intentions to use MARSR [48], the second determinant in order of importance of online purchasing [23], and an essential factor in the context of technology-supported collaborative learning [21]. Based on these arguments, the following research hypotheses were postulated:

Hypothesis 8 (H8). 

The habit of using AR/VR positively affects the intention to use AR/VR applications in heritage monuments.

Hypothesis 9 (H9). 

The habit of using AR/VR positively affects their use in heritage monuments.

As already mentioned, this study suggests incorporating two additional constructs—personal innovativeness (PIN) and involvement (INV)—as a popular extension of the UTAUT2 model and valuable/determining factors in the explored context.

Consumers’ innovativeness greatly influences the adoption of technologies. Personal innovativeness was defined by Agarwal and Prasad [60] in the domain of information technology as “the willingness of an individual to try out any new information technology.” [44,61]. Goldsmith and Hofacker [62] stated that innovativeness is specific to a domain. The role domain-specific innovativeness plays in online purchase intention has been demonstrated in different contexts [44,61,63]. A limited number of studies analyzed the influence of innovativeness in the context of online tourism shopping [23] and urban heritage tourism [15]. Therefore, it is necessary to analyze the influence of the consumers’ innovativeness on their BI to use AR/VR in the specific domain of cultural heritage. Thus, based on this argument, the following hypothesis was advanced:

Hypothesis 10 (H10). 

The consumers’ innovativeness in technology-enhanced environments positively affects the behavioral intention to use AR/VR applications in heritage monuments.

In simple terms, involvement is a concept similar to participation. The concept of social psychological involvement has been considered by academic research mainly from the consumer behavior perspective. In this research realm, Havitz and Dimanche [64], based on suggestions by Rothschild [65], proposed the following definition of involvement in recreation and tourism settings: “a psychological state of motivation, arousal, or interest between an individual and recreational activities, tourist destinations, or related equipment...”. Few studies explored the influence of this factor in virtual environments, mainly in terms of presence [66,67]. In tourism research, Huang et al. [54] endorsed the relationship between involvement and consumer behavioral intention in the context of virtual experience and its impact on tourist decision making. Along the same line, Sun et al. [20] explored the effect of involvement in the Taiwanese context and found that involvement is a determining factor of interaction in virtual experiences/environments. It was found that the consumers’ perceived effectiveness is positively influenced by involvement. In this study, it is hypothesized that involvement will be positively associated with the behavioral intention of visitors to heritage monuments, as follows:

Hypothesis 11 (H11). 

The consumers’ perceived involvement in the AR/VR environments positively affects their behavioral intention to use AR/VR applications in heritage monuments.

The last hypothesis was formulated as follows:

Hypothesis 12 (H12). 

Visitors’ intentions to use AR/VR significantly affect the actual usage of AR/VR applications in heritage monuments.

As already mentioned, the above factors can directly and positively affect visitors’ intentions to use and behavior use of AR/VR in heritage monuments. Therefore, this study posits the following research model on AR/VR applications in heritage monuments, as depicted in Figure 1.

3. Research Methods

The study aimed to investigate the Chinese consumers’/visitors’ perceptions of the factors influencing the adoption and usage of AR/VR in heritage monuments. Twelve hypotheses were postulated, and a survey was conducted to explore the hypothesized relationships. The model was tested through an exploratory study detailed in the following points.

3.1. Research Instrument: Questionnaire and Measurements

This study adopted a set of measurement items based on the literature on technology acceptance—the extended UTAUT model (UTAUT2) and related studies—in line with the extant literature on online shopping and technology-enhanced education, adapted to the specific context of this study on AR/VR [12,18,21,45,67]. The measurement items (observed variables) and the supporting studies are shown in

Table 2. Measurement items.

Factor/Latent Variable	Measurement Items/ Observed Variables	Supporting Studies
Performance expectancy (PE)	PE1: I find AR/VR applications useful in my visit. PE2: Using AR/VR enables me to perform my visit better and more conveniently (effectively). PE3: Using AR/VR increases my productivity and performs my visit quicker (saving time). PE4: Using AR/VR to render my visit more efficient.	Venkatesh et al., 2012 [12], Nunkoo & Ramkissoon 2013 [40], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Ali et al., 2016 [21], Chiao et al., 2018 [22], Gu et al., 2019 [49], Palau-Saumell et al., 2019 [48]
Effort expectancy (EE)	EE1: Learning how to operate and use AR/VR is easy for me. EE2: Using AR/VR does not require a high volume of mental effort. EE3: It is easy for me to become skillful at using AR/VR EE4: My interaction with AR/VR is clear and understandable	Venkatesh et al., 2012 [12], Nunkoo & Ramkissoon 2013 [40], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Ali et al., 2016 [21], Chiao et al., 2018 [22], Gu et al., 2019 [49], Palau-Saumell et al., 2019 [48]
Social influence (SI)	SI1: People who influence my behavior think I should use AR/VR. SI2: People who are important to me think I should use AR/VR to boost my social image. SI3: Most of my relatives, friends, and acquaintances recommend AR/VR. SI4: AR/VR is the current trend, and I recommend them to others.	Venkatesh et al., 2012 [12], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Huang & Kao 2015 [59], Ali et al., 2016 [21], Chiao et al., 2018 [22]
Facilitating conditions (FC)	FC1: Heritage monument has the adequate/necessary to use AR/VR. FC2: I have the knowledge required to use AR/VR. FC3: AR/VR environment is compatible with my smartphone/tablet I use. FC4: A specific person was available for assistance with difficulties in using AR/VR.	Venkatesh et al., 2012 [12], San Martín & Herrero 2012 [44], Huang et al., 2013 [54], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Huang & Kao 2015 [59], Ali et al., 2016 [21], Chiao et al., 2018 [22], Palau-Saumell et al., 2019 [48]
Hedonic motivation (HM)	HM1: Using AR/VR was fun. HM2: Using AR/VR was interesting. HM3: Using AR/VR was enjoyable. HM4: The AR/VR experience is very pleasant.	Venkatesh et al., 2012 [12], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Lai 2015 [51], Huang & Kao 2015 [59], Ali et al., 2016 [21], Palau-Saumell et al., 2019 [48], Jung et al., 2020 [18]
Price value (PV)	PV1: The use of AR/VR is beneficial for me. PV2: The use of AR/VR was worth the money I paid. PV3: AR/VR provides a good value.	Venkatesh et al., 2012 [12], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Huang & Kao 2015 [59], Kim and Qu 2014 [46], Ali et al., 2016 [21], Palau-Saumell et al., 2019 [48]
Habit (HT)	HT1: The use of AR/VR has become a habit for me, natural to me. HT2: I am addicted to using AR/VR. HT3: I must use AR/VR.	Venkatesh et al., 2012 [12], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Huang & Kao 2015 [59], Ali et al., 2016 [21], Palau-Saumell et al., 2019 [48]
Personal innovativeness (PIN)	PIN1. If I heard about new information technology, like AR/VR, I would look for ways to experiment with it. PIN2. I am usually the first to explore new digital technologies among my peers. PIN3. I like to experiment with new digital and smart technologies.	Crespo and del Bosque 2008 [61], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Jung et al., 2015 [34]
Involvement (INV)	INV1: The visual aspects of the AR/VR environment are involving. INV2: The auditory elements of the AR/VR environment are participative/involving. INV3: The AR/VR environment is interesting, interactive, and engaging.	Witmer & Singer 1998 [67], Sylaiou et al., 2008 [66], Huang et al., 2012 [33], Sun et al., 2015 [20]
Behavioral intention to use (BI)	BI1: I am likely to use AR/VR next time I visit a monument. BI2: I recommend AR/VR to my relatives, friends, and peers. BI3: My willingness to visit a heritage monument with AR/VR is higher than to visit those not having AR/VR.	San Martín & Herrero 2012 [44], Venkatesh et al., 2012 [12], Wen 2012 [45], Nunkoo & Ramkissoon 2013 [40], Escobar-Rodríguez & Carvajal-Trujillo 2014 [23], Ali et al., 2016 [21], Jung et al., 2020 [18]
Behavior use (BU)	BU1: I like revisiting this monument after experiencing the AR/VR application.	Chiao et al., 2018 [22], Jung et al., 2020 [18]
	BU2: I like visiting heritage monuments having AR/VR applications.

Source: Retrieved from extant literature and supporting studies in the last column.

.

Following the procedure described a total of 37 items were obtained, as shown in Table 1. The four items measured the constructs of PE, EE, SI, FC, and HM. Price value, HA, PIN, and INV were measured using three items each. Behavioral intention was measured using three items, whereas the use behavior construct was measured using two items.

The design and finalization of the research instrument were performed in five steps. First, the items in the questionnaire were validated based on the opinions of a panel of researchers and experts/industry practitioners (4 persons overall). Based on the panel’s views, some improvements were made to improve clarity. The research instrument (questionnaire) was first drafted in the English language (the language for this paper) and then translated into Chinese (participants). The second step was to check the translation’s quality; two academics performed this task in order, the aim being to verify the mutual understanding between the English and Chinese languages and to ensure consistency between the two versions.

The next step was to perform a pilot test with a group of 15 visitors in a heritage monument (Liangzhu Museum) in Ningbo to ensure that the questions were unambiguous. Participants were asked to fill in the initial questionnaire to check whether all questions were clearly and adequately formulated. Following the pilot test, the questions were revised and finalized accordingly. Based on the outcome of this test, the research team completed all items, rephrasing some items to enhance additional simplicity and easiness.

The research instrument encompassed four sections: (i) AR/VR familiarity and usage (with four questions); (ii) general opinion/perception about using AR/VR in monuments/museums (1 construct with 3 items); (iii) the ten research constructs (with 37 items); and (iv) demographics (with six questions). The responses to each of the items were rated on a 7-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). This procedure is the usual way of measuring variables that are not directly quantifiable or observed.

3.2. Sampling and Data Collection

The present research is an exploratory quantitative study conducted at the Liangzhu Ancient City Site Park and the Liangzhu Museum. The Liangzhu Ancient City peripheral hydraulic system is the earliest known large-scale hydraulic project in China and the earliest dam in the world. The Liangzhu Ancient City was a regional center of power and belief at an early stage in the Taihu Lake area around the lower Yangtze River in China. Liangzhu culture had a far-reaching influence on the development of Chinese civilization for the next 5000 years, and it can be verified that the development characteristics of Chinese civilization—pluralism and unity—have been authentically and completely preserved. It is a large prehistoric settlement site in East Asia with outstanding representation in the history of human civilizations. It is currently able to attract more than 2 million visitors per year. This project explores the factors influencing tourists’ willingness to use AR and VR.

This research used the convenience sampling method in both sites/monuments over a period of two months, August to September 2022. A total of 584 participants were recruited, with 204 participants at the Liangzhu Ancient City and 380 participants were visitors of the Liangzhu Museum. A member of the research team (the first author) randomly searched for potential participants, forming a group with an average size of 20 tourists. The response rate was 80 per cent. A pre-screening question about the previous usage of AR/VR technology (“Have you used AR/VR technology?”) was used to qualify/disqualify potential participants. The research’s purpose was explained, and tourists were asked if they were willing to participate in the study by completing the questionnaire. The researcher informed all participants about the approximate time to complete the survey (i.e., 20 min). We also ensured the confidentiality of the information and the freedom of the response process before the survey began. No incentives were offered to potential participants. From each group 15 visitors were selected.

After excluding the invalid questionnaires as well as those without values/answers, a volume of 538 utilizable questionnaires were collected from Chinese consumers aged 18 years and older and having previous experience in using AR/VR technology. The sample’s characteristics are depicted in

Table 3. Sample’s profile (n = 538).

Characteristic	Options	Frequency	Percentage
4.1 Gender	Male	264	49.1
	Female	274	50.9
4.2 Age group (years)	18 to 25	139	25.84
	26 to 35	131	24.35
	36 to 45	112	20.82
	46 to 55	92	17.20
	56 and older	64	11.89
4.4 Educational level	Junior school and below	30	5.58
	High school or equivalent	125	23.23
	University or equivalent	164	30.48
	Master and above (postgraduate)	219	40.71
4.5 Profession/capacity	Student	36	6.69
	Public servant/official	112	20.82
	Company employee	142	26.39
	Privately or individually owned business	93	17.29
	Farmer	89	16.54
	Freelancer	62	11.52
	Others (corporate personnel)	4	0.74
4.6 Visits/experiences to monuments: number of visits/experiences in monuments (average per year)	1 to 2	116	21.56
	3 to 4	176	32.71
	5 to 7	125	23.23
	8 to 10	83	15.43
	10+	38	7.06

. All items in the questionnaire, except personal information, were measured on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7).

The two genders were evenly distributed. Regarding age, the group of 18 to 25 was almost 26 per cent. One out of three (32.71 per cent) indicated that they have on average 3 to 4 visit experiences per year.

3.3. Data Analysis and Results

This study firstly used SPSS software to summarize and organize the data, correct and remove abnormal data, and perform descriptive row analysis, correlation analysis, and regression analysis. Then, AMOS software was used to perform path analysis, model fit, and hypotheses validation.

3.3.1. Measurement Model

The average is used to measure the variable value’s average level and concentration trend based on a formal research sample. The average values of PE, EE, SI, FC, HM, PV, HT, PIN, and INV are around or higher than 5.00 (5.21, 5.22, 5.12, 5.22, 5.30, 5.00, 4.83, 5.02, and 4.96, respectively). Reliability and validity analyses were also conducted. According to the reference standard, the reliability is acceptable when the Cronbach’s alpha coefficient is between 0.6 and 0.8. When the alpha coefficient is greater than 0.8, the reliability is very good. As shown in

Table 4. Reliability and validity analysis.

Items	Mean	Average Value	Standard Deviation	Factor Loadings	Composite Reliability	AVE	Cronbach’s α
Performance expectancy (PE)	PE1	5.253	1.391	0.77	0.863	0.612	0.861
	PE2	5.152	1.335	0.808
	PE3	5.21	1.356	0.84
	PE4	5.243	1.376	0.734
Effort expectancy (EE)	EE1	5.294	1.402	0.777	0.853	0.593	0.849
	EE2	5.199	1.426	0.827
	EE3	5.193	1.381	0.847
	EE4	5.175	1.466	0.668
Social influence (SI)	SI1	5.186	1.452	0.754	0.871	0.628	0.869
	SI2	5.095	1.443	0.837
	SI3	5.108	1.411	0.844
	SI4	5.167	1.416	0.732
Facilitating conditions (FC)	FC1	5.32	1.358	0.746	0.861	0.609	0.859
	FC2	5.195	1.397	0.833
	FC3	5.195	1.392	0.838
	FC4	5.165	1.434	0.757
Hedonic motivation (HM)	HM1	5.335	1.371	0.746	0.874	0.635	0.87
	HM2	5.206	1.406	0.83
	HM3	5.34	1.388	0.827
	HM4	5.309	1.443	0.708
Price value (PV)	PV1	5.123	1.381	0.816	0.876	0.701	0.875
	PV2	4.952	1.38	0.852
	PV3	5.037	1.388	0.83
Habit (HT)	HT1	4.898	1.466	0.78	0.878	0.707	0.876
	HT2	4.814	1.474	0.872
	HT3	4.851	1.501	0.866
Personal innovativeness (PIN)	PIN1	5.117	1.439	0.804	0.867	0.685	0.865
	PIN2	4.983	1.428	0.823
	PIN3	5.056	1.408	0.799
Involvement (INV)	INV1	4.98	1.535	0.806	0.877	0.703	0.875
	INV2	4.913	1.429	0.857
	INV3	5.095	1.401	0.814
Behavioral intention to use (BI)	BI1	5.002	1.508	0.784	0.871	0.693	0.869
	BI2	4.968	1.442	0.84
	BI3	4.961	1.461	0.798
Behavioral use (BU)	BU1	4.887	1.681	0.872	0.838	0.722	0.838
	BU2	4.881	1.641	0.854

, all Cronbach’s α coefficients are greater than 0.8, indicating that constructs have good reliability. Validity analysis was conducted for convergent validity (CV) and discriminant validity (DV) to reflect the authenticity and validity of the collected data. CV tests included composite reliability (CR) and average variance extraction (AVE). According to Table 4, the factor loadings were higher than the criterion of 0.5. The CR was higher than the criterion of 0.6, indicating a good CR for all constructs. Moreover, the AVE values were higher than 0.5.

The results of DV analysis are shown in

Table 5. Discriminant validity.

	PE	EE	SI	FC	HM	PV	HT	PIN	INV	BI	BU
PE	0.783
EE	0.321	0.770
SI	0.34	0.362	0.792
FC	0.33	0.344	0.357	0.780
HM	0.421	0.411	0.367	0.364	0.797
PV	0.272	0.292	0.318	0.271	0.318	0.838
HT	0.238	0.239	0.315	0.226	0.299	0.295	0.841
PIN	0.343	0.285	0.309	0.245	0.319	0.355	0.404	0.828
INV	0.283	0.266	0.282	0.242	0.353	0.365	0.343	0.448	0.839
BI	0.361	0.259	0.306	0.281	0.352	0.432	0.376	0.437	0.397	0.833
BU	0.29	0.276	0.306	0.31	0.354	0.277	0.236	0.251	0.301	0.229	0.849

Note: Boldface characters are AVE square root values.

. The absolute value of the correlation coefficient is less than 0.5 and smaller than the corresponding square root of AVE. Therefore, there is some correlation between each latent variable and there is a degree of difference between them, indicating that the DV of the data is ideal.

3.3.2. Test of Structural Model and Hypotheses

This study used the structural equation model to examine the relationship between the variables. The model fit test was performed using AMOS and the results are shown in

Table 6. Model fitting effect.

Commonly Used Metrics	χ2	df	p	Chi-Square Degrees of Freedom Ratio χ2/df	GFI	RMSEA	RMR	CFI	NFI	NNFI	SRMR
Criteria	-	-	>0.05	<3	>0.9	<0.10	<0.05	>0.9	>0.9	>0.9	<0.1
value	1268.07	582	0	2.179	0.883	0.047	0.106	0.94	0.895	0.931	0.049

. The model fit test (χ²/df) is lower than 3. The CFI and NNIF are higher than 0.9, and SRMR is smaller than 0.1. These results indicate that the model fits well.

Usually, the standard path coefficient indicates the influence relationship between variables. The results are shown in

Table 7. Path analysis and hypothesis validation.

X	→	Y	Non-Standardized Path Coefficients	Itself	z (CR Value)	p	Standardized Path Coefficients	Supported or Rejected
PE	→	BI	0.185	0.06	3.08	0.002	0.16	H1: Supported
EE	→	BI	−0.014	0.063	−0.224	0.823	−0.011	H2: Rejected
SI	→	BI	0.036	0.06	0.598	0.55	0.031	H3: Rejected
FC	→	BI	0.064	0.064	0.992	0.321	0.051	H4: Rejected
PV	→	BI	0.074	0.067	1.102	0.27	0.061	H5: Rejected
HM	→	BI	0.239	0.057	4.199	0	0.21	H6: Supported
HT	→	BI	0.137	0.054	2.56	0.01	0.126	H7: Supported
PIN	→	BI	0.19	0.062	3.048	0.002	0.167	H8: Supported
INV	→	BI	0.123	0.053	2.322	0.02	0.123	H9: Supported
FC	→	BU	0.504	0.075	6.716	0	0.357	H10: Supported
HT	→	BU	0.228	0.061	3.725	0	0.184	H11: Supported

and Figure 2. If it is significant, it indicates a significant effect between the constructs, supporting or rejecting the hypothesized relationship.

As can be seen from Table 7, PE has a significant positive impact on BI (normalized path coefficient value is 0.185 > 0, z = 3.08, p = 0.002). Likewise, the factor HM has a significant positive influence on BI (normalized path coefficient value is 0.239 > 0, z = 4.199, p = 0). This also stands for HT (normalized path coefficient value is 0.137 > 0, z = 2.56, p = 0.001); PIN (normalized path coefficient value is 0.19 > 0, z = 3.048, p = 0.002); and INV (normalized path coefficient value is 0.123 > 0, z = 2.322, p = 0.02).

The construct EE does not significantly affect the BI (z = −0.224, p = 0.823). This is also valid for the construct SI (z = 0.598, p = 0.55), the FC (z = 1.992, p = 0.321), and the PV (z = 1.102, p = 0.27).

The actual use (BU) was found to be significantly and positively affected by FC (normalized path coefficient value is 0.504 > 0, z = 6.716, p < 0.001) and HT (normalized path coefficient value is 0.228 > 0, z = 3.725, p < 0.001). Therefore, seven hypotheses (H1, H6, H7, H8, H9, H10, and H11) are supported. On the contrary, hypotheses H2, H3, H4, and H5 are not supported. In summary, EE, SI, FC, and HM have no significant positive effect on BI, while PE, PV, HT, PIN, and INV, have a significant positive effect on BI. It is worth pointing out that HM has the most significant effect on BI, and FC has the highest impact on BU.

4. Discussion, Conclusions, and Implications

The adoption and implementation of digital and smart technologies by tourism suppliers and destinations have become mainstream over the last few years. Sometimes the design and implementation of these technologies for managerial and marketing purposes are well thought out and planned. In other instances, tourism destinations and suppliers are improvising, following, and imitating other industry applications and services. This study argues that the design and implementation of AR/VR in tourism services and experiences should be drawn based on good knowledge and apprehension of tourist consumer behavior, their requirements, needs, and expectations. Any application and services provided to enhance, enrich, and facilitate tourists’ experience should consider the factors determining their attitude and behavior. The main purpose of our study was to investigate the key drivers of the adoption and use of VR/AR technologies by tourists. It took a tourist consumer perspective to explore this issue within the context of Chinese cultural monuments based on the theoretical foundations of the UTAUT2 model. An extended conceptual model was suggested positing/assuming that the tourists’/visitors’ willingness to use and actual use of AR/VR technologies and applications are determined by nine factors, namely performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI), hedonic motivation (HM), price value (PV), habit (HT), involvement (INV), and personal innovativeness (PIN). It was also advanced that the factors FC and HT, along with the behavioral intentions, influence the behavior use (actual use) of AR/VR technologies.

This research selected Liangzhu Ancient City and Liangzhu as the study site. It provides implications for how to rely on visualization technology to promote the conservation of cultural heritage resources and stimulate the development potential of tourism destinations. This study is based on visualization technology to promote the conservation of cultural heritage resources and stimulate tourism destinations’ development potential. It was revealed that AR/VR technology plays an important role in influencing the tourism experience at heritage/cultural attractions. More specifically, it was found that PE, HM, HT, PIN, and INV significantly positively affect BI. The most important driver is HM, confirming previous studies [57]. Several studies on online and mobile purchasing environments [46,51] and technology-supported education [21] support the positive impact of perceived HM on consumer BI. The use of AR/VR by tourists during tourism activities and visits is beneficial in terms of value.

This study’s results regarding some key factors are contradicting extant literature. According to extant literature, EE, SI, and FC are key factors in UTAUT2, and they have a positive and significant impact on consumers’ BI. Furthermore, the positive impact of FC was endorsed by previous studies such as [23,48]. This study revealed that FC was not considered by Chinese consumers as a factor influencing their willingness (BI) to use AR/VR in heritage monuments. On the contrary, the factor FC seems to significantly be impacting the actual behavior use (BU). A key finding of this study is that BI is a strong predictor of BU, confirming the strong and positive relationship posited by the UTAUT2 model. Usage behavior occurs when visitors have a strong intention to use, confirming the suggestions of previous studies, e.g., [21,23].

A possible explanation for this finding related to FC is that consumers have long experience, good know-how, and familiarity with digital and smart technologies; thus, they do not need any support or specific resources.

Similarly, another two core variables in the UTAUT2 model (i.e., EE and SI) were found to have insignificant influence on tourists’ BI. The study’s findings indicated that EE and SI did not significantly influence the visitors’ willingness to use AR/VR applications in heritage monuments. This insignificant relationship could be explained and justified by the following elements/reasons. The first reasons could be the familiarity of Chinese consumers with smart technologies in general, AR/VR included. Chinese consumers have the perception that the AR/VR applications are easy to use, and there is no high effort associated with their usage. That is why they believed that this factor could not significantly influence their intention to use them. There is no degree of effort linked to their use, according to their opinion. The second reason or additional element to consider is the time since the publication of the pivotal study by Venkatesh et al. (2012) [12]. Most likely, ten years of extensive and numerous experiences of technological applications by consumers could result in a latent acceptance of these applications, not requiring a high degree of effort nor social influence and recognition. Consumers and visitors are willing to use AR/VR and other smart and digital technologies regardless of the effort required and the opinion of their relatives and peers. Therefore, Chinese visitors to heritage monuments could be more confident about their skills to use smart technologies because they are experienced, and they trust in their utility and value without taking into consideration the influence exerted by others.

These are the findings of our research in the specific context and settings (i.e., cultural heritage monuments in China). According to the study’s findings, their BI is significantly influenced by five factors, i.e., PE, HM, HT, PIN, and INV, not by EE, PV, FC, and SI. Nevertheless, it is worth pointing out that FC was found to have significant impact on actual BU, finding that is in line with the UTAUT2 model. At the end of the day, ultimately, what is critical is the BU and according to our study’s findings FC as a very strong predictor of the BU of AR/VR applications in the investigated context.

The above-outlined findings have both theoretical and practical implications. From an academic/theoretical perspective, this study proposed and validated an extended framework for digital technologies (AR/VR) implemented in the field of visitors’ experience. Our approach considered and incorporated the key factors influencing the adoption and usage of AR/VR technologies in cultural visitor attractions. The proposed framework should constitute a valuable and useful foundation for future research endeavors on tourism experiences enhanced and supported by any digital technological tools (e.g., smart mobile, Artificial Intelligence) within various settings and contexts (other visitor attractions, hotels, restaurants, and the cruise industry).

Likewise, the study’s findings have practical implications (managerial and marketing). Tourism managers and marketers should take seriously into account and incorporate into their services all key drivers determining the use of AR/VR applications. The design and implementation of such a digital technology infrastructure requires high investments. For tourism suppliers and destinations, the challenges and risks of adopting VR/AR technology include the technology’s costs, setup and maintenance skills, commercial considerations, and impact on the visitor experience. One major challenge in the widespread adoption of AR and VR technology is finding the right commercial model [68]. While these technologies can improve the visitor experience, significant and ongoing technological investments are required. Tourism businesses must choose between providing these technologies to attract visitors and stay competitive or charging for their use [5]. The study’s findings indicated that consumers/visitors are cost-minded and have a ‘price-saving orientation’; the factor of PV is very influential. Visitors (at tourist attractions) and customers (of the tourism business) will use AR/VR applications and services when the benefits are more significant than the monetary costs [12]. Therefore, managers and marketers of tourism businesses should be very careful when deciding the extra price fees (costs of use) of digital services. Another interesting finding is that the factors influencing the tourism/visitor experience vary depending on the technology, the context (smart, mobile, and online), the setting (industry and context), and the stage of the customer/visitor decision journey (pre-, during, and post-visit).

AR/VR technologies can also present sustainable development opportunities for cultural heritage tourism destinations in the post-epidemic era. COVID-19 has had a long-term negative impact on the tourism industry and has prompted a rethinking of the tourism growth model. First, government agencies should increase appropriate technology incentives and policy guidance, such as tax breaks, subsidies, and deferred repayment. These measures can stimulate the application of modern technological tools such as AR/VR in cultural heritage tourism places from a policy perspective and promote the sustainability of cultural heritage tourism in the post-epidemic era. Second, tourism companies should focus on the factors that influence tourists’ application of AR/VR technologies. Since tourists’ PE and PV of AR/VR technology significantly affect their willingness to act, enriching AR/VR technology tourism products is important in highlighting virtual reality technology facilitation, tourism experience function, and immersion. Technology developers can consider the demand of tourists for AR/VR tourism products and build a virtual tourism platform employing AR/VR panoramic technology or 3D modeling. Some good ways to enhance the cultural heritage and increase the attractiveness of monuments include the integration of entertainment elements to create highly realistic scenes and increase the perceived immersion.

Moreover, setting reasonable fees/prices for using AR/VR applications and simplifying operational processes could enhance the visitors’ PE and PV. Finally, tourism destination marketing organizations can enrich the relevance of cultural heritage in the post-epidemic era with the power of AR/VR technology. Mass media should rely on the immersion of AR/VR technology to maximize the restoration of heritage’s original appearance and consider creative marketing content and forms to attract potential tourists. Destination managers can enhance the interaction between tourists and cultural heritage resources. Strengthening the entertainment of AR/VR helps tourists deeply participate in tourism activities through virtual ways and establishes a connection between tourists and destinations, thus inducing actual tourism behavior after the epidemic.

The design and offering of digital technologies, such as AR/VR, are useful and valuable as long as they enhance the tourism experience and improve visitor satisfaction. Regarding the designers and developers of smart digital technology applications, these industry practitioners should deeply understand the applicable content and operational implementation of AR/VR in various contexts. This challenge means that they must have a marketing approach and customer orientation and induce positive emotions and emotional involvement, which positively affect behavioral intentions, further extending the engagement and immersive experience that enhances the tourist’s needs. The effective design and implementation of AR/VR technologies in cultural monuments and other visitor attractions should take into account the following elements: (i) aiming to provide additional value to visitors, tourist empowerment, and the value co-creation with tourists, the AR/VR applications should provide experience enhancement, leading to knowledge and exploration; (ii) to attain this aim, the digital applications should include and incorporate the following elements/features for more pervasive and effective use of AR/VR—perceived benefits, perceived attributes of innovation (personal innovativeness), involvement (participation and interaction immersion), and visitor engagement. All these factors and features should result in the experience outcomes requested by visitors/consumers and those expected/desirable by tourism suppliers/attractions.

5. Limitations and Suggestions for Future Research

Although this study has interesting findings and significant contributions, it presents some limitations that should be acknowledged. The first limitation is that the study design is determined and limited by the current context. Some methodological elements were not choices, as they were affected and determined by the current health crisis, the COVID-19 pandemic. The research had to comply with strict traveling and social restrictions in China. The health crisis affected and limited the research team’s options regarding the survey design and data collection. The second limitation was the sample and the survey location. The data collection was conducted in Hangzhou, Zhejiang Province, China, during the holiday season, and the offline respondents were exclusively domestic tourists; no international tourists were interviewed. In addition, more heritage monuments and cultural attractions could be included in the research design. The above limitations indicate possible future endeavors by tourism scholars. One future pathway is to apply the suggested framework to exploring the tourists’ perceptions about similar digital technologies (chatbots and Artificial Intelligence) in different contexts (hotel and restaurant industry) and similar settings (other visitor attractions) and countries. The study’s findings need to be more generalized, since they are related to Chinese heritage monuments. Researchers could conduct similar projects in other countries and continents (Europe, Africa, and America). The suggested conceptual framework could be extended and improved by including some mediating factors, such as the experience outcomes (satisfaction, arousal, and memories) and demographic factors (age group/generational cohort and gender).