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Abstract

Augmented reality (AR) adoption in archeological site museums has attracted growing attention; however, a notable gap remains in understanding how personal traits and technological perceptions interact to influence tourists’ choices. To address this gap, this study conducted at the Sanxingdui Archeological Site Museum employs the Technology Acceptance Model (TAM), a widely used framework for analyzing user acceptance of technology, alongside structural equation modeling to examine the nuanced interplay of Technology Readiness dimensions and their impact on perceptions. The findings reveal that optimism does not significantly influence attitudes toward AR, while innovativeness has a positive effect. Discomfort is negatively associated with AR attitudes, while perceived security concerns have a counterintuitive positive effect. Moreover, perceived ease of use strongly determines the perceived usefulness of AR, which, in turn, directly influences attitudes toward the technology. Perceived ease of use also plays a crucial role in shaping AR attitudes. Additionally, a positive attitude toward AR is strongly linked to future adoption intentions, which, in turn, significantly correlate with tourists’ intentions to visit destinations. These insights not only enhance academic discussions on TAM and AR in tourism but also offer valuable practical implications for the museum sector seeking seamless AR integration.

Plain Language Summary

Exploring how augmented reality changes museum visits: Insights from Sanxingdui Museum, China

Our research focused on understanding why visitors to museums, like the Sanxingdui Museum in China, decide to keep using augmented reality (AR) technology during their visits. AR technology can overlay virtual elements (like images, sounds, or other data) on real-world environments, enhancing the visitors’ experience by making it more interactive and informative. We found that certain personal characteristics and how people perceive technology play crucial roles in this process. For example, we discovered that being open to new experiences (innovativeness) makes people more likely to have a positive attitude toward using AR. However, if people feel uncomfortable with new technology or worry about its security, they might not be as keen on using it. Our study also highlighted the importance of the AR technology itself—specifically, how easy it is to use and how useful people find it. If visitors believe that AR technology can enhance their museum experience and is not complicated to use, they are more likely to use it again in the future. Interestingly, our results showed that just being optimistic about technology doesn’t necessarily make someone more likely to use AR in museums. This was a surprising finding, as one might assume that a general positive outlook on technology would encourage its use. Ultimately, our findings suggest that for cultural heritage museums to successfully integrate AR technology, they need to ensure that it is user-friendly, secure, and genuinely enhances the visitor experience. This way, more people will be inclined to use AR during their visits, enriching their understanding and enjoyment of the museum’s exhibits.

Keywords

augmented reality (AR)technology acceptance model (TAM)tourist technology readiness and acceptance model structural equation modeling (SEM)

Introduction

In recent years, the convergence of tourism and information technology has led to the emergence of novel tourism operational paradigms, with technology becoming a key driver in organizational management, product innovation, and the enhancement of travel intentions (Xiaoluan, 2021). Smart tourism, a product of the rapid evolution of information technology, has introduced new paradigms and experiential models in tourism development, offering unprecedented opportunities for value creation and effective management (Vardopoulos et al., 2023). Since the introduction of the “Fourteenth Five-Year Plan for Tourism Development” in 2022, the symbiotic development of China’s tourism industry with information technology has presented a revitalized aspect (Zhang et al., 2023). By leveraging the advantages of information technology, smart tourism has not only improved the standard of service and management in tourist attractions but also significantly enhanced tourism competitiveness (Puri et al., 2023). The introduction of tourism technology products, such as augmented reality (AR) technology, has revealed substantial potential in delivering personalized tourist experiences, further stimulating the development of tourist intentions (Anand et al., 2023).

In the realm of smart tourism, AR technology enhances the perception of time and space by seamlessly integrating virtual information into tangible objects and spaces, and facilitating interaction between multimedia information and the real world (Azuma et al., 2001; Carrion & Levinson, 2012). This technology immerses tourists in a vibrant interactive environment (Zhuang et al., 2021). In the context of cultural heritage tourism destinations, such as archeological museums, the adoption of AR technology has become a key driver (Law, 2018). It possesses the capacity to virtually reconstruct historical edifices, revive historical lifestyles, and enable tourists to immerse themselves in historical and cultural experiences. Consequently, it diversifies the modes of tourist experiences and substantially elevates the level of tourist satisfaction (Cajiao et al., 2022). The objective of this research is to investigate the receptivity of AR technology within archeological museums. By conducting an in-depth analysis grounded in the enhanced Technology Acceptance Model (TAM), this study aspires to elucidate and interpret this emerging phenomenon (Cheng et al., 2023).

AR technology has the potential to influence tourist intentions and enhance satisfaction, based on the assumption that tourists are inclined to adopt AR technology (Buhalis, 1998). While AR technology offers significant appeal to a broad range of tourists, its adoption is not universal (Davila Delgado et al., 2020). Some individuals may exhibit technological apprehension or skepticism (Wei, 2019). Consequently, attitudes and intentions toward AR technology among tourists are shaped by personal willingness to use it and the positive experiences it provides, which, in turn, contribute to tourism intentions and experiential preferences (Do et al., 2020).

Current research on AR in tourism primarily focuses on its significance, characteristics, and development strategies. However, there is a relative lack of empirical studies examining the specific factors influencing AR adoption in tourism and its impact on shaping tourist intentions. Over the past decades, the TAM has been widely applied in academia to explain individuals’ acceptance and behavior toward emerging technologies (Al-Qaysi et al., 2020; C. Lin et al., 2007; Venkatesh et al., 2003). Recent studies suggest that incorporating additional variables can enhance TAM’s ability to explain attitudes and intentions toward technology adoption (Szymkowiak & Jeganathan, 2022).

Personality traits are fundamental determinants in shaping attitudes and behavioral intentions, significantly influencing technology acceptance and user behavior (Ajzen & Fishbein, 1972). However, contemporary research has not sufficiently examined the impact of personality traits on technology adoption, despite their critical role in individuals’ propensity to embrace emerging technologies (Aldammagh et al., 2021). In the tourism sector, a lack of comprehensive understanding regarding how personality factors influence tourists’ willingness to adopt new technologies and their subsequent attitudes may hinder the full potential of technology in enhancing tourism experiences (McCartney & McCartney, 2020). Such limitations could result in unproductive investments in tourism offerings, diverging from the strategic objective of strengthening technological infrastructure within the industry, particularly amid ongoing supply-side reforms (Fennell, 2021; Saleh et al., 2020). Addressing these gaps is essential to ensuring the effectiveness and long-term viability of technological innovations in tourism.

This study, set in the context of archeological museums, examines the acceptance of AR technology through an expanded TAM. It aims to provide both a robust theoretical foundation and practical insights to enhance the influence of advanced technologies on tourists’ travel intentions.

This paper investigates the acceptance of AR technology in archeological site museums, focusing on the TAM framework integrated with technological readiness and its implications for tourists’ travel intentions. It begins with a comprehensive literature review on the concept of Technology Readiness (TR) and its four dimensions: optimism, innovativeness, discomfort, and insecurity. The study then develops hypotheses exploring potential relationships between these dimensions and tourists’ attitudes toward AR adoption. The methodology section outlines the research setting at the Sanxingdui Archeological Site Museum, followed by data collection and analysis procedures. The findings highlight key themes: (1) the significant role of innovativeness in driving AR adoption, (2) the inhibitory effects of discomfort and insecurity on technology use, (3) the non-significant impact of optimism on AR adoption, and (4) the crucial influence of perceived ease of use and usefulness in shaping positive attitudes toward AR. The paper concludes by discussing the broader implications of AR technology in transforming museum experiences and the tourism industry, acknowledging study limitations, and suggesting directions for future research.

Theoretical Foundations

Augmented Reality

Augmented Reality (AR), as a key component of smart tourism development, offers a novel approach for the tourism industry to create technology-based experiential products, leading to widespread adoption by tourism organizations (Florido-Benítez & del Alcázar Martínez, 2024). AR overlays virtual content onto the real world, enhancing tourists’ interactive experiences. Through smart tourism service platforms established in scenic areas, AR creates an online interactive environment and provides extensive travel information (Chiang et al., 2014). By immersing tourists in unfamiliar settings, AR transforms the tourism experience (Pencarelli, 2020).

In World Cultural Heritage tourism sites, AR facilitates the restoration of cultural relics and historical architecture, reconstructs historical scenes, and enhances the representation and preservation of historical culture, offering tourists an immersive, time-travel experience (Bozzelli et al., 2019). The Olympia Temple in Greece, reconstructed using the ArcheoGuide augmented reality system, was the first ancient temple to incorporate AR technology (Plecher et al., 2019).

With continuous advancements in AR technology, its applications in tourism have expanded. AR provides detailed information about destinations and attractions, enabling tourists to engage in esthetic experiences without requiring a tour guide (H. Lee et al., 2015). Additionally, AR influences tourists’ psychological decision-making processes (Dai et al., 2022), establishing a link between AR and the Technology Acceptance Model (TAM). AR has revitalized the tourism industry, with its adoption driving innovation in tourism organizations and attractions.

Improvement and Applicability Analysis of the Technology Acceptance Model

The TAM has been extensively used to explain individual behavior in adopting information technology (Sharp, 2006; D. Tang & Chen, 2011). TAM posits that an individual’s acceptance and use of new technology are determined by behavioral intention, which is influenced by attitude toward using the technology. This attitude is shaped by perceived usefulness and perceived ease of use, with the latter also affecting perceived usefulness (Abdullah et al., 2016; Granić & Marangunić, 2019). Perceived usefulness and perceived ease of use are the two core variables of TAM. Perceived usefulness refers to the extent to which an individual believes that using new technology will enhance performance, primarily reflecting functional outcomes (Shih & Chen, 2013). Perceived ease of use refers to the extent to which an individual believes that using new technology will be effortless, primarily involving the assessment of the effort required for its use.

While TAM is primarily employed to explain individual behavior in using information technology within organizational settings, its applicability to consumer service environments is limited due to its inability to account for individual personality traits (Marangunić & Granić, 2015). As the tourism industry prioritizes service provision and customer satisfaction, extending TAM by integrating additional variables is essential (Y. Li et al., 2008; Yu et al., 2005). Although some studies have expanded TAM by incorporating external variables (Zhong et al., 2021), they have predominantly focused on perceived usefulness and perceived ease of use without considering the influence of personality traits (L. Chen & Aklikokou, 2020).

Personality traits are fundamental determinants of attitudes and behavioral intentions (Wang et al., 2021). For individuals exhibiting technological apprehension or skepticism, personality traits influence their willingness to adopt and use new technologies (Khasawneh, 2018; Yu et al., 2005). Therefore, the role of personality traits in technology acceptance warrants further investigation (Alyoussef, 2022). Although TAM has been widely applied in tourism research, demonstrating that perceived usefulness and ease of use are key factors influencing tourists’ adoption of new technologies (Taufik & Hanafiah, 2019), there remains a lack of research on the impact of personality traits on tourists’ technology adoption and the influence of new technology on tourists’ intentions (Gao & Huang, 2019). Expanding research on the influence of new technology on tourists’ intentions by incorporating personality traits is therefore essential.

Technology Readiness (TR)

Technology Readiness (TR) is a concept reflecting personality traits, primarily referring to an individual’s inclination to adopt new technology for domestic and work-related purposes (Punzon, 2021). It comprises four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism reflects a positive attitude toward technology, while innovativeness indicates a tendency to advocate for technology and adopt a leadership role in its use. Discomfort refers to perceptions of a lack of control over technology and associated stress (Blut & Wang, 2020). Insecurity pertains to mistrust in technology and skepticism regarding its functionality (Khalil et al., 2023). Among these dimensions, optimism and innovativeness are positive factors of technology readiness, whereas discomfort and insecurity are negative factors. Positive and negative perceptions of technology coexist, influencing technology adoption.

The four dimensions of technology readiness significantly impact an individual’s acceptance and use of technology, shaping the process of adopting new technology (Blut & Wang, 2020). McNamara et al. (2022) suggested that technology readiness should be integrated into the TAM to examine the influence of personality traits on technology adoption (Obeidy et al., 2017). Within TAM, technology readiness is primarily reflected in positive and negative attitudes toward technology use, significantly influencing technology adoption behavior (Seong & Hong, 2022). Incorporating technology readiness into TAM enhances the model’s explanatory power in the context of tourism consumption. This integration provides a comprehensive understanding of tourists’ attitudes toward new technology adoption and the underlying mechanisms shaping their tourism intentions (Zhu & Deng, 2020).

Research Hypotheses

Technology Willingness and Attitude Towards AR Use

In evaluating the adoption of AR technology in archeological site museums, the four dimensions of Technology Readiness—optimism, innovativeness, discomfort, and insecurity—function as personality traits that influence attitudes toward technology use (Mankins, 2009). Optimism and innovativeness generally foster a more receptive attitude, increasing the likelihood of adopting new technology (Chang & Kannan, 2006). In contrast, discomfort and insecurity may lead to apprehension and reluctance toward technology use.

Existing research highlights technology readiness as a key determinant in tourists’ engagement with AR technology at tourism destinations. For example, Lin and Chang (2011) identified a positive correlation between higher levels of optimism and innovativeness and favorable attitudes toward AR adoption. Conversely, Walczuch et al. (2007) found that increased discomfort and insecurity significantly reduce positive attitudes toward new technology adoption. Additionally, Oh et al. (2014) confirmed the substantial influence of both positive and negative dimensions of technology readiness on tourists’ attitudes toward adopting mobile internet services (Parasuraman & Colby, 2015).

AR enhances attitudes toward technology adoption by reshaping tourists’ experiential frameworks and influencing technology readiness levels. Accordingly, this study proposes the following hypotheses:

Optimism, defined as the general belief that technology yields positive outcomes, is expected to enhance tourists’ receptiveness to AR technologies. Optimistic tourists are more likely to perceive AR as a valuable tool that enriches the museum experience, increasing their inclination to view AR favorably (S. Li & Jiang, 2023).

H1. Optimism exerts a positive effect on tourists’ attitudes toward AR usage.

Innovativeness refers to an individual’s openness to adopting new technologies. Tourists with high innovativeness are more likely to be early adopters of AR, recognizing its novelty and potential to transform traditional museum visits into interactive experiences (Çallı et al., 2024).

H2. Innovativeness positively influences tourists’ attitudes toward AR usage.

Discomfort with technology, often arising from unfamiliarity or perceived complexity, may contribute to negative attitudes toward AR. If tourists perceive AR interfaces as overly complex or unintuitive, their overall attitude toward adopting such technologies may be less favorable (Devine et al., 1999).

H3. Discomfort adversely affects tourists’ attitudes toward AR usage.

Insecurity pertains to concerns about technology’s reliability and safety. Issues related to data privacy and the security of personal information when using AR may discourage tourists from engaging with AR applications (To et al., 2020).

H4. Insecurity negatively impacts tourists’ attitudes toward AR usage.

AR Technology Perceptions and Usage Attitudes

In the context of AR technology acceptance in archeological site museums, the relationship between technology perceptions and usage attitudes is crucial (Lai et al., 2013). The TAM posits that tourists’ perceptions of usefulness and ease of use influence their attitudes toward technology adoption. Notably, when a technology is perceived as easier to use, its perceived usefulness increases correspondingly (Gefen et al., 2003). Both variables are key determinants of technology adoption and utilization, as well as criteria for evaluating satisfaction with technology adoption (Saprikis et al., 2020). With the growing integration of tourism and information technology, TAM has become a significant theoretical framework for analyzing tourists’ technology usage attitudes (Gharibi, 2021). For example, Morosan and Jeong (2008) demonstrated that perceived usefulness and ease of use positively influence tourists’ attitudes toward hotel reservation system adoption (Rejeb et al., 2023). Accordingly, this study proposes the following hypotheses:

This study examines how tourists’ perceptions of AR technology’s ease of use enhance their perceptions of its usefulness. According to TAM, ease of use is a key factor influencing users’ perceptions of a technology’s utility. This factor is particularly relevant in cultural heritage museums, where the ease of use of AR technology is critical to enhancing visitor experiences.

Furthermore, by investigating the relationship between ease of use and perceived usefulness in a cultural context such as the Sanxingdui Museum, this study contributes to understanding AR technology acceptance in heritage environments and offers practical guidance for its integration (Madi et al., 2024). This analysis of the relationship between ease of use and usefulness informs museum administrators and technology developers in designing and implementing AR experiences that align with visitor expectations and needs, thereby increasing engagement and satisfaction.

When AR is perceived as useful, providing valuable information and enhancing the visitor experience, tourists are more likely to develop positive attitudes toward its usage (Kubiczek et al., 2024).

H5. Tourists’ perceived ease of use of AR will exert a positive effect on their perceived usefulness of AR.

The less effort required to use AR, the more positive tourists’ attitudes will be toward its adoption. Ease of use ensures that technology does not become a barrier to engagement (Suherlan & Hidayah, 2025).

H6. Tourists’ perceived usefulness of AR will positively influence their attitudes toward AR usage.

When AR technology is easy to use, tourists are more likely to perceive it as accessible and convenient, fostering positive attitudes toward its adoption. A user-friendly interface reduces the cognitive and operational effort required, enhancing engagement and encouraging tourists to integrate AR into their experiences at cultural and heritage sites (Wang et al., 2024).

H7. Tourists’ perceived ease of use of AR will positively affect their attitudes toward AR usage.

AR Usage Attitudes, AR Usage Intentions, and Destination Tourism Intentions

In the context of AR technology acceptance in archeological site museums, the relationships among AR usage attitudes, usage intentions, and destination tourism intentions are critical considerations (Tian-Cole & Crompton, 2003). The literature suggests that attitudes toward novel technology significantly influence usage intentions and behavioral decisions, aligning with the “attitude-intention-behavior” paradigm (Yung et al., 2021). When individuals hold positive attitudes toward new technology, their usage intentions increase, potentially leading to corresponding behaviors (Wu et al., 2021).

In tourism research, the relationships between tourists’ AR usage attitudes, intentions, and behaviors have received extensive empirical support. For example, Chung et al. (2015) identified a strong association between tourists’ AR usage attitudes and intentions, emphasizing the importance of favorable perceptions in sustaining AR experiences and adoption. Additionally, with the rise of smart tourism, AR applications have not only enhanced the sophistication of scenic areas but have also transformed tourists’ experiences and behaviors (P. Lee et al., 2020). The accessibility and novelty of AR can significantly increase tourists’ usage intentions, thereby strengthening destination recognition and fostering tourism and recommendation intentions. Wei (2019) further noted that information technology adoption contributes to destination tourism development, serving as a key determinant of tourists’ travel intentions (H. F. Lin & Chen, 2017).

Based on this analysis, the present study proposes the following hypotheses.

Positive attitudes toward AR are expected to translate into a higher intention to use it. This reflects the basic premise that a favorable disposition toward a technology predicts its continued use (Chung et al., 2018).

H8. Tourists’ AR usage attitudes will exert a positive influence on their AR usage intentions.

Intentions to use AR can enhance the overall destination experience, leading to a greater likelihood of tourists choosing to visit or recommend the destination based on their anticipated or actual technology-enhanced experiences (Tavitiyaman et al., 2021).

H9. Tourists’ AR usage intentions will positively impact their destination tourism intentions.

To visually summarize the above hypotheses and clarify the conceptual framework guiding this research, Figure 1 presents the proposed research model and the hypothesized relationships among key variables. This model integrates the constructs of technology readiness, AR technology perceptions, usage attitudes, usage intentions, and destination tourism intentions, thereby providing a comprehensive overview of the theoretical structure underlying this study.

Figure 1.

Research conceptual model.

Methodology

Sampling Location and Data Collection

This study selected the Sanxingdui Archeological Site Museum as the research site to examine the acceptance of augmented reality (AR) technology in archeological site museums. The Sanxingdui Archeological Site Museum is a prominent cultural site in China that was among the early adopters of AR technology. Through AR, the museum has digitally reconstructed damaged relics and integrated architectural styles and cultural elements into the real world (He et al., 2018). This application allows visitors to engage more deeply with historical culture, enhances interactive experiences, and significantly increases travel intentions. The museum’s use of AR in cultural relic restoration, historical interpretation, and interactive experiences makes it a representative case, providing a strong foundation for its selection as an empirical research site (Shi et al., 2023). This choice not only establishes a specific context for analyzing AR technology acceptance but also offers a robust case study for understanding how AR enhances museum visitation experiences. Figure 2 shows the example of AR-based museum exhibition.

Figure 2.

An example of AR-based museum exhibition.

This study adhered to ethical research principles to ensure participant protection, including minors under the age of 18. For participants below 18 years of age, parental or guardian consent was obtained before participation. Consent forms provided detailed information about the study’s purpose, the voluntary nature of participation, and assurances of anonymity and confidentiality. Additionally, the questionnaire was designed to exclude sensitive or intrusive questions, ensuring its appropriateness for younger participants. To safeguard participant privacy, all data were anonymized during collection, and only aggregated results were analyzed. The study followed standard ethical guidelines, including respect for participants’ autonomy, confidentiality, and risk minimization. These measures ensured the integrity and ethical conduct of the research process.

Between January and May 2023, questionnaires were distributed through online platforms for this study. A total of 318 responses were collected, of which 310 were deemed valid after rigorous screening. The number of valid responses exceeds nine times the number of analytical variables (33), meeting the necessary preconditions for Structural Equation Modeling (SEM). Data collection was facilitated by the Sojump online questionnaire platform, which offers functionalities similar to Amazon Mechanical Turk, tailored for a Chinese audience.

To ensure the authenticity of respondents’ experiences with AR technology at the Sanxingdui Archeological Site Museum, the questionnaire included targeted questions designed to verify firsthand engagement with AR. This approach refined the sample to include only participants with actual AR experience at the museum, ensuring the validity of subsequent analyses. By assessing direct interactions with AR technology, as well as perceived usefulness and future adoption intentions, the study established a robust framework for evaluating user acceptance and attitudes toward AR applications in cultural heritage settings.

A snowball sampling strategy was employed to increase the sample size, with the survey disseminated across various professional networks and social media platforms, including Weibo, WeChat, and QQ. Participation was entirely voluntary, and respondents had the option to withdraw at any stage. To prevent duplicate responses and maintain data integrity, only one response per IP address was permitted.

In this study, SPSS 26.0 was used for descriptive statistical analysis, with detailed results presented in Table 1. Among the respondents, 40.97% were male, and 59.03% were female. Regarding educational background, 49.35% held a bachelor’s degree, 21.94% had a master’s degree or higher, and 28.71% had a high school education or below. In terms of age distribution, 65.16% of participants were between 19 and 30 years old. The high proportion of younger age groups in the study indicates a higher level of readiness and adaptability in adopting augmented reality, particularly in cultural heritage museum settings (Jung et al., 2015; Rauscher & Humpe, 2022).

Table 1.

Demographic Distribution of Respondents.

Demographic category	Sub-category	Count	Percentage (%)
Gender	Male	127	40.97
Gender	Female	183	59.03
Education level	High school and below	89	28.71
	Bachelor’s degree	153	49.35
	Master’s and above	68	21.94
Age group	18 and below	18	5.81
	19–30	202	65.16
	31–45	68	21.94
	46–60	18	5.81
	61 and above	4	1.29
Monthly income	Below 2000 RMB	132	42.58
	2,001–4,000 RMB	64	20.65
	4,001–6,000 RMB	75	24.19
	6,001–8,000 RMB	27	8.71
	Above 8,001 RMB	12	3.87

Questionnaire Design and Variable Measurement

The questionnaire for this study comprised two main sections: demographic variables and specialized research scales. Demographic variables were included to capture the sample’s basic characteristics, while the research scales were adapted from established studies and translated into Chinese using the back-translation method. Considering the specific context of this study, particularly in relation to technology acceptance, the research adopted the framework of Parasuraman (2000) and Lin and Hsieh (2007), categorizing technological readiness into four dimensions: optimism, innovativeness, discomfort, and insecurity. Each dimension was measured using three items, resulting in a total of 12 items. The questionnaire scales and corresponding references are presented in Table 2.

Table 2.

Measurement Model and Sources.

Types	Code	Description	Reference
Optimism	O1	AR technology makes the tourism services and products of the Sanxingdui Museum site easier to use.	Paulauskas et al. (2023)
	O2	AR applications provide me with greater freedom and convenience during my visit to the Sanxingdui Museum site.
	O3	I find using new technologies like AR to be quite exciting.
Innovativeness	N1	Overall, I was among the earliest in my social circle to adopt and use new technologies.	Alalwan et al. (2018)
	N2	I’m always able to grasp new technologies and products on my own without needing help from friends.
	N3	In the fields that interest me, I can keep up with the latest technological developments.
Discomfort	D1	Sometimes I feel that the development of AR systems isn’t suited for the average person.	Godoe and Johansen (2012).
	D2	When I receive technical support from high-tech services, I sometimes feel like I’m being exploited by people who know more than I do.
	D3	New technologies make it easier for governments and companies to monitor people.
Lack Of security	S1	The security risks of new technologies only become apparent after people start using them.	Basak et al. (2016).
	S2	I worry that information I send over the internet might be seen by others.
	S3	When providing information to the internet, I’m usually unsure if it’s secure.
Perceived usefulness	U1	Using AR helps me obtain better information at the Sanxingdui Museum site.	Abdullah et al. (2016).
	U2	AR applications are a great way to tour the Sanxingdui Museum site.
	U3	Overall, I think using AR to explore the Sanxingdui Museum site is very beneficial.
Perceived ease of use	E1	The interface of AR applications is easy to use without requiring much effort.	Abdullah et al. (2016).
	E2	I find AR applications to be very user-friendly.
	E3	Through AR applications, I can conveniently access the tourist information I desire.
Attitude toward AR usage	A1	I enjoy using AR applications when touring the Sanxingdui Museum site.	Zhuang et al. (2021).
	A2	Using AR is a good idea when exploring the Sanxingdui Museum site.
	A3	Utilizing AR enhances my tourism experience at the Sanxingdui Museum site.
Intention to use AR	I1	I intend to continue using AR applications in the future.	Zhuang et al. (2021).
	I2	I anticipate using AR applications more frequently in my future travels.
	I3	The likelihood of me recommending my friends to use AR during their travels is high.
Intention for destination tourism	V1	After experiencing AR, I plan to visit the Sanxingdui Museum site again in the future.	C. Tang et al. (2023).
	V2	Following my AR experience, I plan to visit the Sanxingdui Museum site frequently.
	V3	After trying AR, I will likely recommend my friends to visit the Sanxingdui Museum site for tourism.

The measurement of perceived usefulness and perceived ease of use was based on the studies of Davis (1989) and Chung et al. (2015), with each construct assessed using three items, totaling six items. Attitudes toward AR use and the intention to use AR were measured using three items per construct, based on the frameworks of Venkatesh et al. (2003) and Lin and Hsieh (2007), resulting in a total of six items.

To measure tourism intention, this study referenced the research of C. F. Chen and Tsai (2007), utilizing three items.

All constructs were measured on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The scale was designed to systematically capture respondents’ attitudes and perceptions regarding the acceptance of AR technology in archeological site museums, ensuring a robust foundation for in-depth analysis based on the revised TAM (Ghazizadeh et al., 2012).

Data Analysis Methods and Procedures

This study primarily employed Structural Equation Modeling (SEM) to empirically validate the hypothesized relationships among relevant constructs and evaluate the overall conceptual model.

Initially, the reliability of the questionnaire scales was evaluated using SPSS. All constructs exhibited Cronbach’s alpha values exceeding .7, indicating good stability and internal consistency.

Subsequently, the normality of the data distribution was examined. The results showed that the absolute skewness coefficients for all scale items ranged from 0.050 to 0.988, remaining below the critical threshold of 3, while the absolute kurtosis coefficients ranged from 0.080 to 1.126, below the critical value of 8. These statistical indicators suggest that the data approximately follows a normal distribution, supporting the use of the maximum likelihood estimation method for subsequent parameter estimation.

Finally, Confirmatory Factor Analysis (CFA) was conducted in AMOS 17.0 to assess the reliability and validity of the measurement model. Additionally, an evaluation of the overall fit of the structural model and the study’s hypotheses was performed.

Data Analysis and Hypothesis Testing

Reliability and Validity Assessment

According to the results of the Confirmatory Factor Analysis (CFA; Table 3), the Composite Reliability (CR) of all constructs exceeded 0.6. The Average Variance Extracted (AVE) was above 0.5 for all variables except “lack of security” variable, indicating that the measurement model in this study demonstrates satisfactory reliability (Salloum et al., 2019). The standardized factor loadings of all measurement items were generally above 0.6 (p < .001), supporting good convergent validity (Hammady et al., 2020).

Table 3.

Confirmatory Factor Analysis.

Types	Question Code	Standardized factor loadings	CR	AVE
Optimism	O1	0.835***	0.783	0.550
	O2	0.749***
	O3	0.625***
Innovativeness	N1	0.783***	0.777	0.539
	N2	0.762***
	N3	0.651 ***
Discomfort	D1	0.72***	0.762	0.516
	D2	0.711 ***
	D3	0.724***
Lack of security	S1	0.699***	0.686	0.422
	S2	0.634***
	S3	0.612***
Perceived usefulness	U1	0.641***	0.776	0.538
	U2	0.774***
	U3	0.778***
Perceived ease of use	E1	0.642***	0.760	0.515
	E2	0.737***
	E3	0.767***
Attitude toward AR usage	A1	0.768***	0.739	0.488
	A2	0.697***
	A3	0.622***
Intention to use AR	I1	0.689***	0.818	0.601
	I2	0.767***
	I3	0.860 ***
Intention For destination tourism	V1	0.771***	0.757	0.511
	V2	0.615***
	V3	0.749***

Note. *** indicates statistical significance at p < 0.001 level.

For discriminant validity, the square roots of the AVE of the constructs were compared with the correlation coefficients between constructs. If the square root of the AVE is greater than the correlation coefficients between constructs, the variables exhibit good discriminant validity (X. Z. Li et al., 2022). As shown in Table 4, the square roots of the AVE for all constructs exceeded their correlations with other constructs, confirming satisfactory discriminant validity.

Table 4.

Discriminant Validity Test of Variables.

Latent variable	1	2	3	4	5	6	7	8	9
1. Optimism	0.741
2. Innovativeness	0.340	0.734
3. Discomfort	0.189	0.639	0.718
4. Lack of security	0.421	0.183	0.387	0.649
5. Perceived usefulness	0.457	0.190	0.097	0.372	0.734
6. Perceived ease of use	0.716	0.295	0.152	0.584	0.624	0.717
7. Attitude toward AR usage	0.536	0.307	0.057	0.527	0.712	0.689	0.698
8. Intention to use AR	0.502	0.275	0.042	0.473	0.627	0.712	0.681	0.775
9. Intention for destination tourism	0.493	−0.031	0.470	0.467	0.638	0.708	0.688	0.757	0.715

Note. Bold diagonal values represent the square root of AVE for each construct, which should exceed inter-construct correlations for adequate discriminant validity.

Model Fit and Hypothesis Testing

Building on the reliability and validity analyses, the model’s fit was comprehensively assessed using various indices, including χ²/df, GFI, TLI, CFI, IFI, RMR, and RMSEA. The results (Table 5) indicate that χ²/df = 1.284, falling within the standard 1 to 3 range; GFI = 0.914, CFI = 0.956, TLI = 0.949, and IFI = 0.971, all exceeding the critical threshold of 0.9; RMR = 0.050 and RMSEA = 0.032, both below their respective critical values of 0.05 and 0.08 (Padilla-Meléndez et al., 2013). These indices collectively confirm that the proposed model exhibits a strong fit with the sample data. The standardized parameter estimates for the hypothesized model are presented in Figure 3.

Table 5.

Main Test Indicators for Model Fitting.

Fit Indices	X²/df	GFI	CFI	TLI	IFI	RMR	RMSEA
Judgment criteria	1–3	>0.9	>0.9	>0.9	>0.9	<0.05	<0.08
Value	1.284	0.914	0.956	0.949	0.971	0.05	0.032

Figure 3.

Hypothesized model standardized output results.

The hypothesis testing results are summarized in Table 6, where *p < 0.05; **p < 0.01; ***p < 0.01. Among the nine hypotheses proposed in this study, eight were empirically supported. Factors such as innovativeness, discomfort, lack of security, perceived usefulness, and perceived ease of use significantly influence tourists’ attitudes toward mobile augmented reality adoption. Perceived ease of use has a positive effect on perceived usefulness, usage attitude positively influences usage intention, and usage intention significantly impacts tourists’ destination travel intentions. The regression determination coefficient (R²) for travel intention is 0.821, indicating that the independent variables in the model account for 82.1% of the variance in usage intention, demonstrating substantial explanatory power.

Table 6.

Path Coefficient Estimation and Hypothesis Testing.

Research hypothesis	Standardized path coefficient	Standard error	t-value	Hypothesis testing
H1: Optimism → Attitude toward AR usage	−0.058	0.087	−0.667	Not supported
H2: Innovativeness → Attitude toward AR usage	0.253**	0.083	3.048	Supported
H3: Discomfort → Attitude toward AR usage	−0.286**	0.097	−2.948	Supported
H4: Lack of security → Attitude toward AR usage	0.217**	0.095	2.211	Supported
H5: Perceived ease of use → Perceived usefulness	0.619***	0.073	8.479	Supported
H6: Perceived usefulness → Attitude toward AR usage	0.316***	0.154	2.052	Supported
H7: Perceived ease of use → Attitude toward AR usage	0.475***	0.179	2.654	Supported
H8: Attitude toward AR usage → Intention to use AR	0.887***	0.097	9.144	Supported
H9: Intention to use AR → Intention for destination tourism	0.987***	0.091	10.846	Supported

Note. *p < 0.05; **p < 0.01; ***p < 0.001.

Discussion

To examine the acceptance of augmented reality (AR) technology in archeological site museums, structural equation modeling was employed. The results align with existing paradigms while also revealing unique insights. This comprehensive approach extends beyond statistical interpretations to explore the intricate relationship between technology and culture.

First, although previous studies have commonly linked optimism to technological adoption, the findings indicate that optimism does not significantly influence attitudes toward AR (β = −.058, p > .05). This highlights the complexity of technology acceptance, particularly in the context of cultural heritage museums. It suggests that in historically and culturally rich environments, tourists’ acceptance of technology may depend less on general optimism and more on the specific applications and relevance of the technology to the museum experience (H1). Based on this finding, future research should investigate additional personality traits, such as openness or cultural adaptability, which may influence tourists’ technology acceptance across different cultural and technological contexts.

The analysis indicates that innovativeness has a significant positive effect on attitudes toward AR (β = .253, p < .01), suggesting that a propensity to experiment with new technologies may be more influential in this context than previously recognized (Pradhan et al., 2018). This finding reinforces the notion that in cultural heritage environments, tourists who are open to new experiences may be more receptive to AR technology. Consequently, museum administrators should consider fostering an innovation-friendly environment to attract and engage these visitors, such as by providing more interactive and experiential exhibitions.

Regarding H3, discomfort was found to have a significant negative impact on tourists’ attitudes toward AR (β = −.286, p < .01). This result aligns with the Technology Acceptance Model and theories of user resistance, which indicate that discomfort or unfamiliarity with a technology can lead to negative attitudes. As tourists’ discomfort with technology increases, their acceptance and positive attitudes toward AR decrease. This finding, supported by a significant t-value (−2.948), confirms that discomfort is a key barrier to tourists’ willingness to adopt and use AR technology.

For H4, security concerns have a substantial impact on the acceptance of AR technologies. Lack of security significantly affects attitudes toward AR adoption, mirroring the negative effects of discomfort. These concerns highlight the necessity of robust data protection and privacy measures within AR applications, which are essential for fostering trust and ensuring a secure user experience. This underscores the need for a security-oriented approach in the development and implementation of AR, aiming to mitigate privacy concerns and enhance the overall attractiveness of AR technologies in sensitive environments such as museums.

In the development and promotion of AR applications, particular attention should be given to enhancing the intuitiveness and ease of use of user interfaces to minimize potential user discomfort. A well-optimized user experience design can effectively reduce discomfort, thereby fostering more positive attitudes and intentions toward AR technology adoption (H3).

Furthermore, the significance of perceived usefulness and ease of use in AR technology adoption has been confirmed (β = .619, p < .001 for the impact of perceived ease of use on perceived usefulness; β = .475, p < .01 for the impact of perceived ease of use on AR usage attitudes; β = .316, p < .05 for the impact of perceived usefulness on AR usage attitudes; H5, H6, H7). This suggests that for AR experiences in archeological site museums to achieve widespread acceptance, they must be both beneficial and easy to use (Hess et al., 2014). Managers should prioritize the seamless integration of AR technology into the visitor experience, ensuring that it enhances rather than complicates engagement.

Finally, a relationship was identified between AR usage attitudes and broader tourism intentions. Positive attitudes toward AR significantly predicted future usage intentions (β = .887, p < .001) and overall destination tourism intentions (β = .987, p < .001; H8, H9). This finding indicates that AR technology not only enhances immediate experiences but also fosters future cultural exploration and engagement. Therefore, museums and tourism-related organizations should focus on developing and promoting engaging AR applications that enhance visitor immersion and participation, thereby increasing satisfaction and the likelihood of revisitation (Han et al., 2021).

Conclusion

Theoretical Implications

The evolution of smart tourism has positioned technology, particularly augmented reality (AR), at its core (Bastidas-Manzano et al., 2021). Research on the Sanxingdui Archeological Site Museum further underscores this by investigating how technological willingness and perceptions interact to influence tourists’ adoption of AR in heritage sites. While previous studies have explored the potential of AR, this research highlights the nuanced relationship between personal traits and technological perceptions, particularly in the museum context (Tom Dieck et al., 2016).

This study addresses two significant gaps in the existing literature. First, while extensive research has focused on the technological features and benefits of AR, limited attention has been given to how individual personality traits, such as optimism and innovativeness, influence its adoption in cultural heritage settings. By integrating the dimensions of technology readiness into the Technology Acceptance Model (TAM), this study provides a more comprehensive understanding of AR adoption, particularly in cultural heritage tourism. Second, previous studies have rarely examined the barriers to AR adoption, such as discomfort and insecurity, in detail. The findings indicate that these negative factors play a critical role in shaping tourists’ attitudes and adoption behaviors, emphasizing the need to design AR systems that effectively address these concerns.

This study addresses a critical gap in the existing literature by incorporating the often-overlooked dimension of technology willingness, providing a more comprehensive understanding of AR adoption. It not only reaffirms the relevance of technology acceptance models in tourism but also emphasizes the importance of perceived ease of use and usefulness (Go et al., 2020). The findings indicate that while traits such as innovativeness facilitate AR adoption, concerns regarding discomfort and security act as barriers (Alkhattabi, 2017). Notably, optimism, initially presumed to be influential, was not a significant determinant. The lack of influence of optimism on attitudes toward AR (H1) necessitates a reassessment of how emotional predispositions affect technology acceptance, particularly in cultural heritage contexts where practical applications may take precedence over general sentiment. Collectively, these insights emphasize the necessity for destinations and service providers to tailor AR strategies, ensuring they are user-friendly, secure, and aligned with the diverse characteristics, expectations, and concerns of tourists (Javadian Sabet et al., 2022).

Furthermore, the significant negative impact of discomfort on tourists’ acceptance of AR (H3) underscores its role as a major barrier to technology adoption in the tourism sector. This finding not only advances the theoretical understanding of AR adoption barriers but also offers actionable insights for practitioners. Specifically, it underscores the necessity of addressing psychological resistance by improving AR interface design, enhancing support systems, and ensuring intuitive user experiences. These findings reinforce the importance of designing AR experiences that prioritize user comfort to mitigate resistance and foster positive engagement. As AR continues to shape the tourism landscape, understanding the nuanced factors influencing its adoption in cultural heritage settings remains essential.

This study demonstrates that both positive and negative factors of technology readiness must be considered to develop a balanced framework for AR adoption in tourism. By emphasizing the interplay between human factors and technological features, this study lays the foundation for future research on user-centric AR applications. It contributes to the body of knowledge by highlighting the role of technology readiness in AR acceptance, urging museum administrators and tourism stakeholders to consider both technological and human factors in AR implementations. Future research should further explore these dynamics by examining diverse cultural contexts and incorporating a broader range of personality traits and technological attributes to enhance understanding of AR’s impact on tourism.

Practical Implications

For cultural heritage institutions and policymakers, understanding the complex relationship between technological usability, visitor characteristics, and environmental factors is essential. The findings emphasize that while AR technologies offer transformative potential, their adoption depends not only on their features but also on how seamlessly they integrate into the visitor experience. For example, even if an AR application delivers high-quality content, adoption may decline if users perceive it as difficult to use or insecure. This underscores the importance of user-centric design and robust privacy measures as key factors in driving AR adoption within cultural settings. Museums should prioritize intuitive interfaces and clear tutorials to ensure users feel confident and secure when interacting with AR systems. Policymakers can further support these efforts by establishing standards for user-friendly AR development and data protection policies to enhance visitor trust.

Museum managers and AR developers must also critically assess the role of innovativeness in designing AR experiences. The findings indicate that while traits such as innovativeness can enhance initial visitor satisfaction, they do not necessarily ensure sustained engagement or loyalty. For instance, an AR feature may initially attract visitors due to its novelty, but if it does not provide tangible value, its long-term impact on visitor satisfaction will be limited. Developers should align innovation with practical benefits, ensuring AR features contribute meaningfully to the cultural experience. This necessitates ongoing collaboration between developers, cultural institutions, and end users to ensure AR solutions align with the evolving needs of diverse audiences.

For marketers promoting AR technologies in cultural tourism, the challenge lies in balancing the appeal of novelty with the sustained relevance of their offerings. In an increasingly competitive cultural tourism landscape, alternative attractions can quickly overshadow existing AR applications, even if they are well-developed. This highlights the necessity of a dual-pronged marketing strategy: first, continuously emphasizing the unique aspects of AR solutions that distinguish them in the marketplace; second, regularly updating and enhancing AR content to maintain its relevance. This approach not only reinforces the value of existing applications but also ensures their competitiveness against emerging alternatives.

For visitors and users, understanding the relationship between personal traits and AR adoption offers actionable insights. Individuals hesitant to engage with AR technologies due to discomfort or security concerns may benefit from structured, supportive environments that lower barriers to entry. Additionally, social influence plays a significant role in adoption; when satisfied users share positive experiences, they contribute to broader acceptance of AR technologies in cultural tourism. Visitors are encouraged to provide candid feedback to cultural institutions, facilitating the continuous refinement and optimization of AR experiences. This collaborative ecosystem, built on trust and shared learning, has the potential to create cultural heritage experiences that are both technologically innovative and deeply engaging.

Limitations and Future Research Directions

This study provides valuable insights based on its focused investigation of the Sanxingdui Archeological Site Museum, a significant cultural heritage site designated as a key cultural relic protection unit in China (Fan & Yuen Yan Lai, 2004). While the findings offer important implications, they are derived from a specific context, and the sample may not fully represent the broader tourist demographic. Additionally, the research primarily examined antecedent variables such as technological willingness and perceptions, without considering other potential factors, including cultural motivations, tourist knowledge, and experiential value. Although these findings provide foundational insights for similar prominent cultural heritage sites in China, future research should adopt a broader scope. This includes expanding the range of tourist destinations, collecting a more diverse tourist sample and integrating additional influencing variables to enhance the understanding of augmented reality’s impact on tourist intentions.

Footnotes

Acknowledgements

The authors would like to express sincere gratitude to Universiti Sains Malaysia, Pulau Pinang and Yuncheng University, China, for their invaluable support in the completion of this work.

ORCID iDs

Wenjing Xu

Mazran Ismail

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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.

Ethical Approval

Ethics and Informed Consent Statement

This study was conducted in strict adherence to ethical research principles and the APA Ethical Principles of Psychologists and Code of Conduct (Section 8.05). Since our research involved human participants, we obtained approval from the Universiti Sains Malaysia Ethics Review Committee before data collection commenced. Prior to participation, all respondents were fully informed of the study’s purpose, their voluntary participation, and the confidentiality of their responses. Informed consent was explicitly obtained from all participants, with additional parental or guardian consent secured for those under 18 years old.

Ethical Considerations in Methodology

Consent to Participate

Obtained from all participants.

Consent for Publication

All authors have approved the manuscript for submission and publication.

Minimizing Risk

We ensured that the study posed no physical or psychological risks to participants. The survey design was structured to exclude any sensitive or distressing questions, and participants were explicitly informed that they could withdraw at any point without any repercussions.

Balancing Risks and Benefits

Given the increasing implementation of AR in cultural heritage museums, our study provides critical insights for both academia and industry. The potential societal benefits—including improved museum engagement and accessibility—far outweigh any minimal risks to participants, which were mitigated through careful survey design and anonymization procedures.

Data Anonymization and Confidentiality

To protect participants’ privacy, all data were collected anonymously. No personally identifiable information was recorded, and all responses were processed in aggregate form to ensure confidentiality.

Data Availability Statement

All data generated or analyzed during this study are included in this published article. Data related to this study are also available upon request from the corresponding author.

References

Abdullah

Ward

Ahmed

(2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014

Ajzen

Fishbein

(1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1–9. https://doi.org/10.1037/h0031930

Alalwan

A. A.

Baabdullah

A. M.

Rana

N. P.

Tamilmani

Dwivedi

Y. K.

(2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology and Society, 55, 100–110. https://doi.org/10.1016/j.techsoc.2018.06.007

Aldammagh

Abdeljawad

Obaid

(2021). Predicting mobile banking adoption: An integration of TAM and TBP with trust and perceived risk. Financial Internet Quarterly, 17(3), 35–46. https://doi.org/10.2478/fiqf-2021-0017

Alkhattabi

(2017). Augmented reality as e-learning tool in primary schools’ education: Barriers to teachers’ adoption. International Journal of Emerging Technologies in Learning (iJET), 12(02), 91–100. https://doi.org/10.3991/ijet.v12i02.6158

Al-Qaysi

Mohamad-Nordin

Al-Emran

(2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25, 4659–5002. https://doi.org/10.1007/s10639-020-10197-1

Alyoussef

I. Y.

(2022). Acceptance of a flipped classroom to improve university students’ learning: An empirical study on the TAM model and the unified theory of acceptance and use of technology (UTAUT). Heliyon, 8(12), e12529. https://doi.org/10.1016/j.heliyon.2022.e12529

Anand

Arya

Suresh

Sharma

(2023). Quality dimensions of augmented reality-based mobile apps for smart-tourism and its impact on customer satisfaction & reuse intention. Tourism Planning & Development, 20(2), 236–259. https://doi.org/10.1080/21568316.2022.2137577

Azuma

Baillot

Behringer

Feiner

Julier

MacIntyre

(2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47. https://doi.org/10.1109/38.963459

10.

Basak

S. K.

Govender

D. W.

Govender

(2016). Examining the impact of privacy, security, and trust on the TAM and TTF models for e-commerce consumers: A pilot study [Conference session]. 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 19–26. IEEE. https://doi.org/10.1109/PST.2016.7906922

11.

Blut

Wang

(2020). Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48, 649–669. https://doi.org/10.1007/s11747-019-00680-8

12.

Bozzelli

Raia

Ricciardi

De Nino

Barile

Perrella

Tramontano

Pagano

Palombini

(2019). An integrated VR/AR framework for user-centric interactive experience of cultural heritage: The ArkaeVision project. Digital Applications in Archaeology and Cultural Heritage, 15, e00124. https://doi.org/10.1016/j.daach.2019.e00124

13.

Buhalis

(1998). Strategic use of information technologies in the tourism industry. Tourism Management, 19(5), 409–421. https://doi.org/10.1016/S0261-5177(98)00038-7

14.

Cajiao

Leung

Y. F.

Larson

L. R.

Tejedo

Benayas

(2022). Tourists’ motivations, learning, and trip satisfaction facilitate pro-environmental outcomes of the Antarctic tourist experience. Journal of Outdoor Recreation and Tourism, 37, 100454. https://doi.org/10.1016/j.jort.2021.100454

15.

Çallı

B. A.

Özşahin

Coşkun

(2024). The assessment of organizational innovativeness as a mediator between ICT adoption and firm performance in Turkish SMEs. Sage Open, 14(4). https://doi.org/10.1177/21582440241297939

16.

Carrion

Levinson

(2012). Value of travel time reliability: A review of current evidence. Transportation Research Part A Policy and Practice, 46(4), 720–741. https://doi.org/10.1016/j.tra.2012.01.003

17.

Bastidas-Manzano

A. B.

Sánchez-Fernández

Casado-Aranda

L. A.

(2021). The past, present, and future of smart tourism destinations: A bibliometric analysis. Journal of Hospitality & Tourism Research, 45(3), 103126–103552. https://doi.org/10.1177/1096348020967062

18.

Chang

A. M.

Kannan

P. K.

(2006). Employee technology readiness and adoption of wireless technology and services [Conference session]. Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. 1, p. 42c. IEEE. https://doi.ieeecomputersociety.org/10.1109/HICSS.2006.148

19.

Chen

C. F.

Tsai

(2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28(4), 1115–1122. https://doi.org/10.1016/j.tourman.2006.07.007

20.

Cheng

Pan

Qian

(2023). Enhancing museum visiting experience: Investigating the relationships between augmented reality quality, immersion, and TAM using PLS-SEM. International Journal of Human-Computer Interaction, 40(17), 4521–4532. https://doi.org/10.1080/10447318.2023.2227832

21.

Chen

Aklikokou

A. K.

(2020). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850–865. https://doi.org/10.1080/01900692.2019.1660989

22.

Chiang

T. H. C.

Yang

S. J. H.

Hwang

G. J.

(2014). Students’ online interactive patterns in augmented reality-based inquiry activities. Computers & Education, 78, 97–108. https://doi.org/10.1016/j.compedu.2014.05.006

23.

Chung

Han

Joun

(2015). Tourists’ intention to visit a destination: The role of augmented reality (AR) application for a heritage site. Computers in Human Behavior, 50(1), 588–599. https://doi.org/10.1016/j.chb.2015.02.068

24.

Chung

Lee

Kim

J. Y.

Koo

(2018). The role of augmented reality for experience-influenced environments: The case of cultural heritage tourism in Korea. Journal of Travel Research, 57(5), 627–643. https://doi.org/10.1177/0047287517708255

25.

Dai

Wang

Kirillova

(2022). Travel inspiration in tourist decision making. Tourism Management, 90, 104484. https://doi.org/10.1016/j.tourman.2021.104484

26.

Davila Delgado

J. M.

Oyedele

Beach

Demian

. (2020). Augmented and virtual reality in construction: Drivers and limitations for industry adoption. Journal of Construction Engineering and Management, 146(7), 04020079. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001844

27.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008

28.

Devine

P. G.

Tauer

J. M.

Barron

K. E.

Elliot

A. J.

Vance

K. M.

(1999). Moving beyond attitude change in the study of dissonance-related processes. In Harmon-Jones

Mills

(Eds.), Cognitive dissonance: Progress on a pivotal theory in social psychology (pp. 297–323). American Psychological Association.

29.

H. N.

Shih

Q. A.

(2020). Effects of mobile augmented reality apps on impulse buying behavior: An investigation in the tourism field. Heliyon, 6(8), e04667. https://doi.org/10.1016/j.heliyon.2020.e04667

30.

Fan

Yuen Yan Lai

(2004). Review of the web sites for Chinese archaeology. Aslib Proceedings, 56(4), 255–257. https://doi.org/10.1108/00012530410549286

31.

Fennell

D. A.

(2021). Technology and the sustainable tourist in the new age of disruption. Journal of Sustainable Tourism, 29(5), 767–773. https://doi.org/10.1080/09669582.2020.1769639

32.

Florido-Benítez

del Alcázar Martínez

(2024). How artificial intelligence (AI) is powering new tourism marketing and the future agenda for smart tourist destinations. Electronics, 13(21), 4151. https://doi.org/10.3390/electronics13214151

33.

Gao

Huang

(2019). Understanding interactive user behavior in smart media content service: An integration of TAM and smart service belief factors. Heliyon, 5(12), e02983. https://doi.org/10.1016/j.heliyon.2019.e02983

34.

Gefen

Karahanna

Straub

D. W.

(2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307–321. https://doi.org/10.1109/TEM.2003.817277

35.

Gharibi

(2021). The evolution of predictive models and tourism. Journal of Tourism Futures, 7(2), 259–266. https://doi.org/10.1108/JTF-04-2020-0046

36.

Ghazizadeh

Lee

J. D.

Boyle

L. N.

(2012). Extending the technology acceptance model to assess automation. Cognition Technology & Work, 14, 39–49. https://doi.org/10.1007/s10111-011-0194-3

37.

Godoe

Johansen

T. S.

(2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(1), 38–52. https://doi.org/10.5334/jeps.aq

38.

Kang

Suh

S. C.

(2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)–cutting edge. Tourism Review, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062

39.

Granić

Marangunić

(2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864

40.

Hammady

Strathearn

(2020). Ambient information visualisation and visitors’ technology acceptance of mixed reality in museums. Journal on Computing and Cultural Heritage, 13(2), 1–22. https://doi.org/10.1145/3359590

41.

Han

Yoon

J. H.

Kwon

(2021). Impact of experiential value of augmented reality: The context of heritage tourism. Sustainability, 13(8), 4147. https://doi.org/10.3390/su13084147

42.

Hess

T. J.

McNab

A. L.

Basoglu

K. A.

(2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS Quarterly, 38(1), 1–28. https://doi.org/10.25300/MISQ/2014/38.1.01

43.

X. R.

(2018). When art meets tech: The role of augmented reality in enhancing museum experiences and purchase intentions. Tourism Management, 68, 127–139. https://doi.org/10.1016/j.tourman.2018.03.003

44.

Javadian Sabet

Shekari

Guan

Rossi

Schreiber

Tanca

. (2022). THOR: A hybrid recommender system for the personalized travel experience. Big Data and Cognitive Computing, 6(4), 131. https://doi.org/10.3390/bdcc6040131

45.

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. https://doi.org/10.1016/j.tourman.2015.02.013

46.

Khalil

Kallmuenzer

Kraus

(2023). Visiting museums via augmented reality: An experience fast-tracking the digital transformation of the tourism industry. European Journal of Innovation Management, 27(6), 2084–2100. https://doi.org/10.1108/EJIM-09-2022-0479

47.

Khasawneh

O. Y.

(2018). Technophobia: Examining its hidden factors and defining it. Technology and Society, 54, 93–100. https://doi.org/10.1016/j.techsoc.2018.03.008

48.

Kubiczek

Hadasik

Krawczyńska

Przedworska

Madarász

E. Z.

Ryczko

(2024). Perspective of created value in consumer choice: Comparison of economic and ecological dimensions. Sage Open, 14(1). https://doi.org/10.1177/21582440241238516

49.

Lai

Y. H.

Huang

H. C.

R. S.

Chang

C. M.

(2013). The effects of website trust, perceived ease of use, and perceived usefulness on consumers’ online booking intention: Evidence from Taiwan B&B sector. Life Science Journal, 10(2), 1516–1523.

50.

Law

E. L. C.

(2018). Augmenting the experience of a museum visit with a geo-located AR app for an associated archaeological site. In Vermeeren

Calvi

Sabiescu

(Eds.), Museum Experience Design: Crowds, Ecosystems and Novel Technologies (pp. 205–224). Springer. https://doi.org/10.1007/978-3-319-58550-5_10

51.

Lee

Chung

Koo

(2015). Moderating effects of distrust and social influence on aesthetic experience of augmented reality: Motivation–opportunity–ability model perspective [Conference session]. Proceedings of the 17th International Conference on Electronic Commerce 2015, pp. 1–8. https://doi.org/10.1145/2781562.2781588

52.

Lee

Hunter

W. C.

Chung

(2020). Smart tourism city: Developments and transformations. Sustainability, 12(10), 3958. https://doi.org/10.3390/su12103958

53.

Lin

Chang

H-C.

(2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality, 21(4), 424–444. https://doi.org/10.1108/09604521111146289

54.

Lin

Shih

Sher

P. J.

(2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology and Marketing, 24(7), 641–657. https://doi.org/10.1002/mar.20177

55.

Lin

H. F.

Chen

C. H.

(2017). Combining the technology acceptance model and uses and gratifications theory to examine the usage behavior of an augmented reality tour-sharing application. Symmetry, 9(7), 113. https://doi.org/10.3390/sym9070113

56.

Lin

J. S. C.

Hsieh

P. L.

(2007) The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Computers in human behavior, 23(3), 1597–1615. https://doi.org/10.1016/j.chb.2005.07.006

57.

Jiang

(2023). The technology acceptance on AR memorable tourism experience—the empirical evidence from China. Sustainability, 15(18), 13349. https://doi.org/10.3390/su151813349

58.

X. Z.

Chen

C. C.

Kang

(2022). Research on relevant dimensions of tourism experience of intangible cultural heritage lantern festival: Integrating generic learning outcomes with the technology acceptance model. Frontiers in Psychology, 13, 943277. https://doi.org/10.3389/fpsyg.2022.943277

59.

Shu

(2008). Review of relationships among variables in TAM. Tsinghua Science and Technology, 13(3), 273–278. https://doi.org/10.1016/S1007-0214(08)70044-0

60.

Madi

Al Khasawneh

Dandis

A. O.

(2024). Visiting and revisiting destinations: Impact of augmented reality, content quality, perceived ease of use, perceived value and usefulness on E-WOM. International Journal of Quality & Reliability Management, 41(6), 1550–1571. https://doi.org/10.1108/IJQRM-10-2023-0314

61.

Mankins

J. C.

(2009). Technology readiness assessments: A retrospective. Acta Astronautica, 65(9-10), 1216–1223. https://doi.org/10.1016/j.actaastro.2009.03.058

62.

Marangunić

Granić

(2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81–95. https://doi.org/10.1007/s10209-014-0348-1

63.

McCartney

(2020). Rise of the machines: Towards a conceptual service-robot research framework for the hospitality and tourism industry. International Journal of Contemporary Hospitality Management, 32(12), 3835–3851. https://doi.org/10.1108/IJCHM-05-2020-0450

64.

McNamara

A. J.

Shirowzhan

Me Sepasgozar

(2022). Investigating the determents of intelligent construction contract adoption: A refinement of the technology readiness index to inform an integrated technology acceptance model. Construction Innovation, 24(2), 702–724. https://doi.org/10.1108/CI-10-2021-0191

65.

Morosan

Jeong

(2008). Users’ perceptions of two types of hotel reservation web sites. International Journal of Hospitality Management, 27(2), 284–292. https://doi.org/10.1016/j.ijhm.2007.07.023

66.

Obeidy

W. K.

Arshad

Huang

J. Y.

(2017). An acceptance model for smart glasses-based tourism augmented reality. AIP Conference Proceedings, 1891(1), 020080. https://doi.org/10.1063/1.5005413

67.

J. C.

Yoon

S. J.

Chung

(2014). The role of technology readiness in consumers’ adoption of mobile internet services between South Korea and China. International Journal of Mobile Communications, 12(3), 229–248. https://doi.org/10.1504/IJMC.2014.061460

68.

Padilla-Meléndez

del Aguila-Obra

A. R.

Garrido-Moreno

(2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317. https://doi.org/10.1016/j.compedu.2012.12.014

69.

Parasuraman

(2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001

70.

Parasuraman

Colby

C. L.

(2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730

71.

Paulauskas

Blažauskas

Damaševičius

Maskeliūnas

(2023). Reconstruction of industrial and historical heritage for cultural enrichment using virtual and augmented reality. Technologies, 11(2), 611383. https://doi.org/10.3390/technologies11020036

72.

Pencarelli

(2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism, 22, 455–476. https://doi.org/10.1007/s40558-019-00160-3

73.

Plecher

D. A.

Wandinger

Klinker

(2019). Mixed reality for cultural heritage [Conference session]. 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1618–1622. https://doi.org//10.1109/VR.2019.8797846

74.

Pradhan

M. K.

Lee

(2018). Understanding travelers’ behavior for sustainable smart tourism: A technology readiness perspective. Sustainability, 10(11), 4259. https://doi.org/10.3390/su10114259

75.

Punzon

J. G.

(2021). Augmented reality in shopping tourism: Boosting tourism development through innovation in Barcelona. European Journal of Tourism Hospitality and Recreation, 11(1), 1–10. https://doi.org/10.2478/ejthr-2021-0001

76.

Puri

Mondal

Das

Vrana

V. G.

(2023). Blockchain propels tourism industry—an attempt to explore topics and information in smart tourism management through text mining and machine learning. Informatics, 10(1), 9–15. https://doi.org/10.3390/informatics10010009January.

77.

Rauscher

Humpe

(2022). Traveling the past: Raising awareness of cultural heritage through virtual reality. Journal of Promotion Management, 28(2), 128–143. https://doi.org/10.1080/10496491.2021.1987958

78.

Rejeb

Treiblmaier

(2023). How augmented reality impacts retail marketing: A state-of-the-art review from a consumer perspective. Journal of Strategic Marketing, 31(3), 718–748. https://doi.org/10.1080/0965254X.2021.1972439

79.

Saleh

Abubakar

Suriani

(2020). Determining factors affecting the interest in investment in Bulukumba area of South Sulawesi. Journal of Southwest Jiaotong University, 55(1), 1–17. https://doi.org/10.35741/issn.0258-2724.55.1.35

80.

Salloum

S. A.

Qasim Mohammad Alhamad

Al-Emran

Abdel Monem

Shaalan

(2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467

81.

Saprikis

Avlogiaris

Katarachia

(2020). Determinants of the intention to adopt mobile augmented reality apps in shopping malls among university students. Journal of Theoretical and Applied Electronic Commerce Research, 16(3), 491–512. https://doi.org/10.3390/jtaer16030030

82.

Seong

B. H.

Hong

C. Y.

(2022). Corroborating the effect of positive technology readiness on the intention to use the virtual reality sports game “Screen Golf”: Focusing on the technology readiness and acceptance model. Information Processing & Management, 59(4), 102994. https://doi.org/10.1016/j.ipm.2022.102994

83.

Sharp

J. H.

(2006). Development, extension, and application: A review of the technology acceptance model. Director, 7(9), 3–11.

84.

Shih

Y. Y.

Chen

C. Y.

(2013). The study of behavioral intention for mobile commerce: Via integrated model of TAM and TTF. Quality & Quantity, 47, 1009–1020. https://doi.org/10.1007/s11135-011-9579-x

85.

Shi

Wang

Long

(2023). Exploring the key drivers of user continuance intention to use digital museums: Evidence from China’s Sanxingdui museum. IEEE Access, 11, 81511–81526. https://doi.org/10.1109/ACCESS.2023.3297501

86.

Suherlan

Hidayah

(2025). The surprising truth about attitudes of tourism students toward statistics. Sage Open, 15(1). https://doi.org/10.1177/21582440241294117

87.

Szymkowiak

Jeganathan

(2022). Predicting user acceptance of peer-to-peer e-learning: An extension of the technology acceptance model. British Journal of Educational Technology, 53(6), 1993–2011. https://doi.org/10.1111/bjet.13229

88.

Tang

Han

(2023). Green consumption intention and behavior of tourists in urban and rural destinations. Journal of Environmental Planning and Management, 66(10), 2126–2150. https://doi.org/10.1080/09640568.2022.2061927

89.

Tang

Chen

(2011, May). A review of the evolution of research on information technology acceptance model [Conference session]. 2011 International Conference on Business Management and Electronic Information, vol. 2, pp. 588–591. IEEE. https://doi.org/10.1109/ICBMEI.2011.5917980

90.

Taufik

Hanafiah

M. H.

(2019). Airport passengers’ adoption behaviour towards self-check-in kiosk services: The roles of perceived ease of use, perceived usefulness and need for human interaction. Heliyon, 5(12), e02960. https://doi.org/10.1016/j.heliyon.2019.e02960

91.

Tavitiyaman

Tsang

W. S. L.

Lam

C. W. R.

(2021). The influence of smart tourism applications on perceived destination image and behavioral intention: The moderating role of information search behavior. Journal of Hospitality and Tourism Management, 46, 193–487. https://doi.org/10.1016/j.chb.2015.02.068

92.

Tian-Cole

Crompton

(2003). A conceptualization of the relationships between service quality and visitor satisfaction, and their links to destination selection. Leisure Studies, 22(1), 1–80. https://doi.org/10.1080/02614360306572

93.

Tom Dieck

M. C.

Jung

Han

D. I

. (2016). Mapping requirements for the wearable smart glasses augmented reality museum application. Journal of Hospitality and Tourism Technology, 7(3), 230–253. https://doi.org/10.1108/JHTT-09-2015-0036

94.

W. M.

Gao

J. H.

Leung

E. Y. W.

(2020). The effects of job insecurity on employees’ financial well-being and work satisfaction among Chinese pink-collar workers. Sage Open, 10(4). https://doi.org/10.1177/2158244020982993

95.

Vardopoulos

Papoui-Evangelou

Nosova

Salvati

(2023). Smart ‘tourist cities’ revisited: Culture-led urban sustainability and the global real estate market. Sustainability, 15(5), 4313. https://doi.org/10.3390/su15054313

96.

Venkatesh

Morris

M. G.

Davis

G. B.

(2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

97.

Walczuch

Lemmink

Streukens

(2007). The effect of service employees’ technology readiness on technology acceptance. Information Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005

98.

Wang

Q. C.

Chang

Liu

Jian

I. Y.

Y. T.

Wang

Y. X.

(2021). The impact of personality traits on household energy conservation behavioral intentions–An empirical study based on theory of planned behavior in Xi’an. Sustainable Energy Technologies and Assessments, 43, 100949. https://doi.org/10.1016/j.seta.2020.100949

99.

Wang

Han

Liu

Wang

(2024). Research on the impact of the socio-educational environment on the academic performance of college students: The mediating role of study motivation. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1289064

100.

Wei

(2019). Research progress on virtual reality (VR) and augmented reality (AR) in tourism and hospitality: A critical review of publications from 2000 to 2018. Journal of Hospitality and Tourism Technology, 10(4), 539–570. https://doi.org/10.1108/JHTT-04-2018-0030

101.

Jiang

Liang

(2021). Critical factors for predicting users’ acceptance of digital museums for experience-influenced environments. Information, 12(10), 426. https://doi.org/10.3390/info12100426

102.

Xiaoluan

(2021). Research on social media tourism marketing in the post-epidemic period. Jurnal Audiens, 2(2), 273–288. https://doi.org/10.18196/jas.v2i2.11914

103.

Choi

Rho

(2005). Extending the TAM for a t-commerce. Information Management, 42(7), 965–976. https://doi.org/10.1016/j.im.2004.11.001

104.

Yung

Khoo-Lattimore

Potter

L. E.

(2021). Virtual reality and tourism marketing: Conceptualizing a framework on presence, emotion, and intention. Current Issues in Tourism, 24(11), 47–1525. https://doi.org/10.1080/13683500.2020.1820454

105.

Zhang

Guo

Shen

(2023). Synergistic relationship or not? Understanding the resilience and efficiency of the tourism economy: Evidence from Hainan Province, China. Environment Development and Sustainability, 26, 3793–3825. https://doi.org/10.1007/s10668-022-02858-7

106.

Zhong

Moon

H. C.

(2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology and Society, 64, 101515. https://doi.org/10.1016/j.techsoc.2020.101515

107.

Zhuang

Hou

Feng

Lin

(. (2021). Subjective norms, attitudes, and intentions of AR technology use in tourism experience: The moderating effect of millennials. Leisure Studies, 40(3), 392–406. https://doi.org/10.1080/02614367.2020.1843692

108.

Zhu

Deng

(2020). How to influence rural tourism intention by risk knowledge during COVID-19 containment in China: Mediating role of risk perception and attitude. International Journal of Environmental Research and Public Health, 17(10), 3514. https://doi.org/10.3390/ijerph17103514

Exploring Tourists’ Intentions to Adopt Augmented Reality in Cultural Heritage Museums: Insights From a Modified Technology Acceptance Model

Abstract

Plain Language Summary

Keywords

Introduction

Theoretical Foundations

Augmented Reality

Improvement and Applicability Analysis of the Technology Acceptance Model

Technology Readiness (TR)

Research Hypotheses

Technology Willingness and Attitude Towards AR Use

AR Technology Perceptions and Usage Attitudes

AR Usage Attitudes, AR Usage Intentions, and Destination Tourism Intentions

Methodology

Sampling Location and Data Collection

Questionnaire Design and Variable Measurement

Data Analysis Methods and Procedures

Data Analysis and Hypothesis Testing

Reliability and Validity Assessment

Model Fit and Hypothesis Testing

Discussion

Conclusion

Theoretical Implications

Practical Implications

Limitations and Future Research Directions

Footnotes

Acknowledgements

ORCID iDs

Funding

Declaration of Conflicting Interests

Ethical Approval

Consent to Participate

Consent for Publication

Minimizing Risk

Balancing Risks and Benefits

Data Anonymization and Confidentiality

Data Availability Statement

References