In a groundbreaking study set to redefine the landscape of cultural heritage tourism, researchers Cheng and Qian have unpacked the intricate web of factors influencing tourists’ adoption of mobile augmented reality (MAR) educational applications. Utilizing structural equation modeling (SEM), their findings illuminate how technological, cognitive, and emotional dimensions converge to shape user acceptance of MAR. These insights signal a transformative shift in how heritage sites can leverage cutting-edge technology to engage visitors, blending education and entertainment in unprecedented ways.
At the heart of this study lies an exploration of external and internal motivators that determine tourists’ willingness to embrace MAR applications when exploring cultural tourism destinations. Key external factors identified include trust (TR), information quality (IQ), and enjoyment (EN), which exert a pronounced influence on how users perceive the ease of use and utility of these AR tools. Facilitating conditions (FC) – essentially the technological and contextual support available to users – were also found to directly impact perceived ease of use. Collectively, these variables indirectly drive the intention to use MAR applications, underscoring a complex interplay between user environment and individual motivation.
Notably, enjoyment surfaced as the most pivotal factor affecting perceived usability and ease of use. This finding aligns with prior studies examining intrinsic motivation within digital learning environments, reinforcing the importance of engaging, pleasurable experiences in technological adoption. The fusion of virtual and real-world elements, underpinned by 3D tracking and real-time interactive capabilities, inherently boosts user engagement. The GY-MAR project, a central case in this research, leveraged interactive design features and gamification to amplify entertainment value, effectively balancing visitors’ dual desires for learning and fun.
Interestingly, while some extant literature posits that enjoyment directly spurs users’ intention to adopt novel technologies, Cheng and Qian’s analysis challenges this assumption. Instead, they reveal that enjoyment influences users indirectly, mediated by their perceptions of usefulness and ease of use. This nuanced understanding likely owes itself to the dual cognitive-emotional landscape inhabited by cultural heritage tourists, who seek both knowledge acquisition and stimulating experiences. Thus, enjoyment functions as an initial attractor rather than an immediate decision driver.
Embedded within the GY-MAY project’s innovative framework are multifaceted modules designed to enhance emotional engagement. Virtual characters provide guided navigation, treasure hunt games foster exploration, and ‘talking cultural exhibits’ offer immersive storytelling. These elements go beyond superficial gamification, addressing educational depth while retaining entertainment appeal. As a result, the application effectively reduces visitors’ cognitive load—a critical barrier in heritage interpretation—and fortifies their retention of cultural knowledge, thereby elevating their perceived utility.
Delving deeper into trust and information quality, the research confirms these as vital pillars buttressing tourists’ positive evaluations of MAR applications. Trust emerges from the credible collaboration underpinning the project, involving ceramic culture experts, site staff, tourist representatives, and technology developers. This collective expertise assures users of the application’s reliability and educational value. The rigorous participation process, including application onboarding and support, further alleviates usability concerns, enhancing perceived ease of use and usefulness.
Facilitating conditions, reflecting the availability of technological infrastructure and assistance, hold a significant role in shaping user experience. When environmental support is robust, visitors find it easier to navigate and benefit from MAR applications, thus increasing adoption rates. This highlight draws attention to the often overlooked operational aspects of deploying AR technology in heritage settings, such as Wi-Fi availability, device compatibility, and on-site technical help.
From a theoretical vantage point, findings reinforce the enduring relevance of the Technology Acceptance Model (TAM) within cultural tourism contexts. Both perceived usefulness and ease of use exert substantial influence on tourists’ attitudes toward MAR educational applications. Moreover, an intuitive chain emerges where ease of use bolsters perceptions of utility, facilitating a more favorable attitude and fostering usage intention. Such insights amplify our understanding of the digital transformation of heritage experiences through socially grounded behavioral frameworks.
Perhaps most intriguingly, the study extends TAM by integrating the construct of Relative Advantage (RA), affirming that perceived usefulness significantly impacts RA, which in turn drives intention to use. RA captures tourists’ comparative assessment of MAR applications against traditional interpretive methods, such as guided tours or static displays. When visitors discern a clear advantage—richer content, more interactive experiences, or deeper engagement—they become more inclined to opt for the technological medium. This layered evaluative process mirrors epistemological concepts of progressing from sensory impressions to reflective judgments.
Empirical evidence from the GY-MAR initiative crystallizes these theoretical ideas into tangible design successes. For instance, at the Ancient Kiln site, the application addresses deficiencies in traditional displays by augmenting static explanations with multimedia demonstrations of porcelain-making techniques. This dynamic overlay not only clarifies complex processes but also heightens users’ perception of relative advantage over conventional exhibit boards. Visitors’ enhanced understanding and engagement translate directly into a greater willingness to use the MAR application, validating the project’s targeted approach.
Crucially, the study acknowledges emotional design as a tri-layered construct shaping the user experience. The instinctive layer appeals to sensory attraction and immediate satisfaction; the behavioral layer optimizes functionality and usability; and the reflective layer encourages deeper cognitive engagement and rational evaluation. The GY-MAR project’s explicit integration of these emotional design principles illustrates a sophisticated strategy that aligns technological innovation with human factors and cultural values, setting a benchmark for future heritage AR applications.
This research also carries implications for the sustainable development and innovation trajectories of cultural heritage tourism. While emerging devices such as AR glasses promise novel interfaces, the ubiquity and accessibility of smartphones ensure MAR applications remain a powerful conduit for heritage interpretation for the foreseeable future. Recognizing this, developers and promoters of cultural MAR applications must prioritize visitor experiences that seamlessly blend entertainment and education while enhancing perceptions of reliability and uniqueness.
The study’s progressive model elucidates the cognitive and emotional journey tourists embark upon when encountering MAR educational applications. Initial enjoyment draws interest, followed by assessments of usability and utility, culminating in comparative evaluations that solidify adoption decisions. This comprehensive understanding offers cultural institutions and technology providers a roadmap for crafting interventions that resonate with contemporary tourists’ multifaceted needs and expectations.
Furthermore, the collaborative development model exemplified in the GY-MAR project—incorporating diverse stakeholders spanning cultural experts, tourist representatives, and technical professionals—serves as an effective formula for ensuring content authenticity, technical adequacy, and user relevance. This cooperation also fosters local cultural empowerment and strengthens community ties to heritage preservation, showcasing a socially embedded approach to technological innovation.
In sum, Cheng and Qian’s study markedly advances scholarship on technology acceptance in cultural tourism by weaving together structural equation modeling with nuanced behavioral insights and pragmatic design implementations. Their work highlights how MAR educational applications, thoughtfully designed and contextually grounded, can revolutionize heritage engagement by meeting tourists’ cognitive and emotional aspirations in tandem.
As cultural institutions worldwide grapple with attracting and engaging diverse visitor segments amid evolving digital landscapes, this research provides a beacon illuminating how immersive technologies can be harnessed not merely as gimmicks but as substantive educational tools. The integration of trust, quality information, enjoyment, facilitating conditions, and emotional design forms a robust foundation for sustained MAR application adoption and contributes compellingly to the future of cultural heritage interpretation.
Ultimately, this investigation underscores the transformative power embedded in mobile AR technologies when seamlessly interwoven with authentic content, user-centric design, and rigorous evaluation frameworks. The implications extend beyond cultural tourism, offering fertile ground for further interdisciplinary research and innovation at the nexus of technology, culture, and human experience.
Subject of Research: Factors influencing tourists’ intention to use mobile augmented reality (MAR) educational applications in cultural tourism.
Article Title: Exploring the key factors influencing tourists’ intention to use mobile AR educational applications from the perspective of structural equation modeling.
Article References:
Cheng, J., Qian, J. Exploring the key factors influencing tourists’ intention to use mobile AR educational applications from the perspective of structural equation modeling.
npj Herit. Sci. 13, 119 (2025). https://doi.org/10.1038/s40494-025-01575-4
Image Credits: AI Generated
