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Article

Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History

by
Jiaying Li
1,
Lin Zhou
1,* and
Wei Wei
2
1
School of Design, Jiangnan University, Wuxi 214122, China
2
Department of Product Design, Changshu Institute of Technology, Changshu 215500, China
*
Author to whom correspondence should be addressed.
Information 2025, 16(7), 573; https://doi.org/10.3390/info16070573
Submission received: 4 June 2025 / Revised: 29 June 2025 / Accepted: 1 July 2025 / Published: 4 July 2025
(This article belongs to the Special Issue Information Technology in Society)
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Abstract

Advances in information technology have enabled virtual museums to transcend traditional physical boundaries and become important tools in education. Despite their growing use, the factors influencing the effectiveness of virtual museums in enhancing students’ learning motivation remain underexplored. This study investigates key factors that promote learning motivation among secondary school students using the National Museum of Nature’s Online Virtual Exhibition as a case study. Grounded in the Stimulus–Organism–Response (S-O-R) theoretical framework, a conceptual model was developed and empirically tested using Structural Equation Modeling (SEM) to examine relationships among stimulus variables, psychological states, and learning motivation. Results reveal that affective involvement, cognitive engagement, and perceived presence significantly enhance learning motivation, while immersion shows no significant effect. Among the stimulus factors, perceived enjoyment strongly promotes affective involvement, perceived interactivity enhances cognitive engagement, and content quality primarily supports cognitive processing. Visual aesthetics contribute notably to immersion, affective involvement, and perceived presence. These findings elucidate the multidimensional mechanisms through which user experience in virtual museums influences learning motivation. The study provides theoretical and practical implications for designing effective and engaging virtual museum educational environments, thereby supporting sustainable digital learning practices.

1. Introduction

Advances in modern information technology have created new possibilities for museum-based information delivery, leading to the rise of virtual museums that transcend traditional physical boundaries [1]. Virtual museums are defined as collections of digital artifacts composed of various media, without being confined to a physical location. Their connectivity and accessibility surpass conventional methods of communication and interaction with visitors, enabling global reach [2]. These digital platforms enhance physical museums through personalization, interactivity, enriched user experiences, and diverse content offerings [3]. Virtual museums exemplify the transformation driven by developments in information and communication technology [4], providing access to digital databases, online exhibitions, and 3D object views, thereby facilitating digital access to physical collections [5].
Museums play a vital role in helping students understand historical artifacts and cultural heritage, fostering cultural sensitivity [6]. Learning-oriented museum programs focus on experience and knowledge acquisition [1]. Digital technologies have reshaped traditional learning models by offering not only educational environments but also opportunities for discovery, interaction, and participation [7]. Research shows that virtual museums significantly enhance students’ learning motivation, cognitive development, and critical thinking skills. For example, immersive experiences have been shown to increase students’ interest and support higher-order thinking development [8]. The National Museum of Nature’s Online Virtual Exhibition offers a 360° immersive environment. Virtual Tours (VTs) use multiple 360° images or videos to create realistic representations of physical spaces, allowing users to navigate, change perspectives, and explore interactively on personal digital devices, simulating in-person visits. VTs have been shown to increase interest in visiting physical locations and improve learning outcomes and knowledge retention [9].
Although online and virtual exhibitions are now common in museums, they do not fully replicate the experience of onsite visits [10], and social interaction remains limited [11]. Similarly, the educational application of virtual museums faces challenges such as limited adoption and awareness among educators and students [12]. Furthermore, studies on virtual museums across educational levels are scarce, especially regarding their long-term impact on learning outcomes. To address these gaps, this study focuses on secondary school students and applies the S-O-R model to analyze how virtual museum experiences influence internal psychological states to enhance learning motivation. Using the National Museum of Nature’s Online Virtual Exhibition as a case study, the research aims to provide theoretical insights and practical guidance for future educational practices and virtual museum design.

2. Model Development and Research Hypotheses

2.1. Model Construction

This study is grounded in the Stimulus–Organism–Response (S-O-R) theoretical framework, originally proposed by Mehrabian and Russell in 1974 [13]. The S-O-R model seeks to explain how individuals respond to external environmental stimuli by generating internal emotional or cognitive reactions, which in turn influence their attitudes and behaviors. It comprises three key components: stimulus, referring to external environmental factors; organism, encompassing individuals’ emotional and cognitive responses; and response, indicating resulting attitudes or behaviors. In this research, online virtual museums are conceptualized as typical information environments, where features such as visual aesthetics, perceived interactivity, and content quality serve as external stimuli. These features trigger users’ sensory and psychological reactions, making the S-O-R framework suitable for constructing a research pathway of “perceived stimuli→user experience→learning motivation.”
Although the S-O-R model originated in environmental psychology, its core mechanism—linking external stimuli to emotional/cognitive processing and subsequent behavioral responses—has been widely applied in various digital media contexts, including e-commerce, gaming, MOOCs, and social media. For example, in the context of online retail, atmospheric cues such as color, layout, and imagery function as external stimuli that shape consumers’ emotional states and cognitive evaluations, thereby influencing browsing behavior and purchase intention [14]. In the domain of digital and educational games, visual attractiveness (e.g., graphic quality and animation) and task design (e.g., levels and challenges) significantly enhance users’ immersion and learning motivation [15]. In their study of a virtual exhibition at the National Costume Museum, Wu et al. [16] found that information quality and richness stimulated users’ perceived enjoyment and perceived usefulness, which subsequently enhanced their usage intention. These empirical findings align with the stimulus–psychological response–behavior logic embedded in the S-O-R framework. In addition, Cheng [17] applied the S-O-R model to the context of Massive Open Online Courses (MOOCs), confirming that perceived network externalities, gamification, and media richness positively influenced learners’ behavioral, emotional, and social learning experiences, which in turn fostered sustained usage intentions. More recently, Huang et al. [18] empirically validated the influence pathways of various perceived stimuli on emotional responses in immersive virtual environments using the S-O-R model. Their findings further reinforced the theoretical applicability of the S-O-R framework in examining learning motivation within virtual museum contexts.
However, some theoretical limitations of the S-O-R model must be acknowledged when applied to virtual museum settings. First, the “organism” component provides a relatively simplified representation of internal psychological processes, lacking consideration of individual difference variables such as motivational type, learning style, and self-regulation ability. This may constrain the model’s explanatory power regarding complex user behavior. Second, the model assumes a one-time, linear stimulus–response pathway, which makes it less effective in capturing the dynamic evolution of learning motivation over extended use or across contextual transitions. Third, virtual learning environments are characterized by technological heterogeneity, interaction complexity, and cultural adaptation, yet the S-O-R model has limited capacity to address platform-specific or contextual variations. Despite these limitations, the model serves as a useful foundation for variable selection and path construction in this study. Future research may consider incorporating moderating variables such as platform features or learner characteristics to enhance its adaptability and explanatory depth.
Based on this theoretical foundation, this study conceptualizes learning motivation as the response variable. The organism layer includes four internal psychological states: immersion, affective involvement, cognitive engagement, and perceived presence. Perceived enjoyment, perceived interactivity, content quality, and visual aesthetics as stimulus variables (see Figure 1 for the hypothesis model).

2.2. Research Hypotheses

In the response section of the model, learning motivation is selected as the dependent variable. In the organism section, four mediating variables are proposed: immersion, affective involvement, cognitive engagement, and perceived presence. In the stimulus section, four latent independent variables are included: perceived enjoyment, perceived interactivity, content quality, and visual aesthetics. Based on this structure, the study proposes a set of research hypotheses to explore the complex relationships between stimulus factors, organismic responses, and the enhancement of learning motivation. These hypotheses aim to clarify how the design attributes of virtual exhibitions influence students’ internal psychological experiences and, in turn, affect their motivation to learn.

2.2.1. Organism Dimension

Immersion refers to a psychological state in which individuals perceive themselves as being enveloped, included, and interacting within a virtual environment. This state is the result of the combined effects of sensory stimulation, environmental factors, and individual predispositions [19]. Petersen et al. [20] found that immersion positively influences learners’ embodied presence and embodied learning. Embodied presence refers to the sense of “being there,” while embodied learning involves understanding oneself and the surrounding world “as a living, bodily subject that perceives and acts meaningfully” [21]. Immersion can enhance situational interest, thereby increasing learning motivation. It also encourages deeper engagement in learning activities. Therefore, immersive experiences in online virtual exhibitions may enhance learning motivation. The following hypothesis is proposed:
H1: 
Immersion has a positive effect on learning motivation.
Turner et al. [22] analyzed learning from the cognitive, emotional, and behavioral dimensions and observed that emotion plays a crucial role in classroom environments, significantly affecting students’ motivation and behavior. Emotion is a critical component of student motivation. Reschly et al. [23] found that, among students in grades 7 to 10, more frequent positive emotions in school are associated with higher levels of engagement. This finding was further supported by Olmos-Raya et al. [24]. When learners invest more emotionally in online virtual exhibitions, their engagement in learning increases, thus enhancing learning motivation. Accordingly, the following hypothesis is proposed:
H2: 
Affective involvement has a positive effect on learning motivation.
Cognitive engagement is the result of students’ active involvement in learning environments [25]. Self-efficacy, achievement goals, and perceived instrumentality (the intrinsic value of the task) are three motivational factors influencing cognitive engagement [26]. Corno and Mandinach [27] demonstrated that self-regulated learning is a form of cognitive engagement and is considered a key factor in generating and sustaining student motivation in the classroom. When online virtual exhibitions activate learners’ self-regulated learning and trigger cognitive engagement, learning motivation may be positively influenced. The following hypothesis is proposed:
H3: 
Cognitive engagement has a positive effect on learning motivation.
Perceived presence is defined as the subjective experience of being in a place or environment, even when one is physically situated elsewhere. The effectiveness of a Virtual Environment (VE) is often associated with the degree of perceived presence [19]. The sense of presence or immersion in a 3D environment is attributed to realism, interactivity, or user control [28]. Witmer and Singer [19] demonstrated that, when learners perceive themselves as situated in a computer-generated virtual world, and when control enhances this sense of presence, learning and performance may improve. Previous studies have also confirmed that presence has a positive impact on learning outcomes in educational virtual environments [29,30]. Therefore, interaction with online virtual exhibitions may enhance learners’ perceived presence and subsequently their learning motivation. The following hypothesis is proposed:
H4: 
Perceived presence has a positive effect on learning motivation.

2.2.2. Stimulus Dimension

Perceived enjoyment is defined as “the extent to which the activity of using the technology is perceived to be enjoyable in its own right.” In media environments, enjoyment is viewed as a form of intrinsic motivation that fosters interaction with specific technologies [31]. It contributes to a sense of engagement [32]. According to Holdack et al. [33], perceived enjoyment serves as a mediating factor that significantly influences user attitudes and behavioral intentions. Studies in marketing, communication, and social psychology have shown that perceived enjoyment, as an intrinsic motivator, positively affects technology acceptance and usage behavior [34]. Leveau and Camus [35] confirmed the connection between immersion and enjoyment [36]. Therefore, the following hypothesis is proposed:
H5: 
Perceived enjoyment has a positive effect on immersion.
Perceived enjoyment, the extent of pleasure experienced during media use, is a key component of emotional experience. In virtual learning, positive achievement emotions such as enjoyment and hope, if not sufficiently strong, may hinder optimal performance and engagement. Learners lacking motivation may not engage in cognitive processing even if they are cognitively capable [37]. Yang et al. [38] found that perceived enjoyment significantly enhances learners’ affective involvement and thereby improves learning satisfaction. Thus, the following hypothesis is proposed:
H6: 
Perceived enjoyment has a positive effect on affective involvement.
There are two core constructs in interactivity theory: actual interactivity and perceived interactivity. Actual interactivity, or function-based interactivity, is objectively assessed based on the number and type of interactive features [39]. Perceived interactivity is a psychological state experienced by users during media interaction and is measured by users’ subjective experience [40,41]. Voorveld et al. [42] suggested evaluating perceived interactivity by asking users about their feelings or experiences during website use.
Different media exhibit varying degrees of interactivity. Comparing PCs, video, and virtual environments reveals that virtual environments induce the highest levels of perceived presence. When users experience clumsy interaction with a virtual environment, immersion decreases; when interaction is natural, immersion increases [19]. Therefore, the following hypothesis is proposed:
H7: 
Perceived interactivity has a positive effect on immersion.
Studies have shown that higher levels of perceived interactivity are associated with more positive emotional responses [43]. Wang et al. [44] categorized perceived interactivity into four dimensions, all of which collectively influence trust during usage, thereby enhancing affective involvement and community attachment. Hence, the following hypothesis is proposed:
H8: 
Perceived interactivity has a positive effect on affective involvement.
Oh and Sundar [45] identified two forms of interactivity—modality interactivity and message interactivity. Modality interactivity enhances interface evaluation and cognitive absorption, while message interactivity fosters deeper engagement with information. Online virtual exhibitions feature both types of interactivity, prompting learners to think about content. Perceived interactivity grants users control over the learning experience [46], which positively affects their learning experience [20]. Interactivity is also a prerequisite for experiential learning in virtual environments [47]. Therefore, the following hypothesis is proposed:
H9: 
Perceived interactivity has a positive effect on cognitive engagement.
Moreover, Fortin and Dholakia [48] suggested that the impact of perceived interactivity on engagement is mediated by perceived presence. Wong [49] found that task-oriented interactive activities in virtual environments improved memory recall accuracy, indicating that interactivity enhances perception and presence. Thus, the following hypothesis is proposed:
H10: 
Perceived interactivity has a positive effect on perceived presence.
Lee et al. [50] demonstrated that high-quality content allows learners to be fully immersed in virtual destinations even before physically visiting them. Holdack, Lurie-Stoyanov, and Fromme [33] revisited the role of information richness in immersive and novel technologies, concluding that rich content enhances immersion. Thus, the following hypothesis is proposed:
H11: 
Content quality has a positive effect on immersion.
Cheng et al. [51] found that detailed virtual environments on digital platforms stimulate learners to actively engage in cognitive processing. The immersive features of virtual reality also enhance perceived presence. Hameed and Perkis [52] confirmed that realism and coherence in virtual environments, delivered through multisensory stimulation, contribute to a strong sense of presence. The following hypotheses are proposed:
H12: 
Content quality has a positive effect on cognitive engagement.
H13: 
Content quality has a positive effect on perceived presence.
Visual aesthetics in virtual environments significantly influence user experience. The authors of [53] noted that users prefer realistic environments, where graphical quality and detail affect immersion. Visual aesthetics, as part of aesthetic experience, enhance immersion and overall satisfaction [54]. Accordingly, the following hypothesis is proposed:
H14: 
Visual aesthetics have a positive effect on immersion.
Generally, when users focus more attention on virtual stimuli, they become more emotionally invested, enhancing their perceived presence [19]. Zhou and Li [55] suggested that deeper cognitive experiences with interface interactions and the reliability of visual elements further contribute to engagement. Therefore, the following hypotheses are proposed:
H15: 
Visual aesthetics have a positive effect on affective involvement.
H16: 
Visual aesthetics have a positive effect on perceived presence.

3. Research Design and Methodology

3.1. Case Description

To support the empirical investigation of how virtual museums enhance learning motivation, this study selected the virtual exhibition of the National Museum of Natural History of China as a research case. This exhibition integrates educational content, immersive technologies, and interactive design, making it a representative example of a digital cultural platform.
The exhibition includes multiple themed sections such as “Prehistoric Mammals,” “The Origin of Humans,” “The World of Plants,” and “Wonders of Africa.” Through high-quality 3D models, narrative animations, and multisensory media, the exhibition presents scientific knowledge with strong content quality and visual aesthetics. To enhance the interactive experience, the museum adopts 720-degree 3D display technology for exhibits, supported by digital narrations, thereby improving users’ sense of perceived presence and immersion. Additionally, semantic recognition technologies are applied to provide personalized explanations and contextual information. Visitor comments also contribute to greater cognitive engagement. These features closely align with the psychological mediating mechanisms emphasized in this study. (see Figure 2 for the screenshot of the virtual exhibition webpage).
The reasons for selecting this exhibition are threefold: first, it demonstrates cutting-edge practices in immersive technology integration within virtual museums; second, it possesses a clear science popularization and educational orientation, aligning with this study’s learning-motivation-based model; third, as a significant case of museum digital transformation in China, the exhibition holds strong practical relevance and generalizability.

3.2. Questionnaire Design

This study followed a standardized questionnaire development procedure to ensure scientific validity in measurement. The measurement framework was constructed based on established theoretical models, with all constructs derived from prior literature, as detailed in Table 1. Each item was measured using a 7-point Likert scale ranging from “1 = strongly disagree” to “7 = strongly agree” to capture respondents’ attitudes with high granularity. In addition, a standardized demographic section was included to collect basic personal information such as gender, age, and education level.

3.3. Sample and Data Collection

In this study, participants were required to have prior experience with the online virtual exhibition of the National Museum of Natural History to ensure that their responses were based on actual usage, thereby enhancing the reliability and relevance of the data. Since the research emphasizes users’ perceptions and psychological responses during the virtual exhibition experience, the trial session was essential. However, this process had to be organized and facilitated by researchers onsite, guiding each respondent through the trial, which significantly increased the complexity and workload of data collection. Secondary school students were selected as the target population for three key reasons. First, this age group is at a critical stage in the development of learning motivation and typically possesses sufficient digital literacy and operational competence to meaningfully engage with virtual museum interfaces. This supports both data validity and experimental control. Second, the integration of digital technologies and online resources is accelerating in the current secondary education context. As an extension of educational technology, virtual museums are increasingly accepted and show great potential for implementation in middle schools. Third, the student population offers practical value and educational relevance for exploring motivational mechanisms in virtual learning environments. To enhance sample diversity, participants were recruited through offline field visits to secondary schools, targeting students from different grade levels. Due to the time required for each participant to complete the trial and survey, as well as logistical constraints of onsite implementation, the overall sample size was limited. Upon completion of data collection, a rigorous data cleaning process was conducted. Special attention was paid to detecting and eliminating invalid responses, such as those with excessively short completion times or identical responses across all scale items. This step was essential to ensure the quality and reliability of the dataset used for subsequent analysis. In total, 132 responses were collected, of which 109 were deemed valid, yielding a valid response rate of 82.58%. The final sample spanned multiple grade levels and balanced key demographic characteristics including gender, prior experience with virtual exhibitions, and digital literacy. The gender ratio was approximately 1.12:1 (male to female), with 90.91% of respondents being junior secondary school students aged 13–15. Regarding prior usage, 61.54% of respondents had used the platform occasionally, 24.61% had never used it before, and 13.85% reported using it sometimes or frequently. All participants provided informed consent prior to their involvement, and the study ensured strict confidentiality and privacy protection for all personal data and responses. (see Figure 3 for the questionnaire collected on-site photos for record).

4. Data Analysis

Sample size can influence several aspects of Structural Equation Modeling (SEM). Compared to Covariance-Based SEM (CB-SEM), Partial Least Squares SEM (PLS-SEM) demonstrates higher statistical power with smaller sample sizes and more complex models, and it exhibits better convergence behavior [64]. Therefore, this study employed the PLS-SEM algorithm using SmartPLS 4 software to ensure stable estimation of the complex model involving multiple latent variables and structural paths. A weighted path scheme with up to 3000 iterations and default initial weights was applied. In addition, a non-parametric bootstrapping procedure with 5000 subsamples was conducted to assess the statistical significance of the PLS-SEM results.

4.1. Assessment of Measurement Model

The measurement instruments were rigorously evaluated for reliability and validity in accordance with empirical research standards. For reliability assessment, both Cronbach’s alpha and composite reliability were used to examine internal consistency. As shown in Table 2, the Cronbach’s alpha values for all latent constructs ranged from 0.715 to 0.882, exceeding the threshold of 0.7 recommended by Nunnally [65]. Composite reliability values ranged from 0.839 to 0.927, surpassing the critical value of 0.7 proposed by Gefen et al. [66]. For validity testing, all standardized factor loadings of observed variables exceeded the cutoff value of 0.7 (refer to Table 3), meeting the parameter estimation requirements of the measurement model. These results demonstrate that all constructs satisfy the criteria for convergent validity, supporting their inclusion in further analysis.

4.2. Assessment of Structural Model

This study employed cross-loadings analysis to empirically assess the discriminant validity of the measurement model, a method widely used in validating multiconstruct measurement systems, according to Leguina [67]. As shown in Table 3, the outer loadings of each indicator on its associated construct were significantly higher than any of its cross-loadings on other constructs. Therefore, the cross-loading criterion was satisfactorily met.
In addition to the cross-loadings analysis, this study further applied the Fornell–Larcker criterion to statistically validate the discriminant validity of the measurement model. According to this criterion, if the square root of the Average Variance Extracted (AVE) for each latent construct is greater than the highest correlation it has with any other construct, the model is considered to possess adequate discriminant validity across constructs. As shown in the discriminant validity matrix (Table 4), the square roots of the AVE, presented in bold on the diagonal, consistently exceed the corresponding inter-construct correlation coefficients in the off-diagonal positions. This empirical result meets the critical threshold proposed by Fornell and Larcker [68], confirming that each construct exhibits sound discriminant validity.
To reinforce the findings, the study additionally employed the Heterotrait–Monotrait Ratio (HTMT) method to assess discriminant validity. According to the criterion proposed by Gold et al. [69], when the HTMT values between latent constructs are below 0.90, the measurement model is considered to have adequate discriminant validity. As shown in Table 5, all HTMT values among the latent variables met the required threshold. This result is consistent with the conclusions derived from the Fornell–Larcker criterion test, forming a methodological triangulation. Together, the two methods confirm that each theoretical construct in the measurement model demonstrates statistically significant independence. This dual validation supports the robustness of discriminant validity required in higher-order structural equation models.

4.3. Results

This study employed PLS-SEM to investigate the influence of various factors on the enhancement of learning motivation. The path analysis results are summarized in Table 6. Out of the 16 hypothesized paths, 9 were supported, while H1, H5, H7, H8, H10, H11, and H13 were not supported.
The findings indicate that immersion, affective involvement, cognitive engagement, and perceived presence had varying degrees of influence on learning motivation. Specifically, cognitive engagement had a highly significant positive effect on learning motivation (β = 0.279, p < 0.001), suggesting that higher cognitive engagement can significantly enhance learners’ subsequent motivation. Likewise, affective involvement and perceived presence had significant positive effects on continued intention (affective involvement: β = 0.333, p = 0.003; perceived presence: β = 0.256, p = 0.014). However, the effect of immersion on learning motivation was not statistically significant (β = 0.064, p = 0.530).
Perceived enjoyment had a highly significant positive effect on affective involvement (β = 0.339, p < 0.001), but its effect on immersion was not significant (β = 0.146, p = 0.144).
Perceived interactivity showed a significant positive impact on cognitive engagement (β = 0.276, p = 0.004), indicating its important role in engaging learners cognitively. However, its effects on immersion, affective involvement, and perceived presence were not significant (immersion: β = 0.204, p = 0.058; affective involvement: β = 0.041, p = 0.656; perceived presence: β = 0.024, p = 0.811).
Similarly, content quality significantly influenced cognitive engagement (β = 0.268, p = 0.005), highlighting the critical role of content in shaping cognitive engagement. Nonetheless, its effects on immersion and perceived presence were not significant (immersion: β = 0.166, p = 0.191; perceived presence: β = 0.126, p = 0.294).
In addition, visual aesthetics had significant positive effects on immersion, affective involvement, and perceived presence (immersion: β = 0.292, p = 0.049; affective involvement: β = 0.493, p < 0.001; perceived presence: β = 0.571, p < 0.001). (see Figure 4 for the intentional behavior model).

5. Discussion

5.1. Factors Influencing the Enhancement of Learning Motivation

The results reveal that immersion did not have a significant impact on the enhancement of learning motivation (β = 0.064, p = 0.530). This may suggest that, while immersion enhances the browsing experience within a virtual museum, it does not necessarily translate into increased learning motivation unless it is aligned with specific educational goals and guided learning mechanisms. In contrast, affective involvement was found to have a significant positive influence on learning motivation (β = 0.333, p = 0.003). This supports the view that emotional resonance helps learners internalize value and become goal-driven, thereby fostering sustained motivation. Cognitive engagement also had a highly significant impact on learning motivation (β = 0.279, p < 0.001). This indicates that when learners are cognitively engaged, they process information more deeply and participate more actively in thinking, which fosters intrinsic motivation and a sense of achievement—factors crucial for sustaining learning behavior. Furthermore, perceived presence showed a significant positive effect on learning motivation, suggesting that when learners feel “present” in a virtual environment, their willingness and initiative to learn are enhanced. Perceived presence reflects learners’ sense of realism and engagement in the virtual setting. It facilitates a stronger sense of belonging, focus on learning tasks, and goal-directed behaviors. Specifically, a high level of perceived presence enables users to form both emotional and cognitive connections with the content presented, thereby enhancing their engagement, exploration motivation, and learning expectations.

5.2. Influence of Antecedent Variables on Mediating Variables

Perceived enjoyment had a significant positive effect on affective involvement (β = 0.339, p < 0.001) but no significant impact on immersion (β = 0.146, p = 0.144). This pattern aligns with the earlier results regarding their influence on learning motivation. While users may find an activity enjoyable, this does not automatically lead to a deep immersive state. Immersion often relies more heavily on technological execution and the realism of the environment. Prior studies in the contexts of VR and AR have indicated that enjoyment can positively affect immersion [70,71]. However, in the current study, enjoyment appears to primarily influence the emotional dimension. Enjoyable experiences can elicit positive emotional responses, increasing users’ psychological investment and emotional resonance during learning.
The significant positive influence of perceived interactivity on cognitive engagement (β = 0.276, p = 0.004) supports previous research suggesting that interactivity encourages active cognitive processing. Higher perceived interactivity stimulates users to actively process information, deepening their understanding and critical thinking about the exhibits, which enhances cognitive engagement. In virtual museums, interactive elements such as clicking, rotating, pop-up information, and audiovisual feedback effectively foster users’ curiosity and learning motivation, encouraging deeper engagement with the content [72]. However, perceived interactivity did not significantly affect immersion, affective involvement, or perceived presence (immersion: β = 0.204, p = 0.058; affective involvement: β = 0.041, p = 0.656; perceived presence: β = 0.024, p = 0.811). This may indicate that the interactive design in virtual museums primarily serves functional navigation purposes—such as directional guidance or informational toggling—rather than delivering sensory immersion or situational engagement. In many cases, interaction features are basic or repetitive, and technical limitations (e.g., internet dependence) may hinder users’ immersive experiences or perceived presence [73]. Moreover, if interactivity fails to trigger emotional responses such as surprise, curiosity, or empathy, its impact on emotional involvement will remain limited. Perceived presence, in particular, relies on sophisticated spatial perception, realistic visual feedback, and authentic interactions—attributes not typically realized through simple button clicks or text pop-ups. Furthermore, varying dimensions of presence may respond differently to measurement tools [74], which may further complicate the relationship between interactivity, immersion, and presence.
Content quality showed a significant positive effect on cognitive engagement, consistent with existing literature [75,76]. This finding indicates that high-quality content enhances learners’ information processing and comprehension. However, content quality did not significantly influence immersion or perceived presence. This could be due to its primary function of facilitating cognitive rather than sensory or contextual engagement. Specifically, cognitive engagement is more closely associated with the structured, logical, and educational value of the content, whereas immersion and presence depend on visual presentation, interaction design, and technological support [20]. Additionally, if the content presentation adopts traditional formats, it may be less effective in fostering users’ situational integration and psychological immersion within the virtual space [77].
Visual aesthetics had a significant positive effect on immersion (β = 0.292, p = 0.049), indicating that, as the first perceptual cue upon entering the virtual environment, visual appeal influences users’ overall assessment of quality. Optimized visual design—through appealing interface layout, color harmony, and aesthetic presentation—enhances users’ aesthetic pleasure and encourages deeper immersion [78]. The effect of visual aesthetics on affective involvement was highly significant (β = 0.493, p < 0.001), showing that aesthetically pleasing environments stimulate emotional responses such as joy, curiosity, and interest. These responses, in turn, boost emotional engagement with the virtual content. Users often perceive visual quality as a reflection of the developers’ effort, thereby reinforcing psychological resonance and emotional connection [79]. Additionally, visual aesthetics had a very strong positive effect on perceived presence (β = 0.571, p < 0.001). Visually appealing virtual interfaces increase the perceived realism and credibility of the environment, which contributes to a heightened sense of presence. When the virtual space achieves high visual fidelity and coherence, users are more likely to suspend disbelief and feel “present” in the virtual environment, a critical factor in immersive learning [19].

6. Conclusions

This study conducted an in-depth investigation into how online virtual museums influence learners’ learning motivation by applying the S-O-R framework to examine how antecedent variables affect the outcome variable through mediators. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study comprehensively assessed multiple influencing factors, including learning motivation, immersion, affective involvement, cognitive engagement, perceived presence, perceived enjoyment, perceived interactivity, content quality, and visual aesthetics.
The results highlight the critical roles of affective involvement, cognitive engagement, and perceived presence in promoting learning motivation. Affective involvement strengthens learners’ motivation and goal-directed behavior by evoking emotional resonance and value alignment. Cognitive engagement facilitates the development of intrinsic motivation through deeper information processing and active thinking. Perceived presence fosters a sense of “being there,” enhancing learners’ identification with and concentration on learning tasks, thereby encouraging sustained learning intention. In contrast, immersion did not significantly predict learning motivation, suggesting that sensory immersion alone, without educational guidance mechanisms, may not effectively translate into learning motivation.
Regarding the influence paths of the antecedent variables, perceived enjoyment significantly enhanced affective involvement but had no significant effect on immersion. This indicates that enjoyment primarily activates emotional responses rather than enhancing immersive states. This finding contrasts with previous studies in VR/AR settings, where perceived enjoyment promoted immersion, highlighting the mediating role of different technological environments. Perceived interactivity significantly improved cognitive engagement but did not significantly influence affective involvement, immersion, or perceived presence. This suggests that current interactive designs in virtual museums mainly serve functional navigation purposes and are insufficient to elicit higher-level immersive experiences or emotional resonance. Content quality significantly contributed to cognitive engagement but did not have significant effects on immersion or perceived presence, indicating that content presentation mainly supports cognitive processing rather than sensory or contextual simulation. Visual aesthetics, however, had a significantly positive influence on immersion, affective involvement, and perceived presence, underlining its importance as a primary perceptual entry point in virtual environments. High-quality visual presentation not only enhances users’ aesthetic evaluation of the environment but also strengthens emotional connection and spatial awareness, serving as a key driver for deeper virtual experiences.
Overall, the proposed model illustrates the multipathway mechanisms from perceptual factors to learning motivation within the context of virtual museums. These findings offer valuable insights into understanding motivational processes in digital cultural environments. The study provides practical guidance for designing educationally effective virtual museums: enhancing the cognitive value and visual quality of content, guiding users toward emotional involvement, and creating more immersive and interactive environments to foster learning motivation and sustained engagement.

6.1. Theoretical Contributions

The theoretical contributions of this study are threefold. First, it systematically integrates the cognitive pathway of perception–emotion–motivation into the cultural–educational context of virtual museums. By constructing a structural model of “perceptual factors–mediated experiences–learning motivation,” the study identifies the mediating roles of immersion, affective involvement, cognitive engagement, and perceived presence in promoting learning motivation. It also clarifies the influence paths of antecedent variables such as perceived enjoyment, perceived interactivity, content quality, and visual aesthetics. This framework expands the application of situational perception theory in virtual learning environments and provides theoretical support for explaining how intrinsic motivation is developed in cultural digital platforms.
Second, the study employs a multidimensional modeling approach to examine factors affecting learning motivation in virtual environments. By comparing the effects of various perceptual variables, it reveals the differential impacts of these factors on psychological mediators. For instance, while perceived interactivity activates cognitive engagement, it does not independently foster immersion or perceived presence. In contrast, visual aesthetics influence both emotional and spatial perceptual constructs. These distinctions contribute to a more nuanced understanding of the relationship between user experience and motivation, refining the theoretical basis for motivational modeling.
Finally, the study focuses on the psychological link between user experience and educational goals in virtual museums, offering theoretical insights into the educational efficacy of cultural digital platforms. In particular, it highlights the pivotal roles of affective involvement and perceived presence in shaping motivation, thereby broadening the research paradigm regarding how user experiences impact learning behavior. The proposed affective–cognitive pathway of motivation formation in digital cultural contexts provides a theoretical foundation for further research on immersive educational environments.

6.2. Practical Implications

This study offers several practical implications for the cultural dissemination and educational application of virtual museums. First, the findings provide targeted suggestions for platform design and optimization. Identifying the critical roles of visual aesthetics and content quality in stimulating affective involvement and cognitive engagement can guide design teams to allocate more resources toward content development and interface aesthetics, thus enhancing users’ overall experience and learning motivation. The significant effect of perceived presence also suggests that developers should invest in spatial layout, environmental realism, and interaction feedback to increase users’ sense of presence and engagement.
Second, the study supports the implementation of educational functions in virtual museums. By identifying the bridging roles of affective involvement and cognitive engagement in the development of learning motivation, it argues that educational virtual museums should go beyond information presentation to foster psychological involvement. Platform operators can integrate contextual guidance, emotional design, and task-oriented mechanisms to help users establish personal value identification during interactions, thereby promoting sustained learning intention. These findings offer important insights for education departments, digital museum initiatives, and online cultural course development.
In addition, the study offers a practical pathway for the integration of culture and technology. Although perceived interactivity positively influenced cognitive processing, its limited impact on immersion and emotional involvement indicates room for improvement in current interaction design. Development teams should reconsider the types and depths of interaction systems and shift from basic operational interaction toward context-based and affective interaction to create more immersive cultural learning experiences. These recommendations provide practical guidance for building effective virtual educational platforms targeting youth and the general public.

6.3. Limitations and Future Research

Despite offering new empirical evidence on the mechanisms by which virtual museums influence learning motivation, this study has certain limitations that suggest directions for future research. First, the sample mainly consists of users with a certain level of familiarity and experience with virtual museums, which may lead to an overestimation of cognitive and behavioral tendencies. Future research should expand the sample to include users from diverse cultural backgrounds and digital literacy levels to enhance representativeness and generalizability.
Second, this study is based on cross-sectional survey data, which limits its ability to capture the dynamic evolution of causal relationships between variables. Future studies could adopt longitudinal designs to track changes in users’ psychological and behavioral responses across multiple virtual museum experiences, thereby gaining deeper insights into the developmental trajectory of learning motivation. Experimental designs or behavioral tracking techniques are also recommended to capture users’ emotional reactions, interaction behaviors, and learning performance during actual engagement, improving the accuracy of mechanism identification.
Moreover, the current model does not incorporate all potential factors influencing learning motivation, such as types of immersive technologies (e.g., 2D web-based platforms, 360-degree navigation, immersive VR), individual differences (e.g., learning styles, motivation types), and cultural contexts. Future research should expand the model by including additional latent variables to develop a more comprehensive theoretical framework. Given the potential variance in user perceptions under different technological conditions, comparative studies across platforms are also suggested to refine perceptual mechanisms and support more precise educational and design strategies for virtual museum platforms.

Author Contributions

Conceptualization, J.L. and L.Z.; methodology, J.L.; software, L.Z.; validation, J.L. and L.Z.; investigation, J.L.; resources, L.Z.; data curation, J.L.; writing—original draft preparation, J.L.; writing—review and editing, W.W.; visualization, J.L.; supervision, L.Z.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.

Funding

The 2023 Ministry of Education Humanities and Social Sciences Research Planning Fund Project “A Study on the Design Value of Chinese Gardens Overseas in the Context of Intelligent Media” (Project No. 23YJA760123); the 2024 National Social Science Fund Art Studies Project “Research on the Design of Chinese Gardens Overseas” (Project No. 24BG134).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Changshu Institute of Technology Ministry of Social Science (protocol code No. CIT MSS-E-2025-025, approved on 26 February 2024).

Informed Consent Statement

All the subjects participating in the study and their parents gave informed consent.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

During the writing of this manuscript, ChatGPT-4o provided support with translation and language polishing. This assistance improved both the accuracy and fluency of the content. The author sincerely thanks the ChatGPT team and its service platform.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Learning Motivation Enhancement Hypothesis model.
Figure 1. Learning Motivation Enhancement Hypothesis model.
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Information 16 00573 g002
Figure 2. The virtual exhibition of the National Museum of Natural History of China.
Figure 2. The virtual exhibition of the National Museum of Natural History of China.
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Information 16 00573 g003
Figure 3. Offline questionnaire distribution and data collection at a middle school in Ningbo.
Figure 3. Offline questionnaire distribution and data collection at a middle school in Ningbo.
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Figure 4. The intentional behavior model of learning behavior motivation for middle school students. (* for significant positive effects and ** for highly significant positive effect).
Figure 4. The intentional behavior model of learning behavior motivation for middle school students. (* for significant positive effects and ** for highly significant positive effect).
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Table 1. Measurement Items and Sources.
Table 1. Measurement Items and Sources.
ConstructCodingItemSource
Perceived
Enjoyment		PE1I felt happy while using the online virtual exhibition platform.[56]
PE2I found the online virtual exhibition itself to be interesting.
PE3I would be willing to use it even without a specific learning purpose.
Perceived
Interactivity		PI1I was able to interact with the content of the online virtual exhibition (e.g., clicking, operating, etc.).[57]
PI2The online virtual exhibition platform responded quickly to my actions.
PI3The interactive design of the online virtual exhibition helped me become more engaged.
Content
Quality		CQ1The content of the online virtual exhibition was helpful to me.[58]
CQ2The information provided by the online virtual exhibition was valuable.
CQ3The information presented in the online virtual exhibition was clear and easy to understand.
Visual
Aesthetics		VA1I found the online virtual exhibition visually attractive.[59]
VA2The design of images and animations on the platform made me feel comfortable.
VA3The overall design of the exhibition was creative.
ImmersionIM1I felt as if I were physically present at the exhibition.[19]
IM2I was completely immersed in this virtual experience.
IM3I was able to ignore my surroundings and focus on the exhibition.
Affective
Involvement		AI1I felt happy or excited during the virtual exhibition experience.[60]
AI2The content of the virtual exhibition resonated with me emotionally.
AI3I was deeply concerned and involved with the content presented.
Cognitive
Engagement		CE1I thought a lot during my visit to the online virtual exhibition platform.[61]
CE2I needed to concentrate when using the virtual exhibition platform.
CE3I tried to understand the knowledge behind the exhibition.
Perceived
Presence		PP1I felt as though I were truly inside the exhibition space.[62]
PP2The virtual exhibition platform made my interaction with the exhibits feel natural.
PP3I perceived the virtual exhibition space as “real.”
Learning
Motivation		LM1The online virtual exhibition stimulated my interest in further learning.[63]
LM2I wanted to learn more about the content related to the exhibition.
LM3The experience of browsing the virtual exhibition enhanced my motivation to learn.
Table 2. Descriptive and measurement assessment results.
Table 2. Descriptive and measurement assessment results.
ConstructCronbach’s AlphaCRAVE
Affective Involvement0.8820.9270.808
Cognitive Engagement0.7340.8490.653
Content Quality0.8470.9070.764
Immersion0.8620.9150.783
Learning Motivation0.8720.9220.797
Perceived Enjoyment0.8180.8920.734
Perceived Interactivity0.7150.8390.634
Perceived Presence0.8220.8940.737
Visual Aesthetics0.7610.8630.677
Table 3. Discriminant validity: cross-loading.
Table 3. Discriminant validity: cross-loading.
ConstructCodingAICECQIMLMPEPIPPVA
Affective
Involvement		AI10.878 0.401 0.479 0.574 0.621 0.579 0.452 0.564 0.670
AI20.904 0.509 0.414 0.540 0.559 0.553 0.512 0.562 0.660
AI30.915 0.516 0.566 0.631 0.658 0.706 0.539 0.576 0.660
Cognitive
Engagement		CE10.547 0.815 0.383 0.483 0.516 0.348 0.344 0.457 0.441
CE20.375 0.843 0.316 0.425 0.415 0.233 0.321 0.413 0.305
CE30.346 0.765 0.303 0.297 0.520 0.311 0.348 0.243 0.297
Content
Quality		CQ10.415 0.364 0.874 0.575 0.489 0.569 0.508 0.410 0.418
CQ20.529 0.357 0.856 0.352 0.522 0.609 0.428 0.341 0.548
CQ30.499 0.371 0.892 0.435 0.527 0.551 0.457 0.424 0.487
ImmersionIM10.457 0.301 0.341 0.862 0.502 0.382 0.394 0.704 0.401
IM20.677 0.464 0.519 0.927 0.569 0.563 0.622 0.704 0.604
IM30.563 0.533 0.521 0.864 0.552 0.558 0.451 0.618 0.558
Learning
Motivation		LM10.579 0.458 0.592 0.572 0.888 0.441 0.363 0.567 0.557
LM20.592 0.694 0.439 0.459 0.860 0.443 0.404 0.474 0.583
LM30.656 0.464 0.538 0.611 0.929 0.543 0.496 0.677 0.652
Perceived
Enjoyment		PE10.632 0.407 0.537 0.445 0.504 0.865 0.601 0.412 0.577
PE20.679 0.343 0.587 0.498 0.523 0.904 0.603 0.465 0.676
PE30.429 0.197 0.566 0.548 0.332 0.799 0.456 0.308 0.384
Perceived
Interactivity		PI10.485 0.306 0.445 0.392 0.388 0.527 0.786 0.278 0.532
PI20.409 0.311 0.347 0.380 0.345 0.496 0.793 0.298 0.453
PI30.440 0.377 0.474 0.548 0.396 0.527 0.810 0.440 0.456
Perceived
Presence		PP10.458 0.382 0.319 0.625 0.491 0.342 0.244 0.870 0.511
PP20.629 0.419 0.441 0.726 0.620 0.491 0.480 0.870 0.630
PP30.517 0.377 0.388 0.593 0.529 0.346 0.367 0.835 0.529
Visual
Aesthetics		VA10.668 0.289 0.557 0.503 0.576 0.590 0.504 0.548 0.849
VA20.582 0.385 0.369 0.462 0.487 0.534 0.514 0.513 0.817
VA30.566 0.406 0.414 0.512 0.591 0.469 0.465 0.553 0.802
Table 4. Discriminant validity: Fornell–Larcker criterion.
Table 4. Discriminant validity: Fornell–Larcker criterion.
ConstructAICECQIMLMPEPIPPVA
Affective Involvement0.899
Cognitive Engagement0.529 0.808
Content Quality0.544 0.416 0.874
Immersion0.649 0.498 0.530 0.885
Learning Motivation0.684 0.605 0.584 0.614 0.893
Perceived Enjoyment0.685 0.373 0.656 0.576 0.535 0.857
Perceived Interactivity0.558 0.420 0.535 0.562 0.474 0.650 0.796
Perceived Presence0.631 0.459 0.451 0.761 0.643 0.466 0.434 0.858
Visual Aesthetics0.737 0.435 0.547 0.599 0.671 0.647 0.601 0.654 0.823
Table 5. Discriminant validity: Heterotrait–Monotrait Ratio (HTMT).
Table 5. Discriminant validity: Heterotrait–Monotrait Ratio (HTMT).
ConstructAICECQIMLMPEPIPPVA
Affective Involvement
Cognitive Engagement0.650
Content Quality0.633 0.523
Immersion0.731 0.613 0.596
Learning Motivation0.776 0.747 0.684 0.705
Perceived Enjoyment0.793 0.468 0.792 0.680 0.624
Perceived Interactivity0.701 0.570 0.674 0.691 0.594 0.842
Perceived Presence0.731 0.589 0.529 0.900 0.750 0.552 0.539
Visual Aesthetics0.898 0.579 0.685 0.727 0.821 0.806 0.817 0.818
Table 6. Structural assessment result.
Table 6. Structural assessment result.
HypothesisPathβSTDEVTpResult
H1IM→ LM0.064 0.102 0.628 0.530 Invalid
H2AI→ LM0.333 0.112 2.963 0.003 Valid
H3CE→LM0.279 0.070 4.004 0.000 Valid
H4PP→ LM0.256 0.105 2.448 0.014 Valid
H5PE→ IM0.146 0.100 1.463 0.144 Invalid
H6PE→ AI0.339 0.087 3.884 0.000 Valid
H7PI→ IM0.204 0.108 1.895 0.058 Invalid
H8PI→ AI0.041 0.093 0.445 0.656 Invalid
H9PI→ CE0.276 0.096 2.880 0.004 Valid
H10PI→ PP0.024 0.101 0.239 0.811 Invalid
H11CQ→ IM0.166 0.127 1.309 0.191 Invalid
H12CQ→ CE0.268 0.095 2.840 0.005 Valid
H13CQ→ PP0.126 0.120 1.050 0.294 Invalid
H14VA→ IM0.292 0.148 1.969 0.049 Valid
H15VA→ AI0.493 0.084 5.849 0.000 Valid
H16VA→ PP0.571 0.099 5.771 0.000 Valid
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Li, J.; Zhou, L.; Wei, W. Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History. Information 2025, 16, 573. https://doi.org/10.3390/info16070573

AMA Style

Li J, Zhou L, Wei W. Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History. Information. 2025; 16(7):573. https://doi.org/10.3390/info16070573

Chicago/Turabian Style

Li, Jiaying, Lin Zhou, and Wei Wei. 2025. "Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History" Information 16, no. 7: 573. https://doi.org/10.3390/info16070573

APA Style

Li, J., Zhou, L., & Wei, W. (2025). Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History. Information, 16(7), 573. https://doi.org/10.3390/info16070573

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