A Multidimensional Perspective on the Impact of Gamification on Visitors’ Emotions and Revisit Intention in Virtual Museum Spaces: A Case Study of the Southern Han Mausoleums Museum

Abstract

An empirical analysis was conducted by evaluating the emotional responses of 30 university students in a virtual museum environment using a combination of subjective scales and physiological monitoring technologies. The experimental samples were divided into a control group (without gamification) and four experimental groups featuring different combinations of gamification elements. The results showed a significant increase (p < 0.05) in emotional arousal (both subjective and physiological) and intention to revisit in the experimental groups compared to the control group, indicating that gamification elements effectively enhance visitors’ emotional engagement and loyalty. However, no significant differences were observed in the impact of different gamification combinations on physiological emotions and revisit intention, suggesting that visitors are more concerned with the presence of gamification elements than their specific forms. Correlational analysis revealed a significant positive correlation between heart rate (HR) and subjective positive emotions and revisit intention, indicating its potential as a critical indicator of emotional engagement. This study confirms the practical value of gamification elements in virtual museums, emphasizing the priority of essential elements and the balance between challenge and reward mechanisms. The inclusion of physiological indicators provides a multidimensional perspective for emotion assessment, addressing the limitations of traditional subjective methods.

1. Introduction

Virtual museums, as a new form of cultural dissemination in the digital age, demonstrate significant potential for economic revenue generation and tourism development. Traditional museums serve as venues for public exhibitions and as custodians responsible for the maintenance and preservation of artifacts [1]. In the context of rapid advancements in information technology, traditional museums face challenges such as limited audience reach and high operational costs. Virtual museums, on the other hand, enable the global sharing of cultural resources via the Internet, offering a new digital dimension for physical museums [2]. Virtual reality allows virtual museums to transcend geographical and temporal limitations, providing new impetus for economic growth. These museums can diversify their revenue streams through online exhibitions, digital ticket sales, and virtual tour services [3]. Amid the lockdowns caused by the COVID-19 pandemic, virtual museums became the sole means by which domestic and international visitors in many countries could experience museum offerings. Even after the lifting of restrictions, people have continued to prefer low-risk experiences and view virtual reality (VR) as an alternative to traditional travel [4]. The Secretary-General of the United Nations World Tourism Organization has remarked that virtual reality is among the solutions facilitating the recovery of the tourism industry post-COVID-19 [5]. Therefore, it is evident that in the context of global economic digitalization, the development of virtual museums is not only an innovation in cultural dissemination but also a crucial engine for economic growth.

Revisit intention is crucial from the perspective of developing economic revenue for virtual museums. It is a key indicator of tourists’ behavioral intentions after their visits. It reflects their loyalty to the virtual museum, enhancing which can significantly contribute to the sustainable profitability of virtual museums [6]. Users with a firm intention to revisit are more likely to join paid membership programs, purchase exclusive content, or participate in virtual events, directly increasing the revenue streams of virtual museums. Furthermore, beyond direct profitability, the word-of-mouth effect generated by revisit intention can reduce marketing costs while increasing traffic [7]. For instance, repeat user visits boost online traffic and improve the website’s search ranking, attracting more new visitors. This beneficial cycle helps expand the audience base, bringing more advertising revenue and sponsorship opportunities to the museum [8].

The reasons behind tourists’ intention to revisit include destination attachment, tourists’ travel memories, guided tour experiences, emotional experiences, and the specific cultural atmosphere [6,9,10,11]. Throughout the entire viewing process, the comprehensive experience of tourists is the result of external stimuli. The feelings generated through the experience reflect cognitive processes, and the revisit intention triggered by these cognitive processes represents a manifestation of a behavioral response [9]. In recent years, researchers have begun to pay more attention to the relationship between tourists’ emotions and their intention to revisit, emphasizing the importance of emotions in virtual museums [1]. Emotions such as comfort, joy, and distress significantly influence tourists’ willingness to revisit a destination in the future [12]. Studies have found that emotions play a moderating role in revisit intention [13]. Positive emotions among tourists positively moderate the relationship between satisfaction and revisit intention, while negative emotions negatively moderate this relationship. Emotions are crucial in user decision-making processes, especially in cultural and creative fields [14]. In virtual spaces, users’ emotions are not merely reactions to content but are also a comprehensive perception of the overall environment and interactive design. Negative emotions can lead to a reluctance to revisit [15]. Therefore, in the design and operation of virtual museums, it is important to create an experience environment that can evoke positive emotions, enhancing user emotional engagement through interactivity and immersion [16]. By optimizing emotional experiences, virtual museums can increase users’ revisit intentions more effectively, achieving higher economic benefits and user satisfaction.

Users’ emotional reactions reflect their evaluation of environmental stimuli [17], influencing their decision-making [18]. In research related to emotional enhancement, gamification is widely recognized as an effective method [19,20,21]. By transforming traditional actions into game-like behaviors, gamification evokes positive experiences and motivations in users, thereby influencing their behavior [22]. The design strategies of gamification aim to foster pleasurable experiences, attract user interest, and establish emotional connections to drive user behavior, thereby achieving desired objectives. Gamification has been demonstrated to enhance tourists’ emotional experiences during travel; compared to traditional touring modes, it can present serious and less perceptible cultural content in a lively and engaging manner, making it easier for tourists to engage willingly. Additionally, gamification encourages interaction and communication, enhancing participation and immersion. When applied to cultural tourism, it can significantly improve the tourism experience for visitors [23].

Gamification was first introduced in 2002 by British engineer Nick Pelling to apply game elements and mechanisms to non-game environments to enhance engagement and enjoyment [23]. The PBL (Points, Badges, Leaderboards) triad proposed by scholar Kevin Werbach and others is widely recognized as a fundamental component in gamification design. The core of gamification lies in utilizing these PBL elements and specific tasks and reward systems to make traditionally dull tasks or learning activities more attractive and challenging [24]. In recent years, gamification has been widely applied as an innovative approach in fields such as education, management, and healthcare, aiming to motivate user participation and achieve specific goals by creating engaging experiences [25]. Research indicates that adopting gamification strategies can stimulate autonomy and enhance positive attitudes and engagement in learning and work [25]. Although researchers may define gamification differently, it is generally considered a process rather than a product designed to improve experiences in non-game environments through game thinking and mechanisms [17].

Gamification techniques have also been applied to the service processes and tactile interfaces of museums and virtual museums to help integrate traditional game elements into non-game environments. For example, in the museum application, users can try to make a Cycladic figurine through a non-graphic puzzle game. These gamification elements stimulated the audience, attracted their interest, and finally let them return to the museum, which mainly targeted young people with a variety of interface experiences [26,27]. The literature [28,29] highlights the importance of controlling the difficulty of tasks when gamifying museum scenarios, as this can significantly affect visitors’ emotions; overly challenging tasks may lead to anxiety, while overly simple tasks may result in boredom. Various gamification elements determine the complexity of tasks. Although there has been some progress in research on gamification in museum experiences, confirming its positive impact on user experience, the optimal combination of different gamification elements to enhance visitor emotions and stimulate their intention to revisit remains unclear. Particularly in virtual museum settings, empirical evidence is lacking on balancing the challenge and reward mechanisms within gamification elements. Therefore, this study aimed to analyze the impact of different combinations of gamification elements on visitors’ emotions and their intention to revisit from this perspective.

This study investigated two key aspects: first, whether gamification can effectively enhance visitors’ emotions and their intention to revisit, and second, how gamification elements can be optimally combined to boost these emotions and revisit intentions. Beyond the research background, this study includes the following sections: The Section 2 is the experimental part, where we describe our design of a control group sample and the use of various combinations of gamification elements to create experimental group samples, totaling five different experimental samples. We employed a combination of subjective evaluation and physiological monitoring to analyze the impact of these samples on visitors’ emotions. In Section 3, we analyze the experimental data to identify gamification elements influencing visitors’ emotions. The Section 4 discusses the causes of the experimental results in conjunction with existing research. In Section 5, we summarize the entirety of this study.

2. Methods

2.1. Research Framework

This study explored the impact of gamification elements in enhancing visitors’ emotions in virtual museums. The overall research framework is depicted in Figure 1. The framework comprises three components. In the virtual museum section, we incorporated various gamification elements into existing exhibits and integrated them into the virtual museum setting to form the experimental samples for this study. Regarding human factors, we discuss the selection of physiological emotion indicators. We focused on selecting questionnaires that could effectively reflect visitors’ emotions for subjective evaluation. Subsequently, we conducted data analysis on the collected elements, quantitatively analyzing the factors related to visitors’ emotions and offering recommendations for optimizing emotional experiences in future virtual museums.

2.2. Southern Han Mausoleums Museum

The subject of this study is the virtual artifact exhibition hall of the Southern Han Mausoleums Museum. Located in Panyu District, Guangzhou, the Southern Han Mausoleums Museum is a specialized museum built on the Southern Han Mausoleum site. It opened in 2019, showcasing the historical evolution of Guangzhou over 2200 years and the culture of the Southern Han Kingdom through permanent exhibitions and public archeological activities. The museum’s design integrates site preservation with cultural heritage, featuring Han and Tang architectural styles and Lingnan characteristics while adopting modern esthetics. The museum boasts a rich collection of artifacts, including the precious Shang dynasty tusk zhang, the De Mausoleum celadon, and the mourning documents unearthed from Kang Mausoleum, all of which hold significant academic value, particularly in providing tangible evidence for the study of Tang–Song society and Southern Han history.

We chose the Southern Han Mausoleums Museum as the subject of our experiment because it is the only imperial mausoleum-themed museum in South China, carrying significant cultural memories from the Five Dynasties and Ten Kingdoms period. Its rich collection of artifacts and high-standard construction provides an excellent academic research and cultural dissemination platform. However, due to its remote location and lack of interactivity, the museum has relatively weak appeal, necessitating digital optimization. Thus, it serves as an ideal subject for testing the effectiveness of gamification strategies in enhancing public engagement. Through this study, we aim to help the museum achieve the digital exhibition and integration of culture and tourism, enhancing its social impact and becoming a new highlight in the development of Guangzhou as a cultural city.

2.3. Design and Application of Gamification Elements

In this experiment, we designed a control group sample (n = 1) and experimental group samples with gamification elements (n = 4). The experimental group samples were further divided into different combinations: a basic PBL (Points, Badges, Leaderboards) sample, a PBL plus competition sample (PBL + C), a PBL plus points store sample (PBL + PS), and a PBL plus content unlocking sample (PBL + CU).

The virtual space we designed allows participants to freely explore the environment, which includes various artifacts (as shown in Figure 2). When they interact with these artifacts, the interface switches to an artifact appreciation mode, and the samples for this experiment are presented within this mode.

2.3.1. Control Group Sample

Figure 3 illustrates the design of the control group sample for this experiment. This sample provides a 360-degree rotatable 3D model, allowing players to observe the artifact using mouse drag operations from multiple angles. An information panel on the right also displays the artifact’s name, era, excavation background, and functional description. To enhance interactivity, we incorporated a coloring game segment (Figure 4). Upon entering the coloring segment, players see a brush button on the left, which allows adjustments to the brush size, color, and stroke. A timer appears in the upper right corner, and players can color the artifacts. The task is complete when the entire artifact is covered in color.

2.3.2. PBL Sample

Figure 5 shows the design of this study’s experimental group sample. In addition to retaining all the functions of the control group, the experimental group also introduced a settlement interface after the coloring was completed. Here, the system records the players’ game time, awards them points and honor badges (Figure 5), and ranks them among their friends based on their completion time (Figure 6).

2.3.3. PBL + C Sample

Unlike the PBL sample, the PBL + C sample introduces a new dynamic where the system matches the player with an opponent upon entering the coloring scene, and both undertake the coloring task simultaneously. During the coloring process, players can view their opponent’s progress through a small window in the upper left corner, with the system displaying both players’ time usage (as shown in Figure 7). Once the coloring is complete, players click to finish and enter the settlement interface, where the time taken by both players is compared. The system evaluates participants based on the duration and battle outcome, awarding points and honor badges accordingly, and ranks them based on completion time.

2.3.4. PBL + PS Sample

The PBL + PS sample with an integrated points store shares the same gameplay elements as the PBL sample. The key difference is that players can use accumulated points to redeem virtual items in the points store (Figure 8), such as badges or skins, as rewards, which can be displayed on the leaderboard for other players to see.

2.3.5. PBL + CU Sample

The content unlocking mechanism enables players to unlock additional artifacts for coloring once they reach a certain points threshold. Players can earn unique achievement rewards and display the number of “unlocked artifacts” on the leaderboard (Figure 9).

2.4. Physiological Emotional Indicators

Research based on physiological monitoring technologies provides crucial objective data support for studying emotions. These technologies can capture users’ physiological responses in real time during interactive processes, offering immediate and objective emotional feedback for user experience assessment. Compared to traditional methods such as questionnaires and interviews, physiological data are not influenced by participants’ subjective biases or memory distortions, making them more accurate and reliable [30].

Electrodermal activity (EDA) and electrocardiography (ECG) are two common physiological indicators widely used in emotion measurement research. They can reflect changes in emotional states in different dimensions [31].

EDA is a key psychophysiological indicator of emotional arousal, allowing the real-time and precise capture of changes in arousal levels. When an individual experiences emotional stimuli, increased sweat gland secretion significantly enhances skin conductivity, which is referred to as EDA [32]. This measurable change in skin conductance (an increase in skin conductance equates to a decrease in skin resistance) reflects the intensity of emotional arousal. EDA is closely related to emotion, arousal, and attention, making it one of the factors most often measured in physiological response systems [33]. In terms of specific EDA parameters, EDA can typically be quantified based on the skin conductance level (SCL) or skin conductance response (SCR) [31]. For example, a higher average SCL may indicate that the subject is in a state of higher physiological arousal or long-term stress [34,35]. The SCR is an extremely sensitive indicator for examining emotional arousal, favoring short-term emotional monitoring compared to SCL. Typically, the SCR uses the change in skin conductance (i.e., the difference between experimental values and baseline values) to indicate the degree of somatic physiological activation [36].

Electrocardiography (ECG) provides valuable information about emotional states by recording heart rate (HR) and heart rate variability (HRV) [37,38]. For example, HR increases when the body is excited and decreases when it is calm. Additionally, the frequency domain measure—the ratio of the low frequency to the high frequency (LF/HF) in HRV—reflects the relative activity of the sympathetic and parasympathetic nervous systems [39]. A high LF/HF ratio indicates relatively increased sympathetic activity, which is often associated with emotional states such as stress, anxiety, and excitement. Conversely, a low LF/HF ratio suggests dominant parasympathetic activity, typically aligned with emotions such as relaxation, calmness, and pleasure.

By combining EDA and ECG indicators, changes in emotional states can be reflected from different perspectives. Using diverse emotional measurement methods allows for a more comprehensive assessment of basic emotions.

2.5. Subjective Emotion Indicators

To enhance the precision and reliability of subjective emotion measurement, we employed a combination of two emotion assessment scales: the Self-Assessment Manikin (SAM) and the Positive and Negative Affect Schedule (PANAS).

The SAM, developed by Bradley and Lang in 1994 [30], is a non-verbal, pictorial assessment tool designed to evaluate emotional states rapidly. The SAM allows participants to quickly understand and judge the rating criteria, minimizing inaccuracies and variations due to language descriptions and enabling them to express their emotional states without verbal descriptions accurately. The validity and reliability of SAM are comparable to those of semantic differential scales, with high internal consistency [40]. It is widely used globally for assessing immediate emotional changes triggered by specific events, making it particularly suitable for contexts requiring quick and intuitive emotion measurement.

The PANAS was developed by Watson and colleagues in 1988 as a text-based scale grounded in the two-dimensional structure of emotion, designed to measure positive affect (PA) and negative affect (NA) [41]. Each dimension contains 10 emotional adjectives; for example, positive affect includes terms such as “active” and “optimistic”, while negative affect includes terms such as “distressed” and “sad”. Participants rate the intensity of each emotion. Research has shown that the PANAS exhibits high sensitivity in emotion induction experiments [41]. Larsen and Ketelaar found that guiding participants to imagine scenarios eliciting positive or negative emotions could significantly increase PA. However, it might not necessarily decrease NA. This feature enhances the reliability and scientific validity of the PANAS scale [42]. The PANAS is used not only for assessing short-term emotional states but also for research on long-term mental health and subjective well-being. It has been widely applied globally, becoming one of the most used tools for emotion assessment [43].

Each of these two scales has its distinct characteristics. The SAM offers an intuitive visual rating system, while the PANAS provides detailed emotional characteristics through quantitative textual descriptions. Combining these two scales helps us to obtain more diverse emotional data, thereby supporting more complex emotional analyses.

In addition, we included a revisit intention scale (as described in [44]) in our study to evaluate the performance of the gamified sample. The revisit intention scale consists of three items: “visit again” (“this experience makes me willing to visit the Southern Han Mausoleums Virtual Museum again”), “recommend to friends” (“I am willing to recommend the Southern Han Mausoleums Virtual Museum to my friends”), and “choose again” (“when there are other virtual museums to choose from, I will still choose to visit the Southern Han Mausoleums Virtual Museum”).

In terms of scale scoring, the SAM scale uses a 9-point rating system. For subjective valence, the scores range from 1 to 9 (1 indicating very unpleasant, 5 indicating neutral, and 9 indicating very pleasant). For subjective arousal, the scores also range from 1 to 9 (1 indicating very calm, 5 indicating neutral, and 9 indicating very excited). This study utilized the abbreviated PANAS scale (consisting of 10 items). Carla et al. found that the results of the 20-item PANAS scale and the 10-item abbreviated PANAS scale were broadly consistent [45]. The PANAS scale was presented as a textual questionnaire, with items divided into positive and negative emotion groups. Each item has 5 options, ranging from “very slightly or not at all” to “extremely”, scored from 1 to 5. The revisit intention scale consists of three items, requiring participants to respond based on their level of agreement with each question. Each item has 5 options, ranging from “strongly disagree” to “strongly agree”, scored from 1 to 5.

2.6. Participants

Regarding the number of participants, relevant research suggests that at least 10 subjects are needed to obtain statistically significant emotional research results [46]. Furthermore, the study in [47] found that the most used range of study participants is 10–30 (46.15% of the papers used this number range). Additionally, after reviewing recent studies on emotional research [48,49,50], we ultimately recruited 30 university students to participate in the emotional stimulation experiment. In addition to their advantages in experimental control and data reliability, college students also tend to be more familiar with digital interfaces and adapt to new interface control methods, which makes them particularly suitable for participants in emotion measurement research.

The participants were between 18 and 25 years old, with normal or corrected-to-normal vision, no tactile impairments or history of neurological disorders, and standard olfactory capabilities. We required all the participants not to consume alcohol, drink coffee, or stay up late within 24 h before the experiment. This study received approval from the University’s Human Research Ethics Committee. The participants signed informed consent forms, acknowledging their understanding of the study’s purpose and potential risks. All data were processed anonymously to protect privacy, and the participants were free to withdraw from the study at any time without penalty.

2.7. Experimental Procedure

The research assistant guided the participants into the laboratory (Figure 10) and seated them in the preparation area. The research assistants first gave the participants a personal information form, which included the following: 1. basic information: age, name, and gender; 2. physical condition information: physical health status, mental health status, vision condition, and whether they were right-handed or left-handed; 3. signature for informed consent. The participants filled out the form according to their actual circumstances and signed it. Once completed, the forms were collected. The assistant then explained the detailed procedure and requirements of the experiment to the participants. The research assistant then equipped the participants with physiological monitoring devices and adjusted them until the signals were stable. After all the equipment was properly adjusted, the formal experiment began.

The formal experiment procedure is illustrated in Figure 11. The research assistant guided the participants from the preparation area to the experiment area and seated them. To ensure standardization, the assistant was responsible for changing the experimental samples (virtual museum scenes), guiding the participants through operations, monitoring and recording physiological signals, and distributing and collecting subjective scales. To effectively record experimental data, the assistant simultaneously pressed the camera and computer record buttons, directing the camera lens towards the computer’s time bar to facilitate later segmentation and calculating time discrepancies.

The assistant explained the procedure and precautions of the experiment, prompting the participants to rest with their eyes closed for 30 s to prevent excessive tension or excitement from inflating the baseline physiological data. After resting, the participants opened their eyes and began evaluating the experimental samples. Initially, the assistant played the control group sample of the Southern Han Mausoleums Virtual Museum, guiding the participants through the coloring task and distributing the subjective evaluation scales, instructing them to fill out the scales based on their subjective feelings. Once completed, the scales were collected, marking the end of this sample’s evaluation.

The assistant then sequentially played the experimental group samples, guiding the participants through the tasks associated with each sample. After all four samples were evaluated, the experiment was completed. The formal experiment section lasted approximately 15 min.

To analyze the impact of the sample on emotions and revisit intention, we used the statistical analysis software SPSS 19 for data analysis. First, we conducted a t-test analysis to examine the differences between the control and experimental groups. Subsequently, we performed a one-way ANOVA using the experimental group samples as the grouping variable to analyze whether different combinations of gamification elements resulted in differences in emotions and revisit intention. Finally, we employed correlation analysis to explore the relationships between subjective and physiological emotions and revisit intention.

3. Results

3.1. Differences Between the Control Group and the Experimental Group

3.1.1. Subjective Emotions

Table 1. Results of subjective evaluation of experimental samples with and without gamification elements.

	Sample Grouping (Mean ± Standard Deviation)		t	p
	Control Group	Experimental Group
PA	14.30 ± 2.71	15.36 ± 3.25	−1.645	0.102
NA	7.47 ± 2.78	7.04 ± 2.30	0.869	0.386
Valence	6.50 ± 1.20	6.97 ± 1.32	−1.761	0.080
Arousal	4.80 ± 1.42	6.20 ± 1.75	−4.062	0.000 *

* p < 0.01.

presents the results of subjective evaluations for experimental samples with and without gamification elements. The data show that experimental samples with gamification elements resulted in higher positive affect (PA) scores and lower negative affect (NA) scores. Additionally, the valence scores for the experimental group were slightly higher than those for the control group. Although there were no significant differences between the experimental and control groups regarding PA, NA, and valence, the arousal scores were significantly higher in the experimental group with gamification elements (p < 0.001). This indicates that gamification elements have a positive effect on participants’ arousal levels. These results suggest that gamification elements influence participants’ subjective emotional states by enhancing their sense of engagement and positive experiences. The significant increase in arousal implies that participants may experience stronger emotional reactions and higher energy levels when faced with gamified tasks, which could help stimulate their positive behaviors.

3.1.2. Physiological Emotions

Table 2. Results for the physiological emotional aspects of the experimental samples with and without gamification elements.

	Sample Grouping (Mean ± Standard Deviation)		t	p
	Control Group	Experimental Group
SCL	6.80 ± 4.15	8.70 ± 4.49	−2.11	0.04 *
SCR	0.37 ± 0.35	0.33 ± 0.25	0.76	0.45
HR	0.40 ± 4.02	−0.78 ± 6.02	1.02	0.31
LF/HF	0.83 ± 2.20	−0.39 ± 5.27	1.24	0.22

* p < 0.05.

displays the differences in physiological indicators between the control and experimental groups. From the table, it is evident that the experimental group showed a significantly higher SCL than the control group, indicating that adding gamification elements can effectively enhance participants’ physiological arousal levels. This finding aligns with the results for emotional arousal, suggesting that incorporating gamification elements into the virtual museum experience can simultaneously enhance emotional arousal from both subjective and physiological perspectives. However, the results for the SCR did not show significant differences, which may indicate that gamification elements do not have a notable impact on short-term physiological responses.

Additionally, based on the analysis of electrocardiogram signals, we compared the differences in heart rate (HR) and LF/HF between the experimental and control groups. Although the experimental group exhibited lower HR and LF/HF values than the control group, there were no statistically significant differences (p > 0.05).

3.1.3. Revisit Intention

According to the t-test analysis results for revisit intention scores (

Table 3. Results for the experimental samples with and without gamification elements in terms of revisit intention.

	Sample Grouping (Mean ± Standard Deviation)		t	p
	Control Group	Experimental Group
Visit again	3.47 ± 0.73	3.92 ± 0.70	−3.180	0.002 **
Recommend to friends	3.67 ± 0.84	4.02 ± 0.67	−2.109	0.041 *
Choose again	3.17 ± 0.79	3.62 ± 0.79	−2.788	0.006 **

* p < 0.05; ** p < 0.01.

), the experimental group with gamification components showed significantly higher intentions to visit again, recommend to friends, and choose again than the control group without these components. Specifically, the experimental group scored 3.92, 4.02, and 3.62, respectively, while the control group scored 3.47, 3.67, and 3.17. The t-test results indicated significant differences (p < 0.05 or p < 0.01). This result suggests that gamification elements effectively enhanced participants’ intention to revisit, likely due to the increased sense of a positive experience, enjoyment, and engagement, thereby boosting their interest in participating again.

The increase in revisit intention implies that participants experienced higher satisfaction during the process, which could translate into greater customer loyalty. In a competitive market, gamification strategies to enhance customer revisit intention may strengthen market competitiveness.

3.2. Differences in the Combination of Gamification Elements

In the previous section, we demonstrated that t-tests showed that experimental samples with added gamification elements significantly enhanced participants’ emotions and revisit intentions. In this section, we further analyze the differential impact of various gamification elements on emotions and revisit intentions. This analysis aims to guide future gamification designs in virtual museums.

3.2.1. Subjective Emotions

According to the one-way ANOVA results (

Table 4. ANOVA results for the samples in the experimental group in terms of subjective emotions.

	Sample (Mean ± Standard Deviation)				F	p
	PBL	PBL + C	PBL + PS	PBL + CU
PA	16.23 ± 2.80	16.17 ± 3.29	13.90 ± 3.34	15.13 ± 3.14	3.63	0.02 *
NA	6.53 ± 1.78	8.33 ± 2.58	6.50 ± 1.94	6.80 ± 2.38	4.73	0.00 **
Valence	7.20 ± 1.00	7.00 ± 1.41	6.60 ± 1.48	7.07 ± 1.34	1.15	0.33
Arousal	6.20 ± 1.49	6.73 ± 1.53	5.57 ± 2.06	6.30 ± 1.73	2.36	0.08

* p < 0.05; ** p < 0.01.

), there were significant differences in PA and NA among different groups. The multiple comparison results (Figure 12) show that the PBL sample had the highest PA score (16.23), significantly higher than that of PBL + PS (p < 0.01). PBL + C ranked second in terms of PA (16.17), also significantly higher than PBL + PS (p < 0.01). PBL + UC was third (15.13), but it did not show a significant difference from PBL + PS (p > 0.05).

Regarding NA, PBL + PS had the lowest score (6.50), indicating the lowest degree of negative emotions induced in participants, with PBL scoring relatively low (6.53). Conversely, PBL + C had the highest negative emotion score (8.33), significantly higher than the scores of the other three samples (p < 0.01).

The experiment indicates that adding more game elements to the basic gamification components (PBL triad) does not necessarily enhance participants’ subjective evaluations. The PBL sample performed best in increasing PA, possibly because the foundational PBL elements are simple and direct, effectively motivating users without requiring additional complexity.

PBL + PS had the lowest NA, suggesting that it effectively reduces negative emotions. This may be because the points store provides a continuous reward mechanism, reducing user frustration. On the other hand, PBL + C had the highest NA, significantly above that of other combinations. This suggests that while the competition element can increase PA, it may also elevate stress or negative emotions due to competitive pressure. These findings imply that while gamification can enhance engagement and satisfaction, the type and complexity of gamification elements must be carefully balanced to avoid unintended negative emotional impacts.

Although valence and arousal did not show significant differences, their results were highly consistent with PA and NA. The PBL + C combination resulted in the highest arousal (6.73 ± 1.53). This result was close to significance (F = 2.36, p = 0.08), suggesting that adding a competitive element with other players on top of the PBL components effectively increased participants’ arousal (as shown in Figure 13). However, the relatively high NA score (8.33 ± 2.58) for this combination indicates that increased arousal does not always lead to positive outcomes; it can also introduce tension and anxiety, which may negatively impact participants’ evaluations of the virtual museum experience.

Conversely, the PBL + PS combination resulted in the lowest arousal score (5.57 ± 2.06) and also the lowest valence score (6.60 ± 1.48), suggesting that arousal scores do not necessarily equate to valence. High arousal can be accompanied by anxiety, while low arousal might lead to boredom and a loss of interest among participants. This highlights the importance of balancing engagement and emotional responses in gamification design to optimize the user experience.

3.2.2. Physiological Emotions

Table 5. ANOVA results for samples in the experimental group in terms of physiological emotions.

	Sample (Mean ± Standard Deviation)				F	p
	PBL	PBL + C	PBL + PS	PBL + UC
SCL	0.49 ± 0.56	0.32 ± 0.62	0.28 ± 0.38	0.15 ± 0.52	2.13	0.10
SCR	0.13 ± 0.12	0.20 ± 0.20	0.19 ± 0.19	0.19 ± 0.21	0.76	0.52
HR	−0.33 ± 5.53	0.30 ± 4.68	−1.90 ± 9.13	−1.20 ± 3.14	0.77	0.51
LF/HF	0.61 ± 1.73	−0.73 ± 5.30	−0.77 ± 5.50	−0.67 ± 7.17	0.48	0.70

presents the changes in physiological indicators across the four gamification samples. The results indicate that while there were differences in physiological measures among the samples (such as SCL, approaching significance), none of these differences reached statistical significance (p > 0.05). This suggests that the combinations of gamification elements examined had a limited impact on participants’ physiological responses or that a larger sample size or longer experimental duration might have been needed to observe potential effects. When considering the earlier comparisons with the control group, it remained clear that the presence or absence of gamification elements significantly impacted participants. This implies that, while the specific type of gamification element may not drastically alter physiological responses, the inclusion of gamification did have an observable effect on engagement and emotional arousal.

3.2.3. Revisit Intention

As shown in

Table 6. ANOVA results for samples in the experimental group in terms of revisit intention.

	Sample (Mean ± Standard Deviation)				F	p
	PBL	PBL + C	PBL + PS	PBL + UC
Visit again	3.93 ± 0.74	3.93 ± 0.64	3.83 ± 0.75	4.00 ± 0.69	0.28	0.84
Recommend to friends	4.10 ± 0.66	4.03 ± 0.67	3.83 ± 0.75	4.10 ± 0.61	1.05	0.37
Choose again	3.50 ± 0.78	3.67 ± 0.80	3.60 ± 0.86	3.70 ± 0.75	0.37	0.78

, although different combinations of gamification strategies had some influence on participants’ willingness to revisit during this experiment, these effects did not reach statistical significance (the p-values were all greater than 0.05). This may suggest that the participants were more concerned about whether gamification elements were included in the virtual museum experience. However, how these elements were combined did not substantially affect their evaluation of their willingness to revisit.

3.3. Relationship Analysis of Various Indicators

3.3.1. Subjective and Physiological Emotions

Table 7. Coefficients of correlations between subjective and physiological emotions.

	PA	NA	Valence	Arousal
SCL	−0.10	−0.08	−0.07	−0.13
SCR	0.01	−0.13	−0.01	0.01
HR	0.25 **	−0.08	0.18 *	0.21 *
LF/HF	0.12	−0.04	0.03	0.02

* p < 0.05; ** p < 0.01.

shows a significant positive correlation between HR and PA, valence, and arousal, while the correlations between other physiological indicators and subjective emotions did not reach significance. This experiment highlights the important role of HR in emotional experiences. An increase in HR often indicates heightened emotional arousal, suggesting that participants are relatively excited. Additionally, we found that HR is associated with participants’ positive emotions and pleasure. This experiment demonstrates that changes in HR provide a real-time, physiological indicator of emotional responses, enabling us to better understand participants’ emotional engagement in a virtual museum environment.

3.3.2. Subjective Emotions and Revisit Intention

The results from

Table 8. Coefficients of correlations between subjective emotions and revisit intention.

	PA	NA	Valence	Arousal
Visit again	0.68 **	−0.06	0.56 **	0.58 **
Recommend to friends	0.58 **	−0.13	0.58 **	0.49 **
Choose again	0.50 **	−0.17 *	0.45 **	0.40 **

* p < 0.05; ** p < 0.01.

indicate that subjective emotions generally correlate significantly with revisit intention. NA had a weaker correlation with revisit intention. However, it showed a significant negative correlation with the “choose again” option, suggesting that negative emotions may reduce participants’ inclination to select the virtual museum again. All the other indicators showed a significant positive correlation with revisit intention. These positive correlations suggest that when participants experience increased positive emotions, pleasure, and arousal, they are more likely to have revisit intention.

3.3.3. Physiological Emotions and Revisit Intention

Table 9. Coefficients of correlations between physiological emotions and revisit intention.

	SCL	SCR	HR	LF/HF
Visit again	−0.07	−0.01	0.20 *	0.07
Recommend to friends	−0.05	0.05	0.16 *	0.06
Choose again	−0.05	0.04	0.20 *	0.07

* p < 0.05.

illustrates the correlation between physiological emotions and the revisit intention. Like subjective emotions, HR showed a significant positive correlation with various indicators of revisit intention. In contrast, the correlations between the SCL and SCR were weaker. This highlights the important role of HR in revisiting intention. The correlation between LF/HF and revisit intention indicators was also relatively weak. This suggests that while specific components of heart rate variability play a role in expressing emotions and intentions, their impact is less significant than that of HR. By incorporating the correlation coefficients of subjective emotions, this study highlights the vital role of HR in comprehending and forecasting visitors’ intentions to return. The results suggest that focusing on elements that can trigger positive heart rate responses may enhance visitors’ subjective emotions and willingness to revisit when designing virtual museum experiences.

4. Discussion

4.1. Gamification’s Performance in Improving Tourists’ Emotions

The results of this study indicate that gamification elements have a significantly positive effect on enhancing the emotional experience of visitors to virtual museums. Although the differences between the experimental and control groups in PA, NA, and valence scores did not reach significance, the significant increase in arousal (p < 0.001) demonstrates that gamification can effectively enhance participants’ emotional engagement. This increase in arousal is closely related to the characteristics of gamification elements. Including gamification elements adds interactivity, challenge, and immediate feedback to the exhibits in a virtual museum, which is believed to elicit stronger emotional responses [51]. In this study, by analyzing the environment and exhibit characteristics of the selected virtual museum, the research was mainly conducted using coloring and game elements integrated with other game elements, and corresponding research results were obtained to demonstrate its ecological effectiveness in the virtual museum environment. For example, increased interactivity enhances engagement and immersion during the visit, while challenges stimulate curiosity and the desire to explore, thereby boosting arousal.

Furthermore, changes in physiological indicators, such as the significant increase in SCL, further support the notion that gamification enhances emotional arousal. Research indicates that gamification affects emotional states not only at the subjective experience level but also significantly at the physiological level.

Kevin Werbach and Dan Hunter emphasizes that gamification can significantly enhance user experience and engagement [52]. For virtual museum designers, gamification elements offer an effective method for enhancing users’ emotional experiences. By increasing emotional arousal, museums can improve visitor engagement and satisfaction, boosting revisit intention and customer loyalty. This enhancement in emotional experience may also translate into more positive behaviors, such as frequent visits and a higher willingness to recommend the museum to others.

The results of this study provide empirical support for the application of gamification in virtual museums. They highlight emotional arousal as an important dimension for measuring user experience and suggest that gamification design could become a crucial strategy for enhancing cultural experiences.

4.2. Performance of Gamification Element Combinations in Improving Tourists’ Emotions

Through a one-way ANOVA, we found significant differences in positive and negative emotion scores across different combinations of gamification elements in the experimental group, but no similar changes were observed in physiological indicators and revisit willingness.

Specifically, basic gamification elements (PBL: Points, Badges, Leaderboards) performed best in enhancing PA. This indicates that simplified gamification designs can improve users’ positive emotional experiences. Hamari et al.’s research shows that sustained reward mechanisms can reduce participants’ frustration and negative emotions [53]. In our experiment, the combination of PBL + PS showed the best results in reducing NA, suggesting that the points store mechanism effectively regulates participants’ emotions.

Regarding emotional arousal, the PBL + C combination resulted in the highest score, approaching significance (p = 0.08), indicating that competitive elements can stimulate higher emotional arousal but may also induce tension and anxiety (with the highest negative emotion scores). Kreibig suggested that competitive mechanisms might trigger a “challenge–threat” state, leading to increased sympathetic nervous system activity [54], which aligns with the elevated NA observed in this study. The elevated emotional arousal accompanied by high negative emotion scores suggests that designers must weigh the potential positive and negative impacts of competitive elements in gamification applications.

The experimental results indicate that the changes in physiological indicators across different gamification combinations did not reach statistical significance. This may suggest that the way gamification elements are combined has a relatively minor impact on participants’ physiological responses or that a larger sample size or a longer experimental duration may be needed to observe potential effects. The current results indicate that the presence or absence of gamification elements has a more significant impact on physiological responses. In contrast, the specific combinations of these elements have a comparatively weaker effect.

The results regarding revisit intention are similar to those for physiological emotion outcomes. Although different combinations of gamification strategies had some influence on participants’ willingness to revisit, they did not reach statistical significance (the p-values were all greater than 0.05). This may imply that participants are more concerned about whether the virtual museum includes gamification elements rather than the specific way these elements are combined.

These findings suggest that when designing gamified experiences, designers should prioritize the effective incorporation of gamification elements rather than focusing excessively on their specific combinations.

4.3. Interaction Analysis of Emotions and Behaviors

Our correlation analysis revealed the relationships between subjective emotions, physiological emotions, and revisit intention. Innovatively studying the correlation between physiological and emotional indicators, even if the correlation may be weak or present a mixed relationship, can also provide methodological or improvement conditions for future research, thereby providing valuable guidance for subsequent design implementation. HR, as a significant physiological emotion indicator, showed a positive correlation with positive emotion scores, valence, and arousal. In contrast, other physiological indicators, such as the SCL and SCR, did not correlate significantly with subjective emotions. SCR typically reflects transient emotional arousal, and the current task design may not have elicited sufficiently intense stimuli to trigger strong phasic responses. As for the LF/HF ratio, future studies could explore these measures under more extended or stress-inducing conditions to better assess the validity. This suggests that the SCL and SCR are less sensitive to emotional responses in the current experimental setup, while HR is an important physiological signal reflecting participants’ emotional engagement. An increase in HR often indicates heightened emotional arousal, suggesting that participants may be in an excited or optimistic emotional state [54]. By monitoring changes in heart rate, virtual museum designers can better understand and assess user emotional engagement, optimizing gamification design to enhance user experience.

In 1980, the model proposed by Oliver and others suggested that consumer satisfaction influences attitude changes and purchase intentions [55]. In this study, subjective emotions, particularly positive and emotional arousal, showed a significant positive correlation with revisit intention. When participants experienced higher levels of positive emotions and emotional arousal, they were more inclined to revisit or recommend the virtual museum. However, NA was significantly negatively correlated with the “choose again” response, indicating that negative emotions might reduce users’ willingness to choose to revisit.

The interaction analysis of emotions and behavior suggests that enhancing users’ positive emotional experiences and managing physiological indicators such as HR can effectively enhance user engagement and revisit intention. By integrating data from subjective and physiological indicators, designers can gain a more comprehensive understanding of user experience, allowing them to develop more effective gamification strategies.

The sample of this study is 30 college students. Although it meets the basic statistical requirements, it limits the generalizability of the findings. The age range (18–25 years old) and background are relatively homogeneous, which may limit the generalizability of the conclusions. Future research can expand the sample range to include different age groups (such as teenagers, middle-aged, and elderly) and non-student groups to verify the applicability of gamification strategies among other users. In addition, increasing the diversity of cultural backgrounds (such as international tourists) can further explore the impact of cultural differences on emotional experience and improve the external validity of the research. By considering different user groups, cultural backgrounds, and habits, it will be more helpful to promote gamified research results to other more typical virtual museum interactions in the future, such as fully immersive VR virtual museums, or richer museum tours such as artifact exploration, information seeking, or tour guides.

5. Conclusions

This study experimentally validated the role of gamification elements in enhancing visitors’ emotions within a virtual space. The research demonstrated that adding gamification elements can significantly increase participants’ emotional arousal and enhance their revisit intention. Additionally, this study analyzed the differential impacts of various gamification combinations.

Specifically, the experimental group with added gamification elements showed significant improvements over the control group regarding subjective emotional arousal and physiological indicators (SCL), indicating that gamification design can enhance user immersion and engagement. Additionally, the experimental group demonstrated a significantly higher willingness to revisit than the control group, suggesting that gamification elements successfully increase user loyalty and the potential for conversion (as evidenced by the increased revisit intention) by enhancing participants’ positive emotions.

Essential gamification elements (PBL: Points, Badges, Leaderboards) were most effective in enhancing positive emotions, whereas complex combinations (such as PBL + C) might be associated with increased negative emotions, such as anxiety. The points store mechanism effectively reduces user frustration, resulting in the lowest negative emotion scores. These findings suggest that essential gamification elements should be prioritized, as PBL can enhance emotional arousal while avoiding the negative experiences associated with complex combinations, making it suitable as a primary optimization strategy for virtual museums. Additionally, design should balance challenge and reward mechanisms to avoid excessive competition (such as through battle elements) that can trigger negative emotions. For example, maintaining user interest through continuous feedback mechanisms such as a points store can be beneficial.

In addition to the research findings, this study made significant methodological innovations by incorporating physiological emotion indicators, thereby enhancing the accuracy and objectivity of emotional experience assessments. Traditional research on user experience primarily relies on subjective scales, which, although effective in capturing participants’ emotional states, are susceptible to self-report bias and environmental influences. This study provides a more objective tool for evaluating emotions by integrating physiological monitoring devices.

Through these methodological innovations, this study offers a new perspective on assessing the effectiveness of gamification design in virtual museums. It explores new pathways for future user experience research and design practices. The introduction of physiological emotion indicators marks a shift from a single-dimensional to a multidimensional approach in user experience evaluation, laying the groundwork for a deeper understanding and enhancement of user experiences.