Abstract
In the digital era, increasing attention has been paid to how social media reshapes virtual tourism, yet little research has explored the mechanisms of digital healing in disembodied travel and the differentiated effects of landscape types. This study takes Erhai Scenic Area in China as an example and combines text coding with questionnaire survey. Study 1 used web crawling to collect review texts, followed by semantic analysis and landscape coding. Study 2, based on the classifications derived from Study 1, conducted video experiments and questionnaire surveys. The results of both studies were then compared and analyzed. Findings indicate that emotion plays a partial mediating role in the mechanism of "disembodied landscape perception—embodied emotional change—embodied travel intention," with personality traits (openness and neuroticism) serving as moderators. Moreover, the four landscape types: ecological-natural, commercial-leisure, historical-cultural, and rural-pastoral demonstrate differentiated effects in emotional healing, promoting travel intentions, and facilitating the transformation from disembodied to embodied perception. This study provides a theoretical foundation for destination marketing, healthy landscape design, and digital healing, contributing to the development of new health tourism models in the social media context.
Introduction
In the context of an era where virtual technologies are deeply embedded in daily life, human spatial experiences exhibit a dual characteristic of both “embodied” and “disembodied” elements. Currently, social media, short video platforms, and virtual technologies are creating a new tourism paradigm where “disembodied” experiences on social media (such as travel photos, videos, movies, etc.) inspire “embodied” travel intentions. This “disembodied tourism” acts as a precursor or substitute for physical travel, fulfilling a growing desire for escapism amidst the constraints of modern, high-pressure lifestyles, providing a new type of travel consumption model. Embodied Cognition Theory (ECT) posits that cognition is shaped by bodily states, memory, and the interaction between the body and the environment1. It is highly dependent on the characteristics of the body2, providing a theoretical foundation for analyzing tourist experiences from the perspective of “bodily presence,” as well as how these experiences shape destination image perception3, emotional responses, and behavioral intentions4. Most existing studies5 focus on the immediacy of offline embodied experiences6, where the interaction between body and environment influences emotions and behaviors1, yet they tend to overlook how digital media can, through “quasi-embodiment” mechanisms, evoke similar emotional responses7. This study challenges the traditional notion that embodied experiences in tourism must rely on the absolute physical presence of the body, extending ECT into the digital and virtual domain, and thereby laying the groundwork for future research on perceptual experiences in disembodied contexts. With the advancement of digital technologies, social media has introduced image-based virtual interactions that replace physical contact, shifting social activities toward disembodied experiences8. Disembodied experience refers to a state in which mental cognition is somewhat separated from the physical body9. In digital contexts, this is typically achieved through interfaces8, avatars, or audiovisual content10, which can effectively evoke and regulate emotions, triggering genuine affective reactions. AI companions can simulate empathy through affective algorithms, creating interactive experiences with emotional warmth11. Experiments involving two-dimensional virtual hands and VR have demonstrated that virtual body representations can be flexibly adjusted to a disembodied state10. Research on virtual identity and consumption has enabled anonymous, bodiless interactions12,13,14. Media such as painting, film, video, and games immerse viewers in simulated worlds15. For example, the game “Black Myth: Wukong” provides a two-dimensional, disembodied experience that stimulates the senses16. Such virtual interactions may also induce changes in bodily perception7, while enhancing environmental awareness and engagement17. These technologies eliminate physical barriers to evoke a “pseudo-presence,”17,18 indicating that physical presence is not a prerequisite for shaping emotions. Digital technologies can activate body schemata and emotional memory by simulating embodied mechanisms, thereby producing effects comparable to those of actual embodied experiences.
Health tourism is a dynamic phenomenon19,which is generally understood as tourism in which individuals travel to other places to engage in health-related activities, including both disease prevention and medical treatment20. The model of health tourism examined in this study, however, is defined by its core function of promoting physical and mental well-being. Within the context of digital media, the scope is further extended to encompass virtualized disembodied experiences. Landscape perception, as an important theoretical foundation for the development of health tourism, refers to tourists’ subjective interpretation of and emotional response to the environment. It constitutes the core of the tourism process, directly influencing satisfaction and health outcomes21. The concept of “restorative environmental perception” explains how specific environments promote emotional recovery and stress relief22. A large body of research indicates that exposure to natural environments, such as forests and waterscapes, can reduce environmental stressors23 and alleviate negative emotions24. Coastal environments, in particular, have been shown to exert positive effects on both physical and mental well-being25, demonstrating the health and wellness benefits of nature-based tourism for humans26. In the post-pandemic era, tourists’ attention to health has significantly increased, with a tendency to choose health tourism as a means to improve physical and mental well-being27. Within the process of health tourism, the core objective is the therapeutic effect on both body and mind. Contemporary scholars define “healing” as the holistic restoration of individuals from a state of imbalance, encompassing both physiological recovery and subjective psychological perception28,29. As an emerging paradigm, digital healing refers to the mechanisms through which digital media content or interactive experiences positively influence users’ emotional states, alleviate stress, and facilitate psychological recovery. Research has demonstrated that disembodied experiences possess psychological restorative effects, enhance psychological resilience, and improve environmental perception. Creative video therapy, building on cinema therapy and neurocinematics, unlocks the healing potential of media30. Animated videos can soothe and heal psychological wounds31, while pet and food-related content facilitates emotional release and reduces distress32. Studies confirm that visual and tactile stimuli positively impact emotions33. Digital art environments and virtual reality also elicit therapeutic emotional responses34. During COVID-19, virtual social interaction was shown to enhance psychological resilience35 and reduce virus-related anxiety36. These studies highlight the benefits of digital healing in promoting mental health and create new opportunities for exploring the healing functions of landscape perception within health tourism.
Current research on the therapeutic effects of landscape perception on psychological well-being is already extensive; however, much of the literature primarily focuses on on-site experiences, while relatively little attention has been paid to the digital or disembodied travel contexts. Bastiaansen et al. employed electroencephalography to measure tourists’ emotions when viewing destination videos and images37, whereas Zhang et al. utilized computer-based systems to detect emotional differences among tourists38. Since emotions can be triggered by external stimuli39, the online environments created by virtual tourism can effectively alleviate stress40. Research on virtual cultural heritage tourism has explored the positive impact of VR sensory technologies on users’ emotional states41. Experiments with digital museums demonstrate that digital technologies preserve cultural authenticity while enhancing therapeutic potential42. Experimental evidence from VR further confirms that video experiences can facilitate emotional healing43, providing empirical support for the industrial development of therapeutic tourism44. In addition, personality traits can influence emotion regulation strategies and cognitive processing45. The Big Five personality traits have been shown to be associated with various emotion regulation mechanisms, including reappraisal and mindfulness46. Specifically, openness has been demonstrated to moderate the relationship between travel videos and travel inspiration47, whereas neuroticism affects emotional responses under different arousal conditions48. Research on the therapeutic effects of health tourism has been steadily deepening, confirming the close connection between travel videos and viewers’ emotions. Nevertheless, most existing studies have approached this from the perspective of a single landscape or overall environment, with few studies deconstructing the video content itself to explore how the landscapes and activities presented influence viewers’ emotions49. This has led to a lack of differentiation among landscape types, as well as the absence of quantitative comparisons of the therapeutic effects of different landscape types in both real and video environments. Based on this, this study approaches the topic from the perspective of landscape perception in travel videos and propose the following H1. In Study 1, H1 was further refined based on landscape analysis results, comparing the differential effects of perceived landscape types on emotional changes. At the same time, considering the moderating role of personality traits (openness and neuroticism) in this process, we propose the following hypotheses:
H1
The landscape perception brought by disembodied travel video has a positive impact on people’s emotional changes.
H2a
Openness positively moderates the impact of landscape perception to emotional changes.
H2b
Neuroticism positively moderates the impact of landscape perception to emotional changes.
According to the cognitive-behavioral approach50, travel intention is a key factor in shaping travel experiences. Although virtual tourism can partially substitute for on-site travel, expectations of actual travel experiences typically exceed the visual imagination of the objects51. Embodied travel intention refers to the degree of effort users are willing to invest in real-world travel activities corresponding to their virtual tourism experiences52. Research has shown that tourists’ attitudes toward smartphones influence travel intention, which in turn affects immersive engagement53. Positive sensory experiences related to destinations on social media increase tourists’ attachment to and identification with those destinations54. In today’s digital environment, short-form travel content is widely prevalent, and its design, along with destination attractiveness, can enhance viewers’ desire to travel47. Sensory-rich culinary tourism videos can elicit emotions and stimulate behavioral intentions16. Moreover, viewers’ affinity for travel videos can generate emotional connections that motivate them to visit the destination54. Virtual tourism experiences may have a positive influence on subsequent behavioral decisions, as travel videos can simulate real-life scenarios to evoke travel experiences55. Recent studies have begun to explore the transition between embodied and disembodied tourism, aiming to clarify the mechanisms linking disembodied landscape perception, emotional changes, and travel intention, and to examine whether emotional changes play a mediating role, we propose the following three hypotheses and establish the basic framework of this study (Fig. 1):
Basic mechanism framework (self-drawn by the author).
H3
Disembodied landscape perception has a positive influence on embodied travel intention.
H4
Emotional changes positively influence travel intention.
H5
Emotional changes have a mediating effect on the relationship between disembodied landscape perception and embodied travel intention.
Study 1
Research area
Erhai Lake Scenic Area, located in Dali City, Yunnan Province, boasts rich natural and cultural resources (Fig. 2). As China’s seventh largest freshwater lake, it serves as a key ecological zone. According to the results of a survey on Chinese tourists’ ideal health tourism destinations, the top three health tourism destinations are Hainan Province, Yunnan Province, and Guangxi Province27. With rural tourism driving rural revitalization, tourism around Erhai Lake has rapidly expanded56. Development began in 1997 with the Dali Bai Autonomous Prefecture Tourism Industry Development Group, attracting general tourists after 2007 and peaking in 201657. In recent years, tourism has surged again. The area’s pastoral scenery and traditional settlements, combined with Erhai’s natural environment58, form a strong foundation for Dali’s tourism appeal and have made it a focus of domestic tourism research. Choosing the Erhai Lake Scenic Area as the research area provides a rich sample of landscape perception types, making it highly representative.
Location analysis map (self-drawn by the author, base map satellite image from Amap, https://ditu.amap.com/).
Data source
The data for this study was collected from online reviews on tourism websites, focusing on the perceptions of visitors after experiencing the sites. To ensure the comprehensiveness and representativeness of the research data, genuine perception evaluations from visitors were analyzed. The data was gathered from multiple channels, including three popular Chinese travel websites: Ctrip, Qunar, and Mafengwo.
Methods of study 1
Scholars have employed various landscape classification methods based on principles such as functional morphology, dominant factors, and scale attributes to partition large-scale spaces. Focusing on tourists’ perceptions of the Erhai Scenic Area, this study notes that conventional classification approaches lack regional specificity and perceptual relevance. To better refine tourists’ embodied landscape perception types and translate these into disembodied perception, this study innovatively re-codes landscape classification by using online review data. Through NVivo qualitative analysis, a three-level classification of spatial landscape perception characteristics for the Erhai Scenic Area was derived. Study 1 integrated online text semantic analysis and Grounded Theory coding methods59,60 to extract the main types of tourists’ landscape perceptions of the Erhai Scenic Area from evaluation texts after embodied tourism, and conducted sentiment analysis on text evaluations of different types of landscapes. This provided a classification basis for exploring differences in disembodied perception and emotional healing effects among different landscape types in Study 2.
Data collection and cleaning
Network comments about the Erhai Lake Scenic Area were collected from three websites using the Octopus Cloud Service Cluster combined with Python. The data collection and scraping tasks are mainly carried out through the Octopus service platform. After setting up the program on this platform, data can be automatically crawled. Python is mainly used to clean and process the crawled data, such as removing HTML tags, spaces, line breaks and other irrelevant information. The data collection process fully complies with the experimental terms and user agreements of each website. The above-mentioned apps selected for the research are all legal and legitimate, and the online comments within them are all open-source data voluntarily and publicly posted by users. Therefore, the online comments are publicly accessible and the data can be obtained. From January 2019 to January 2025, a total of 9,347 comments were collected. The collected data was filtered and cleaned by removing irrelevant comments that were not related to the scenic spots or themes, those with obvious marketing or advertising content, non-text comments (such as symbols, images, etc.), and duplicate comments. In the end, 8,072 valid comments were obtained.
Text data semantic analysis
After the data was collected and cleaned, this study utilized the Rost Content Mining System (Rost CM) to perform an initial semantic analysis of the comments59. After conducting tokenization with Rost CM, word frequency analysis was conducted to extract high-frequency words. The high-frequency word results were then analyzed using social network and semantic network analysis to build a network graph, further revealing the inherent relationships and structures within the comments.
Grounded theory coding and classification
This study, based on the methodology of Grounded Theory (GT)61, utilized NVivo 12 qualitative analysis software for coding analysis, in conjunction with the results from text semantic analysis. Following the principles of GT, the study employed a combination of manual and automatic coding methods, reading the texts line by line and performing three stages of coding: Open Coding, Axial Coding, and Selective Coding. With reference to the three-level classification logic, the text content was refined into three levels: embodied perception landscape types (first level), landscape sequence elements (second level), and specific landscape components (third level). In Open Coding, researchers extracted initial concepts and summarized the potential themes of tourists’ original perceptual evaluations. Subsequently, the initial concepts were combined with high-frequency words and social semantic networks for the classification and integration in Axial Coding. Finally, core categories-landscape types were extracted and the three-level classification was detailed. The details of the coding process are shown in the figure, including statistics on reference points of coded files and a hierarchical chart of coding nodes (Fig. 3).To ensure the reliability and validity of the coding process, this study employed multiple coders in combination with theoretical review and validation59.
Encoding node diagram (self-drawn by the author).
Sentiment analysis by landscape type
Based on the coding results, relevant online review texts corresponding to each landscape type were integrated and processed, and sentiment polarity analysis was conducted for each type using Rost CM. A horizontal comparison of emotional responses across the different landscape types was performed, along with a vertical comparison between these emotional responses and the perceived restorative strength of disembodied experiences identified in Study 2.
Results of study 1
Network text semantic analysis
Semantic networks are an application of complex network technology in the field of natural language processing. The frequency of word usage is closely related to the visitor’s experience and plays an important role in forming a sense of place62. Using Rost CM to screen the top 50 high-frequency words from online reviews of the Erhai Scenic Area, irrelevant words related to travel experience, landscape perception, and emotions were removed. The main focus of visitors’ embodied travel experiences in Erhai is on “scenery, photography, ancient town, beautiful, local customs, sun” etc. (Fig. 4 left). Based on the frequency results, a social and semantic network map of the high-frequency words was constructed and divided into three levels, revealing the underlying social relationship characteristics in the reviews. Among them, “scenic spots, Bai nationality, islands, nature, ancient town, guesthouses” formed the core group of high-frequency word nodes, with related words like “Cangshan, romantic, local customs, sunshine,” forming a secondary word circle (Fig. 4 right).
Network text analysis (left = word frequency, right = semantic network diagram).
Landscape perception coding classification model
The core level represents embodied perception landscape types, coded according to the overall landscape functional attributes and spatial distribution sequence: offshore ecological-natural landscape, shore-side commercial-leisure landscape, coastal historical-cultural landscape, and shoreline rural-pastoral landscape. The second level includes landscape sequence elements, categorized based on recognizable spatial elements and decoding sequences, which consist of 12 items: religious cultural landscapes, fishing village dock landscapes, and natural lake islands, etc. The third level consists of specific landscape components, totaling 21 items (Fig. 5).
Landscape classification code distribution diagram (Self-drawn by the author).
By coding and classifying embodied landscape perceptions, this study divides tourists’ landscape perceptions of the Erhai Scenic Area into four categories. To compare the healing differences and specific transformation effects among landscape types, based on the existing hypotheses H1 and H3 combined with the classification results, the hypothetical model framework of this study is refined, and the following hypotheses are proposed (Fig. 6). Among them, H1a-d verify the differences of these four types of landscapes in terms of emotional changes, aiming to reveal which landscape type can more effectively achieve digital healing. H3a-d verify the differentiated driving forces of the four landscape types on travel intentions, so as to explore the specific transformation effects of different landscapes from disembodied perception to embodied travel intentions. The verification results of the hypotheses will fill the research gap in the absence of a mechanism for differentiating landscape types, provide precise guidance for short video content creation and destination marketing, and maximize health benefits and commercial transformation.
Hypothesis conceptual model (self-drawn by the author).
H1a
Disembodied perception of offshore ecological-natural landscapes has a positive effect on emotional changes.
H1b
Disembodied perception of shore-side commercial-leisure landscapes has a positive effect on emotional changes.
H1c
Disembodied perception of coastal historical-cultural landscapes has a positive effect on emotional changes.
H1d
Disembodied perception of shoreline rural-pastoral landscapes has a positive effect on emotional changes.
H3a
Disembodied perception of offshore ecological-natural landscapes has a positive effect on travel intention.
H3b
Disembodied perception of shore-side commercial-leisure landscapes has a positive effect on travel intention.
H3c
Disembodied perception of coastal historical-cultural has a positive effect on travel intention.
H3d
Disembodied perception of shoreline rural-pastoral landscapes has a positive effect on travel intention.
Emotional analysis of the four embodied perception landscape types
Based on the results of coding and classification, sentiment analysis was conducted on evaluative texts corresponding to the four core landscape types (Table 1). The findings show that, following embodied experiences, all four landscape types elicited predominantly positive emotional responses from visitors. Among them, historical-cultural and rural-pastoral landscapes generated the highest levels of emotional positivity, followed by ecological-natural landscapes. In contrast, commercial-leisure landscapes exhibited the highest level of negative sentiment.
Study 2
Methods of study 2
Questionnaire design
Building upon the findings of Study 1, Study 2 was designed to examine the effect of disembodied 2D travel videos on emotional changes and embodied travel intention, as well as to explore the relationships among these three variables. Additionally, the study aimed to assess whether two personality traits—openness and neuroticism—moderate the relationship between landscape perception and emotional changes. In accordance with the ethical review requirements of Central South University, this study does not involve life science or medical research and thus does not require ethical approval. However, informed consent must be obtained from all participants and their legal guardians during the survey. An informed consent form containing relevant ethical requirements is placed at the beginning of the questionnaire, and only participants who provide their consent are permitted to proceed with the subsequent survey. In addition, this study has been reported to the Academic Committee of the School of Architecture and Art at Central South University, and the entire survey was strictly conducted in accordance with the relevant requirements under the supervision of the supervisor. This study used a survey questionnaire method, which includes four sub-scales: the Big Five Personality Scale63, the Emotional Change Scale64, the Sensory Perception Dimension Scale65, and the Travel Intention Scale66 and a video segment (https://www.bilibili.com/video/BV1xrfhYpELX/?spm_id_from=333.1387.homepage.video_card.click). In the process of questionnaire distribution, to facilitate participants' completion, all Chinese translation versions employed have undergone validation and been published in academic literature67,68,69. These scales measure nine variables with a total of 59 items. Following questions about demographic characteristics, the first set assessed participants’ levels of openness and neuroticism using the eight and ten items respectively from the Big Five Inventory63. The items were rated on a 5-point Likert scale, ranging from 1 (“disagree strongly”) to 5 (“agree strongly”). Then comes the experimental part, participants were asked to watch a 5-min video. To ensure that each participant carefully watches the videos, we implemented a page redirect mechanism at the video link section in the questionnaire. Participants must click the video link, jump to the video webpage, and complete the viewing before they can proceed to the next step of questionnaire filling. Meanwhile, the number of viewers and views will be tracked through the video platform’s backend statistics. The video was divided into two distinct sections, the first section lasts for 90 s and depicts the daily work, study, and life of individuals from various professions and age groups, helping the participants recall their daily routines. Following this, there is a 20-s subtitle prompt encouraging participants to reflect on and recall their emotional state throughout the day. Then, there is a 10-s pause, allowing participants to return to their regular emotional state. The second section features a 3-min travel video of Erhai Lake, showcasing four types of landscapes in sequence: offshore natural landscapes, shore-side commercial landscapes, coastal cultural landscapes, and shoreline pastoral landscapes. Each landscape type is displayed for 45 s. After viewing the video, participants answered six questions aimed at assessing emotional changes before and after watching the travel video, based on the scale developed by Rogers et al64. This 6-descriptor scale, derived from the Positive and Negative Affect Schedule (PANAS), includes four negative emotions (nervous, irritable, upset, distressed) and two positive emotions (excited, and happy). The original 5-point scale was modified to assess emotion variance, with the following scale: 1 = much less than before, 2 = less than before, 3 = about the same, 4 = more than before, 5 = much more than before64. Subsequently, four sets of questions were presented to evaluate participants’ perception of four landscape types. Referencing Li’s study65, each set included eight dimensions, with perception intensity rated after each dimension. Finally, participants were asked to complete the travel intention scale based on their feelings after watching the video. This marks the completion of the entire experimental procedure66.
In the design of the travel videos, several approaches were attempted to avoid mutual influence between different landscape types. Initially, the four types of landscape videos were presented separately, with different participants viewing distinct categories of landscapes. However, the results were significantly affected by individual differences among participants. We also experimented with randomizing the playback order of the four videos, yet this approach still failed to prevent inter-video influence and comparison, while also leading to sequence confusion in participants’ questionnaire responses. After discussions within our team, we recognized that while the comparison between participants might enhance the differentiation of the landscape types, this form of differentiated experimental result better reflects the data and achieves the experimental goal by emphasizing these differences. Moreover, we considered that landscape perception constitutes a holistic experience of the environment, often arising from the interplay of various landscape elements. Therefore, imposing strict controls over inter-landscape influences could disrupt the natural experiential perception of the participants. Additionally, we implemented baseline emotional calibration and visual variable control to minimize other interfering factors. Before each viewing, participants were given 90 s to recall daily life scenarios, followed by 20 s of subtitle guidance and a 10-s black screen pause to help return their emotions to baseline. We also standardized the production of the four types of landscape videos to ensure uniformity in duration, resolution, color saturation, and background music.
Data collection
During the questionnaire distribution process, a combined method of online and offline distribution was employed. An online questionnaire was created using the Wenjuanxing platform. Initial data collection was conducted through online and offline convenience sampling from 27, January to 1st, February, 2025. Online questionnaires were filled out through WeChat forwarding and group messaging, while in terms of offline, researchers went to communities and surrounding public places to find volunteers to help fill it out. During the offline data collection, since some participants were unwilling to scan the QR code or had difficulty opening the video, electronic devices were provided. 95 questionnaires were completed in the pre-experiment, with 94 valid ones. The reliability and validity analysis of the data met the standards, with a KMO value of 0.790 and a Cronbach’s α value of 0.955, indicating good reliability and validity in the pre-experiment (See supplementary material).The second-stage collection used a snowballing method from 1st, February to 7th, February, 2025, resulting in a total of 462 completed questionnaires, of which 409 responses were valid, with an effective rate of 88.5%. Participants were mostly from Mainland China, representing a diverse range of careers and age groups. The participants were consisted of 49.1% male participants (n = 201) and 50.9% female participants (n = 208). As shown in Table 2, the sample was dominated by people aged 18 to 50, accounting for 83.3%. This study primarily focuses on alleviating emotions through travel-scenery videos within virtual networks in the digital age. Individuals aged 18 to 50 constitute both the main force of the internet era and the demographic facing the greatest pressures in daily life. Therefore, the sample population was highly consistent the primary target audience of this research58.
Results of study 2
Reliability and validity test
SPSS 27.0 was used in data analysis, and all scales in the study were maturity scales used and cited by previous researchers. As shown in Table 3, the Cronbach’s α value of Neuroticism and Emotional changes scales were all > 0.60, which was acceptable, while the rest ranged between 0.788 to 0.937 for all variables, which were all > 0.70, indicating good reliaiblity. The composite reliability (CR) and were between 0.896 and 0.930, and the standardized factor loading and average variance extracted (AVE) values of all items were also calculated to assess the reliability of the questionnaire. All standardized factor loading and AVE values exceeded the 0.50, and the all CR values were greater than 0.70, indicating that both reliability and convergent validity were acceptable. Finally, KMO and Bartlett’s test are used to assess the validity of data, the KMO value was above 0.90, and the Bartlett’s test result was significant, indicating marvelous validity.
Correlation analysis
In order to test whether the hypotheses 1, 3, and 4 were supported, correlation tests were first conducted between all variables, excluding the personality traits. As shown in Table 4, a Pearson correlation analysis was performed to examine the relationships between landscape perception (total and four types), emotional changes, and travel intention scores. The analysis revealed significant positive correlations for each pair of variables, with Pearson’s r values ranging from 0.354 to 0.820. These results suggest moderate to strong relationships between the variables, indicating that further regression analysis can be conducted.
Main effect test
The study continued employing linear and hierarchical multiple regression analysis using SPSS 27.0, with Model 1 and Model 2, to test the Hypotheses 1, 3, 4, along with their corresponding sub-hypotheses (H1a, H1b, H1c, H1d, H3a, H3b, H3c, and H3d). Respondents’ demographic variables, including gender, age, education, occupation, and travel frequency, were included as control variables in the analysis. The majority of respondents reported increases in positive affect and decreases in negative affect after watching the disembodied travel video, where the response counts and histograms for each item in the Mood Change Scale are shown in Table 5. As shown in Table 6, the results of the main effect indicated that overall landscape perception had a significant positive effect on both emotional changes (β = 0.412, p < 0.001) and embodied travel intention (β = 0.591, p < 0.001). Additionally, emotional changes were found to significantly predict travel intention (β = 0.378, p < 0.001). These findings supported Hypotheses 1, 3, and 4. Further analysis using multiple regression revealed that, with the exception of the rural landscape, all other landscape types had a significant positive effect on emotional changes. Similarly, all landscape types, except for shore-side commercial landscapes, were positively associated with travel intention. These results provide support for H1a, H1b, H1c, H3a, H3c, and H3d. H1d and H3b were not supported.
Analysis of moderating effect
The study used PROCESS extension v4.0 (Model 1) to conduct hierarchical regression analysis to test the moderating effects of personality factors (neuroticism and openness) on the relationship between landscape perception (X) and emotional change (Y), while controlling the demographic variables of gender, age, occupation, education, and travel frequency (Fig. 7). As shown in Table 7, the results showed that neuroticism and openness significantly moderated the effect of X on Y, supporting H2a and H2b.
Moderating effect results (left = neuroticism, right = openness).
The result revealed a significant interaction between X and openness (B = 0.357, p < 0.001, 95% CI [0.245, 0.469]), with the explanatory power of R2 = 0.348, indicating that the relationship between X and Y was moderated by openness level. Specifically, when openness was at a higher level (+ 1SD), the relationship between X and Y was stronger (B = 0.554, p < 0.001), suggesting that openness enhances the effect of landscape perception. However, lower opennes level (-1SD) had no significant moderating effect (p = 0.157). The regression equation is expressed as:
The neuroticism moderation model was significant overall (F = 22.142, p < 0.001), with an explanatory power of R2 = 0.307. The interaction term between neuroticism and landscape perception significantly predicted emotional changes (B = 0.316, p < 0.001, 95% CI [0.181, 0.450]), indicating that neuroticism significantly positively moderates the relationship between landscape perception and emotional changes. Specifically, when neuroticism was lower (-1SD), the moderating effect exists (B = 0.188, p < 0.01). When neuroticism was at a higher level (+ 1SD), the relationship between X and Y was stronger (B = 0.554, p < 0.001). The regression equation is expressed as:
Analysis of mediating effect
As shown in Table 7, after controlling for gender, age, occupation, education level, and travel frequency, the results showed that emotional change plays a significant partial mediating role in the relationship between landscape perception and travel intention. The total effect of landscape perception on travel intention was 0.896 (p < 0.001), with the direct effect being 0.805 (p < 0.001) and the indirect effect being 0.091 (95% CI [0.038, 0.152]), which accounts for 10.2% of the total effect. The partial mediation effect was significant. Therefore, Hypothesis H5 is supported. As shown in the path analysis diagram (Fig. 8), this study reports the results of all hypothesis tests. Except for the non-significant effects of the coastal commercial landscape on travel intention and the shoreland pastoral landscape on emotional changes, all the proposed paths in the model show significant positive correlations.
Path model of the effects (self-drawn by the author).
Comparison of study 1 and study 2 results
A comparative analysis was conducted between the emotional responses elicited by embodied experiences of the four landscape types in Study 1 and the perceived restorative strength in disembodied contexts in Study 2 (Table 8). This analysis included both a horizontal comparison across the four landscape types and a vertical comparison between the degree of disembodied perception and the valence of emotions following embodied experiences, aiming to capture the emotional shifts from virtual viewing to on-site experience. To more intuitively compare the degree of embodied versus disembodied perception, we represent it using a numerical model:
In this model, a, b, and c represent the coefficients for positive, neutral, and negative emotions, respectively, assigned values of 1, 0, and -1. Horizontal analysis reveals that in embodied emotional perception: historical-cultural > rural-pastoral > ecological-natural > commercial-leisure (ME:0.771 > 0.748 > 0.707 > 0.697); in the degree of disembodied perception: rural-pastoral > ecological-natural > historical-cultural > commercial-leisure (MD:0.264 > 0.260 > 0.232 > 0.026). Vertical analysis shows that during the transition from disembodied to embodied perception, the change value is: commercial-leisure > historical-cultural > rural-pastoral > ecological-natural.
Discussion
Against the backdrop of digitalization and the rise of “cloud tourism,” disembodied travel has emerged as a new form of tourism. To uncover the mechanism of “perception–emotion–behavior” in disembodied landscape experiences and the differentiated effects of landscape types, this study adopts a combination of quantitative and qualitative methods. A qualitative analysis of online text data was conducted using grounded theory, followed by video experiments and questionnaire surveys based on the coding results. The survey data were then subjected to statistical analysis. Findings aim to maximize the health benefits and commercial potential of “cloud tourism”, establishing a new paradigm for health tourism research in the digital intelligence era.
Theoretical contribution: extending the landscape—emotion link into the virtual context
Social media enables emotional connection and healing through virtual experiences. This study establishes a mediating mechanism of “disembodied landscape perception → embodied emotional change → embodied travel intention (B = 0.896, p < 0.001),” supporting environmental restorative theory, attention restoration theory(ART), stress recovery theory(SRT), and human–nature connectedness theory(HNC)70,71. It extends Embodied Cognition Theory(ECT)1 into virtual contexts by introducing environmental psychology and HNC, while proposing a bidirectional “disembodied-embodied” transformation model. The results confirm that disembodied perception can evoke embodied-like emotional responses7, consistent with findings on digital nature’s effectiveness in stress reduction72,73. Visual nature representations such as images and videos were shown to alleviate anxiety and benefit mental health74,75. Additionally, the study aligns with existing research indicating that positive short video experiences strengthen travel motivation76,77 and support tourism promotion78. The stimulus-organism-response(SOR)79 framework is also extended to social media’s disembodied context80. These findings highlight that although landscape videos offer healing potential, they cannot replace real-world interaction. Instead, they should function as mediators encouraging physical travel. Finally, the study underscores personality’s moderating role in landscape healing (Openness: B = 0.357, p < 0.001, Neuroticism: B = 0.316, p < 0.001), establishing a personalized healing paradigm81,82,83. Questionnaire results validate neuroticism’s bidirectional moderation and openness’s threshold effect, supporting the notion that “the same landscape may be perceived differently” based on individual traits84.
Analysis of landscape healing and transformative effects differences
The findings address the research gap concerning differential restorative effects across landscape types85,86. Through multivariate regression analysis of four primary landscapes in the Erhai Scenic Area, results indicate that all types positively influenced emotions or travel intentions to varying degrees87, supporting the applicability of environmental restorative theory in digital contexts22 (p < 0.05, H1abc&H3acd were supported). However, rural-pastoral landscape had no significant impact on emotional changes(p = 0.232, H1d was not supported), disembodied perceptions of farmland, docks, villages, agricultural labor, and fishing scenes do not effectively alleviate emotions. Commercial-leisure landscapes can produce a psychological healing effect ,but can’t significantly influence travel intention and further stimulate travel intentions and real-world tourism consumption(p = 0.630, H3b was not supported). Comparative data from Studies 1 and 2 indicate landscape-specific effects in the transition from embodied to disembodied perception. Ecological-natural landscapes show high perceptual accessibility but limited emotional shift (ME = 0.707, MD = 0.260), supporting “digital nature therapy.” Historical-cultural landscapes achieve high conversion, relying strongly on embodied on-site interaction (ME = 0.771, MD = 0.232). Commercial-leisure landscapes are weak in both perceptions, reaffirming the limitations of standardized environments, yet their high emotional transition rate suggests potential within the “check-in economy.”(ME = 0.697, MD = 0.026) A notable paradox was observed in rural-pastoral landscapes, which performed well perceptually but elicited weak emotional response (ME = 0.747, MD = 0.264, H1d was not supported). While empirical data cannot fully explain this discrepancy, we attempted to speculate on the reasons: this paradox may stem from the social reality of rural areas. They are often perceived as lacking convenient transportation, adequate services, education, and employment opportunities, prompting many young people to leave88,89. As a result, rural youth experience a conflicted relationship with their hometowns, marked by both attachment and detachment, pride and entrapment90,91. While urban youth often lack emotional connections to the countryside altogether. This tension may help explain the observed contradictions.
Practical implications for health tourism under social media
Future health tourism are likely to integrate both disembodied and embodied experiences, forming a closed loop of “embodied–disembodied” travel. While social media can provide psychological comfort and serve as a supplementary tool for mental health, it cannot substitute for the effects of on-site travel51. Based on the new tourism model constructed in this study (Fig. 9), a novel model centered on the “digital-physical” value transformation closed loop, the core principle of “precision healing” is established at the conceptual framework level. Its core dimensions adhere to the specificity of landscape types. Drawing on empirical results, it distinguishes the distinct emotional restoration efficacy and behavioral transformation pathways of the four landscape types across the two dimensions of “disembodied perception” and “embodied experience.” Furthermore, the healing effects are moderated by the user’s personality traits, identifying “Neuroticism” and “Openness” as key moderating variables and proposing the “personality-landscape matching” principle. At the path mechanism level, the model optimizes the “digital-physical” conversion process. Targeting the “disembodied perception → emotional change → travel intention” pathway, short videos serve as the initial touchpoint, providing immediate psychological solace by triggering a “sense of pseudo-presence.” For digital front-end outreach, content platforms and creators should leverage algorithms for personalized content recommendations. For instance, users high in Neuroticism could be directed towards ecological natural landscapes to establish emotional safety, while users high in Openness could be offered historical and cultural landscapes to stimulate cognitive interest, thereby maximizing the efficiency of front-end emotional arousal. At the critical conversion node, destination marketers should allocate resources based on the unique conversion characteristics of each landscape type. For example, focus on developing in-depth experience products for historical and cultural landscapes, which show high conversion rates, while for commercial leisure landscapes, the marketing emphasis should be placed on their immediate psychological solace value. Finally, the embodied experience of physical travel feeds back into the digital ecosystem through user-generated content sharing, enhancing the model’s sustainability and creating a value closed loop of “digital preview—physical experience—social feedback.” At the level of managerial implications, content creators should integrate the creative principles of health and healing into short video production, evoking user emotional resonance and psychological relaxation. This involves prioritizing the integration of restorative content such as natural, cultural, and commercial landscapes. Destination marketers should reduce reliance on homogenized commercial landscapes. By translating inspiration into concrete action, such as launching short-term “Weekend Healing Getaways,” they can lower the time and economic thresholds for participation. This guides a shift from transient online digital healing to sustained real-world ecological restoration, which can not only help prevent social media addiction but also construct a virtuous cycle for the healthy tourism model. For the overall industry, there is a need for collective vigilance against the “traffic-first” algorithm tendency, fostering a more positive digital environment, and guiding the industry away from fleeting sensory stimulation towards a new healthy tourism model offering sustained therapeutic value.
Schematic diagram of new health tourism (self-drawn by the author).
Implicit ethical challenges of disembodied digital tourism
The psychedelic digital space, chaotic space proliferation, ambiguous spatial boundaries, and poor design ethics could threaten human safety and undermine human dominance as space subjects. Humans are influenced by the spatial–temporal dynamics of information and virtual spaces, blurring the boundaries between embodied and disembodied spaces. This study highlights concerns over implicit digital ethics in the age of media-driven tourism. In the new health tourism, it is necessary to establish a clearer boundary between disembodied and embodied space, and optimize the immersion of disembodied experience without replacing the real experience. At the technical level, a “reality anchoring system” could be developed by setting thresholds for digital immersion (e.g., 60 min) and integrating biofeedback sensors such as heart-rate and eye-tracking to monitor cognitive load, thereby dynamically adjusting information flow to prevent overload and imbalance. Within the “influencer economy,” it is equally crucial to establish a responsible communication ecosystem that avoids excessive filters and misleading promotions, which distort user perceptions and trigger herd effects. Instead, algorithms should be guided to channel traffic in ways that encourage ecological protection of destinations. Ultimately, addressing these challenges requires sustained collaboration among governments, researchers, content creators, and users. By aligning technological innovation with ethical governance, a balanced and symbiotic relationship between “digital twins” and the real world can be achieved.
Limitations and future research directions
This study has several limitations. First, there may be differences in perception based on age and generational factors. Since this study primarily focuses on younger and middle-aged groups, individuals under 40 years old occupied 75.5% of the total sample. Therefore, the sample group may not experience the same level of embodied emotional connection with pastoral landscapes influenced by short video “filters” as older adults do, which leads to the non-significant effect of rural-pastoral landscape on emotional change (H1d is not supported). Furthermore, there are also some potential factors that may have influenced participants’ responses to the video stimuli. For instance, participants’ cross-comparisons of different landscape videos and variations in playback order could have introduced biases into the experimental results. Future studies should address and refine these design flaws. In addition, the duration of the video can cause changes in the interaction threshold. Research indicates that short videos (< 1 min) are more likely to trigger emotional peaks but with poor persistence, while long videos (> 5 min) promote deeper cognition but may lead to aesthetic fatigue92. This study used a 3-min landscape video for the experiment, and future research could establish a “duration-emotion curve” model to optimize the matching of content duration with landscape types. Moreover, subsequent experiments could further explore the healing effects of various disembodied perceptual elements, such as media formats (e.g., images, videos, VR), background music, and filming techniques.
Conclusion
This study aims to explore the digital healing mechanisms of disembodied landscape perception through social media, focusing on differences in restorative effects across landscape types and their specific transformation effects. The findings reveal: (1) The healing mechanism of “disembodied landscape perception—embodied emotional change—embodied travel intention” was validated, showing emotional change as a partial mediator between perception and travel intention, with personality traits (openness and neuroticism) exerting moderating effects; (2) All four landscape types positively influenced either emotions or travel intention to varying degrees, only rural-pastoral landscapes showing no significant impact on emotional change and commercial-leisure landscapes showing no significant effect on travel intention; (3) When comparing the strength of disembodied perception with the positive and negative emotions of embodied experiences, commercial-leisure and historical-cultural landscapes exhibited the highest transformation rates, whereas rural-pastoral and ecological-natural landscapes showed relatively smaller changes. This study highlights the influence of social media travel videos on mental health, extends the theoretical boundaries of “landscape restoration,” and confirms the psychological healing potential of disembodied travel perception. It further advances the dual evolution of “Internet traffic economy” and “health healing” in “cloud tourism,” contributing to a new model of health tourism that integrates disembodied and embodied experiences in the digital-intelligence era.
Data availability
The data that supports the findings of this study are available in the supplementary material of this article.
References
Varela, F. J., Thompson, E. & Rosch, E. The Embodied Mind: Cognitive Science and Human Experience. (The MIT Press, 2017). https://doi.org/10.7551/mitpress/9780262529365.001.0001.
Sixtus, E., Krause, F., Lindemann, O. & Fischer, M. H. A sensorimotor perspective on numerical cognition. Trends Cogn. Sci. 27, 367–378 (2023).
Zhang, S. & Chen, Y. A study on embodied experience of surfing tourism based on grounded theory—Take China’s Hainan province as an example. Behav. Sci. 12, 407 (2022).
Xu, X. & Dai, G. The transformative power of embodied behaviour: Influencing tourists’ experience in the guangzhou marathon as a mass participant sports event. Behav. Sci. 15, 90 (2025).
Cohen, E. & Cohen, S. A. Current sociological theories and issues in tourism. Ann. Tour. Res. 39, 2177–2202 (2012).
Veijola, S. & Jokinen, E. The body in tourism. Theory Cult. Soc. 11, 125–151 (1994).
James, M. M. & Leader, J. F. Do digital hugs work? Re-embodying our social lives online with digital tact. Front. Psychol. 14, (2023).
Stanghellini, G. & Sass, L. The bracketing of presence: Dematerialization and disembodiment in times of pandemic and of social distancing biopolitics. Psychopathology 54, 113–118 (2021).
Wei, X. From, “disembodied cognition” to “embodied cognition”:the generating path of college students’ sense of gain in ideological and political course. Theory Pract. Educ. 40, 34–36 (2020).
Eck, J., Dignath, D., Kalckert, A. & Pfister, R. Instant disembodiment of virtual body parts. Atten Percept. Psychophys. 84, 2725–2740 (2022).
Araujo, T. Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Comput. Hum. Behav. 85, 183–189 (2018).
Bargh, J. A., McKenna, K. Y. A. & Fitzsimons, G. M. Can you see the real me? Activation and expression of the “true self” on the internet. J. Soc. Issues 58, 33–48 (2002).
Koles, B. & Nagy, P. Virtual customers behind avatars: The relationship between virtual identity and virtual consumption in second life. J. Theor. Appl. Electron. Commer. Res. 7, 87–105 (2012).
Zhao, S. The digital self: Through the looking glass of telecopresent others. Symb. Interact. 28, 387–405 (2005).
Kim, J. Synthetic vision in virtual reality documentaries. Film-Philos. 25, 321–345 (2021).
Hutchinson, W., Djafarova, E., Liu, S. & Abdelrahman, M. Investigating the impact of food tourism vlogger entrepreneurs’ language characteristics on audiences’ attitude and behaviours. Int. J. Entrep. Behav. Res. 30, 735–772 (2024).
Ahn, S. J. (Grace) et al. Experiencing Nature: Embodying Animals in Immersive Virtual Environments Increases Inclusion of Nature in Self and Involvement with Nature. J. Comput. Mediat. Commun. 21, 399–419 (2016).
Barsalou, L. W. Simulation, situated conceptualization, and prediction. Philos. Trans. R Soc. Lond. B Biol. Sci. 364, 1281–1289 (2009).
Jiang, L., Wu, H. & Song, Y. Diversified demand for health tourism matters: From a perspective of the intra-industry trade. Soc. Sci. Med. 293, 114630 (2022).
Mueller, H. & Kaufmann, E. L. Wellness tourism: Market analysis of a special health tourism segment and implications for the hotel industry. J. Vacat. Mark. https://doi.org/10.1177/135676670100700101 (2025).
Trong Nhan, N. & Huynh, V. Exploring the impact of tourism environment on tourist satisfaction in tourist sites: An example of Phong Dien Tourism Village, Vietnam. Case Stud. Environ. 8, (2024).
Li, C. & He, J. Restorative environmental perception’s influence on post-tour behavior of desert off-road self-driving tourists: the mediating role of flow experience. Sustainability 15, 12934 (2023).
Markevych, I. et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environ. Res. 158, 301–317 (2017).
Shi, H., Luo, H., Wei, Y. & Shin, W.-S. The influence of different forest landscapes on physiological and psychological recovery. Forests 15, 498 (2024).
Bell, S. L., Phoenix, C., Lovell, R. & Wheeler, B. W. Seeking everyday wellbeing: The coast as a therapeutic landscape. Soc. Sci. Med. 142, 56–67 (2015).
Zhou, L., Liu, A., Wang, L., Li, Y. & Cheng, X. Perceived health benefits of nature-based tourism: The influences of tourists’ involvement, restorative environment and health consciousness. Int. J. Tour. Res. 25, 606–622 (2023).
Zhong, L., Sun, S., Law, R. & Qi, X. Tourists’ perception of health tourism before and after COVID-19. J. Tour. Res. 26, e2620 (2024).
Yang, J. Y., Paek, S., Kim, T. (Terry) & Lee, T. H. Health tourism: Needs for healing experience and intentions for transformation in wellness resorts in Korea. Int. J. Contemp. Hosp. Manag. 27, 1881–1904 (2015).
Kim, J.-H., Ritchie, J. R. B. & McCormick, B. Development of a scale to measure memorable tourism experiences. J. Travel Res. 51, 12–25 (2012).
Shi, Z., & Wang, Y. Creative video therapy in the digital age: Theoretical exploration and media practice of wound healing. J. Beijing Film Acad. 36–44 (2024).
Zhang, P. Micro-narratives, big picture: The symbolic meaning mechanism of “healing” short video animations. New Films 104–109 (2022).
Li, J. A Research On The Emotional Transmission Of Emotional Healing Videos Under The New Media Context. (Dalian University of Technology, 2023). https://doi.org/10.26992/d.cnki.gdlqc.2021.000003.
Batat, W. Phygital customer experience in the metaverse: A study of consumer sensory perception of sight, touch, sound, scent, and taste. J. Retail. Consum. Serv. 78, 103786 (2024).
Xia, Y., Deng, Y., Tao, X., Zhang, S. & Wang, C. Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Hum. Soc. Sci. Commun. 11, 1–15 (2024).
Paul, I., Mohanty, S. & Sengupta, R. The role of social virtual world in increasing psychological resilience during the on-going COVID-19 pandemic. Comput. Hum. Behav. 127, 107036 (2022).
Turkle, S. Parallel Lives: Working on Identity in Virtual Space. (SAGE Publications, 1996).
Bastiaansen, M., Straatman, S., Mitas, O., Stekelenburg, J. & Jansen, S. Emotion measurement in tourism destination marketing: A comparative electroencephalographic and behavioral study. J. Travel Res. 61, 252–264 (2022).
Zhang, S., Chen, N. & Hsu, C. H. C. Facial expressions versus words: Unlocking complex emotional responses of residents toward tourists. Tour. Manage. 83, 104226 (2021).
Koole, S. L. The psychology of emotion regulation: An integrative review. Cogn. Emot. 23, 4–41 (2009).
Cheng, X. & Li, J. The impact of virtual tourism experience on stress and emotion under the stay-at-home restrictions due to COVID-19 epidemic. Tour. Tribune 35, 13–23 (2020).
Lee, J., Jung, T., Tom Dieck, M. C., García-Milon, A. & Kim, C.-S. Affordance, digital media literacy, and emotions in virtual cultural heritage tourism experiences. J. Vacation Mark. 13567667241255383 (2024) https://doi.org/10.1177/13567667241255383.
Wu, J., Tuo, Y., Bai, C. & Lin, Z. Curating wellness: Exploring healing experiences in digitally transformed museums. Tour. Manage. 111, 105207 (2025).
Li, C., Zhang, C., Li, H. & Liu, H. The healing effect of tourism videos: A comparative study based on multidimensional emotional measurement. Tour Sci. 39, 24–48 (2025).
Li, N., Li, L., Chen, X. & Wong, I. A. Digital destination storytelling: Narrative persuasion effects induced by story satisfaction in a VR context. J. Hosp. Tour. Manag. 58, 184–196 (2024).
Parks-Leduc, L., Feldman, G. & Bardi, A. Personality traits and personal values: a meta-analysis. Pers. Soc. Psychol. Rev. 19, 3–29 (2015).
Barańczuk, U. The five factor model of personality and emotion regulation: A meta-analysis. Pers. Individ. Differ. 139, 217–227 (2019).
Fang, X., Xie, C., Yu, J., Huang, S. & Zhang, J. How do short-form travel videos trigger travel inspiration? Identifying and validating the driving factors. Tour. Manag. Perspect. 47, 101128 (2023).
Siyaguna, T., Myhre, S. K., Saxton, B. T. & Rokke, P. D. Neuroticism and emotion regulation predict attention performance during positive affect. Curr. Psychol. 38, 1542–1549 (2019).
Silaban, P. H., Chen, W.-K., Nababan, T. S., Eunike, I. J. & Silalahi, A. D. K. How travel vlogs on youtube influence consumer behavior: A use and gratification perspective and customer engagement. Hum. Behav. Emerg. Technol. 2022, 1–16 (2022).
Herbert, M. 5.02 - Clinical Formulation. in Comprehensive Clinical Psychology (eds. Bellack, A. S. & Hersen, M.) 25–55 (Pergamon, Oxford, 1998). https://doi.org/10.1016/B0080-4270(73)00117-6.
Gan, L., Xie, W., Jia, X. & Zhou, T. Could the VR experience replace on-site travel? Experimental analysis of the VR scene of valspar peak. Tour. Tribune 34, 87–96 (2019).
Li, X., Li, Y., Song, C., Lu, W. & Zhang, Q. Exploring consumer behavior in virtual reality tourism using the theory of planned behavior. Tour. Tribune 36, 15–26 (2021).
Ghaderi, Z., Hatamifar, P. & Ghahramani, L. How smartphones enhance local tourism experiences?. Asia Pac. J. Tour. Res. 24, 778–788 (2019).
Alamäki, A., Pesonen, J. & Dirin, A. Triggering effects of mobile video marketing in nature tourism: Media richness perspective. Inf. Process. Manage. 56, 756–770 (2019).
Cui, T. & Jiang, H. Influence of virtual tourism experience, authenticity and satisfaction on on-site tourism intention: A case study of the Mogao Grottoes in Dunhuang. Geogr. Geo-Inf. Sci. 39, 122–129 (2023).
Li, C. et al. Spatial differentiation and driving factors of rural settlement in Plateau lake: A case study of the area around the Erhai. Econ. Geogr. 42, 220–229 (2022).
Wang S. & Sun J. Bidirectional mechanisms of tourism resource(re)development and community heterogeneity: J. Nat. Resour. 39, 1531–1547 (2024).
Fang, S., Wang, W. & Wang, Y. Measuring and implementing spatial justice in lakeside tourist destinations: A study on the perception of indigenous people on the west coast of Erhai lake. Trop. Geogr. 43, 2035–2048 (2023).
Guo, R. et al. Semantic comparison of online texts for historical and newly constructed replica ancient towns from a tourist perception perspective: A case study of Tongguan Kiln ancient town and Jinggang ancient town. Land 13, 2197 (2024).
Weng, F. et al. Study on multidimensional perception of national forest village landscape based on digital footprint support-Anhui Xidi Village as an Example. Forests 14, 2345 (2023).
Chen, Y. & Sun, Y. Determinants of platform ecosystem health: An exploration based on grounded theory. J. Bus. Econ. Manag. 22, 1142–1159 (2021).
Pan, X. & Xue, Y. Advancements of artificial intelligence techniques in the realm about library and information subject-a case survey of latent dirichlet allocation method. IEEE Access 11, 132627–132640 (2023).
John, O. P. & Srivastava, S. The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In Handbook of personality: Theory and research, 2nd ed 102–138 (Guilford Press, New York, NY, US, 1999).
Rogers, A. A., Ha, T. & Ockey, S. Adolescents’ perceived socio-emotional impact of covid-19 and implications for mental health: results from a U.S.-based mixed-methods study. J. Adolesc. Health 68, 43–52 (2021).
Li, S., Chen, T., Chen, F. & Mi, F. How does the urban forest environment affect the psychological restoration of residents? A natural experiment in environmental perception from Beijing. Forests 14, 1986 (2023).
Luong, T.-B. The moderating role of e-word of mouth in the relationships between destination source credibility, awareness, attachment, travel motivation, and travel intention: A case study of Vietnamese film tourism. Int. J. Tour. Res. 26, e2729 (2024).
Chen, H., Qiu, L. & Gao, T. Application of the eight perceived sensory dimensions as a tool for urban green space assessment and planning in China. Urban For. Urban Green. 40, 224–235 (2019).
Ma, Y. & Zhang, Y. Overly filtered travel photos: The effect of cognitive dissonance on travel intentions among potential tourists in social media context. Tour. Tribune. 40, 28–41 (2025).
Zhang, B. et al. The big five inventory-2 in China: A comprehensive psychometric evaluation in four diverse samples. Assessment 29, 1262–1284 (2022).
Ives, C. D. et al. Reconnecting with nature for sustainability. Sustain Sci. 13, 1389–1397 (2018).
Li, Y., Li, W. & Liu, Y. Remedies from nature: exploring the moderating mechanisms of natural landscape features on emotions and perceived restoration in urban parks. Front. Psychol. 15, 1502240 (2025).
Kjellgren, A. & Buhrkall, H. A comparison of the restorative effect of a natural environment with that of a simulated natural environment. J. Environ. Psychol. 30, 464–472 (2010).
Brancato, G., Van Hedger, K., Berman, M. G. & Van Hedger, S. C. Simulated nature walks improve psychological well-being along a natural to urban continuum. J. Environ. Psychol. 81, 101779 (2022).
Vetter, D. et al. Effects of art on surgical patients: A systematic review and meta-analysis. Ann. Surg. 262, 704–713 (2015).
Krause-Sorio, B. et al. Your brain on art, nature, and meditation: a pilot neuroimaging study. Front. Hum. Neurosci. 18, (2025).
Cao, X., Qu, Z., Liu, Y. & Hu, J. How the destination short video affects the customers’ attitude: The role of narrative transportation. J. Retail. Consum. Serv. 62, 102672 (2021).
Chen, X. & Cheng, Z. The impact of environment-friendly short videos on consumers’ low-carbon tourism behavioral intention: A communicative ecology theory perspective. Front. Psychol. 14, 1137716 (2023).
Liao, S.-S., Lin, C.-Y. & Xie, X.-Z. Effects of short-form video application users’ guanxi on intention to visit rural tourism destinations: The moderating role of tourism fatigue. J. Vacat. Mark. 30, 782–804 (2024).
Jiang, J., Hong, Y., Li, W. & Li, D. A study on the impact of official promotion short videos on tourists’ destination decision-making in the post-epidemic era. Front. Psychol. 13, 1015869 (2022).
Wu, S. & Peng, X. How can short travel videos facilitate tourists’ decision-making about traveling to cultural heritage destinations? Evidence from the Wudang Mountain Complex. APJML https://doi.org/10.1108/APJML-05-2024-0577 (2025).
Sella, E. et al. The influence of individual characteristics on perceived restorativeness and benefits associated with exposure to nature in a garden. Front. Psychol. 14, (2023).
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2, 313–345 (2007).
Berto, R., Barbiero, G., Barbiero, P. & Senes, G. An individual’s connection to nature can affect perceived restorativeness of natural environments. Some observations about Biophilia. Behav. Sci. 8, 34 (2018).
Nisbet, E. K., Zelenski, J. M. & Murphy, S. A. The nature relatedness scale: linking individuals’ connection with nature to environmental concern and behavior. Environ. Behav. 41, 715–740 (2009).
Wilkie, S., Platt, T. & Trotter, H. Impact of an online micro-restorative experience on mood, emotion, and perceived restorativeness: Matters of place preference - SURE. In International Association of Person-Enviroment Studies Bi-Annual Conference (Lisbon, Portugal, 2022).
Zhang, P., He, Q., Chen, Z., Li, X. & Ma, J. An empirical study on the promotion of students’ physiological and psychological recovery in green space on campuses in the post-epidemic era. IJERPH 20, 151 (2022).
Quoidbach, J. et al. Emodiversity and the emotional ecosystem. J. Exp. Psychol. Gen. 143, 2057–2066 (2014).
Østergaard, J., Pless, M., Blackman, S. & MacDonald, R. Introduction to special issue: youth, rural places and marginalisation. J. Youth Stud. 27, 1227–1239 (2024).
McLaughlin, D. K., Shoff, C. M. & Demi, M. A. Influence of perceptions of current and future community on residential aspirations of rural youth. Rural. Sociol. 79, 453–477 (2014).
Pedersen, H. D. & Gram, M. ‘The brainy ones are leaving’: The subtlety of (un)cool places through the eyes of rural youth. J. Youth Stud. 21, 620–635 (2018).
Nordberg, K. The dynamic rural stayer—Analysing the dynamics of the staying process in rural areas. J. Rural. Stud. 111, 103423 (2024).
Zhang, M. Research on the application of AR interactive mode in tourism situational experience from a embodied perspective. (Jiangnan University, 2019).
Acknowledgements
We thank all the volunteers who participated in this study by filling out the online and offline questionnaires, as well as all the commenters who provided online text data. Thanks to Yuchen Zhong, Manlu Liu, Jie Zhang, and Xuecong He for their great help in the data analysis process. Thanks to Prof. Xubin Xie for his patience in tutoring the article.
Funding
This work was supported by the “High‑End Think Tank” Project of the Central South University: Study on Rural Space Design and Development Strategy under the New Development Philosophy [grant numbers 2022znzk12]; and the National Local Joint Engineering Laboratory of Digital Protection and Creative Utilization Technology for Traditional Village and Town Culture [grant number 2024HSKFJJ005].
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Conceptualization, R.G. and Y.Q.; methodology, R.G.; software, R.G.; validation, Y.Q. and R.L.; formal analysis, Y.Q.; investigation, X.X.; resources, X.X.; data curation, R.L.; writing—original draft preparation, R.G.; writing—review and editing, Y.Q.; visualization, R.L.; supervision, R.L.; project administration, X.X.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.
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Guo, R., Qi, Y., Xie, X. et al. Health tourism model in the digital age: emotional healing effects of disembodied landscape perception through social media. Sci Rep 16, 6785 (2026). https://doi.org/10.1038/s41598-026-36305-8
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DOI: https://doi.org/10.1038/s41598-026-36305-8
