Bridging the Distance: Understanding the Role of Destination Anthropomorphism in Videos on Tourists’ Travel Intention

Abstract

Destination anthropomorphism has emerged as a significant promotional strategy for destination marketers, but how to effectively embed anthropomorphic elements in destination promotional videos remains unclear. Based on anthropomorphism theory and social presence theory, this study uses a mixed method approach combining empirical analysis and experiments to explore the effect and mechanism of destination anthropomorphism in videos on tourists’ travel intention. Specifically, the empirical analysis based on secondary data captured from TikTok indicates that anthropomorphic destinations can elicit greater tourists’ travel intention than non-anthropomorphic destinations in videos, particularly for warm destinations or videos with human elements. The results of scenario-based experiments reveal that this effect is mediated by social presence. The destination stereotype and human elements in videos play moderated mediating roles in these effects. This study provides theoretical implications for research on destination anthropomorphism in the context of new media and offers valuable insights for destination marketers in video marketing.

Keywords

destination anthropomorphism destination promotional video social presence travel intention mixed methods

Introduction

In the digital era, destination marketing has evolved to encompass richer content and a wider array of forms (Valsesia et al., 2020). Facing the intensifying competition in the market, destination marketing organizations (DMOs) have tried to leverage innovative strategies to attract tourists (Liang et al., 2017). Among these strategies, anthropomorphism, which involves portraying destinations as human or infusing them with human-like characteristics, has garnered significant attention. An illustrative example is Amsterdam, which employs the tagline “I Amsterdam” to create an image of openness and friendliness. According to anthropomorphism theory, this strategy facilitates the establishment of perceived similarity between tourists and destinations, thereby fostering social connections and enhancing tourists’ sense of belonging to a particular destination (Chan & Gohary, 2023).

Anthropomorphism approaches include visual anthropomorphism, which relies on human-like appearances or virtual representations (e.g., destination mascots; Radomskaya & Pearce, 2021), and textual anthropomorphism, which employs human-like communication (e.g., referring to a destination using “I” or “she” instead of “it”; Pachucki et al., 2022). Previous studies have examined the effectiveness of both approaches (Letheren et al., 2017; Radomskaya & Pearce, 2021). They suggested that visual anthropomorphism is more effective for promoting single products, as it provides consumers with direct visual stimulation (Radomskaya & Pearce, 2021). However, as a destination includes a diverse range of elements or targets a broad consumer base, it is challenging to ensure that the chosen mascot resonates with all consumers (Byun & Jang, 2018; Shen et al., 2019). In such cases, textual anthropomorphism is a more commonly used and flexible strategy. Therefore, this study focuses on textual anthropomorphism in the context of destination marketing.

Previous studies have found that for tourists with high anthropomorphism tendencies, viewing text-personified advertisements elicits stronger travel intentions compared to non-personified ads (Letheren et al., 2017). However, prior research on destination anthropomorphism primarily examined traditional static advertisements (e.g., Pachucki et al., 2022; Radomskaya & Pearce, 2021), with very few studies focusing on the impact of destination anthropomorphism within promotional videos (e.g., Zhu et al., 2025). Unlike static media, videos, through the multidimensional combination of dynamic images and narration, make destination marketing more immersive, thereby effectively evoking social presence (Tang et al., 2025). Based on social presence theory, social presence may influence the paths of emotional transfer or information communication between tourists and anthropomorphic destinations (Liu et al., 2023), thus affecting tourists’ responses. However, there is currently a lack of systematic investigation into how and why anthropomorphic strategies impact tourist behavior in the context of videos, particularly regarding the role of social presence in this process.

In addition, we expect that the effect of destination anthropomorphism in promotional videos will have varying impacts depending on the tourists’ different impressions of the destination and the different visual elements included in videos. On the one hand, the effectiveness of anthropomorphic strategies in destination videos may be related to tourists’ prior perceptions (i.e., stereotypes) of the destination (Feng et al., 2022). Extensive research has shown that the same style of destination advertisements can produce different effects under varying destination stereotypes (Byun & Jang, 2018; L. Su et al., 2024). Anthropomorphizing different types of destinations may strengthen or weaken tourists’ expected destination images (Chan & Gohary, 2023), thereby aligning or misaligning them more with tourists’ travel motivations. However, to date, no studies have explored the interaction between destination anthropomorphism in videos and stereotypes. On the other hand, human elements, a type of visual elements in videos, may equally evoke tourists’ perceived similarity (Park et al., 2021). Some studies have proposed that including human elements in destination promotional images can serve as a potential medium for establishing social connections between tourists and the destination (Back et al., 2020; Nikjoo & Bakhshi, 2019; Park et al., 2021). However, it remains unclear whether human elements in videos facilitate this connection or distract tourists from imagining anthropomorphic destinations.

To bridge the above research gaps, we propose a conceptual framework that investigates how, why and when destination anthropomorphism in videos affects tourists’ travel intention. Specifically, we hypothesize that the destination anthropomorphism in videos promotes tourists’ travel intention by stimulating the formation of social presence. We analyze when destination anthropomorphism in videos exerts different effects based on the stereotypes associated with different destinations and the salience of human elements in videos. To overcome the limitations of previous studies based only on the experimental methods (F. Li & Ma, 2023; L. Su et al., 2024), this study first uses the secondary data from TikTok, combined with video analysis techniques and empirical models, to analyze the direct effect of destination anthropomorphism on tourists’ travel intention, as well as the moderating effects of stereotypes, and human elements in videos (Liang et al., 2025). Subsequently, we conduct five scenario experiments to further validate the direct and moderating effects and to verify the mediating effect of social presence.

This study makes several contributions to tourism research. First, this paper extends studies on destination anthropomorphism from the context of static advertising to that of dynamic video. By identifying and validating social presence as a new influencing mechanism and response pathway of destination anthropomorphism, this study enriches and extends anthropomorphism theory. Second, this research contributes to the development of social presence theory by providing the new antecedent variables and boundary conditions. Third, by integrating verbal components, visual factors, and tourists’ perceptions, this paper provides a more comprehensive and referable theoretical framework for the literature of destination video marketing. This research offers new insights to DMOs on how to leverage anthropomorphism strategies to enhance the effectiveness of destination video marketing.

Literature Review and Hypotheses Development

Literature Review on Destination Marketing

As an important link of tourism management, destination marketing has always been a key research field. Research on destination marketing initially focuses on languages and images in traditional static advertising (He et al., 2021; Sun et al., 2024), and has revealed the crucial role of language styles (Byun & Jang, 2018), message appeals (Feng et al., 2022), image elements (Q. Su & Li, 2023), and image styles (L. Su et al., 2024; Sun et al., 2025). These influencing factors are commonly combined with tourists’ tendencies (Letheren et al., 2017), travel motivations (Tang et al., 2025), destination types (F. Li & Ma, 2023), and tourism activity types (L. Su et al., 2024) to explore the congruence effect on tourists’ travel intentions and the underlying mechanisms. Among them, destination anthropomorphism, as a language style or strategy, has been recognized as a highly applicable strategy in destination marketing practices, influencing tourists’ travel intentions (e.g., Kwak et al., 2020).

With the advancement in media technology, videos have emerged as a widely accepted communication format for tourists. Scholars have explored the direct impact of unique video features on travel intention from the visual or verbal perspective (Barnes, 2024; Gan et al., 2023; X. Li et al., 2019). Visual factors such as visual perspective (Gan et al., 2023) and visual content (e.g., environment, infrastructure, human; X. Li et al., 2019) have been proven to affect travel intention. Regarding verbal factors, prior studies have predominantly examined the impact of background music and phonetic features on tourists’ travel intention (Barnes, 2024; X. Wang et al., 2023). Y. Wang et al. (2024) have discovered that speech rate, loudness, and pitch would affect tourists’ feelings and behavior patterns. Recognizing the differences between dynamic videos and static advertising, some scholars have begun focusing on identifying the effects of features that are previously studied in static advertising from the perspective of videos (e.g., Gan et al., 2023; Tang et al., 2025). However, this line of research remains relatively limited, particularly in terms of discussing the role and influencing mechanism of destination anthropomorphism within videos.

Destination Anthropomorphism in Videos

Destination anthropomorphism is defined as the process of attributing humanlike features (e.g., emotions, beliefs, and ideas) to a destination (Chan & Gohary, 2023). According to anthropomorphism theory, people tend to anthropomorphize non-human entities during communication due to cognitive and social motivations (Delbaere et al., 2011). Destination promotion is inherently a cross-cultural communication process (Lee & Oh, 2021), which makes destination anthropomorphism a pivotal strategy for enhancing travel intention. As previously mentioned, anthropomorphism strategies typically manifest in visual and textual anthropomorphism. In comparison to visual anthropomorphism, textual anthropomorphism is more suitable for destination marketing because destinations encompass numerous elements that cannot be fully captured by a single visual image (Byun & Jang, 2018; Delbaere et al., 2011). Unlike the predetermined anthropomorphic imagery created by DMOs, textual anthropomorphism, especially when supported by dynamic visuals and sounds in videos, can provide tourists with greater control, and imaginative freedom (Packard et al., 2018). Tourists can envision a destination image that is “communicating” with them based on their unique preferences (Shen et al., 2019).

According to anthropomorphism theory, anthropomorphic designs activate human schemas, enabling individuals to perceive similarities in non-human objects (Kwak et al., 2020). Therefore, when tourists envision a personified destination image in videos, this perceived similarity is triggered. From an emotional perspective, perceived similarity is considered a dimension of reducing psychological distance. Decreasing psychological distance may strengthen the psychological connection between tourists and anthropomorphized destinations (Chan & Gohary, 2023). Cognitively, perceived similarity simplifies tourists’ cognitive processes, particularly in cross-cultural communication, facilitating more efficient information transfer (Kwak et al., 2020; Letheren et al., 2017). Overall, anthropomorphizing destinations in videos offers opportunities to enhance tourists’ perceived similarity, thereby fostering emotional connections and efficient information exchange. This ultimately elicits more positive tourist responses and boosts travel intentions. The hypothesis is proposed as follows:

H1: Destination anthropomorphism in videos has a positive effect on tourists’ travel intention.

Social Presence as a Mediator

Social presence is concerned with “the degree of salience of the other person in the interaction and the consequent salience of interpersonal relationships” (Short et al., 1976, p. 65), which is a perception generated by individuals in the virtual network environment. It focuses on perceptions such as warmth, socialization, and sensitivity that individuals experience from others in communication-mediated interactions (Ying et al., 2022). Based on social presence theory, social presence can be achieved by constructing participant identities or providing the means for actual interaction with others (Han et al., 2016). Anthropomorphizing a destination can enhance the social connections between tourists and the destination, allowing them to perceive the destination as a “friend.” By using “socially-rich” language to interact with tourists, anthropomorphic destinations amplify tourists’ perceived warmth about interpersonal relationships (Liu et al., 2023). Additionally, compared to static advertising, destination videos can transport tourists into the destination promotion scene by showing images, sounds, and emotions, effectively providing a sense of immersion (Ying et al., 2022). This sense of immersion allows tourists to more effectively perceive that they are communicating with a “person” similar to them rather than a cold destination. This helps bridge the temporal and spatial distance between the tourists and the destination, thereby fostering a sense of social presence (Radomskaya & Pearce, 2021). Therefore, we expect that destination anthropomorphism in videos can encourage the formation of social presence.

Meanwhile, we also expect that social presence can stimulate the tourists’ travel intention. Previous research on virtual environments, such as website design and online service improvement, has confirmed that a network environment with social presence is positively associated with users’ satisfaction, perceived interaction quality, and positive communication behavior (Gao et al., 2017; Hwang & Lim, 2015; Lu et al., 2016). Destination videos are transmitted on social platforms, and their context also belongs to the virtual network environment. Thus, the social presence evoked by anthropomorphic destination videos should have a similar effect. Specifically, destination videos with high social presence can effectively convey emotions, allowing tourists to experience similar emotional states (Y. Li et al., 2023). This emotional resonance and shared experience can inspire tourists’ imitative behaviors, encouraging them to recreate these experiences (Letheren et al., 2017). Moreover, information in destination videos with high social presence is presented gradually alongside the interaction between tourists and the destination (Richardson et al., 2017). When tourists actively engage in the process of exploring and understanding information, they are more likely to remember and trust the key details of the destination, and develop positive expectations for their experience, thereby strengthening their travel intentions (Liu et al., 2023). Therefore, we propose that the social presence evoked by anthropomorphic destination videos can stimulate tourists’ intention. The hypothesis is proposed as follows:

H2: Social presence mediates the relationship between destination anthropomorphism in videos and tourists’ travel intention.

The Moderating Effect of Destination Stereotype

A stereotype is a set of simplified and generalized beliefs about an individual or a group. Its cultural universality has been shown to apply to social perceptions of individuals from different cultures (Fiske, 2018), and thus it has been applied to analyze tourists’ evaluations of destinations (e.g., Diamantopoulos et al., 2017; Shen et al., 2019). According to the stereotype content model, people’s pre-existing perceptions of destinations are always based on two dimensions (Feng et al., 2022). The warmth dimension refers to tourists’ perceived warmth or enthusiasm of a destination, and the competence dimension refers to tourists’ perceived beliefs about the capabilities and resources of a destination (Septianto et al., 2022). Tourists form anticipated destination images based on their perceptions and then select destinations that align more closely with their travel motivations (Shen et al., 2019). Previous extensive research categorizes destination stereotypes into warm and competent based on tourists’ perceptions of these two dimensions (e.g., Feng et al., 2022; F. Li & Ma, 2023). They all found that for destinations with different stereotypes (e.g., warm or competent destinations), tourists always respond differently to various information cues. Thus, anthropomorphic videos may have varying effects for different types of destinations.

As mentioned above, the main function of destination anthropomorphism is to encourage emotional interaction (Kwak et al., 2020), so anthropomorphic destination videos could be considered as emotional videos. Tourists’ motivations for visiting warm destinations also tend to be emotional, relating to leisure, ease, and so on (Stepchenkova & Zhan, 2013). The emotional interaction triggered by anthropomorphism further reinforces tourists’ anticipated image of a warm destination. When tourists’ perception of a destination aligns with their motivations, it fosters a positive intention to visit (Lee & Oh, 2021). Conversely, motivations for visiting a competent destination tend to be more rational, such as seeking information or advanced goods (Fiske, 2018). This disconnect between rational motivations and emotional interactions inherent in destination anthropomorphism fails to motivate tourists. Therefore, the effect of anthropomorphic videos on tourists’ travel intention is weaker for competent destinations compared to warm destinations. The following hypothesis is examined:

H3a: Compared to competent destinations, the positive effect of anthropomorphic videos on tourists’ travel intention is stronger for warm destinations.

According to social presence theory, social presence is influenced by the structure of social networks, including social relations and social processes (Short et al., 1976). When considering a warm destination, tourists tend to focus on the experiential attributes of the destination (Shen et al., 2019). The emotional values provided by the destination anthropomorphism in videos can meet tourists’ requirements and expectations regarding the experiential attributes of warm destinations (Kwak et al., 2020). This further encourages tourists to establish friendly social relationships with the destinations, and the social presence evoked by the videos will also be enhanced. However, when tourists are faced with a competent destination, they tend to pay more attention to the informational attributes such as resources and development. Given that tourists’ need for interpersonal relationships is lower in this context, destination anthropomorphism in videos fails to effectively promote interaction between tourists and the destination, thereby limiting the increase in tourists’ perceived social presence. Based on this, the hypothesis is proposed as follows:

H3b: Compared to competent destinations, the mediating effect of social presence between anthropomorphic videos and tourists’ travel intention is stronger for warm destinations.

The Moderating Effect of Human Elements in Videos

In destination marketing materials, human elements are important visual content (Zhang et al., 2023). In this paper, different from using textual anthropomorphism to humanize destinations, the embedding of human elements in videos refers to the inclusion of real people (such as tourists, locals, or guides) in the videos (Back et al., 2020). Previous research has found that human elements, as a visual feature, can enhance the richness, and appeal of photographs, making them more real and trustworthy (Hwang & Lim, 2015; Nikjoo & Bakhshi, 2019). We expect that the embedding of human elements in videos may also affect the effectiveness of destination anthropomorphism in videos.

Embedding more human elements in videos enables tourists to perceive the presence of these figures distinctly (Cyr et al., 2009). Videos with high human presence may influence the impact of textual destination anthropomorphism on tourists’ travel intentions in two directions. First, tourists may project the image of an anthropomorphic destination onto the human elements in the video. In this case, human elements serve as projections and substitutes for textual destination anthropomorphism (Park et al., 2021). For example, when tourists notice that DMOs use textual anthropomorphism strategies in the video, the inclusion of human elements (such as tourists) concretizes the textual anthropomorphism into a real human image. This dual stimulation would further strengthen the perceived similarity among tourists, thereby increasing their travel intentions toward the destination (Reavey et al., 2018). On the other hand, tourists may also simply regard human elements as part of the visual content rather than substitutes for the anthropomorphic destination image. In this scenario, human elements provide tourists with a more authentic imagined travel experience by portraying real human behaviors and interactions (Nikjoo & Bakhshi, 2019). This authentic experience feedback enhances tourists’ trust in the anthropomorphic destination, which further complements the emotional appeal provided by textual anthropomorphism from a cognitive perspective (Luo et al., 2021). In conclusion, embedding more human elements in videos will amplify the positive impact of textual anthropomorphism on tourists’ travel intention. We thus propose the following hypothesis:

H4a: The positive effect of destination anthropomorphism on tourists’ travel intention is stronger in videos with more human elements than in videos with fewer human elements.

Similarly, we expect that embedding more human elements in videos would evoke higher social presence. First, when tourists project the human elements as representations of the destination’s textual anthropomorphism, the dual stimulation may lead to deeper emotional engagement among tourists due to increased perceived similarity (Han et al., 2016). Higher emotional engagement enables tourists to feel a more direct connection with the destination, thereby fostering stronger social presence (Kwak et al., 2020). On the other hand, even if tourists view human elements merely as part of the visual content, as previously mentioned, additional human elements in videos enhance the tourists’ trust in the destination. Increased trust encourages tourists to associate their unknown experiences with the human behaviors depicted in the videos (Luo et al., 2021). Through the perspectives of the figures in the videos, tourists can experience the cultures and landscapes of the destination, which further enhance their social presence. Additionally, social presence theory emphasizes that information richness in online contexts positively influences social presence (Herrewijn & Poels, 2015). As a “socially rich” visual component, the presence of human elements significantly enhances the informational richness of destination videos (Hwang & Lim, 2015), thus can facilitate the formation of social presence. Therefore, videos with more human elements can evoke higher social presence among tourists. The hypothesis is proposed as follows:

H4b: The mediating effect of social presence between destination anthropomorphism and tourists’ travel intention is stronger in videos with more human elements than in videos with fewer human elements.

The conceptual framework is provided in Figure 1. Two studies are conducted by using a mixed method approach and multiple data sources to achieve a more complete understanding from different perspectives. Study 1 aims to test H1, H3a, and H4a through empirical analyses based on secondary data captured from TikTok. Study 2 conducts five scenario-based experiments to verify H2, H3b, H4b and further complement and validate the findings from empirical analyses.

Figure 1.

The conceptual framework.

Study 1: Empirical Study

Research Method of Study 1

Data Collection

As one of the most popular social media platforms in China, TikTok has become an important channel for destination marketing and accessing tourism information (Gan et al., 2023; X. Wang et al., 2023). This study focuses on destination videos on TikTok. We select “tourism promotion,”“city promotion,”“travel recommendation officer,” and “travel big player” as search keywords and use Python to capture the characteristics of videos. To ensure the reliability of findings, we exclude samples posted less than a week ago and exclude videos with less than 100 likes. About 1,219 videos published from May 15th, 2018 to December 8th, 2023 are obtained. Due to inconsistency between keywords and the actual content in some videos, we manually screen the videos whose actual content is not related to tourism. To exclude the influence of video plots, the videos with situational stories and special themes are also eliminated. Ultimately, 761 eligible video metadata entries are retained.

Variable Measures

Travel Intention (Intention)

Interactions (i.e., likes, comments, favorites, reposts) can be regarded as tourists’ behavioral responses driven by the videos (Hughes et al., 2019). Following prior work (Leung et al., 2022), we select the number of favorites as the measure of travel intention. To confirm the robustness of the findings, the number of likes, comments, and reposts are also considered as alternative dependent variables.

Destination Anthropomorphism (Anthrop)

As previously mentioned, the destination anthropomorphism in videos is primarily manifested through text-based anthropomorphism (Letheren et al., 2017; Pachucki et al., 2022). Therefore, we conduct the anthropomorphism analysis on the titles and narrations of the videos. The audio-text transcription interface is used to convert the narrator audio in videos into text. According to classification standards, the value of Anthrop is 1 when the destination in the video is anthropomorphic and 0 for Non-anthrop. As shown in Figure 2, this study randomly select 200 videos for manual classification. The labeled results are used as a training set, and the unannotated videos are predicted and classified by the logistic regression model.

Figure 2.

Classification process of destination anthropomorphism.

Destination Stereotype (Stereotype)

We label each destination according to its province or city, involving 55 provinces and cities. First, we summarize the classification results in previous studies for reference (Shen et al., 2019). Then, we categorize 55 provinces and cities into five distinct clusters, with each cluster being assigned to three researchers for labeling. Warmth and competence are considered in two dimensions respectively (warmth: enthusiastic, sincere; competent: competent, capable; Septianto et al., 2022). Stereotype takes the value 1 when the destination is warm and 0 for competent. The Kappa coefficients for all clusters exceed 0.80, thus successfully meeting the criteria for inter-rater reliability. For destinations with inconsistent labeled results, the category endorsed by the majority is selected as the definitive outcome.

Human Elements (Human)

We analyze the human elements by extracting keyframes. As shown in Figure 3, we utilize PySceneDetect to get the duration and key frames of each scene (He et al., 2021). Then, the content of key frame is identified through the “general object and scene recognition” function in Baidu API (X. Li et al., 2019). Based on the categories of the keywords identified, we calculate the proportion of the duration of scenes containing the “people” attribute relative to the total video length. Many studies suggested that the presence of human elements in a video must persist within the retention range of viewers’ memory to influence their viewing experience (Baddeley, 2003; Cowan, 2010). Information retention times in short-term memory are typically 15 to 30 s, and even shorter in transient memory (Jonides et al., 2008). Based on this, we refer to the work of Zhang et al. (2023) and conduct observational experiments using videos with different thresholds. Ultimately, we adopt a threshold criterion of 10%. Videos where the proportion is less than 10% are considered as videos with fewer human elements (Human = 0). Vice versa for videos with more human elements (Human = 1).

Figure 3.

Human elements identification process.

Control Variables

Drawing on the studies by Schwenzow et al. (2021) and Cascio Rizzo et al. (2023), we incorporate five dimensions of factors as control variables: video, title, narration, publisher, and published date.

Aspects of Video

Schwenzow et al. (2021) demonstrate that Scene Cut Frequency (Frequency) serves as a critical aggregate feature at the video level. This is due to the fact that more scene cuts increase short-term attention and structural complexity of media, which ultimately enhance viewers’ favorability (Lang et al., 2007). We define and measure Frequency by the average duration of each scene (Zhou et al., 2021). Scene cuts and duration are derived using the PySceneDetect tool.

Aspects of Title

Given that TikTok is a video-sharing social network, tourists tend to focus only on the most prominent information in the title. Titles consist of text, punctuation, and hashtags. Nouns are the most salient elements within the text, conveying direct information and enhancing the title richness (Berger & Milkman, 2012). Accordingly, we use the Jieba thesaurus to segment and lexically annotate the title and then measure Title Richness (Richness) by the number of nouns. Punctuation can alter the tone or style of a statement. Abundant research has indicated that titles with exclamatory or interrogative sentences capture viewers’ interest more effectively (Koh et al., 2021). We assess Title Style (Style) by examining punctuation: titles containing exclamation marks or question marks are assigned a style score of 1; otherwise, the score is 0. Hashtags, often keywords in titles or videos, increase the likelihood of a video being found in searches (Kumar et al., 2022). We measure Hashtags by counting the number of hashtags (“#” as a marker).

Aspects of Narration

Narration is the most information-dense and attention-grabbing text for tourists. Comprehensibility, appeal, and logicality are key indicators of the narration effectiveness (X. Wang et al., 2023). According to Cascio Rizzo et al. (2023), we identify Speaking Speed (Speed), Expressiveness, and Logicality as essential features of narration. Speed influences the narration comprehensibility, as a moderate pace allows tourists enough time to fully grasp the conveyed information. We measure Speed by the average number of words per second. Expressiveness impacts the narration appeal, as highly expressive narration effectively captures the tourists’ attention (Xu & Zhao, 2022). We assess Expressiveness by the average number of degree words, rhetorical words, and idioms per second. Finally, narrations with high logicality facilitate clearer information delivery. We measure Logicality by the proportion of logical words in the narration (Timoshenko & Hauser, 2019).

Aspects of Publisher

Publishers with traffic tend to get more engagement. Compared to the number of followers (De Veirman et al., 2017), the total number of likes is a more reasonable reflection of Publisher’s Authority (Authority). This study chooses the logarithm of the total number of likes received by the publisher as the measure variable (Kupfer et al., 2018).

Aspect of Published Date

As time progresses, the video engagement tends to increase (Mariani et al., 2018). Therefore, we control for Published Time (Time). Time is the duration between the published date and the deadline (measured in weeks).

Additional Control

Finally, we control for destination-level fixed effect. The sources of all variables are shown in Figure 4.

Figure 4.

Variables sources in Study 1.

Empirical Models

Intention is a non-negative counting variable, and the data is excessively discrete. Thus, this study chooses negative binary regression to test the model. To test H1, the main effect model is set as formula (1).

\begin{matrix} \ln [E (Intentio n_{ij})] = α_{0} + α_{1} Anthro p_{ij} + β Contro l_{ij} \\ + u_{j} + ε_{ij}, \end{matrix}

(1)

where i denotes video ID, and j denotes destination ID. $α_{0}$ is constant. Anthrop_ij represents the anthropomorphism status of each video. Control_ij is a vector including all control variables mentioned above. u_j denotes the destination-level fixed effect. $ε_{ij}$ is random error. By adding the moderating variables, we further present model (2) and (3) to include the interaction terms in the model.

\begin{matrix} \ln [E (Intentio n_{ij})] = α_{0} + α_{1} Anthro p_{ij} \\ + α_{2} Anthro p_{ij} \times Stereotyp e_{ij} \\ + α_{3} Stereotyp e_{ij} + β Contro l_{ij} + u_{j} + ε_{ij}, \end{matrix}

(2)

\begin{matrix} \ln [E (Intentio n_{ij})] = α_{0} + α_{1} Anthro p_{ij} \\ + α_{2} Anthro p_{ij} \times Huma n_{ij} \\ + α_{3} Huma n_{ij} + β Contro l_{ij} + u_{j} + ε_{ij}, \end{matrix}

(3)

Results of Study 1

Descriptive Statistics

According to the above measures, we process the data of variables. The descriptive statistics are shown in Table 1.

Table 1.

Descriptive Statistics Results.

Variable	Mean	Mean	Max	Min	Std
Intention	7,996.381	348	515,871	2	33,300.22
Anthrop	0.339	0	1	0	0.474
Frequency	4.570	2.092	239	0.775	11.473
Richness	4.507	4	47	1	3.744
Style	0.357	0	1	0	0.480
Hashtags	2.983	3	14	0	2.048
Speed	2.187	1.610	10.625	0.002	1.996
Expressiveness	0.111	0.069	0.522	0	0.122
Logicality	0.007	0.004	0.125	0	0.001
Authority	14.440	14.409	22.077	5.533	2.753
Time	92.820	92.429	291.571	1.143	62.306

As shown in Table 1, there is a large disparity among the tourists’ travel intention. Most videos are at a medium level of interaction (Intention: Max = 515,871, Min = 2, Mean = 348). There are relatively few videos in the sample that use anthropomorphic forms (Anthrop: Mean = 0.339). The sample coverage is wide, which meets the requirement of data diversity. VIF are all less than 5, indicating that there is no multicollinearity in the model.

Effect Test Results

The effect test results are presented in Table 2.

Table 2.

Negative Binary Regression Results.

Variable	Intention
Variable	Model 1	Model 2	Model 3	Model 4
Anthrop	1.051*** (0.148)	1.128*** (0.135)	1.053*** (0.136)	0.968*** (0.124)
Stereotype			0.003 (0.127)
Anthrop × Stereotype			0.803*** (0.247)
Human				0.289** (0.126)
Anthrop × Human				0.543** (0.254)
Control	N	Y	Y	Y
Destination fixed effect	N	Y	Y	Y
Constant	8.498*** (0.086)	3.930*** (0.909)	4.037*** (0.907)	3.832*** (0.906)
N	753	753	753	753
R ²	0.004	0.058	0.058	0.058
χ ²	53.07***	767.21***	777.63***	776.77***

p < .05. ***p < .01; standard errors in parentheses.

Model 1 presents the results of main effect without any control variables, while Model 2 incorporates additional control variables. All regression coefficients pass the significance test, and their signs and significance remain unchanged with the addition of control variables, indicating model stability. Destination anthropomorphism significantly enhances tourists’ travel intention (α = 1.128), which supports H1. Model 3 is used to verify the moderating effect of destination stereotype. According to the results, the regression coefficient of Anthrop × Stereotype (α = .803) is significantly positive, which supports H3a. Model 4 is used to test the moderating role of human elements. The regression coefficients of Human (α = .289) and Anthrop × Human (α = .543) are significantly positive. Thus, H4a is also supported.

Robustness Checks

To further test the robustness of the model, this study adopts two ways. The results are shown in Table 3.

Table 3.

Robustness Check Results.

Variable	IIndex			Intention
Variable	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
Anthrop	1.109*** (0.150)	1.022*** (0.152)	0.949*** (0.155)	1.085*** (0.132)	1.175*** (0.133)	1.136*** (0.137)
Stereotype		0.293** (0.140)
Anthrop × Stereotype		0.464* (0.286)
Human			0.360** (0.140)
Anthrop × Human			0.586** (0.290)
Control	Y	Y	Y	Y	Y	Y
Destination fixed effect	Y	Y	Y	Y	Y	Y
Constant	2.535*** (1.127)	3.688*** (1.135)	3.704*** (1.132)	3.818*** (0.874)	2.235*** (1.054)	3.893*** (0.914)
N	753	753	753	713	729	737
R ²	0.339	0.344	0.348	0.059	0.062	0.058
χ ²				754.15***	789.10***	751.98***
F	7.04^***	6.99^***	7.07^***

p < .1. **p < .05. ***p < .01; standard errors in parentheses.

Replacing the Dependent Variable

Except for favorites, there are also three interactive behaviors: likes, comments, and reposts. Referring to the calculation formula of interactive index in Qingbo index (see Equation 4), the interactive index (IIndex) is replaced as dependent variable (Chen et al., 2023). OLS model results (Model 5–7) show that the regression coefficients maintain their signs and significance. This indicates the good sensitivity of our empirical results.

\begin{matrix} IIndex = 0 . 17^{*} \ln (like + 1) + 0 . 37^{*} \ln (comment + 1) \\ + 0 . 46^{*} \ln (repost + 1), \end{matrix}

(4)

Changing Sample Size

As the platform regularly cleans the collection data of inactive users, some videos may have fewer favorites. Thus, we exclude the top 5% of the favorite data to test the model (Model 8). Promotional videos with too low video duration may be unable to convey the main information, while those with too high video duration may not attract tourists’ interest. Therefore, this study indent the data at the 1% to further analyze according to video duration (Model 9). We also reduce the sample based on publisher’s followers at the 1% (Model 10). The signs and significance of all regression coefficients remain unchanged, indicating the model is robust.

Study 2: Experimental Study

Overview of All Experiments

In Study 2, we conduct five online experiments to explore the underlying mechanisms. Specifically, Experiment 1a and Experiment 1b are employed to test the main effect of destination anthropomorphism in videos (H1) and the mediating effect of social presence on the tourists’ travel intention (H2). Experiment 2a and Experiment 2b are designed to examine the moderating effect of destination stereotypes (H3). Experiment 3 is used to understand the moderating role of human elements (H4). The overall experimental procedure is presented in Figure 5.

Figure 5.

Experimental procedure framework.

Choice of Destinations and Videos

Based on the coding results of destination stereotypes and human elements from Study 1, we first conduct pilot studies to select destinations and videos for following experiments. For Experiment 2, which aims to manipulate destination stereotypes, we first screen destinations with unanimous stereotype assessments in Study 1. Following the classification criteria, 15 researchers vote to select two destinations with the highest stereotype differences in the warmth and competence categories respectively. Ultimately, Haikou and Chengdu are selected as warm destinations, while Shanghai and Guangzhou are selected as competent destinations, which is consistent with prior studies such as Shen et al. (2019).

For Experiment 3, we examine the coding results for human elements across all videos of the above four destinations in Study 1. Only Shanghai has suitable raw videos (one with more human elements, the other with fewer human elements), so we choose the two raw videos of Shanghai as the experimental materials. Since specific destination types or human elements are not required in Experiment 1, we randomly select raw videos of the four destinations as the experimental materials to avoid bias. Referring to Gan et al. (2023), the length of all experimental videos is adjusted to around 90 s with minimal variation, which is consistent with the average and median length of the destination videos in Study1.

Experiment 1

In Experiment 1, destinations with different characteristics are selected for repeated experiments (i.e., Haikou for Experiment 1a, Chengdu for Experiment 1b). The conclusions of the two experiments are consistent. We report Experiment 1a here, and Experiment 1b is shown in Appendix C.

Stimuli

For the destination anthropomorphism manipulation, the Haikou’s raw video is made into two experimental videos by adding the anthropomorphic or non-anthropomorphic title and narration. As shown in Figure 6, the first-person title and narration are used in the anthropomorphic video, and third-person title and narration are used in the non-anthropomorphic video. The background music and the content and tone of the narrations are basically consistent to mitigate the potential impact. Detailed experimental materials are reported in Appendix B.

Figure 6.

Experimental materials comparison in Experiment 1a.

Participants, Procedure and Measures

One-factor (anthrop vs. non-anthrop) between-subject experiment is conducted. 168 Chinese participants are recruited (61.3% female, 75.6% are aged 19–49) from Wenjuanxing to complete this experiment. Appendix G provides the detailed demographic information. Participants are randomly assigned to different groups.

The basic process is presented in Figure 5. Participants read a general description at the beginning of the questionnaire, “Please imagine that you are planning to travel soon and are looking for an ideal destination. While searching online, you come across a Haikou promotional video.” Then, participants click on the link to watch the corresponding experimental video. Following this, participants are asked to answer manipulation check questions regarding their perceived destination anthropomorphism (e.g., “This destination seems to have its own mind”; α = .92; Hur et al., 2015). Next, participants comment on social presence and travel intention. Social presence is measured with six statements, “I’m immersed in the video’s scenes,”“I’m caught up in the video’s atmosphere,” and so on (α = .94; Lu et al., 2016; Gao et al., 2017). Travel intention is measured by the items, “I’m interested in this destination,”“I want to travel to this destination,”“I’m willing to travel to this destination” (α = .90; L. Su et al., 2024). All questions are based on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Finally, participants report their demographic information. All measurement items are shown in Appendix A.

Results

Manipulation Checks

As expected, the independent samples t-test results indicate that the participants can correctly distinguish anthropomorphic destination and non-anthropomorphic destination (M_anthrop = 6.15, SD = 1.02 vs. M_non-anthrop = 4.14, SD = 1.20; t(166) = 11.72, p < .001), and the manipulation check is successful.

Main Effect

A one-way ANOVA is conducted to examine the effect of destination anthropomorphism in videos. Simple effects analyses suggest that anthropomorphic destination (M_anthrop = 6.10, SD = 0.99) can encourage higher travel intention than non-anthropomorphic destination in videos (M_non-anthrop = 5.20, SD = 1.11; F(1,166) = 30.37, p < .001, $η_{p}^{2} = 0.15$ ). The one-way ANOVA on social presence shows similar results. Compared to the non-anthropomorphic destination (M_non-anthrop = 4.94, SD = 1.13), the anthropomorphic destination in videos (M_anthrop = 6.01, SD = 1.07; F(1,166) = 39.58, p < .001, $η_{p}^{2} = 0.20$ ) will evoke higher social presence. As shown in Figure 7, the results support H1.

Figure 7.

Travel intention and social presence in Experiment 1a.

Mediating Effect

PROCESS Model 4 is utilized to test the proposed mediating role of social presence. Destination anthropomorphism (anthrop = 1 vs. non-anthrop = 0) is set as the independent variable. Travel intention is the dependent variable, and social presence is the mediator variable. The control variables of gender, age, income, educational level are included as covariates. A 95% CI of the parameter estimates is obtained by bootstrapping the samples 5,000 times. As depicted in Table 4, destination anthropomorphism exhibits a significant positive effect on social presence (β = 1.04, SE = 0.17), and social presence has a significant positive effect on travel intention (β = .75, SE = 0.05). Although the direct effect is not significant (effect = 0.10, SE = 0.11, 95% CI [−0.13, 0.32]), the indirect effect is significant (effect = 0.78, SE = 0.14, 95% CI [0.51, 1.06]). This suggests that social presence fully mediates the effect of destination anthropomorphism on travel intention, supporting H2.

Table 4.

Results of the Mediation Model in Experiment 1a.

Variable	M (Presence)	Y (Intention)
Constant	5.19*** (0.37)	1.67*** (0.33)
X (Anthrop)	1.04*** (0.17)	0.10 (0.11)
M (Presence)		0.75*** (0.05)
Control	Y	Y
R ²	0.273	0.694
	F(5, 162) = 12.157***	F(6, 161) = 60.827***
	Effect	95% CI
Direct effect	0.10 (0.11)	[−0.13, 0.32]
Indirect effect	0.77 (0.14)	[0.51, 1.06]
Total effect	0.87 (0.16)	[0.56, 1.18]

***

p < .001; Standard error in parentheses.

Experiment 2

Two destinations are selected to manipulate destination stereotypes in Experiment 2a. The differences resulting from these two destinations may not only be due to stereotypes, but to the differences between the two destinations themselves. To exclude the possibility, a single destination is utilized to manipulate the destination stereotype in Experiment 2b. Experiment 2b is reported in Appendix E, and Experiment 2a is as follows.

Stimuli

For the destination stereotype manipulation, Haikou is selected to represent the warm destination, while Shanghai is selected to represent the competent destination. Then, we design two videos for each destination, using anthropomorphic or non-anthropomorphic titles and narrations. The narration content of the two videos for each destination is kept largely consistent. All videos share the same background music and narration tone. Detailed experimental materials are reported in Appendix D.

Participants, Procedure and Measures

A 2 × 2 (anthrop vs. non-anthrop × warmth vs. competence) between-subject experiment is conducted on the Wenjuanxing platform. About 265 valid responses are collected (60.8% female, 78.5% are aged 19–49). More details of participants’ demographic profile are presented in Appendix G.

As shown in the basic process in Figure 5, participants read a general description identical to that of Experiment 1. In order to further activate participants’ perceived destination stereotypes, an additional introduction about warm dimension is added to this description in the Haikou group (i.e., Haikou is a warm and comfortable city, always full of poetry). In the Shanghai group, the extra introduction about competent dimension is added to this description (i.e., Shanghai is a resource-based city, representing action and efficiency). And then, they are asked to watch the corresponding video and to indicate their perceived destination anthropomorphism (α = .91) and perceived destination stereotype (“What kind of city do you think Haikou / Shanghai is?,” 1 = very capable, 7 = very enthusiastic; Septianto et al., 2022). After this, they comment on social presence (α = .93), travel intention (α = .84), destination familiarity (“The destination is very familiar to me before taking this test,” 1 = strongly disagree, 7 = strongly agree; Artigas et al., 2015) and the demographics. The measurement scales for other variables are the same as those in Experiment 1.

Results

Manipulation Checks

The results of ANOVAs show that the participants can distinguish different destination videos and stereotypes. The anthropomorphic destinations are perceived to be more humanized than non-anthropomorphic destinations (M_anthrop = 5.85, SD = 1.18 vs. M_non-anthrop = 4.18, SD = 1.22, F(1, 264) = 126.86, p < .001). Participants who watch Haikou’s videos perceive more warmth than those who watch Shanghai’s videos, and participants in Shanghai group perceive more competence than those in Haikou group (M_Haikou = 5.11, SD = 1.32 vs. M_Shanghai = 2.93, SD = 1.24; F(1, 263) = 191.01, p < .001). The manipulations are successful.

Moderating Effect

A two-way ANOVA is used to test the moderating effect of destination stereotype on the relationship between destination anthropomorphism and travel intention. As visualized in Figure 8, the results yield significant main effects of destination anthropomorphism (F(1, 261) = 89.61, p < .001, $η_{p}^{2} = 0.26$ ) and stereotype (F(1, 261) = 31.56, p < .001, $η_{p}^{2} = 0.11$ ), and a significant interaction effect between destination anthropomorphism and stereotype (F(1, 261) = 8.69, p = .003, $η_{p}^{2} = 0.04$ ) on travel intention. Simple effects analyses reveal that for warm destinations, using anthropomorphic videos significantly increases participants’ travel intention compared to non-anthropomorphic videos (M_anthrop = 6.08, M_non-anthrop = 4.89, ∆M = 1.19, F(1, 261) = 77.95, p < .001, $η_{p}^{2} = 0.10$ ); for competent destinations, although using anthropomorphic videos also increases participants’ travel intention, the increase is less than observed for warm destinations (M_anthrop = 5.26, M_non-anthrop = 4.64, ∆M = 0.62, F(1, 261) = 21.00, p < .001, $η_{p}^{2} = 0.23$ ). H3a is supported. In order to exclude the impact of destination familiarity, destination familiarity is introduced as the covariates to repeat the two-way ANOVA. The results show that the main effects of the destination anthropomorphism (F(1, 261) = 89.49, p < .001, $η_{p}^{2} = 0.26$ ) and stereotypes (F(1, 261) = 30.61, p < .001, $η_{p}^{2} = 0.11$ ) and the interaction effect (F(1, 261) = 8.70, p = .003, $η_{p}^{2} = 0.04$ ) are still significant. The impact of destination familiarity on travel intention is not significant (F(1, 261) = 2.08, p = .151, $η_{p}^{2} = 0.01$ ), indicating it does not significantly change the patterns of findings discussed above.

Figure 8.

Travel intention in Experiment 2a.

Moderated Mediating Effect

PROCESS Model 8 is used to examine the moderated mediating effect of destination stereotype. Destination stereotype (warmth = 1 vs. competence = 0) serves as the moderator. Other variables are consistent with those of Experiment 1a. As shown in Table 5, the moderated mediating effect of destination stereotype (index of moderated mediation = 0.24, SE = 0.12, 95% CI [0.02, 0.47]) is significant. Specifically, for warm or competent destinations, the mediating effects of social presence are all significant. However, the mediating effect in the warm destination group is stronger than that in competent destination group (warmth: indirect effect = 0.51, SE = 0.11, 95% CI [0.31, 0.73] vs. competence: indirect effect = 0.27, SE = 0.09, 95% CI [0.11, 0.45]), supporting H3b.

Table 5.

Results of the Moderated Mediating Model in Experiment 2a.

Variable	M (Presence)	Y (Intention)
Constant	4.97*** (0.32)	2.88*** (0.30)
X (Anthrop)	0.59*** (0.17)	0.30 (0.11)
M (Presence)		0.46*** (0.04)
M (Stereotype)	0.40** (0.17)	0.04 (0.11)
Anthrop × stereotype	0.51* (0.24)	0.39** (0.16)
Control	Y	Y
R ²	0.309	0.575
	F(8, 256) = 14.307***	F(9, 255) = 38.261***
	Stereotype	Coefficient	95% CI
Conditional direct effect	0	0.30 (0.11)	[0.08, 0.53]
Conditional direct effect	1	0.70 (0.12)	[0.46, 0.93]
Conditional indirect effect	0	0.27 (0.08)	[0.12, 0.45]
Conditional indirect effect	1	0.51 (0.11)	[0.31, 0.74]
Index of moderated mediation		0.24 (0.12)	[0.02, 0.49]

p < .05. **p < .01. ***p < .001; Standard error in parentheses.

Experiment 3

Stimuli

Two raw videos of Shanghai are chosen to manipulate human elements. One video contains more human elements, while the other contains fewer human elements. Each raw video is designed as two videos by using anthropomorphic or non-anthropomorphic titles and narrations, forming four experimental videos. Details are shown in Appendix F.

Participants, Procedure, and Measures

About 298 participants (57.7% female, 75.5% are aged 19–49) recruited from Wenjuanxing are randomly assigned to one of the four destination video conditions (destination: anthrop vs. non-anthrop × human: more vs. fewer). Detailed demographic profile is reported in Appendix G.

Basic process is also presented in Figure 5. Participants are asked to read a general description identical to that in Experiment 1. In order to further activate participants’ perceived human elements in videos, the extra introduction about videos with more human elements (i.e., Shanghai, as a popular city, attracts a large number of tourists with its unique charm. ) or videos with fewer human elements (i.e., Shanghai, as a popular city, boasts unique urban landscapes and a rich historical and cultural heritage.) is added to this description. Participants are asked to read the description and to watch the corresponding video. Following this, they rate the scale regarding perceived destination anthropomorphism (α = .93) and perceived human elements (“What do you think of the human presence in the video?,” 1 = very inconspicuous, 7 = very obvious; Magnini & Kim, 2016). Subsequently, they complete items measuring social presence (α = .93), travel intention (α = .90), and demographics.

Results

Manipulation Checks

The one-way ANOVA on destination anthropomorphism reveals significant differences between anthropomorphic and non-anthropomorphic destinations (M_anthrop = 5.92, SD = 1.15 vs. M_non-anthrop = 4.76, SD = 1.69; F(1, 296) = 47.76, p < .001). The one-way ANOVA on perceived human elements yields a significant difference between the two videos (M_more = 5.61, SD = 0.94 vs. M_fewer = 3.77, SD = 1.42; F(1, 296) = 176.25, p < .001), which indicates that participants have noticed whether there are any human elements in videos. The manipulations are successful.

Moderating Effect

A two-way ANOVA is used to examine the moderating effect of human elements. The results yield significant main effects of destination anthropomorphism (F(1, 294) = 41.52, p < .001, $η_{p}^{2} = 0.14$ ), human elements (F(1, 294) = 10.52, p < .01, $η_{p}^{2} = 0.04$ ) and the interactive effect (F(1, 294) = 8.51, p < .01, $η_{p}^{2} = 0.03$ ) on travel intention. As illustrated in Figure 9, simple effects analyses reveal that for videos with more human elements, using destination anthropomorphism significantly increases participants’ travel intention compared to destination non-anthropomorphism (M_anthrop = 5.82, M_non-anthrop = 4.85, ∆M = 0.97, F(1, 294) = 25.80, p < .001, $η_{p}^{2} = 0.08$ ); for videos with fewer human elements, the use of destination anthropomorphism can improve travel intention, but the increase is limited (M_anthrop = 5.18, M_non-anthrop = 4.82, ∆M = 0.36, F(1, 294) = 4.10, p < .05, $η_{p}^{2} = 0.01$ ). These results support H4a.

Figure 9.

Travel intention in Experiment 3.

Moderated Mediating Effect

A moderated mediation analysis is conducted (PROCESS Model 8) to test the role of human elements, where “human elements” (more = 1 vs. fewer = 0) serves as moderator and other variables remain the same as in Experiment 1a. The results indicate that the moderating effect of human elements (index of moderated mediation = 0.39, SE = 0.18, 95% CI [0.03, 0.74]) is significant. For videos with more human elements group, the moderated mediation effect is significant (indirect effect = 0.65, SE = 0.12, 95% CI [0.41, 0.90]). However, it is not significant for videos with fewer human elements group (indirect effect = 0.26, SE = 0.14, 95% CI [−0.01, 0.54]), supporting H4b. This view is consistent with Kwak et al. (2020), that is, the presence of human elements will increase consumers’ understanding of video content.

Discussion and Implications

Main Results

This study proposes a conceptual framework to analyze the relationship and mechanism between destination anthropomorphism in videos and tourists’ travel intention. The results confirm that destination anthropomorphism in videos enhances tourists’ travel intention by stimulating the formation of social presence. Specifically, anthropomorphism in videos encourages tourists to establish social relationships with anthropomorphic destinations by offering perceived similarity (Packard et al., 2018). This enables tourists to more accurately and positively anticipate their tourism experience, thereby enhancing their travel intention (Park et al., 2021). Meanwhile, according to social presence theory, such social relationships and the immersion provided by videos are also essential conditions for evoking social presence. Therefore, social presence is proposed as the potential mechanism in this paper. The framework proposed in traditional static advertising contexts may differ from the conceptual framework presented in this paper due to the lack of conditions for forming social presence.

We further examine the moderating effects of tourists’ prior perceptions about destinations (i.e., destination stereotypes) and visual content (i.e., human elements in videos). Our findings indicate that anthropomorphism is more effective in stimulating tourists’ travel intention in videos of warm destinations compared to competent destinations. Previous research has found that warm destinations evoke relatively emotional travel motivations (Feng et al., 2022; Fiske, 2018), which aligns with the emotional engagement offered by destination anthropomorphism. The results of this paper confirm that such alignment may enhance tourists’ expectations of the destination, thereby increasing their travel intentions. Additionally, the results show that the positive effect of destination anthropomorphism is stronger when more human elements are embedded in videos. This finding further highlights the significance of incorporating human elements in promotional videos, particularly those employing textual anthropomorphism, based on previous research focusing on the effect of embedding human elements in destination images (Back et al., 2020; Nikjoo & Bakhshi, 2019; Zhang et al., 2023).

Finally, we conduct scenario-based experiments to examine the underlying mechanism behind the effect of destination anthropomorphism in videos. We find that social presence mediates the relationship between destination anthropomorphism in videos and tourists’ travel intention. The results also reveal the moderating mediation roles of destination stereotypes and human elements in videos. On the one hand, the emotional value provided by destination anthropomorphism aligns more closely with tourists’ focus on experiential attributes of warm destinations (Stepchenkova & Zhan, 2013), which reinforces tourists’ perception of social connections. Thus, anthropomorphizing warm destinations in videos can stimulate higher social presence. On the other hand, according to social presence theory, the information richness of media positively impacts social presence (Han et al., 2016; Herrewijn & Poels, 2015). As a “socially rich” visual element, our findings show that the salience of human elements influences tourists’ perception of social presence, which is consistent with previous research focusing on social presence theory (Hwang & Lim, 2015; Nikjoo & Bakhshi, 2019).

Theoretical Implications

Three theoretical contributions are made in the present research. First, as one of the first attempts to explain how anthropomorphic strategies in destination videos influence tourists’ emotional responses and behavior, this study enriches and expands anthropomorphism theory. Previous research has extensively applied anthropomorphism theory to explain the impact of anthropomorphic imagery or text in product marketing on consumer perceptions and behaviors (e.g., Letheren et al., 2017; Y. Li et al., 2023; Liu et al., 2023). These studies have largely been confined to traditional static image-based advertising formats. There has been no research exploring how destination anthropomorphism affects tourists’ perceptions and behaviors in the context of dynamic and multimodal formats, such as videos. Thus, this study extends the application context of anthropomorphism theory. On the other hand, existing research has discussed how anthropomorphism affects tourists’ behaviors by influencing their psychological connections, emotions, or perceived warmth (e.g., Chan & Gohary, 2023; Liu et al., 2023). However, these mechanisms do not fully capture the differences in how destination anthropomorphism influences tourists’ emotional responses and behaviors when promotional materials have a sense of immersion. This study identifies and validates social presence as a new influencing mechanism and pathway in the context of applying anthropomorphic strategies in videos, providing a more nuanced perspective for understanding destination anthropomorphism effects in a multimedia context.

Second, this study makes a significant contribution to the advancement of social presence theory. Prior studies on social presence focused on domains such as e-commerce, online services, and virtual communication environments, exploring the effect of factors such as interface design or information richness on social presence (e.g., Pachucki et al., 2022; Richardson et al., 2017). However, the application of social presence in the context of destination marketing remains unexplored. By identifying the formation conditions of social presence, this study analyzes how destination anthropomorphism in videos evokes social presence, thereby expanding the existing body of knowledge on the antecedents of social presence. Additionally, it has been confirmed that social presence is influenced by social relations and the information richness of media (Herrewijn & Poels, 2015; Short et al., 1976). By linking destination stereotypes to social relations and human elements in videos to information richness, we delve into the circumstances under which destination anthropomorphism engenders heightened social presence. These findings enrich the theoretical framework of social presence by introducing novel and crucial boundary conditions, while also underscoring the dynamic nature of social presence in tourism communication contexts.

Finally, this study constructs a more comprehensive theoretical framework for destination video marketing by integrating verbal elements, visual components, and tourists’ perceptions. Previous research on video marketing primarily focused on the impact of individual video features, yet it often overlooked the exploration of their underlying influence mechanisms (Gan et al., 2023; X. Wang et al., 2023; Y. Wang et al., 2024). The comprehensive framework presented by us helps future studies gain a deeper understanding of the factors influencing tourists’ travel intentions in the context of destination video marketing. Furthermore, based on anthropomorphism theory, social presence theory, and the unique context of video marketing, this study emphasizes the key mediating role of social presence, thereby providing a deeper understanding of the underlying mechanisms of these interactions. Our findings and research designs offer new insights for future research, enabling them to further explore the influence mechanisms of other elements in destination promotional videos on tourists’ perceptions and behaviors.

Managerial Implications

The findings provide new insights for DMOs seeking to optimize their marketing strategies. First, DMOs should emphasize the application of destination anthropomorphism in promotional videos. Traditional marketing strategies, which primarily focused on straightforward attraction introductions or cultural showcases, are no longer sufficient in today’s highly competitive tourism landscape (Valsesia et al., 2020). To address this challenge, DMOs can strategically incorporate destination anthropomorphism to enhance the appeal of their promotional videos. For instance, a video can portray the destination as a welcoming host who is eager to share its story. This approach can be introduced into a video series where the destination “communicates” directly to the tourists, expressing its personality traits through anthropomorphic tones or elements like friendly avatars or virtual guides.

Simultaneously, DMOs should continuously refine their anthropomorphic strategies to ensure effective and seamless integration of these elements. One practical method is conducting A/B testing on videos with different anthropomorphic tones, while monitoring engagement metrics (e.g., likes, comments) to identify what resonates most with tourists. By regularly updating content based on real-time feedback, DMOs can craft a destination image that resonates deeply and maintains long-term appeal.

Second, DMOs should tailor destination videos based on tourists’ perceived stereotypes. For warm destinations, anthropomorphic strategies that emphasize human-like traits such as friendliness, sincerity, and hospitality are more likely to be effective. For example, a promotional video for a tropical island can immerse tourists in local culture and attractions through a first-person conversational tone, creating an impression of warmth and approachability. In contrast, for competent destinations, DMOs might de-emphasize anthropomorphism and instead highlight unique natural landscapes, cutting-edge technology, or well-organized infrastructure, aligning content with the destination’s image of efficiency and reliability. Given that perceived destination stereotypes can vary significantly based on tourists’ cultural backgrounds or even specific events, DMOs must continuously evaluate changing perceptions. This can involve monitoring social media conversations, conducting surveys, or analyzing customer feedback to ensure that promotional videos align with current stereotypes. By adjusting strategies to reflect evolving tourist expectations, DMOs can ensure that their marketing messages remain relevant, authentic, and impactful.

Third, the findings of this study show that anthropomorphic destination videos with human elements will increase tourists’ visit intention. This suggests that DMOs can properly introduce figures in videos to help stimulate tourists’ visit intention (Zhang et al., 2023). DMOs can choose to include representative characters in the video to enhance the appeal and inclusion sense, such as representative characters in local folklore or tour guides with specific images. In the absence of representative figures, tourists who share a common identity with viewers can also be a good choice, making videos more cohesive, and authentic.

Limitations and Future Research

This research also has some limitations to be further discussed in the future research. First, the anthropomorphism has been subdivided into overt anthropomorphism (i.e., I, we) and subtle anthropomorphism (i.e., she, he, they; Reavey et al., 2018). We do not further subdivide this variable due to the limitations in the available video data. Future research can benefit from categorizing and comparing these forms of anthropomorphism to understand their distinct impacts on tourists’ perceptions and behaviors. Second, the role of dual anthropomorphism, combining textual and virtual anthropomorphism in destination videos, remains unexplored (Radomskaya & Pearce, 2021). If avatars are heavily adopted in destination videos in the future, understanding whether these anthropomorphic forms complement or amplify each other will be a valuable addition to the destination marketing field. Third, we primarily examine the situational effect of human elements without delving deeply into their proportions or prominence. Emerging research suggests that the percentage or visibility of human elements within visual content can significantly shape viewers’ perceptions and engagement (Zhang et al., 2023). Future studies can explore the optimal balance of human elements in anthropomorphic destination videos to maximize their effectiveness. Fourth, using TikTok as the only secondary data source may introduce platform-specific biases, such as those related to the algorithmic management or user characteristics unique to the platform. Future studies should consider incorporating data from a wider array of social media platforms such as Instagram, YouTube, or Facebook to assess whether the observed effects persist across different contexts and audiences. Finally, future research can also explore the role of tourists’ cultural differences and other demographic characteristics (e.g., gender and age) in shaping tourists’ responses to destination anthropomorphism. These studies contribute to a deeper understanding of the heterogeneous effects of destination anthropomorphism on different visitors, thereby enhancing the effectiveness of customized destination anthropomorphism strategies.

Supplemental Material

sj-pdf-1-jtr-10.1177_00472875251341304 – Supplemental material for Bridging the Distance

Supplemental material, sj-pdf-1-jtr-10.1177_00472875251341304 for Bridging the Distance by Jiahui Gao, Sai Liang, Shaolong Sun and Shouyang Wang in Journal of Travel Research

Footnotes

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments on an early version of this paper.

Author Contributions

Jiahui Gao: Data curation; Investigation; Methodology; Visualization; Writing - original draft. Sai Liang: Conceptualization; Formal analysis; Funding acquisition; Supervision; Writing - original draft. Shaolong Sun: Conceptualization; Funding acquisition; Supervision; Writing - review & editing. Shouyang Wang: Formal analysis; Funding acquisition; Supervision; Writing - review & editing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research work was partially supported by the Shaanxi Provincial Innovation Capability Support Program (2024ZC-KJXX-003), the National Natural Science Foundation of China (72101197, 72372077, 72002107, 71988101), the Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business Bniversity (SZSK202309), the Fundamental Research Funds for the Central Universities (63253074) and the Major Project in Philosophy and Social Science Research from Ministry of Education of China (No. 23JZD014).

ORCID iDs

Jiahui Gao

Sai Liang

Shaolong Sun

Shouyang Wang

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Jiahui Gao is a PhD candidate of Management Science and Engineering at School of Management, Xi’an Jiaotong University, Xi’an, China. Her research interests include tourist behaviors and destination marketing. Contact her at gaojh7@163.com.

Sai Liang is an Associate Professor in College of Tourism and Service Management at Nankai University, Tianjin, China. His research areas include peer-to-peer markets, sharing economy, online reviews, and e-tourism. His works have been published in leading journals including Tourism Management, Journal of Travel Research, Annals of Tourism Research.

Shaolong Sun (Corresponding Author) is a Professor at School of Management, Xi’an Jiaotong University, Xi’an, China. His research interests include big data mining, smart tourism, economic forecasting, and financial management. His works have been published in leading journals including Tourism Management, Journal of Travel Research.

Shouyang Wang is a Bairen Distinguished Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. He is/was a co-editor of 16 journals and has published more than 450 papers in international academic journals. His research interests include decision analysis, risk management, economic analysis and forecasting.

References

Artigas

E. M.

Vilches-Montero

Yrigoyen

C. C.

(2015). Antecedents of tourism destination reputation: The mediating role of familiarity. Journal of Retailing and Consumer Services, 26, 147–152. https://doi.org/10.1016/j.jretconser.2015.06.005

Back

R. M.

Park

J. Y.

Bufquin

Nutta

M. W. W.

Lee

S. J.

(2020). Effects of hotel website photograph size and human images on perceived transportation and behavioral intentions. International Journal of Hospitality Management, 89, 102545. https://doi.org/10.1016/j.ijhm.2020.102545

Baddeley

(2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839. https://doi.org/10.1038/nrn1201

Barnes

S. J.

(2024). Smooth talking and fast music: Understanding the importance of voice and music in travel and tourism ads via acoustic analytics. Journal of Travel Research, 63(5), 1070–1085. https://doi.org/10.1177/00472875231185882

Berger

Milkman

K. L.

(2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353

Byun

Jang

(2018). “To compare or not to compare?”: Comparative appeals in destination advertising of ski resorts. Journal of Destination Marketing & Management, 10, 143–151. https://doi.org/10.1016/j.jdmm.2018.09.003

Cascio Rizzo

G. L.

Berger

De Angelis

Pozharliev

. (2023). How sensory language shapes influencer’s impact. Journal of Consumer Research, 50(4), 810–825. https://doi.org/10.1093/jcr/ucad017

Chan

Gohary

(2023). To whom does destination anthropomorphism appeal? Power and perceived control. Journal of Travel Research, 62(4), 859–877. https://doi.org/10.1177/00472875221095215

Chen

Wang

Jin

Zhang

(2023). Comovements between multidimensional investor sentiment and returns on internet financial products. International Review of Financial Analysis, 85, 102433. https://doi.org/10.1016/j.irfa.2022.102433

10.

Cowan

(2010). The magical mystery four: How is working memory capacity limited, and why? Current Directions in Psychological Science, 19(1), 51–57. https://doi.org/10.1177/0963721409359277

11.

Cyr

Head

Larios

Pan

(2009). Exploring human images in website design: A multi-method approach. MIS Quarterly, 33(3), 539–566. https://doi.org/10.2307/20650308

12.

Delbaere

McQuarrie

E. F.

Phillips

B. J.

(2011). Personification in advertising. Journal of Advertising, 40(1), 121–130. https://doi.org/10.2753/JOA0091-3367400108

13.

De Veirman

Cauberghe

Hudders

. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828. https://doi.org/10.1080/02650487.2017.1348035

14.

Diamantopoulos

Florack

Halkias

Palcu

(2017). Explicit versus implicit country stereotypes as predictors of product preferences: Insights from the stereotype content model. Journal of International Business Studies, 48(8), 1023–1036. https://doi.org/10.1057/s41267-017-0085-9

15.

Feng

Liu

(2022). Emotional or rational? The congruence effect of message appeals and country stereotype on tourists’ international travel intentions. Annals of Tourism Research, 95, 103423. https://doi.org/10.1016/j.annals.2022.103423

16.

Fiske

S. T.

(2018). Stereotype content: Warmth and competence endure. Current Directions in Psychological Science, 27(2), 67–73. https://doi.org/10.1177/0963721417738825

17.

Gan

Shi

Filieri

Leung

W. K. S.

(2023). Short video marketing and travel intentions: The interplay between visual perspective, visual content, and narration appeal. Tourism Management, 99, 104795. https://doi.org/10.1016/j.tourman.2023.104795

18.

Gao

Liu

(2017). How does social presence influence SNS addiction? A belongingness theory perspective. Computers in Human Behavior, 77, 347–355. https://doi.org/10.1016/j.chb.2017.09.002

19.

Han

Min

Lee

(2016). Building relationships within corporate SNS accounts through social presence formation. International Journal of Information Management, 36(6), 945–962. https://doi.org/10.1016/j.ijinfomgt.2016.06.004

20.

Herrewijn

Poels

(2015). The impact of social setting on the recall and recognition of in-game advertising. Computers in Human Behavior, 53, 544–555. https://doi.org/10.1016/j.chb.2014.06.012

21.

Deng

(2021). How to “read” a destination from images? Machine learning and network methods for dmos’ image projection and photo evaluation. Journal of Travel Research, 61(3), 597–619. https://doi.org/10.1177/0047287521995134

22.

Hughes

Swaminathan

Brooks

(2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78–96. https://doi.org/10.1177/0022242919854374

23.

Hur

J. D.

Koo

Hofmann

(2015). When temptations come alive: How anthropomorphism undermines self-control. Journal of Consumer Research, 42(2), 340–358. https://doi.org/10.1093/jcr/ucv017

24.

Hwang

Lim

J. S.

(2015). The impact of engagement motives for social TV on social presence and sports channel commitment. Telematics and Informatics, 32(4), 755–765. https://doi.org/10.1016/j.tele.2015.03.006

25.

Jonides

Lewis

R. L.

Nee

D. E.

Lustig

C. A.

Berman

M. G.

Moore

K. S.

(2008). The mind and brain of short-term memory. Annual Review of Psychology, 59, 193–224. https://doi.org/10.1146/annurev.psych.59.103006.093615

26.

Koh

Kim

Vaughan

(2021). How you name your Airbnb’s title matters: Comparison of seven countries. Journal of Travel & Tourism Marketing, 38(1), 93–106. https://doi.org/10.1080/10548408.2021.1875105

27.

Kumar

Qiu

Kumar

(2022). A hashtag is worth a thousand words: An empirical investigation of social media strategies in trademarking hashtags. Information Systems Research, 33(4), 1403–1427. https://doi.org/10.1287/isre.2022.1107

28.

Kupfer

A. K.

Pähler Vor der Holte

Kübler

R. V.

Hennig-Thurau

(2018). The role of the partner brand’s social media power in brand alliances. Journal of Marketing, 82(3), 25–44. https://doi.org/10.1509/jm.15.0536

29.

Kwak

Puzakova

Rocereto

J. F.

Moriguchi

(2020). When the unknown destination comes alive: The detrimental effects of destination anthropomorphism in tourism. Journal of Advertising, 49(5), 508–524. https://doi.org/10.1080/00913367.2020.1800537

30.

Lang

Park

Sanders-Jackson

A. N.

Wilson

B. D.

Wang

(2007). Cognition and emotion in TV message processing: How valence, arousing content, structural complexity, and information density affect the availability of cognitive resources. Media Psychology, 10(3), 317–338. https://doi.org/10.1080/15213260701532880

31.

Lee

(2021). Anthropomorphism and its implications for advertising hotel brands. Journal of Business Research, 129, 455–464. https://doi.org/10.1016/j.jbusres.2019.09.053

32.

Letheren

Martin

B. A. S.

Jin

H. S.

(2017). Effects of personification and anthropomorphic tendency on destination attitude and travel intentions. Tourism Management, 62, 65–75. https://doi.org/10.1016/j.tourman.2017.03.020

33.

Leung

F. F.

Zhang

J. Z.

Palmatier

R. W.

(2022). Influencer marketing effectiveness. Journal of Marketing, 86(6), 93–115. https://doi.org/10.1177/00222429221102889

34.

Liang

Schuckert

Law

Chen

C. C.

(2017). Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations. Tourism Management, 60, 454–465. https://doi.org/10.1016/j.tourman.2017.01.007

35.

Liang

Yang

Yin

(2025). The paradox of positivity: How overly positive responses by hosts can backfire on peer-to-peer rental platforms. Journal of Travel Research, 64(5), 1085–1099. https://doi.org/10.1177/00472875241245042

36.

(2023). The effectiveness of the destination logo: Congruity effect between logo typeface and destination stereotypes. Tourism Management, 98, 104772. https://doi.org/10.1016/j.tourman.2023.104772

37.

Liu

Chen

Hou

(2023). The impact of social distancing on tourists’ preferences for anthropomorphism. Journal of Hospitality and Tourism Management, 55, 383–398. https://doi.org/10.1016/j.jhtm.2023.05.002

38.

Shi

Wang

(2019). Video mining: Measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216–231. https://doi.org/10.1016/j.ijresmar.2019.02.004

39.

Wan

L. C.

Luo

(2023). If museum treasures could talk: How anthropomorphism increases favorable visitor responses. Annals of Tourism Research, 99, 103540. https://doi.org/10.1016/j.annals.2023.103540

40.

Fan

Zhou

(2016). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior, 56, 225–237. https://doi.org/10.1016/j.chb.2015.11.057

41.

Luo

Tang

Kim

(2021). A picture is worth a thousand words: The role of a cover photograph on a travel agency’s online identity. International Journal of Hospitality Management, 94, 102801. https://doi.org/10.1016/j.ijhm.2020.102801

42.

Magnini

V. P.

Kim

(2016). The influences of restaurant menu font style, background color, and physical weight on consumers’ perceptions. International Journal of Hospitality Management, 53, 42–48. https://doi.org/10.1016/j.ijhm.2015.11.001

43.

Mariani

M. M.

Mura

Di Felice

(2018). The determinants of Facebook social engagement for national tourism organizations’ Facebook pages: A quantitative approach. Journal of Destination Marketing & Management, 8, 312–325. https://doi.org/10.1016/j.jdmm.2017.06.003

44.

Nikjoo

Bakhshi

(2019). The presence of tourists and residents in shared travel photos. Tourism Management, 70, 89–98. https://doi.org/10.1016/j.tourman.2018.08.005

45.

Pachucki

Grohs

Scholl-Grissemann

(2022). No story without a storyteller: The impact of the storyteller as a narrative element in online destination marketing. Journal of Travel Research, 61(8), 1703–1718. https://doi.org/10.1177/00472875211046052

46.

Packard

Moore

S. G.

McFerran

(2018). (I’m) happy to help (you): The impact of personal pronoun use in customer–firm interactions. Journal of Marketing Research, 55(4), 541–555. https://doi.org/10.1509/jmr.16.0118

47.

Park

J. Y.

Back

R. M.

Bufquin

Nutta

M. W. W.

(2021). Attraction, social presence, sociability, and booking intentions: The moderating role of homophily. Journal of Hospitality & Tourism Research, 45(6), 1044–1068. https://doi.org/10.1177/1096348020988898

48.

Radomskaya

Pearce

P. L.

(2021). Adding character: The role of destination mascots in tourism development. Tourism Management, 84, 104248. https://doi.org/10.1016/j.tourman.2020.104248

49.

Reavey

Puzakova

Larsen Andras

Kwak

(2018). The multidimensionality of anthropomorphism in advertising: The moderating roles of cognitive busyness and assertive language. International Journal of Advertising, 37(3), 440–462. https://doi.org/10.1080/02650487.2018.1438054

50.

Richardson

J. C.

Maeda

Caskurlu

(2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402–417. https://doi.org/10.1016/j.chb.2017.02.001

51.

Schwenzow

Hartmann

Schikowsky

Heitmann

(2021). Understanding videos at scale: How to extract insights for business research. Journal of Business Research, 123, 367–379. https://doi.org/10.1016/j.jbusres.2020.09.059

52.

Septianto

Seo

Zhao

(2022). The effects of competence and warmth appeals on luxury and sustainable brand advertising: The moderating role of construal level. Journal of Advertising, 51(3), 369–384. https://doi.org/10.1080/00913367.2021.1921633

53.

Shen

Lin

(2019). Application of the stereotype content model to destination image: Evidence from residents of mainland China. Journal of Destination Marketing & Management, 14, 100375. https://doi.org/10.1016/j.jdmm.2019.100375

54.

Short

Williams

Christie

(1976). The social psychology of telecommunications. John Wiley & Sons.

55.

Stepchenkova

Zhan

(2013). Visual destination images of Peru: Comparative content analysis of DMO and user-generated photography. Tourism Management, 36, 590–601. https://doi.org/10.1016/j.tourman.2012.08.006

56.

Huang

(2024). Does destination nostalgic advertising enhance tourists’ intentions to visit? The moderating role of destination type. Tourism Management, 100, 104810. https://doi.org/10.1016/j.tourman.2023.104810

57.

Sun

Wang

(2025). Beyond visual appeal: The impact of multisensory experience of hotel marketing and review images on sales. Journal of Travel Research. https://doi.org/10.1177/00472875241310818

58.

Sun

Wang

(2024). Let pictures speak: Hotel selection-recommendation method with cognitive image attribute-enhanced knowledge graphs. International Journal of Contemporary Hospitality Management, 36(12), 4296–4318. https://doi.org/10.1108/IJCHM-12-2023-1849

59.

(2023). How cute mascots affect relationships with tourism destinations: A moderated mediation model. Tourism Management, 99, 104782. https://doi.org/10.1016/j.tourman.2023.104782

60.

Tang

Y. T.

Ruan

W. Q.

Zhang

S. N.

Zhou

(2025). What makes you palpitate with excitement? The differentiation effect and mechanism of message appeal in tourism short videos. Journal of Travel Research, 64(4), 767–782. https://doi.org/10.1177/00472875231225396

61.

Timoshenko

Hauser

J. R.

(2019). Identifying customer needs from user - generated content. Marketing Science, 38(1), 1–20. https://doi.org/10.1287/mksc.2018.1123

62.

Valsesia

Proserpio

Nunes

J. C.

(2020). The positive effect of not following others on social media. Journal of Marketing Research, 57(6), 1152–1168.

63.

Wang

Lai

I. K. W.

Liu

(2023). Narrative or non-narrative? The effects of short video content structure on mental simulation and resort brand attitude. Journal of Hospitality Marketing & Management, 32(5), 593–614. https://doi.org/10.1080/19368623.2023.2196271

64.

Wang

Yang

Wang

Zheng

Peng

(2024). How do voice characteristics affect tourism interpretation purchases? An empirical study based on voice mining. Journal of Travel Research, 63(2), 481–495. https://doi.org/10.1177/00472875221151070

65.

Zhao

(2022). Examining the influence of linguistic characteristics of online managerial response on return customers’ change in satisfaction with hotels. International Journal of Hospitality Management, 102, 103146. https://doi.org/10.1016/j.ijhm.2022.103146

66.

Ying

Tang

Tan

Wei

(2022). Virtual reality in destination marketing: Telepresence, social presence, and tourists’ visit intentions. Journal of Travel Research, 61(8), 1738–1756. https://doi.org/10.1016/j.ijhm.2022.103146

67.

Zhang

Yang

(2023). The influence of human elements in photographs on tourists’ destination perceptions and intentions. Tourism Management, 95, 104684. https://doi.org/10.1016/j.tourman.2022.104684

68.

Zhou

Chen

G. H.

Ferreira

Smith

M. D.

(2021). Consumer behavior in the online classroom: Using video analytics and machine learning to understand the consumption of video courseware. Journal of Marketing Research, 58(6), 1079–1100. https://doi.org/10.1177/00222437211042013

69.

Zhu

Cheng

Wang

(2025). Viewer in-consumption engagement in pro-environmental tourism videos: A video analytics approach. Journal of Travel Research, 64(3), 716–735. https://doi.org/10.1177/00472875231219634

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