Immersion and Behavioral Intention in Social Media: TikTok’s ‘Travel Live Streaming’ Perspective

Travel Live Streaming

In contemporary times, the integration of tourism with information and communications technology (ICT) has become increasingly prevalent and dynamic. Both enterprises and individuals are actively seeking innovative approaches to embed ICT into their products and services (Guttentag, 2010). Within the travel industry, there has been a notable surge in attention from social media influencers (SMIs), live streamers, marketers, and destination managers toward live streaming. This trend aims to create and disseminate travel experiences and enhance the appeal of destinations, giving rise to the phenomenon of ‘travel live streaming’ (TLS). Through TLS, live streamers broadcast their travel experiences and destination scenery in real time, engaging interactively with their audience.

Live streaming is defined as a form of media that is displayed through live video (X. Wang & Wu, 2019), recorded and displayed by the live anchor in real time, and through which various types of interactions between the live anchor and the user occur (M. Hu et al., 2017). In the live platform, each live streamer has a home page where viewers are free to join the live streaming, post comments, and exit at any point in time (J. Zhou et al., 2019). Through live streaming, ordinary people can create content that is relevant to their interests and reach out to people with similar interests (Lu et al., 2019). By combining users with social media in the context of tourism, potential visitors can gain a new immersive experience (Flavián et al., 2019). The importance of travel live streaming has led to a growing body of literature, with some academics finding that travel live streaming can provide consumers with a more fun, synchronized, and social multi-sensory experience (Deng, 2019; Deng et al., 2019). Compared to traditional travel information, travel live streaming provides users with a more dynamic and comprehensive view of the destination, and the dynamic live streaming mode presents a stronger sense of interaction and participation. The huge travel market and potential tourists are making travel strategies increasingly innovative and travel live streaming is the new trend. According to Economics (2021), travel live streamers have created nearly 30,000 episodes of content, which has been played more than 60 billion times on the entire network, and the number of fans has exceeded 100 million.

Live streaming has risen as a new force especially in China (X. Liu et al., 2022). Lv et al. (2022) found that informativeness, entertainment, and interactivity all had a positive impact on immersion, immersion positively influenced viewers’ interest in tourism products and live streaming. The visuals and interactivity of travel live streaming have a positive impact on tourism consumers’ willingness to engage, with spatial presence and flow experience playing a mediating role (Ye et al., 2022). T.-T. Yang et al. (2023) examine the motivations for virtual travel and the differences between virtual and traditional travel. Laddering interviews with 32 respondents were conducted, and they found that self-satisfaction was the most important value-led motivation. For travel live streamers, F. Li et al. (2022) used interviews to examine the motivations of live streamers to share their travel experiences, their study found that information sharing and entertainment were identified as the most important motivators. The local tour guides (LTGs) used sharing economy platforms to arrange flexible tour guide services (Shang et al., 2023). The impact of the media on the tourism industry exists in a wide range of areas. Some academics have found a high correlation between film-induced tourism and theme park attractions (Florido-Benítez, 2023). The literature on the social interaction, telepresence, immersion, user satisfaction, and behavioral intentions of travel live streaming is still a research gap for the time being.

Theoretical Background

The uses and gratifications theory is one of the most popular and important theories in communication (Rauniar et al., 2013), it is related to social media (Whiting & Williams, 2013). The theory examines the psychological and behavioral utility of mass communication to people by analyzing the audience’s motivations for media exposure and what needs these exposures satisfy. Research has shown that the satisfaction gained is a good predictor of user use and repeated use of media (Kaye & Johnson, 2002; Palmgreen & Rayburn, 1979). Gan and Wang (2015) used the uses and gratifications theory to find that users will have different levels of satisfaction when using different social media. Recent research by Omar and Dequan (2020) has also applied the uses and gratifications theory to better understand the use of TikTok. In this study, to explore the motivations and influences of users watching live travel on TikTok, we draw on the uses and gratifications theory for theoretical support.

Mehrabian and Russell (1974) proposed the stimulus-organism-response (S-O-R) model, it is based on the behaviorist stimulus-response (S-R) model, which evolved as people’s psychological understanding changed and they gradually realized that human information processing starts with a physical stimulus, followed by the absorption of the external stimulus through the senses, which is worked on by the nervous system to make a decision and then produce the output of an action response, that is, the S-O-R model (Mehrabian & Russell, 1974). The key point of the theory is that the individual’s response to environmental stimuli does not directly influence the individual’s behavior, but rather acts as a mediator through emotions to influence the final behavior (Bitner, 1992; S.-B. Kim et al., 2014). In the tourism industry, the S-O-R model can be very effective in explaining the relationship between stimuli, processes, and responses (Jani & Han, 2015). Ying et al. (2022) extended the S-O-R model by including telepresence and social presence as stimuli and elucidating the interplay of these factors in jointly shaping users’ perceptions and willingness to visit. The results found that VR with a higher sense of immediacy more positively influenced visit intention. Han et al. (2023) applied the S-O-R model to discover that telepresence and the consumers’ interactivity is the experience of a new technology, called VR shopping mall environment, which affects enjoyment and behavioral intention. Using the S-O-R model as a theoretical support, this study attempts to contribute to the extension of the S-O-R model by considering social interaction and telepresence as stimuli, immersion and user satisfaction as the organism, and the user’s behavioral intention as the response.

Kardefelt-Winther (2014) proposed the compensatory Internet usage model (CIUM). This theory attempts to clarify people’s psychological characteristics, suggesting that people make up for their unrealized offline needs through specific online activities. The empirical literature documents a wide range of compensatory strategies that facilitate the use of different online activities. For example, self-presentation, belonging, social gratification, entertainment, and information are psychological aspects associated with social media use (G. M. Chen, 2015; Seidman, 2013). In the context of travel, users can compensate for their regret of not having been to the travel destination by watching tourism live-streaming content. Simultaneously, they can create their immersive feelings through the live-streaming content. The internet compensation model can be used to explain the remote presence and immersion of users when watching travel live streaming, and the relationship with user satisfaction.

Effect of Social Interaction on Immersion and User Satisfaction

Social interaction makes users change from passive to active mode. Specifically, users change from being bystanders to becoming participants, and they produce a lot of new user-generated content through interaction and other behaviors (Szolnoki et al., 2018). The fast-growing live-streaming industry has been accepted by the tourism industry, and new forms of interaction and communication have emerged (Xie et al., 2022). Travel live streamer can record their travel activities in live streaming and interact with viewers in real-time (Deng et al., 2021). Interaction mainly refers to the interaction between the audience and live streamers. Audiences interact by chatting, praising, virtually gifting, or guiding anchors. For live streamers, online interaction can pay attention to the audience’s speeches and needs in real time by acknowledging the audience’s existence and responding to the audience in time with their favorite language and behavior (Deng et al., 2022). This opportunity can be utilized in research and education where the public can watch and be involved in real-time conversations about live-streaming events (Battrawi & Muhtaseb, 2014). Different from traditional media, users of new media (such as social media) can interact with netizens on online platforms. Several prior studies have confirmed that one of the biggest motivations for users to use websites is social interaction (C.-Y. Huang et al., 2007; Nardi et al., 2004). Pentina et al. (2008) pointed out that people use information technology to connect with others and establish relationships with people with common interests.

The social interaction theory explains the interactive relationship between users and live streamers (Hou et al., 2019). It clarifies the interactive processes between people and is used to predict interactive behavior in different social settings (Varey, 2008). In the rapidly developing internet market, interactivity is important as it can improve the interflow quality and reduce the uncertainty of consumption (B. Li et al., 2018; Ou et al., 2014).

Users are easily immersed in the internet experience (Gutierrez et al., 2008; Raptis et al., 2018), where they feel that they are communicating with their friends in a real environment (T. H. Jung & tom Dieck, 2017; Mennecke et al., 2011). Choi et al. (2016) study on the impacts of social media marketing in the hotel industry found that social media marketing had no significant influence on interactivity and user satisfaction. However, Ramanathan et al. (2017) argued that when social media is used in retail network operation and marketing, interactive comments on social media greatly affect customer satisfaction. Therefore, the following hypotheses are proposed:

Effect of Telepresence on Immersion and User Satisfaction

Steuer (1992) stated that telepresence occurs when a person feels transferred to an intermediary environment created by technology (such as computers and television). It can create and display psychological states and interactive feelings within a virtual environment (Sukoco & Wu, 2011). Live streaming, in particular, gives people an immersive feeling (Mueser & Vlachos, 2018), and as this increases, users may feel that they are in a virtual environment. Such ‘immersion’ concerning psychological involvement and the concrete element of behavioral participation (Biocca et al., 2003) can affect users’ satisfaction during media communication. In this study, telepresence is considered different from immersion. As defined by Biocca and Delaney (1995), immersion is ‘the extent to which the virtual environment submerges the user’s perceptual system with computer-generated stimuli’. In other words, immersion is the degree to which all modes of sensory input are manipulated by the medium.

Immersion on the Internet is made possible through various activities (Novak et al., 2000; Pearce, 2009), such as social interaction. Hoffman and Novak (1996) suggest that telepresence increases the subjective intensity of consumers’ immersion. Pelet et al. (2017) studied the influence of immersion on social media. And expanded the richness of the immersion theory by developing a model, they found that telepresence had a positive impact on the five dimensions of immersion, and indicated that the passage of time is forgotten when using social media. Wu and Wang (2014) found that the lasting influence of telepresence on satisfaction is reasonable. Zhu et al. (2023) found that mental imagery and vividness positively predict visitor telepresence and satisfaction. At the same time, VR ads with a higher level of immersion will trigger a stronger intent to (re)visit (Ying et al., 2022). Telepresence and social presence positively influence customer purchase intentions through trust and perceived usefulness (G. Liu et al., 2022). C. Chen and Yao (2022) believe that a highly immersive media experience combined with the in-depth transmission of the narrative enhances the viewer’s positive attitude toward the overall experience. Against this background, we developed the following hypotheses:

Effect of Immersion on User Satisfaction and Behavioral Intention

Consumers’ participation in enjoyable tasks will make them immersed, thus achieving the best performance (Ilsever et al., 2007). In recent years, immersion has been gradually applied to the research of information systems and the Internet. Webster et al. (1993) opine that immersion should include four components: control, concentration, curiosity, and inner interest. Immersion is an abstract term, a metaphorical expression of the body’s immersion under water. In essence, we have a similar psychological immersion experience after jumping into the ocean or swimming pool, which occupies our sensory organs and concentration. Scholars emphasize that to measure immersion, we must regard it as a psychological state, that is, users’ subjective perception (Witmer & Singer, 1998).

Numerous studies have found that immersion can effectively and significantly predict user satisfaction (C.-C. Chang, 2013; Y. P. Chang & Zhu, 2012; Xin Ding et al., 2010). Immersion has also been proven to be an important predictor of audience satisfaction (D. Kim, 2016; M. Kim, 2011). In addition, immersive video content can enhance emotional participation and attitudes toward environmental behaviors (Fonseca & Kraus, 2016). In their research on VR, T. H. Jung and tom Dieck (2017) found that immersion affected users’ behavior intention repeatedly. Focusing on the leisure and tourism aspects of these studies, most of the results show that immersion has a positive impact on satisfaction and behavioral intention (Hansen & Mossberg, 2017). We thus developed the following hypotheses:

User Satisfaction and Behavioral Intention

‘Consumer’s satisfaction response’, which involves judging the function of a product or service or the product or service itself, provides the pleasure level of satisfaction related to consumption (Oliver, 1997). In terms of social media, the feeling of ‘being there’ may have an impact on the formation of consumers’ best online experience and their willingness to re-use digital media (Y. Jung, 2011). When the experience of social media websites matches users’ expectations, it will generate satisfaction, which will satisfy online users and then generate behavioral intentions (Hwang et al., 2018).

Behavioral intention has been widely discussed in research on hotels and tourism (Lim & Ayyagari, 2018), because it is of great significance to identify consumers’ actual purchasing behaviors (Hsu & Huang, 2012; Pelet et al., 2017; L. Wang et al., 2015). In addition, Oliver (2014) described behavioral intention as ‘the established possibility of engaging in behavior’. In this case, behavioral intentions include revisiting intentions and public praise intentions (Jani & Han, 2011). Researchers believe that the reason for behavior intention is customer satisfaction (Baker & Crompton, 2000). H. C. Lee et al. (2019) believed that satisfaction would significantly affect behavioral intention, which was consistent with the results of C.-F. Chen and Chen (2010). Therefore, we propose Hypothesis 7 to illustrate this relationship (Figure 1):

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Figure 1.

Proposed research model.

Participants and Research Method

Quantitative methods are considered to involve objective, formal, and systematic processes in which data are used to quantify or measure the object of study (Teddlie & Tashakkori, 2003), this study employs a quantitative research design. Users watch travel live streaming on TikTok, mostly of destinations that they have never been to. Such users are the participants of this study. This study attempts to divert attention from the promotion methods of tourism videos used in the past (Chua & Chang, 2016; Hogan, 2010; Seidman, 2013) to the novel content and perspectives of ‘travel live streaming’, to understand the relationship between users’ psychology and behavioral intention and to provide insights toward the future development of related industries.

Questionnaire Development and Measurements

First, at the beginning of the questionnaire, an introductory cover letter was provided, which outlined the objectives of the study and assured participants of the confidentiality and anonymity of their responses. Participants were informed that their participation was entirely voluntary, that no personally identifiable information would be collected, and that their responses would be used solely for academic research purposes. By proceeding with the questionnaire, participants were deemed to have given informed consent. Second, the questionnaire used in this study was divided into three parts. The first part, which aimed to determine qualified research participants, comprised a screening question. Only users who have watched at least one travel live stream on TikTok and have never been to the travel destination could continue to fill in the questionnaire. The second part evaluated five aspects of this study, namely, social interaction, telepresence, immersion, user satisfaction, and behavioral intention, and consisted of 20 questions in total. The third part is the demographic section.

In this study, we used scales that provide evidence of their psychometric quality in previous studies. Each item is scored on a 7-point Likert scale, with ‘strongly disagree’ represented by 1 and ‘strongly agree’ represented by 7. The development of the measurement scales for the research constructs was based on relevant literature. The internal consistency reliability coefficients (Cronbach’s α) for the constructs were as follows: social interaction (.838), telepresence (.820), immersion (.884), user satisfaction (.818), and behavioral intention (.858). The overall Cronbach’s α for the tool was .925, indicating high reliability. We measured social interaction using the scale developed by Ko et al. (2005), with a total of four items, an example item is ‘I wonder what other people said’. The scale, although distant, measures consumers’ motivation to use the Internet and fits well with the current study. Telepresence was measured by three items formulated by M. Lee et al. (2020). A sample item is ‘I feel I am in the place’. Immersion was measured using six items developed by Engeser and Rheinberg (2008), an example item is ‘I don’t notice time passing’. User satisfaction was measured using a tool developed by Rauniar et al. (2009) and Rauniar et al. (2013). The behavioral intention was measured using four items developed by Y.-C. Huang et al. (2013, 2016), an example item is ‘I would like to visit the place that I saw in the tourism-related VR activity’.

The last part of the questionnaire included items to collect participants’ demographic information. To facilitate online respondents to understand the questions more clearly and easily, the full questionnaire was adapted and translated into Chinese. To make the original scale more relevant to this study, three experienced translators were first invited to translate the original scale into Chinese independently of each other, and then to discuss and reach a consensus. Then, two translators with good language skills and no knowledge of the original scale were invited to translate the Chinese version of the scale into English. After further discussion, comparison, analysis, and revision between the authors and the five translators, a Chinese scale was finally obtained that was more appropriate to the context and semantics of the study. For example, one item designed for measuring social interaction revising from ‘I wonder what other people said’ to ‘I want to see what other audiences said when watching the travel live streaming in TikTok’. This is more suitable for the travel live streaming, and it is easier for the participants to understand.

Sampling, Data Collection, and Analysis

Confirmatory Factor Analysis (CFA)

The confirmatory factor analysis (CFA) results indicated good model fit: χ²/df = 1.264, RMSEA = 0.031, GFI = 0.929, AGFI = 0.907, CFI = 0.985, TLI = 0.982, IFI = 0.985, PRATIO = 0.842, PNFI = 0.785, PCFI = 0.829. Therefore, all goodness-of-fit indices—encompassing absolute fit indices, incremental fit indices, and parsimonious fit indices—satisfy the criteria established by the academic community (L. T. Hu & Bentler, 1999). This confirms that the model demonstrates an adequate level of goodness of fit. The standard factor loadings for each item range from 0.665 to 0.890, all of which exceed the academic standard of 0.5 (Byrne, 2010). In addition, the combined reliability (CR) of each dimension was greater than 0.7 and the average variance extracted (AVE) was greater than 0.5 (Nunnally, 1978). The detailed results are presented in Table 1.

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Table 1.

Results of the Confirmatory Factor Analysis (N = 274).

Constructs/items	Mean	SD	Skew	Kurt	Cronbach’s α	Standard loadings	CR	AVE
Social interaction (SI)	4.710	1.314			.838		0.841	0.571
I want to see what other audiences say when watching the travel live streaming on TikTok.	4.930	1.792	−0.582	−0.642		0.872
I want to follow the content and process of the anchor’s tour.	4.690	1.498	−0.124	−0.812		0.747
I hope to express my views freely in the comment area of the live-streaming platform.	4.620	1.546	−0.109	−0.661		0.665
I want to meet people who share my interests in the TikTok travel live-streaming platform.	4.590	1.557	−0.178	−0.585		0.724
Telepresence (TP)	4.883	1.369			.820		0.823	0.609
TikTok’s travel live streaming has created a new virtual world for me as if I really exist in this place in the live streaming.	5.130	1.732	−0.698	−0.416		0.857
When the travel live-streaming I watched ended, I felt as if I had returned to the real world after traveling with the host.	4.760	1.567	−0.153	−0.812		0.740
Watching TikTok’s live travel streaming is a supplement to my real travel.	4.760	1.480	−0.166	−0.711		0.739
Immersion (IM)	4.592	1.255			.884		0.884	0.562
When I watched the travel live streaming, I didn’t notice that time was passing.	4.880	1.806	−0.475	−0.710		0.848
I usually concentrate on the live content.	4.490	1.505	0.006	−0.512		0.678
I was completely lost in thought.	4.540	1.475	−0.027	−0.615		0.737
It’s easy for me to concentrate when watching travel live streaming.	4.580	1.511	−0.096	−0.550		0.719
My mind is clear.	4.560	1.557	−0.124	−0.566		0.760
My thoughts and actions are completely spontaneous.	4.510	1.581	−0.039	−0.665		0.744
User satisfaction (US)	4.754	1.414			.818		0.822	0.607
I like TikTok’s live travel function very much.	4.970	1.799	−0.739	−0.375		0.851
The content (text, image, video, etc.) of TikTok travel live streaming meets my needs.	4.690	1.581	−0.308	−0.446		0.721
I am satisfied with the social service of TikTok’s travel live streaming.	4.600	1.564	−0.159	−0.592		0.759
Behavioral intention (BI)	4.652	1.388			.858		0.859	0.606
I want to watch the live streaming related to tourism again.	4.920	1.899	−0.744	−0.461		0.890
I will recommend the live content related to travel to people around me.	4.630	1.599	−0.124	−0.698		0.716
I want to tell others something positive about the content of live travel.	4.530	1.583	−0.164	−0.566		0.747
I will share positive things about live travel with others.	4.530	1.529	−0.245	−0.483		0.749

Note. SD = standard deviation; Skew = skewness; Kurt = Kurtosis; CR = composite reliability; AVE = average variance extracted.

Discriminant Validity

Table 2 illustrates that the square root of the AVE for each dimension surpasses the correlation coefficients among these variables, thereby confirming the satisfactory correlation and discriminant validity of the study (Hung et al., 2010).

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Table 2.

Correlation and Discriminant Validity Results.

Constructs	BI	US	IM	TP	SI
Behavioral intention (BI)	.778
User satisfaction (US)	.651	.779
Immersion (IM)	.570	.588	.750
Telepresence (TP)	.633	.649	.590	.780
Social interaction (SI)	.582	.575	.527	.554	.756

Note. SI = social interaction; TP = telepresence; IM = immersion; US = user satisfaction; BI = behavioral intention. The bold diagonal values represent the square root of the AVE for each construct.

Structural Equation Model Results

The overall model demonstrates a good fit with the data according to the model fit criteria outlined by L. T. Hu and Bentler (1999): χ²/df = 1.369, RMSEA = 0.037, GFI = 0.923, AGFI = 0.900, IFI = 0.979, CFI = 0.978, TLI = 0.975, PRATIO = 0.853, PNFI = 0.789, PCFI = 0.834. Furthermore, the test results of the structural equation model and the validation criteria described above provide positive evidence supporting seven hypotheses. Several key hypothesis testing results are presented in Figure 2 and Table 3. For viewers of TikTok travel live streaming, social interaction positively influenced immersion and user satisfaction, telepresence positively influenced immersion and user satisfaction, immersion positively influenced user satisfaction and behavioral intention, and user satisfaction positively influenced behavioral intention.

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Figure 2.

Structure model and path coefficient.

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Table 3.

Hypotheses Testing.

Hypothesis path		β	t	p	Result
H1	SI—>IM	.294	3.957	<.001	Support
H2	SI—>US	.265	3.556	<.001	Support
H3	TP—>IM	.434	5.501	<.001	Support
H4	TP—>US	.405	4.745	<.001	Support
H5	IM—>US	.207	2.710	.007	Support
H6	IM—>BI	.271	3.762	<.001	Support
H7	US—>BI	.526	6.618	<.001	Support

Note. SI = social interaction; TP = telepresence; IM = immersion; US = user satisfaction; BI = behavioral intention.

Conclusion

Based on our results, we found that when users watch travel live streaming, social interaction and telepresence are important factors for user immersion and satisfaction. Users want to interact with anchors and other users on the live-streaming platform, and they believe that such interaction can make the viewing experience more vivid and memorable. The experience is comparable to users taking a journey and returning to the real world at the end of the live stream. When users experienced strong telepresence, they forgot about the passage of time and were more likely to focus and less likely to be completely lost in thought. We propose that such experiences can make users feel good about the functions of the TikTok platform and live streaming and think that such live streaming of travel can meet their individual needs.

In addition, when users are immersed in ‘cloud travel’ with the anchor, they are satisfied with this experience and want to share it with more people, and in the future, they hope they can visit the destination in person. China’s live-streaming industry has received considerable attention and is innovative in many ways (M. Zhou et al., 2021). Parallelly, the live-streaming industry has changed the lifestyle and travel mode of stakeholders and introduced more fashionable content (Lu et al., 2019). With the emergence of live streaming, the tourism industry has entered a new period and mode.

Theoretical Implications

Ledbetter (2014) argues, that since the media era, the association between the media and the information disseminated has become stronger, and its related theories need to be subdivided further and developed in a more detailed manner. Due to the short development history of network streaming, this new form of social media has not received enough attention in academia (Wongkitrungrueng & Assarut, 2020). With the development of the Internet, although research on social media has attracted attention in various fields, travel live streaming has important research implications as a new form of tourism in the face of the loss, stress, and uncertainty associated with the COVID-19, and an increasing number of scholars have begun to study the live streaming of tourism (Zhang et al., 2021).

First, this study extends the application of the S-O-R model in the social and tourism industry. In a previous study, Ying et al. (2022) drew on the S-O-R model to find that the social presence and telepresence of the VR experience creates intent to visit intent, and this study extends the S-O-R model for immersions in the field of travel live streaming. Ming et al. (2021) confirms the correlation between the S-O-R model and enhanced telepresence and consumer trust, with higher levels of remote presence increasing consumer trust when users purchase online via live streaming. This study expands the theoretical value of the field of live tourism in the context of related theories.

Second, on the one hand, this study makes a relevant theoretical contribution to the immersion experience of travel live streaming. In previous studies, some scholars believed that VR experience promoted the formation of positive attitudes and behavioral outcomes (Tussyadiah et al., 2018). VR technology is like quick and easy social media live streaming. However, few studies have examined social media immersion experiences in the context of travel. In this context, some important questions emerge: Why are people keen on live streaming in the media era? Will the immersion of live streaming affect future life? Prior research on live streaming has mostly focused on webcasting services, and the motivation of enterprises’ network streaming and audience participation is discussed from the level of external factors (Cai & Wohn, 2019; C.-C. Chen & Lin, 2018). On the other hand, although immersion has been widely used in today’s research and technology, relatively few studies have examined the immersion process and the factors affecting it (Shafer et al., 2019; Weech et al., 2019). The impact of short-term immersion experiences on the subsequent long-term personal and professional lives of participants is not well known (Zorn, 1996), and although they are important, few studies have investigated social media immersion experiences in the context of travel. In particular, theoretical research that considers social media, user psychology, immersion experience, user satisfaction, and behavioral intention is limited. The current study aims to fill these knowledge gaps in the literature and inform future research in the field.

Finally, this study generates a theoretical contribution to the field of psychological research on TikTok. Despite TikTok being a distinct and highly successful media software in the world and its success in terms of the number of users, psychological research on its users is lacking (Montag et al., 2021; Shao & Lee, 2020). In particular, the psychological changes of TikTok users on certain occasions, this study explores the changes in user immersion experience, satisfaction, and behavioral intention through social interaction and telepresence.

Practical Implications

The tourism industry is currently facing various types of challenges and problems, and in this context, the present study is of practical significance. The emergence of live streaming provides a way for potential tourists to experience scenic spots, museums, and festival activities before making a visit decision. The coexistence and development of social media and tourism is an inevitable trend at present. People are keen on tourism, and they also enjoy watching travel live streaming. Indeed, social media enables audiences access to all kinds of information about the destination without leaving home. The advantages of social media have been evident, across many fields such as marketing, management, medical care, and education (Gikas & Grant, 2013; Latif et al., 2019; Nadaraja & Yazdanifard, 2013; Roebuck et al., 2013; Slovensky & Ross, 2012), and the ways to enhance the value of social media in tourism has already become a popular research topic globally. This study focused on the relationships between users’ experience, satisfaction, and behavior intention in live tourism streaming, and expanded the general definition of live tourism to live streaming with real-time explanation in tourist attractions.

First, this study has a degree of relevance for destination managers, marketers, and stakeholders. It can help them develop more attractive tourism policies and promotional programs. What potential visitors say on the air is noteworthy, and immersive viewing and interaction can easily enhance satisfaction, entice viewers to continue watching, and build trust with the anchor and the destination. It is worth mentioning that social media should not be blindly combined with tourism but should be combined with the actual situation of local users to attract more potential tourists and business opportunities through live travel.

Second, how can this study help users immerse themselves in the live travel experience and enhance their satisfaction when watching live travel? Social interaction is worth mentioning, and its combination with remote proximity will easily happen with immersion and satisfaction enhancement. In addition, this study also provides practical inputs for travel anchors on social media, who should pay attention to strengthening interaction and communication with users and respond to users’ questions in a timely manner. Further, a more colorful and vivid way of explanation should be adopted to immerse the audience in the anchors’ explanations as much as possible. In this way, audience satisfaction can be enhanced, and more users will recognize and appreciate the travel anchor, thereby leading to an increase in exposure and popularity of the account.