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Article

Does Social Media Make Tourists More like Special Forces? The Impact of Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism

by
Jianzhen Zhao
1,
Yiyan Wang
2,
Shuaifang Liu
2,
Jun (Justin) Li
2,3 and
Qinglin Wang
4,*
1
College of Business Administration, Guangdong Baiyun University, Guangzhou 510450, China
2
School of Tourism Management, Higher Education Mega Center, South China Normal University, Guangzhou 510006, China
3
Faculty of International Tourism and Management, City University of Macau, Taipa, Macau
4
Faculty of Humanities and Arts, Macau University of Science and Technology, Taipa, Macau
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 10033; https://doi.org/10.3390/su162210033
Submission received: 30 September 2024 / Revised: 12 November 2024 / Accepted: 12 November 2024 / Published: 18 November 2024
(This article belongs to the Section Tourism, Culture, and Heritage)
Browse Figures
Versions Notes

Abstract

:
In the post-COVID-19 era, tourism and cultural industries have begun to bounce back, and their “revenge tourism” desire has forced “Special Forces-style tourism”, which was popularized throughout Chinese social media in 2023. This study explores a current knowledge gap in understanding how social media inspires Generation Z’s engagement in Special Forces tourism and implications for practical sustainable tourism. By leaning on the SOR model, Social Identity Theory, and Social Learning Theory, this study investigates how supportive and interactive social media environments shape the sustainable engagement intentions of Generation Z. We experimentally find that perceived supportive communication about sustainability issues significantly inflates sustainable engagement intentions in high-interactivity settings, by encouraging flow experiences and vicarious reinforcement that facilitate socially responsible travel decisions. Key findings include identification of supportive online interactions that can facilitate sustainable tourism among born-digital travelers, who enable informed, socially and environmentally responsible tourism behaviors.

1. Introduction

The tourism industry is undergoing significant changes in the digital era, especially with the rise of social media platforms shaping travel behaviors. Among these changes, “Special Forces-style tourism”, characterized by high-intensity, rapid visits across multiple destinations, has become increasingly popular among Generation Z, a demographic distinguished by their digital fluency and desire for personalized experiences. However, while previous studies highlight the general impact of social media on tourism, limited research delves into the specific ways that perceived supportive communication on these platforms affects Generation Z’s engagement in Special Forces-style tourism.
Against this backdrop, it is worth exploring how social media has fueled the rise of Special Forces-style tourism and its role in shaping the travel behaviors and social interactions of younger generations [1]. In the era of “mobile + internet”, the rise of Special Forces-style tourism as an emerging travel culture has become increasingly prominent, driven by social media, and has evolved into a unique cultural symbol and social phenomenon [2,3]. The formation of this phenomenon is inextricably linked to user interactions on social media. Through interactive behaviors such as commenting, liking, and sharing, users not only expand the breadth and depth of information dissemination [4] but also actively participate in co-creating the emerging experience of Special Forces-style tourism [5]. Research on social media user interactions encompasses a wide range of topics, from the characteristics of interaction (such as immediacy, autonomy [6], and indirectness [7]) and interaction patterns (such as openness [8] and diversity [9]) to the integration of these interactions with other functions (such as information retrieval [10], dissemination [11], and group interactions [7]). These topics have become focal points of academic inquiry. One of the most significant aspects of user interaction is perceived supportive communication [12]. Currently, perceived supportive communication among social media users is primarily facilitated through channels such as comments, private messages, and likes. These forms of interaction enable users to experience emotional support and share information in both public and private exchanges. Furthermore, daily observations of social media phenomena would suggest that supportive communication about these platforms not only reinforces users’ confidence in their travel decisions but also activates their desire to participate. For example, if Generation Z consumers post Special Forces-style travel experiences on social media and receive supportive responses from others, perceived supportive communication will facilitate their travel intention and behavior. For example, users may be encouraged to try new travel styles or post more travel experiences after receiving likes and comments from friends. Consequently, the question of whether and in what way perceived supportive communication about social media influences Generation Z’s engagement in Special Forces-style tourism remains to be addressed.
Basic contact among the users of this medium has also been found to have several effects on tourists. It has been observed that “new” social media is becoming increasingly the first channel for tourist information, re-establishing new tourism models based on the principles of interaction and sharing. For example, Dedeoğlu et al. (2020) [13] identified that user-generated content in various types of social media has influenced the decisions of tourists either for or against tourism. It offers critical reviews, virtual tourism experiences, and unbiased information that is very significant in destination choice and development of the trip itinerary, hence affecting the process of planning a trip. In the same vein, Armutcu et al., 2023 [14], indicated that social media and digital marketing have a high influence on the behavior of tourists. In addition, the influential drivers of tourists’ travel intention and eWOM behavior will encompass online tourism content quality, tourist satisfaction, as well as digital marketing interaction or experience. Most importantly, partially sharing these antecedents is perceived supportive communication. To clarify, perceived supportive communication enhances tourists’ confidence in making decisions through trust development, emotional support, and sharing authentic experiences [15]. Such interactions strengthen the role of social media as an important source of information about tourism and, at the same time, a partial substitute for traditional tourist information channels. In these papers, investigators researched how social media creates a thorough transformation in the pattern of tourists’ behavior and travel experiences because of enhanced interactivity, speedier information diffusion, and virtual communities.
This research addresses this knowledge gap by analyzing the impact of interactive versus non-interactive social media platforms on the tourism intentions of Generation Z consumers, through flow experience and vicarious reinforcement. Anchored in the Stimulus–Organism–Response model, complemented by Social Identity Theory and Social Learning Theory, this research aims to identify motivational drivers toward better tourism intention within supportive virtual communities. Theorizing perceived supportive communication as a means to analyze its influence, this research offers new insights into how social media fosters an emerging dynamic tourism culture that resonates especially with the younger generation. Additionally, our findings contribute practical insights for tourism marketers interested in engaging this audience through supportive, interactive digital environments. This will be the scientific purpose of our research: to investigate those mechanisms through which perceived supportive communication on social media influences Generation Z’s intentions to engage in Special Forces-style tourism. Hence, this present work will attempt to fill some critical gaps in understanding digitally driven social interactions that shape tourism behaviors particularly among young consumers who are deemed digitally native and highly influenced by interactive platforms. Using the Stimulus–Organism–Response (SOR) model, Social Identity Theory, and Social Learning Theory, this research investigates how supportive communication fosters a sense of belonging, influences social learning, and promotes engagement intentions. By examining these processes, this study contributes to a broader understanding of social media’s role in modern tourism behavior, with potential applications for both theory and practice, such as optimizing tourism marketing strategies targeted at Generation Z.

2. Theoretical Background and Hypotheses

2.1. The Main Effect of Perceived Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism

Current research on perceived supportive communication primarily focuses on the medical field, where it refers to the emotional, informational, and tangible support that individuals receive through interactions with others, which helps improve their psychological state [16]. When extended to the realm of social media, perceived supportive communication manifests in behaviors where commenters and supporters exchange and seek support by posting supportive messages and facilitating dialogues within online platforms [17]. Within the framework of the Stimulus–Organism–Response (SOR) theory, external environmental stimuli act upon the internal state of Generation Z, and through interactive communication and observational learning, individuals adjust their response mechanisms to these stimuli, thereby influencing their behavioral intentions [18]. Specifically in the context of tourism, support and recognition from one’s social group become key factors in tourists’ decision-making processes, while the presence and attitudes of reference groups also play a critical role in shaping these decisions [19]. Previous research has revealed that reading others’ comments on social media is a key step in obtaining online social support [20], and the level of interaction within comments is a crucial dimension for measuring the strength of this support [21]. With the continuous recovery of the tourism market, social media platforms are playing an increasingly important role in encouraging individuals to engage in travel behaviors. In the context of this study, supportive interactions on social media are viewed as having a significant stimulating and guiding effect on Generation Z’s intention to engage in Special Forces-style tourism. Based on this, the following research hypothesis is proposed:
H1. 
Perceived supportive communication (interactive vs. non-interactive) has a positive effect on Generation Z’s intention to engage in Special Forces-style tourism.

2.2. The Mediating Effect of Group Identity

Group identity refers to the recognition, value, and emotional connections that team members have with their group, as well as the potential behavioral changes that may occur as a result [22]. According to Social Identity Theory, individuals tend to achieve self-identification by building connections with valued social groups, thereby gaining a sense of personal worth and belonging [23]. For Generation Z, as active users of social media [24] and individuals who are in a phase of life where they actively seek social belonging [25,26], perceived supportive communication helps them establish strong social bonds and enhances their sense of belonging and identification with specific communities. When they experience care and recognition from others through supportive interactions on social media, they are more likely to develop a sense of identification with the group [27].
Current theoretical and empirical research has confirmed the correlation between group identity and tourism intentions [28], such as the willingness of food tourists to consume local cuisine [29], the intention to participate in adventure tourism [30], and sports tourism behavior [31]. Special Forces-style tourism can foster and enhance Generation Z’s group identity through social media interactions. Group identity not only strengthens their connections with other members but also further motivates them to mimic or engage in similar behaviors, such as following the trend of Special Forces-style tourism. Based on this, the following hypothesis is proposed for further exploration:
H2. 
Group identity mediates the relationship between perceived supportive communication and Generation Z’s intention to engage in Special Forces-style tourism.

2.3. The Mediating Effect of Flow Experience

Flow experience can be described as an experience of full absorption and deep involvement of a person in some activity, usually with high concentration, often with a loss or distortion of time and a balance between the challenge of the task and one’s own abilities [32]. While examining the impacts of perceived supportive communication on consumers’ flow experience, a number of scholars have identified that the degree of perceived supportive communication influences consumers’ perceived enjoyment and concentration significantly [33], thereby enabling tourism consumers to achieve a state of flow. Several studies have identified flow experiences as one of the major drivers that enable people to continue performing a certain behavior. It is also claimed that experiencing flow in any activity yields a satisfactory level of associated pleasure and satisfaction, motivating individuals to invest more time and energy in activities related to it. This is well reflected in tourism-intention-related areas such as browsing tourism websites [34], dark tourism behavior intentions [35], and outdoor leisure and adventure tourism behavior intentions [36]. Therefore, it is expected that Generation Z users will be more likely to feel states of flow in discussions involving Special Forces-style tourism. As such, this may strengthen their flow experience and their motivations to participate in online discussions related to Special Forces-style tourism. Based on this, the following hypothesis is proposed:
H3. 
Flow experience mediates the relationship between perceived supportive communication and Generation Z’s intention to engage in Special Forces-style tourism.

2.4. The Moderating Effect of Vicarious Reinforcement

Based on Bandura’s (1977) Social Learning Theory, vicarious positive outcomes and vicarious negative outcomes refer to the phenomena where observers are more likely to mimic and increase a particular behavior after witnessing someone else being rewarded for it, and likewise avoid or decrease that behavior after seeing someone experience negative consequences for the same action [37]. The core of this theory lies in demonstrating how observers can learn from the behaviors of others and the consequences that follow, forming expectations of outcomes in similar situations [37].
This study explores this theoretical framework in depth, not only by considering the internalization process of Generation Z’s psychological responses but also by creatively introducing the behaviors and outcomes of two different reference groups as external stimuli. The behaviors and outcomes of these reference groups will serve as critical points of reference for tourists’ decision-making processes, significantly influencing their subsequent reactions to engaging in Special Forces-style tourism. This perspective not only expands the application of Social Learning Theory within the context of tourism behavior analysis but also provides new academic insights and theoretical support for understanding tourist behavior.
Based on this, the following hypothesis is proposed:
H4. 
Vicarious reinforcement (vicarious positive outcomes vs. vicarious negative outcomes) positively moderates the relationship between perceived supportive communication and Generation Z’s intention to engage in Special Forces-style tourism.
Based on the above analysis, a model is constructed to illustrate the influence of perceived supportive communication on Generation Z’s intention to engage in Special Forces-style tourism (Figure 1).

3. Methods

3.1. Pretest

To test whether the experimental materials could effectively manipulate the perceived interaction level of supportive communication on social media, a pretest was conducted prior to the main experiment. To control for prior preferences and familiarity, both groups were first shown the same post about Special Forces-style tourism on Weibo (a popular Chinese social media platform), ensuring consistency in the social media environment before the experimental conditions. The materials were based on user comments about the Special Forces-style tourism topic on a Chinese social media platform.
While this study focuses on Generation Z’s interactions on Weibo, it is important to note that Weibo is a widely used microblogging platform in China, popular among diverse demographics beyond those aged 16 to 22. Weibo acts as the social central hub for many users in the 30–40 age ranges and is applied to a great many fields, extending from professional networking to public discussion and niche interest communities. The broadness of this userbase provides a rich environment for studying social interactions; however, this research specifically analyzes how Generation Z users interact in the context of Special Forces-style tourism. This study is an experiment designed to investigate the effect of perceived supportive communication on Generation Z’s engagement intentions within Special Forces-style tourism. It used a broadcasting methodology because it could be conducted in real time within a simulated social media dataset and provides insight into the influence of interactive and non-interactive communications on user engagement.
High-interaction condition: The participants in this group viewed a series of responses and supportive comments about Special Forces-style tourism. Taken together, the various replies, likes, and encouraging feedback created an ecology of high interactivity. For instance, whenever one of the members commented on the post given by the researcher, several other members would then provide supportive comments such as “That’s really great travel planning!” or “I did this last year—what a blast!” Other members liked those comments and gave further comments in support. In this case, the researcher tried to create a supportive virtual social environment where posting individuals would feel that their contribution has been noted and commended.
Low-interaction condition: In the support-reduced condition, users received isolated comments lacking responses, likes, or any engagement from other members. For example, a user would say, “I’m thinking about doing a Special Forces-style tour”, but it would not receive any responses or encouragement from others. The display was designed to represent a low-supportive communications environment in which users did not receive acknowledgment or engagement from the online community.
For measuring the level of interaction of social media content, we adapted the approach of Chun et al. [17], reusing experimental materials designed by Li, Li, and Feng (2015) [38]. An important manipulation in these materials was the level of involvement present in the user comments. Specifically, the interactive group’s materials featured secondary comments that built upon the original comments, creating a higher overall level of interaction. In contrast, the non-interactive group’s materials displayed independent comments with little visible interaction.
In this pretest, a sample of 64 university students was randomly divided into two groups. After viewing the images, participants were asked to evaluate the perceived level of supportive communication [39] on the social media platform, using a single item to measure comment interaction: “How would you rate the level of interaction with your comments on Special Forces-style tourism content on this social media platform?” [17]. A 7-point Likert scale was used (1 = “strongly disagree”, 7 = “strongly agree”). Independent-samples t-test results revealed that the high-interaction group had a significantly higher mean score than the low-interaction group (M_high = 2.44, M_low = 0.23, t = 8.167, p < 0.01). This indicates that the manipulation of perceived supportive communication was effective, and the main experiment can proceed based on this foundation.

3.2. Study 1

Experiment 1 employed a between-subject design with a single factor (perceived supportive communication: interactive vs. non-interactive) to examine the impact of perceived supportive communication on social media on Generation Z’s intention to engage in Special Forces-style tourism, as well as the underlying mediating mechanisms.

3.2.1. Participants and Procedure

A total of 201 participants (59% female; M_age = 22, age range: 18–27, 68% holding a bachelor’s degree) were recruited for the experiment. While this study successfully used a sample of 201 participants to understand the social dynamics influencing Generation Z’s engagement in Special Forces-style tourism, future research could benefit from leveraging Weibo’s API (application programming interface) to perform a more extensive analysis. By utilizing the API over a three-month period, researchers could collect larger-scale data and create a data lake to capture a broader spectrum of interactions and sentiments on the platform. This approach would allow for advanced sentiment analysis, enabling a deeper understanding of users’ behaviors and emotional responses over time. Such an analysis could enhance the findings by revealing patterns of engagement and sentiments toward Special Forces-style tourism on a larger scale, providing a more comprehensive perspective on social-media-driven tourism behaviors. Through API-driven data collection, a large-scale dataset could be created to observe supportive communication patterns, vicarious reinforcement, and flow experience directly within the natural social media environment. This approach would enable the tracking of real-time user responses to content, such as comments, likes, shares, and replies, providing a longitudinal perspective on user behavior. Such data could then be analyzed to extract insights into group identity formation, reinforcement of social learning through observed behaviors, and the impact of perceived supportive communication on engagement intentions.
After obtaining informed consent, participants were randomly assigned to either the experimental group (high interaction level) or the control group (low interaction level). Participants were asked to imagine themselves as active social media users (e.g., Wenyujiang202110 or Mei You Yu Wan Chao Mian) in the following scenario: They are browsing a Special Forces-style tourism post on social media and leaving a comment. Following their comment, participants in the experimental group observed several replies and likes to their post, while the control group saw no such interaction.
After viewing the experimental materials, participants evaluated several aspects, including the perceived level of supportive communication, group identification with Special Forces-style tourism, flow experience, tourism intentions, and demographic characteristics. To ensure data quality, attention check questions, such as “What year is it now?” or “Please select ‘strongly agree’ as your answer”, were embedded in the survey. At the conclusion of the experiment, participants were informed that all materials were fictional and created solely for the purposes of the study.
This study, along with all subsequent experiments, employed a seven-point Likert scale to measure perceived supportive communication, group identity, and flow experience (1 = strongly disagree, 7 = strongly agree), and a five-point Likert scale to measure the Special Forces-style tourism intention (1 = strongly disagree, 5 = strongly agree). The measurement of perceived supportive communication was consistent with that used in the pretest. The measurement of group identity was based on classic research paradigms both domestically and internationally [40]. It was structured into four dimensions, progressing step by step from basic cognition to self-concept to emotional connection to emotional peak. Additionally, this study considered the context of Chinese internet culture to ensure relevance and practicality by selecting and modifying the items accordingly. The measurement of flow experience was primarily based on Koufaris’s research [33], which focused on two dimensions: attention concentration and enjoyment, with a total of five items. Both constructs were measured using a 7-point Likert scale, where 1 = “strongly disagree” and 7 = “strongly agree”.

3.2.2. Analysis of Experimental Results

Manipulation check: An independent-samples t-test showed a significant difference in the level of perceived supportive communication on social media between the two experimental groups (M_interactive = 5.81, M_non-interactive = 2.66, t = 26.80, p < 0.05). Specifically, participants in the high-interaction group perceived a higher level of supportive communication, while participants in the low-interaction group perceived a lower level of supportive communication.
Main effect test: Based on Hypotheses 1–3, an independent-samples t-test was conducted for the interactive and non-interactive groups. The results indicated that group identity (M_interactive = 5.66, M_non-interactive = 3.29, p < 0.001), flow experience (M_interactive = 5.63, M_non-interactive = 3.15, p < 0.001), and Generation Z’s intention to engage in Special Forces-style tourism (M_interactive = 3.94, M_non-interactive = 2.34, p < 0.001) were significantly higher in the interactive scenario than in the non-interactive scenario. These findings, supported by independent-samples t-tests, confirm Hypothesis 1, which posits that interactive communication on social media has a significant positive effect on Generation Z’s intention to engage in Special Forces-style tourism, group identity, and flow experience (See Table 1 and Table 2).
Mediation effect test: In the interactive scenario, compared to the non-interactive scenario, perceived supportive communication had a significantly stronger positive effect on Generation Z’s intention to engage in Special Forces-style tourism (β = 0.3013, t = 5.1115, p = 0.0000). This indicates that when social media platforms exhibit more frequent interactions, the supportive communication perceived by tourists more effectively stimulates their interest and intention to engage in Special Forces-style tourism.
For the mediation effect of group identity, perceived supportive communication had a significant positive impact on group identity (β = 0.8966, t = 27.252, p = 0.000). However, the effect of group identity on Generation Z’s intention to engage in Special Forces-style tourism was not significant (LLCI = −0.14, ULCI = 0.06, passing through 0), suggesting that group identity does not have a significant mediating effect on their tourism intention (β = −0.0392, t = −0.7716, p = 0.44) (See Table 3).
Regarding the mediation effect of flow experience, perceived supportive communication had a significant positive effect on flow experience (β = 0.93, t = 29.54, p = 0.000), and flow experience had a significant positive effect on Generation Z’s intention to engage in Special Forces-style tourism (β = 0.3744, t = 7.0641, p = 0.00). Even after controlling for the mediating effects of group identity and flow experience, the direct effect of perceived supportive communication on Generation Z’s intention to engage in Special Forces-style tourism remained significant, further confirming the validity of Hypothesis 1 (β = 0.6151, t = 24.802, p = 0.0000).
First, the main effect was significant, as the bootstrap confidence interval (BootLLCI = 0.5662, BootULCI = 0.6641) did not contain 0, indicating that perceived supportive communication has a significant positive effect on Generation Z’s intention to engage in Special Forces-style tourism. Second, the mediation effect of flow experience was also significant, with a bootstrap confidence interval (BootLLCI = 0.1996, BootULCI = 0.5049) that did not contain 0, demonstrating that flow experience plays an important mediating role between perceived supportive communication and tourism intentions. Finally, after controlling for the mediation effect of flow experience, the direct effect of perceived supportive communication on tourism intentions remained significant, with a bootstrap confidence interval (BootLLCI = 0.1853, BootULCI = 0.4174) that did not contain 0. However, the mediation effect of group identity was not significant (BootLLCI = −0.1252, BootULCI = 0.0533, containing 0) (See Table 4).
Based on the experimental results, we can conclude that flow experience plays a full mediating role in the relationship between perceived supportive communication and Generation Z’s intention to engage in Special Forces-style tourism. This finding reveals the pathway through which perceived supportive communication influences Generation Z’s behavioral intentions, further emphasizing the key role of flow experience in this process. Therefore, Hypothesis 2 was not supported, while Hypothesis 3 was confirmed.

3.3. Study 2

Study 2 employed a 2 (perceived supportive communication: interactive vs. non-interactive) × 2 (vicarious reinforcement: positive vs. negative) between-subject design to examine the moderating effect of vicarious reinforcement.

3.3.1. Experimental Operations and Procedures

The manipulation procedure in Experiment 2 was similar to that of Experiment 1, with the addition of vicarious reinforcement groups and new instructional materials. Participants were provided with the following prompt: “When you browse the topic of Special Forces-style tourism on social media, you will see the latest news about Special Forces-style tourism” along with relevant images.
A total of 188 participants were recruited via the Credamo platform. After reading the experimental materials, participants were asked to rate perceived supportive communication, demographic characteristics, and other related measures. After excluding invalid questionnaires due to incomplete or incorrect responses, 174 valid samples were obtained (52.4% female; M_age = 22, age range 18–29, 78% with a bachelor’s degree). Participants were randomly assigned to four groups: 44 participants in the high perceived supportive communication/positive vicarious reinforcement group, 42 in the high perceived supportive communication/negative vicarious reinforcement group, 43 in the low perceived supportive communication/negative vicarious reinforcement group, and 43 in the low perceived supportive communication/positive vicarious reinforcement group.
The manipulation of perceived supportive communication was consistent with the comment interactivity approach used in Study 1. Regarding the manipulation of vicarious reinforcement, this study drew on the methods for manipulating negative vicarious outcomes from Sitren et al. (2006) [41], with appropriate modifications tailored to the specific experiences of tourists in a social media context. Through carefully designed experimental materials, we aimed to present different forms of vicarious reinforcement (including both positive and negative outcomes) to observe how these outcomes influence tourists’ perceptions of their intentions to engage in Special Forces-style tourism. This manipulation helps us better understand the mechanism by which vicarious reinforcement affects individual decision-making in a social media environment.
Given the large amount of discussions related to Special Forces-style tourism behavior on the internet, we paid particular attention to positive and negative consequences. The most important positive consequences have been honorary titles and increased social recognition, which could be beneficial for initiating participation in Special Forces-style tourism. Negative consequences included physical discomfort, which may raise doubts or concerns about participating in Special Forces-style tourism. These experimental materials were designed to clearly present forms of positive or negative consequences to provide a more concrete simulation of such outcomes, so that participants could directly perceive how each different outcome might affect their behavioral intentions.
First, the participants were exposed to real online cases. Both groups responded to manipulation check questions after reviewing the material. They were tasked with rating the outcome of Special Forces-style tourism in the presented scenarios on a scale of 1–7, where 1 represents a negative outcome and 7 represents a positive outcome [42]. In the experiment, participants were asked to first rate the likelihood of experiencing either a positive or negative vicarious outcome in the presented scenario. The next item asked the following contingencies question: “If you were to do the same thing as the person you just observed, ‘how likely or unlikely do you think it is’? that you would receive the reinforcement or experience the negative consequence? Please circle a number from 1 to 10, with higher numbers indicating higher likelihood”.
Finally, all participants completed the measurements of Generation Z’s intention to undertake Special Forces-style tourism, based on the two manipulated conditions of the social media interaction—namely, interactive versus non-interactive—and the two vicarious reinforcement conditions of positive outcome versus negative outcome.

3.3.2. Result

The manipulation check involved randomly assigning participants to two different scenarios: an alternative positive outcome and an alternative negative outcome. An independent-samples t-test revealed a significant difference between the two groups, with a p-value less than 0.05. Further analysis of the data (as shown in Table 5) indicated that the mean for the positive-outcome group was 6.1, while the mean for the negative-outcome group was 2.51. This substantial difference validates the effectiveness of the constructed scenarios for alternative positive and negative outcomes in this study, confirming that the manipulation method for the independent variable achieved the intended results.
Hypothesis Testing. When examining the moderating effect of vicarious reinforcement (including both vicarious positive and negative outcomes) on Special Forces-style tourism intentions, this study employed rigorous verification through the General Linear Model in SPSS 26.0 software. To ensure the accuracy of data analysis, we applied a square root transformation to the data on Special Forces-style tourism intentions. Levene’s test for homogeneity of variance indicated F = 1.29, p = 0.278, which is greater than the significance level of 0.05, confirming that the assumption of homogeneity of variance was met, allowing us to proceed with subsequent statistical analysis.
The results of the ANOVA indicated that there was a significant interaction between the perceived supportive communication context and vicarious reinforcement on Generation Z’s Special Forces-style tourism intentions (F = 4.67, p = 0.03 < 0.05). This finding suggests that vicarious reinforcement moderates the relationship between perceived supportive communication and Generation Z’s Special Forces-style tourism intentions, thereby supporting Hypothesis 4 (See Table 6).
Furthermore, the results showed that vicarious reinforcement had a significant main effect on the dependent variable (F = 6.77, p = 0.01 < 0.05). In particular, participants exposed to vicarious positive outcomes (M = 3.31, SD = 0.60) in an interactive supportive communication context exhibited higher Special Forces-style tourism intentions compared to those in the vicarious negative outcome context (M = 3.00, SD = 1.08). Therefore, positive outcomes had a more pronounced moderating effect on the primary relationship (see Table 7 and Table 8).
The simple slope analysis in Figure 2 demonstrates the moderating effect of vicarious reinforcement on Generation Z’s intention to engage in Special Forces-style tourism, based on perceived supportive communication in social media contexts. Specifically, the analysis reveals that in the interactive condition, participants exposed to vicarious positive outcomes showed significantly higher tourism intentions compared to those exposed to negative outcomes. Conversely, in the non-interactive condition, the difference between positive and negative outcomes on tourism intention was less pronounced. These findings support the hypothesis that vicarious reinforcement strengthens the relationship between supportive communication and tourism intention, especially under interactive conditions where positive outcomes enhance engagement more effectively.

4. Conclusions and Discussion

Conclusions

This study examined how perceived supportive communication on social media affects Generation Z’s engagement intentions for Special Forces-style tourism and was guided by four key hypotheses. The findings give support to the hypothesis that interactive social media environments have positive effects on tourism intentions in that perceived supportive communication improves engagement more when it takes place under conditions of high rather than low interactivity. We find that flow experience mediates this relationship and thus supports H2, while group identity, as mediated, was not significant and hence failed to support H3. In addition, the data supported the moderating role of vicarious reinforcement, such that a positive vicarious outcome increases tourism intentions more in supportive than non-supportive communication contexts, hence supporting H4.
These findings provide a deeper understanding of the dynamics of supportive communication and engagement intentions in Special Forces-style tourism, particularly among digital-native consumers. Based on these insights, the findings of this study provide practical implications for tourism marketers looking to design proper strategies matching the social media engagement pattern of Generation Z. In this regard, future studies may extend these findings into more sophisticated data-collection techniques, such as using API access to widen qualitative sentiment analysis.
These findings provide a particularly good way to meaningfully view how perceived supportive communication on social media affects the intention of engagement in Special Forces-style tourism across Generation Z, especially in interactive conditions. These findings consequently support the study of Yuan and Hong, in which it has been shown that digital interactivity plays a role in enhancing tourism engagement through new experiences. Similar to the work on the importance of an interactive social media environment to create an immersive experience and intensify engagement intentions, our research also puts forth this theme.
In addition, the mediating effect of flow experience supports the work of Koufaris (2002) [33] in indicating that flow is vital in online consumer engagement. Our findings, therefore, extend Koufaris’s with evidence that flow experience makes a difference in general online behavior and is also a pivotal factor in the context of tourism, at least as far as Generation Z is concerned. Secondly, vicarious reinforcement acts as a moderator of findings from Bandura’s Social Learning Theory, wherein observing other people having a positive experience enhances the behavioral intentions of an individual. Our study reinforces this idea within the realm of tourism, suggesting that Generation Z users are more likely to engage in Special Forces-style tourism when they observe positive reinforcement from social interactions. These connections underscore the relevance of perceived supportive communication in digital tourism contexts and support the broader understanding of social media’s role in shaping user behavior. By relating our findings to the existing literature, we contribute to a nuanced understanding of the factors driving Generation Z’s tourism engagement and offer a fresh perspective on the importance of interactive environments and observed social validation in shaping behavioral intentions.

5. Implications

5.1. Theoretical Contributions

First, this research is situated at the intersection of tourism studies, social psychology, and digital marketing, contributing specifically to our understanding of social media’s role in influencing consumer behavior in tourism. By focusing on the impacts of perceived supportive communication within the social media context, this study adds to the field of tourism behavior, where existing research primarily addresses functional aspects of social media, such as information sharing and user-generated content. Unlike traditional studies in tourism marketing that emphasize destination promotion (e.g., Dedeoğlu et al., 2020 [13]), this research investigates how supportive communication shapes user intentions on a behavioral level, especially among Generation Z, who engage heavily with interactive digital environments.
Second, the study deepens the application of the SOR framework, Social Identity Theory, and Social Learning Theory within the tourism domain. While prior research (e.g., Bandura, 1977 [37]) has explored these theories in consumer psychology and social learning, our study uniquely applies them to analyze interactive social media’s influence on tourism behavior, highlighting flow experience and vicarious reinforcement as essential mechanisms. This focus provides new insights into the psychological processes underlying Generation Z’s digital interactions and how these contribute to their engagement intentions.
Third, by extending these theoretical frameworks into the tourism context, this study offers a refined perspective that diverges from the existing literature by emphasizing supportive, interactive environments as primary drivers of engagement intentions. This novel approach not only enriches the field of tourism psychology but also broadens the applicability of social interaction theories in studying emerging digital behaviors within tourism.

5.2. Managerial Implications

This study provides key practical insights for the design and implementation of tourism marketing strategies targeting Generation Z.
First, the findings indicate that perceived supportive communication on social media positively influences Generation Z’s intentions to engage in Special Forces-style tourism. In light of this, it is recommended to build Special Forces-style tourism communities aimed at young consumers, focusing on creating interactive and supportive online environments that foster flow experiences. This can be achieved by encouraging user interaction, such as likes, comments, and direct engagement between users and travel influencers. The immersive nature of these interactions can help boost travelers’ confidence and their commitment to more ambitious travel plans.
Second, the study delves into the role of vicarious reinforcement in the entire process of forming tourism intentions, particularly comparing the effects of positive and negative outcomes on consumers’ Special Forces-style tourism intentions. While vicarious reinforcement does not directly lead to subsequent behavior changes, its influence in the tourism sector should not be underestimated. Marketers should be mindful of the powerful impact that shared vicarious experiences on social media can have. Special attention should be given to managing negative reviews or shared discomforts experienced during travel, as these can disproportionately suppress interest and deter potential travelers.

6. Research Limitations and Future Research Directions

6.1. Limitation One: Focus on Generation Z as a Specific Group

The major limitation of the current study involves the sample scope, which primarily includes members of Generation Z. Inasmuch as this population represents the very essentials of special tourism styles, the findings of this study are highly relevant in measuring how social media may influence such behavior. However, this is a limiting factor when journaling findings. Generation Z might possess special psychological characteristics that are not fully representative of other age groups or socio-demographic backgrounds, such as consumption habits or the structure of social networks. Future research could expand the sample to include tourists of different age groups, professions, and different income levels in order to provide new insights into how social media shapes tourism behavior. Moreover, a further step for the future would be to compare differences in touristic behaviors across different demographic groups driven by social media influences, thus better capturing the complexity and diversity of the impact of social media on this tourism behavior in different populations.

6.2. Limitation Two: Influence of Chinese Cultural Factors

Existing studies have primarily relied on social media for data and the phenomenon of Special Forces-style tourism within the context of Chinese culture, which could have led to biased results. Chinese culture places great emphasis on collectivism, social identity, and also on “face” as a concept. This may impact how Generation Z perceives and interprets social media information and their willingness to engage in Special Forces-style tourism. Further studies could compare tourists of different cultural backgrounds in order to further understand how social media shapes tourism behavior. Such a study would indicate the relevance of the cultural element in the relationship between social media and tourism behavior by looking at differences in how social media affects tourism behavior in both Eastern and Western cultures. This would also help businesses to operate tours or marketers to devise more specific marketing strategies targeted toward tourists from diverse cultural backgrounds.
Supplementing these foregoing analyses, extra statistical methods could still enrich this study in light of providing deep insights into understanding the engagement intentions of Generation Z within Special Forces-style tourism. For example, multi-group analysis of various responses across the age subgroups of Generation Z—16–18 and 19–22—would afford a finer-grained look at how differences in engagement are varied. In addition, the open-ended responses were put under proper and deep sentiment analysis, which had a good representation of nuance in users’ emotional involvement. In addition, the inclusion of SEM would offer more robust analysis in establishing the relationships among perceived supportive communication, flow experience, and tourism intention, requiring more validations of the proposed hypotheses. These analyses would strengthen the conclusions of this study and enhance contributions to the understanding of social-media-driven tourism behaviors.

6.3. Limitation Three: Lack of Sentiment Analysis

This study did not incorporate sentiment analysis, which could provide deeper insights into the emotional tone of supportive communication on social media. Future research could benefit from applying sentiment analysis to examine how the positivity or negativity of comments impacts users’ tourism engagement intentions. By quantifying emotional responses, such an approach could reveal additional layers of influence, offering a more nuanced understanding of how supportive communication shapes tourism behaviors.

Author Contributions

Conceptualization, Q.W.; writing—original draft preparation, Y.W.; writing—review and editing, S.L.; supervision, J.L.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Social Science Foundation of China (Grant Number: 24BGL143).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 16 10033 g001
Figure 1. Research model.
Figure 1. Research model.
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Sustainability 16 10033 g002
Figure 2. The moderating role of vicarious reinforcement between perceived supportive communication and Special Forces-style tourism intentions.
Figure 2. The moderating role of vicarious reinforcement between perceived supportive communication and Special Forces-style tourism intentions.
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Table 1. The group statistics for interactive vs. non-interactive conditions.
Table 1. The group statistics for interactive vs. non-interactive conditions.
ConditionNMeanSDSEM
Group identificationInteractive103.005.660.830.08
Non-interactive98.003.291.480.15
Flow experienceInteractive103.005.630.820.08
Non-interactive98.003.151.490.15
Tourism intentionInteractive103.003.940.600.06
Non-interactive98.002.341.080.11
Table 2. Independent-samples t-test.
Table 2. Independent-samples t-test.
Levene’s Test for Equality of Variancest-Test for Equality of Means
FSig.tdfSig.MDSED95% Confidence Interval
Lower LimitUpper Limit
Group identificationAssumed variance32.640.0014.08199.000.002.370.172.042.70
Non-assumed variance13.90150.260.002.370.172.032.71
Flow experienceAssumed variance33.740.0014.70199.000.002.480.172.152.81
Non-assumed variance14.51149.800.002.480.172.142.82
Tourism intentionAssumed variance26.100.0013.00199.000.001.600.121.351.84
Non-assumed variance12.83149.370.001.600.121.351.84
Table 3. Testing the mediation model of group identification and flow experience.
Table 3. Testing the mediation model of group identification and flow experience.
PathCoeff.setpLLCIULCIR-sqF
Perceived supportive communication → tourism intention0.30130.05895.1150.000.190.420.81274.42
Perceived supportive communication → tourism intention
(after controlling for the mediator)		0.61510.024824.8020.000.570.660.76613.22
Perceived supportive communication → group identification0.89660.032927.2520.000.830.960.79741.40
Perceived supportive communication → flow experience0.93210.031629.4970.000.870.990.81872.64
Group identification → tourism intention−0.03920.0508−0.77160.44−0.140.060.81274.42
Flow experience → tourism intention0.37440.05307.06410.000.270.480.81274.42
Table 4. Total effect, direct effect, and mediated effect.
Table 4. Total effect, direct effect, and mediated effect.
PathEffectSELLCIULCI
Total effect0.61510.02480.56620.6641
Direct effect0.30130.05890.18530.4174
Mediated effect (group identification)−0.03520.044−0.12520.0533
Mediated effect (flow experience)0.3490.07740.19960.5049
Table 5. Positive/negative outcomes.
Table 5. Positive/negative outcomes.
MSDSEMLevene’s Test for Equality of Variancest-Test for Equality of Means
FSigtSig.
Alternative positive outcomes6.11.2350.12410.6870.00 17.7230.00
Alternative negative outcomes2.511.610.16
Table 6. Levene’s test for equality of error variances a,b.
Table 6. Levene’s test for equality of error variances a,b.
Levene’s Test StatisticsDF1DF 2Sig
Special Forces-style tourism behavior intentionBased on mean1.29331970.278
Based on median2.18331970.091
Based on median with adjusted degrees of freedom2.1833177.920.092
Based on trimmed mean1.91131970.129
Null hypothesis: “the error variances of the dependent variable are equal across all groups”.
a Dependent variable: Special Forces-style tourism behavior intention. b Design: intercept + positive/negative + interactive/non-interactive + positive/negative × interactive/non-interactive.
Table 7. Between-subject effects test.
Table 7. Between-subject effects test.
SourceType III Sum of SquaresDFMean SquareFSigPartial Eta Squared
Adjusted model135.853 a3.0045.2862.660.000.49
Intercept1977.411.001977.412735.970.000.93
Interaction/non-interaction127.731.00127.73176.730.000.47
Positive/negative outcome4.901.004.906.770.010.03
Positive/negative outcome × interaction/non-interaction3.371.003.374.670.030.02
Error142.38197.000.72
Total2282.22201.00
Adjusted total278.23200.00
Dependent variable: Special Forces-style tourism behavior intention.
The R-squared is 0.488 (adjusted R-squared = 0.480).
a indicates that this value was calculated based on the adjusted or modified model.
Table 8. Descriptive statistics.
Table 8. Descriptive statistics.
Interactive/Non-InteractivePositive/NegativeMSDSample Size
InteractiveNegative outcome3.910.5951.00
Positive outcome3.960.6152.00
Total3.941.28103.00
Non-interactiveNegative outcome2.051.1149.00
Positive outcome2.630.9949.00
Total2.341.0598.00
TotalNegative outcome3.000.60100.00
Positive outcome3.311.08101.00
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MDPI and ACS Style

Zhao, J.; Wang, Y.; Liu, S.; Li, J.; Wang, Q. Does Social Media Make Tourists More like Special Forces? The Impact of Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism. Sustainability 2024, 16, 10033. https://doi.org/10.3390/su162210033

AMA Style

Zhao J, Wang Y, Liu S, Li J, Wang Q. Does Social Media Make Tourists More like Special Forces? The Impact of Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism. Sustainability. 2024; 16(22):10033. https://doi.org/10.3390/su162210033

Chicago/Turabian Style

Zhao, Jianzhen, Yiyan Wang, Shuaifang Liu, Jun (Justin) Li, and Qinglin Wang. 2024. "Does Social Media Make Tourists More like Special Forces? The Impact of Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism" Sustainability 16, no. 22: 10033. https://doi.org/10.3390/su162210033

APA Style

Zhao, J., Wang, Y., Liu, S., Li, J., & Wang, Q. (2024). Does Social Media Make Tourists More like Special Forces? The Impact of Supportive Communication on Generation Z’s Intention to Engage in Special Forces-Style Tourism. Sustainability, 16(22), 10033. https://doi.org/10.3390/su162210033

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