“Just pick up and go”: how user-generated social media content shapes tourists' behavioral willingness

In the era of the digital economy, social media content (SMC) is deeply embedded in the whole decision-making process of modern tourists. It not only becomes the main source and channel for tourists to search for tourism information (Wang and Park, 2022), but also user-generated content (UGC) in the form of text, pictures, or videos plays an important role in guiding the wishes and behaviors of potential travelers (Yao and Jia, 2021; Marcello et al., 2019). Content on social media platforms, such as messages from netizens on Instagram, travel destination notes on rednote (an interactive Chinese platform for lifestyle-related images/short video), short video live broadcasts on TikTok, etc., have highly interactive likes, comments and are often forwarded, triggering some tourists to form the intention of “many people like it, so I want to go too.” Some young tourists have the intention of starting immediately on a journey, “just pick up and go.”

Scholars have examined the correlation between SMC and tourists' consumption behavior, especially the influence of UGC on millennials' travel behavior (Jia, 2017; Ana and Istudor, 2019). SMC strengthens tourism destination image perception and marketing management (Milwood, 2012; Deng, 2019). It helps visitors with information search and behavioral decision-making (Verma, 2012; Nezakati et al., 2015). The high interactivity of users' participation in SMC and sharing stimulates behavioral willingness among potential tourists (Ben-Shaul and Reichel, 2018; Chang et al., 2017; Filieri, 2015). The SMC of tourist destinations affects the travel decision-making process of potential tourists, and directly affects tourists' cognition and evaluation of destinations (Kong, 2021; Zhang et al., 2016). SMC is more conducive to bringing tourists into the destination scenario, improving the experience effect and influencing tourists' behavioral intention (Zeng and Gerritsen, 2014). The quality of experience at tourist destinations has been shown to affect their behavioral intentions (Rui, 2024). Those papers that study the influence mechanism of SMC at tourist destinations on tourists' decision-making behavior and behavioral willingness focus on mediating variables methods and constructing theoretical models. Dastane (2020) examined the impact of digital marketing by social media on consumers' purchase intention via customer relationship management as a mediating variable. Wang (2025) tested the influence of UGC on tourism intention, using self-consistency and trust as mediating variables. Duan et al. (2025) selected Douyin (the Chinese version of TikTok) as their research context and examined the impact of attitudes and subjective norms on travel video sharing behaviors among Generation Z users. Fang et al. (2023) found that short social media videos can significantly affect tourists' behavioral willingness (TBW) through empirical tests. The studies mostly focus on the mediating variables that explain the influence of SMC on the TBW and analyze its differential influence on potential tourists from a demographic perspective.

The term SMC in this article refers to content generated by users on social media platforms. It is used interchangeably with UGC and ensures consistency across all subsequent analyses.

We found that TBW is influenced not only by external stimuli from SMC but also by internal stimuli such as tourism expectations and experiences. Furthermore, tourists from different cultural backgrounds exhibit varying degrees of susceptibility to SMC stimuli. Therefore, our research questions are as follows:

To address complex questions, this paper integrates symbolic interaction and embodied cognition theory. Tourist destinations can be conceptualized as symbolic systems (e.g. romantic Paris, adventurous Amazon), where travelers understand and experience the destination through interactions with various symbols (e.g. landmarks, locals, fellow tourists, cuisine, souvenirs). In this process, they construct and revise their self-perception of the destination (Culler, 1981). Cognition is deeply rooted in the visitor's full-body engagement and multi-sensory interactions throughout the travel experience (Agapito, 2014). Unforgettable embodied experiences drive tourists' strong recommendations and desire to revisit (Kim, 2010). However, existing research hasn't examined TBW by integrating symbolic interaction theory and embodied cognition theory.

We take on this task in this research. Integrating and extending these two theories into the TBW context, we collected and analyzed UGC data from rednote, the most influential and widely spread Chinese social platform. We argue that SMC enhances TBW through the cognitive and experiential pathway of symbolic interaction-induced expectation and embodied practice-generated experience. Furthermore, we explore CI as a moderator that negatively regulates the relationship between SMC and tourism expectation. CI refers to an individual's ability to collect, process and adapt to cultural information in new cultural contexts (Earley and Ang, 2003). Unlike traditional cultural level or cultural quality, cultural intelligence is more dynamic and emphasizes the practical capacity to interpret and respond to cultural cues in SMC. We expect our findings to provide practical guidance for tourism destinations about how they might optimize their UGC-driven content strategies, construct immersive embodied experience scenarios and tailor marketing approaches to tourists with varying levels of cultural intelligence. Specifically, we examine whether SMC shapes TBW through two possible routes: the independent mediation of tourism expectation or tourism experience, or a sequential expectation-experience chain.

Originating from Stimulus–Organism–Response (SOR) theory, symbolic interaction theory examines how individuals construct social reality and self-awareness through interpreting and interacting with shared meanings of symbols (Blumer, 1986). Symbol meanings are generated and modified through interaction. This theory can elucidate the UGC – potential tourist interactive process. SMC consists of text, pictures and videos users upload based on their experience or cognition (Wang et al., 2024). With social media, tourism interaction includes cultural symbolic interactions on social media platforms in addition to those in the tourist destination's physical space (Xie et al., 2025). Potential tourists interpret tourist destinations via symbolic representations, transforming destination culture, context and user experience into a rich symbolic system with cultural, emotional and informational symbols. Through symbolic interaction, symbol meanings are translated into behavioral significance, prompting potential tourists to develop travel expectations and willingness to travel.

TBW refers to the willingness to recommend, revisit and pay a premium (Kim and Bonn, 2016). It is based on tourists' own needs, attitudes and value judgments under the influence of antecedent variables like social media information contact and perception of tourism experience. It has three core dimensions: willingness to share means tourists' willingness to convey destination-related information via social media or offline; willingness to revisit is the tendency to return to the destination for consumption; willingness to pay a premium is the willingness to pay extra for unique products, services, or experiences. Armutcu et al. (2023) found that online content of tourist destinations positively impacts TBW. Fang et al. (2023) showed short social media videos can significantly affect TBW. UGC about the destination is a symbol collection, offering cognitive guidance by updating information on cultural activities, user emotions, etc. It stimulates tourists' interest by shaping destination cognitive images and fostering willingness to share, revisit and pay premiums (Das, 2024). Hence, this study proposes:

As a cognitive construct before travel, tourism expectation is a belief system based on external information acquisition, past travel experience and social and cultural norms. Its core function is to provide key reference standards for post-travel satisfaction evaluation (Wang et al., 2016). The potential expectations are usually based on prior travel experience, word-of-mouth, media publicity, advertising and common knowledge (Chon, 1991, 1992; Baloglu, 1997). UGC covers tourists' experiences, sharing with a significant impact on the formation of travel expectations. Wu et al. (2023) explored the influence of different short travel video contents on potential tourists' travel willingness, as well as the mediating effect of customer inspiration and the moderating effect of consumption orientation through three experiments. Through symbolic interpretation on social media, tourists construct expectations about tourist destinations, which become a psychologically driving force for action. With the expected interest and desire rising, tourists will strengthen TBW. Hence, this study proposes:

Embodied Cognition Theory emphasizes that cognition is rooted in bodily senses, movements, physiological states and the interaction between the body and the external environment. Potential tourists develop travel expectations through visual representations (images, videos) and emotional engagement (user reviews), which serve as symbolic interactions providing indirect embodied simulation material. If the actual physical experience aligns with expectations, it reinforces positive experiences, thereby transforming into sustained TBW.

Because of expectations, tourists' experience and evaluation will be affected by the initial interaction with destination symbols during on-the-spot tourism experience. Tourism experience is defined as the sum of multi-dimensional perception (cognitive, emotional, sensory, social), value judgment and psychological feedback generated by tourists in the whole process of tourism activities. Uriely (2005) believes that tourism experience is a multi-dimensional, dynamic subjective process that integrates emotion, cognition, behavior and meaning. Li et al. (2018) argue that tourists will form a proactive impression before visiting the destination, based on information from social media. Tourism experience can affect tourism satisfaction (Guan et al., 2023). Tourists' participation in on-site activities can effectively improve their satisfaction and TBW (Hung et al., 2019). Hence, this study proposes:

Tung (2011) studied the key role of tourism expectations in tourism experiences, stating that product or service expectations affect tourism experience quality. SMC can evoke tourism expectations and motivate tourists. During travel, tourists integrate social media symbols into destination consumption, creating new symbols that can lead to behaviors like repeat visits. In summary, SMC's symbolic interactions can stimulate tourism expectations (Zhong et al., 2025), which influence tourism experiences and TBW.

From the perspective of symbolic interaction theory, SMC is fundamentally a set of long-distance symbolic systems (Culler, 1981), which constructs the symbolic imagination of tourists through symbolic elements such as images, narratives and emotional labels. However, this type of tourism expectation is essentially a disembodied cognitive presupposition that has not been tested against reality. It lacks the support of physical participation and sensory verification, which cannot directly drive TBW. Consequently, when tourism expectation is used as an intermediary independently, its effect will be attenuated by the uncertainty of the gap between imagination and reality and it cannot be effectively transmitted to behavioral willingness.

In connection with the embodied cognitive theory, tourism experience refers to the knowledge of presence established through tourists' multi-sensory interactions, including visual, auditory and gustatory interactions, within the destination context (Kiverstein and Miller, 2015). The construction of experience must adopt expectations as a reference framework. In the absence of tourism expectations as an evaluation benchmark, tourism experience merely consists of a series of scattered sensory events. As posited by the assimilation-contrast theory in social psychology (Oliver, 1980), an individual's behavioral response is not determined by the experience itself but by the outcome of the comparison between the experience and the expectations. Therefore, when tourism experience is solely employed as an intermediary, due to the absence of the expectation reference framework, its effect cannot be independently transmitted to behavioral willingness. Hence, this study proposes:

CI refers to an individual's ability to gather, process and adapt information effectively in unfamiliar cultural environments (Earley and Ang, 2003). Unlike cultural competence associated with one's level of formal education, CI is dynamic and emphasizes cognitive and behavioral capacity for interpreting and responding appropriately to culturally diverse stimuli. Research on CI often examines its impact on cross-cultural adaptation by treating it as an antecedent variable. Li et al. (2012) examined the influence of CI on the cross-cultural adaptation levels of international students in China. Few studies have introduced it as a moderating variable in tourism contexts. Tourists with high CI can rationally analyze and evaluate SMC, which develops more specific and realistic travel expectations and are less susceptible to being overly influenced by superficial symbols in social media (such as mere visual impact or emotional manipulation) (Leung, 2010). This reduces expectation gaps arising from misinterpreted information or cultural differences. Conversely, individuals with low CI have a weak ability to acquire information and engage in cognitive processing, relying deeply on superficial emotional responses (Ng, 2007). Browsing SMC with a lack of rational judgment, they are easily swayed by emotions and dominated by the immediate feelings evoked by the content (Hossain et al., 2023). Hence, this study proposes:

The proposed model of the study is shown in Figure 1.

In order to systematically reveal the influence mechanism of SMC on TBW, this study adopts a hybrid research design combining the structural equation model (SEM) and experimental method. The two methods are complementary and progressive. SEM evaluates the hypothesized structural relationships and the overall empirical pattern among the focal variables, while experiments further explore the causal relationship on the proposed ordering of these variables under manipulated SMC conditions.

To investigate how SMC influences TBW, this study used snowball sampling to collect questionnaire data from online and offline visitors to the Grand Tang Mall. Xi'an Grand Tang Mall is a 2,100-m-long and 500-m-wide pedestrian mall located in the center of Xi'an, a city of more than ten million people. Xi'an is the thirteenth largest city in China, and formerly the capital of the Tang Dynasty (618–907). The ancient Giant Wild Goose Pagoda is the landmark of the Grand Tang Mall, which is the most popular free tourist attraction. Since 2019, Xi'an Grand Tang Mall has been at the top of the search list for every Golden Week (a 7-day public holiday in China) due to the extraordinary popularity of the interactive programs at this tourist site on social media (see Figure 2). It is a culture-themed destination in Xi'an that provides an appropriate context for examining how symbolically rich SMC shapes tourists' expectations, experiences and behavioral willingness.

Data were collected from May 16 to July 11, 2024. Of 423 questionnaires distributed, 39 were invalid, yielding 384 valid responses (90.8% effective rate). The sample was predominantly female (59.38%), with most tourists under 40 years old (84.63%). The majority held at least a college diploma (92.18%). Students comprised about two-fifths of the sample, while enterprise and institution employees accounted for roughly one-third. Approximately half of the respondents had a monthly income below 3,000 RMB (the official currency of the People’s Republic of China).

Measurement items were adapted from validated scales. SMC was derived from Kim et al. (2017), comprising five dimensions: relevance, timeliness, completeness, entertainment and value-added. Tourism expectations were adapted from Wang et al. (2016). The tourism experience scale came from Agyeiwaah et al. (2024) and Kim et al. (2012). TBW – including willingness to recommend/share, revisit and pay a premium – was developed by Huang (2009), Prayag (2015) and Baker et al. (2000).

Independent-sample t-tests revealed no significant gender differences in SMC, tourism expectation, tourism experience, or cultural intelligence (p > 0.05). However, a significant difference emerged in TBW (p < 0.05), with male tourists showing higher willingness to recommend, share, revisit and pay premium prices (Table 1).

ANOVA results indicated no significant age-related differences across any construct (p > 0.05). Similarly, income did not significantly influence any construct (p > 0.05). Regarding education, no differences were found in TBW, expectations, experiences, or cultural intelligence (p > 0.05). However, significant variation was observed in SMC (p < 0.05). Post-hoc analysis revealed that respondents with lower education levels were less influenced by SMC, likely due to preferences for simpler content and reliance on offline interactions. Occupational differences were significant for TBW and cultural intelligence (p < 0.05). Farmers consistently scored lower than other groups (e.g. government employees, corporate staff, freelancers), reflecting economic constraints, limited tourism exposure and fewer cross-cultural experiences. Post-hoc pairwise analysis results are shown in Table 2.

To ensure the study's reliability and specificity, it's necessary to evaluate measurement consistency and accuracy. As Table 3 shows, the composite reliability values of all latent variables range from 0.767 to 0.920, and the Cronbach's α values range from 0.766 to 0.919, both exceeding the 0.7 threshold, indicating high internal consistency. Confirmatory factor analysis (CFA) demonstrated that standardized factor loadings of the 34 measurement items ranged from 0.572 to 0.866, indicating good convergent validity.

Average variance extracted (AVE) values ranged from 0.563 to 0.700, exceeding the 0.5 threshold. The square roots of AVE values (Table 4) were greater than the correlations among latent variables, indicating satisfactory discriminant validity.

To ensure the validity of single cross-sectional survey studies, it's necessary to test if artificial covariation between variables due to common methodological bias confounds real results. In this study, one-factor CFA is used to test the common method bias. A latent variable for the bias is introduced into the SEM, and the goodness of fit of models with and without it is compared. The results showed that (see Table 5) the theoretical model, constructed by assigning measurement items to corresponding latent factors as per the research hypothesis, fitted well (χ2 = 1381.708, p < 0.001, GFI = 0.820 > 0.8, AGFI = 0.893 > 0.8, CFI = 0.906 > 0.8, TLI = 0.897 > 0.8, Root Mean Square Residual (RMR) = 0.053 < 0.1, Root Mean Square Error of Approximation (RMSEA) = 0.066 < 0.08). In contrast, the single-factor model with all measurement items assigned to one latent factor had poor data fitting and unsatisfactory results (χ2 = 3443.296, p < 0.001, GFI = 0.537 < 0.8, AGFI = 0.478 < 0.8, CFI = 0.681 < 0.8, TLI = 0.661 < 0.8, RMR = 0.107 > 0.1, RMSEA = 0.120 > 0.08). These results indicate that there is no serious common method bias among constructs in this study, and no coefficient estimation error affecting result reliability.

In this study, a systematic model fitness test (see Table 6) confirmed the good match between the theoretical model and the actual data, and a series of test results fully verified the model's overall fitness.

The Bootstrap method has high test power and is widely recommended for testing the mediation effect (Wen et al., 2004). Specifically, with Bootstrap sampling times set to N = 5000 and a 95% confidence level for bias correction, the results are interpreted as whether 0 is included within the 95% confidence interval. If 0 is excluded, the path is significant; otherwise, it is not (Chen et al., 2013). The test results are shown in Figure 3 and Table 7.

The direct effect of SMC on TBW is 0.636, p < 0.01, indicating that SMC can effectively improve TBW and hypothesis H1 is supported.

In the path of SMC→tourism expectation→TBW, SMC positively affects tourism expectation (β = 0.787, p < 0.01), while tourism expectation has no significant support for TBW (β = 0.004, p = 0.941), and hypothesis H2 is not supported.

In the path of SMC→tourism experience→TBW, SMC has no significant impact on tourism experience (β = 0.044, p = 0.354) and tourism experience positively affects TBW (β = 0.134, p < 0.01), and hypothesis H3 is not supported.

In the chain path of SMC→tourism expectation→tourism experience→TBW, SMC can positively affect tourism expectation, tourism expectation can positively affect tourism experience and tourism experience can positively promote TBW. Besides, tourism expectations and tourism experience play a significant chain mediating role, demonstrating a value of 0.078. Hypothesis H4 is supported.

This study used hierarchical regression to examine CI's moderating effect, with results in Figure 4. With tourism expectations as the dependent variable, Model 1 included age as a control variable, Model 2 added the independent variable and CI and Model 3 incorporated the interaction term SMC × CI to judge the moderating effect by its coefficient significance. The interaction coefficient of SMC and CI was significant. The standardized regression coefficient of SMC was 0.511 (p < 0.01), and that of the interaction term was −0.402 (p < 0.01). The SMC × CI interaction had a significant negative effect on tourism expectation, showing CI has a significant negative moderating role between SMC and tourism expectation. Thus, Hypothesis H5 was verified.

Based on Study 1, Study 2 was conducted from November 1–30, 2025, using Xi'an tourist attractions as experimental materials. Participants were randomly divided into high and low SMC groups, controlling for gender, age, education, income and occupation. Standardized stimuli were developed with real Xiaohongshu UGC, and pre-experiments ensured valid independent variable manipulation. Unified experiments, instructions and measurement tools guaranteed good internal validity. Then, the chain mediation and moderation mechanisms were further analyzed to clarify causal paths and boundary conditions, enhancing the rigor and explanatory power of research conclusions.

Before the formal experiment, experimental materials with high or low levels of SMC were pretested (see Figures 5 and 6). This study selected the graphic pages on rednote with the keyword “Xi'an Travel Guide”, using posts with the highest-liked and lowest-liked as SMC stimuli. Participants received unified reading guidelines before viewing to ensure authentic browsing and objective feedback. Valid participants, limited to rednote users with one-year travel experience, were recruited on the questionnaire platform. The system removed unqualified samples like non-rednote users, those without recent travel experience and those failing the attention test. The minimum sample size of 128 was calculated by G*Power 3.1 (effect size = 0.25, significance level = 0.05, statistical power = 0.8).

138 subjects were recruited in the pretest experiment. They were randomly assigned to one of two groups, filled in the experimental material perception scale and provided demographic information. One-way ANOVA results showed significant SMC differences (Cronbach's α = 0.982) between the two groups (M _high = 4.31, M _low = 2.13; F _[1,136] = 274.219, p = 0.000). This indicates the independent variable operation was successful and the high/low level SMC experimental material was well-constructed.

160 subjects were recruited for this experiment, and a total of 154 valid samples were obtained. The purpose was to test whether SMC has significant differences at different levels (high vs low). In this experiment, a single-factor two-level (high SMC versus low SMC) completely randomized inter-group design was employed. In this design, the level of SMC served as the independent variable between subjects, while the TBW was the dependent variable.

Experimental results: (1) Independent variable manipulation test. One-way ANOVA found significant differences in the level of SMC (Cronbach's alpha = 0.983) between the high and low groups (M _high = 4.38, M _low = 2.34; F _[1,152] = 219.853, p = 0.000) and the independent variable manipulation was successful. (2) Dependent variable test. One-way ANOVA on the dependent variable visitor behavioral willingness (Cronbach's α = 0.972) showed that visitor behavioral willingness was significantly higher in the high-level SMC group than in the low-level SMC group (visitor behavioral willingness M _high = 4.42, M _low = 2.32; F _[1,152] = 209.111, p = 0.000). The effect of gender on TBW was not significant (F _[1,152] = 0.342, p = 0.56), nor was the effect of age on TBW (F _[1,152] = 0.174, p = 0.677). The subsequent analysis controlled for gender and age of the sample, and the analysis results showed that the influence of gender and age was not significant.

Given that the independent variables were randomly assigned through experimental manipulation and the dependent variables were measured immediately after the manipulation, the conditions of both groups, excluding the experimental materials, were controlled. As a result, the observed differences in TBW can be ascribed to the disparities in SMC levels. Therefore, this experiment can support the causal conclusion of SMC → TBW.

To satisfy the primary causal effects, we further examine the chain mediation mechanism of tourism expectation and tourism experience in SMC influencing TBW, clarifying the causal path and intrinsic transmission mechanism from independent variable to dependent variable. In this experiment, 228 subjects were recruited for this experiment and a single-factor two-level (high SMC vs. low SMC) completely randomized inter-group design was adopted. The level of SMC was the independent variable between subjects; the TBW was the dependent variable, and the tourism expectation and tourism experience were the mediating variables. The independent variables were randomly grouped by experimental manipulation. After the independent variables were manipulated, the two mediating variables of tourism expectation and tourism experience were measured in chronological order, and finally, the dependent variables were measured.

Experimental results: (1) Independent variable manipulation test. One-way ANOVA found significant differences in the level of SMC (Cronbach's α = 0.984) between the high and low groups (M _high = 4.49, M _low = 2.97; F _[1,226] = 144.049, p = 0.000), indicating that the independent variable manipulation was successful. (2) Dependent variable test. One-way ANOVA on the dependent variable visitor behavioral willingness (Cronbach's α = 0.974) showed that visitor behavioral willingness was significantly higher in the high-level SMC group than in the low-level SMC group (M _high = 4.54, M _low = 2.97; F _[1,226] = 140.986, p = 0.000). (3) Test of mediating variables. The results of one-way ANOVA on the mediator variables tourism expectation (Cronbach's α = 0.923) and tourism experience (Cronbach's α = 0.940) showed that the high-level SMC group had significantly higher tourism expectation and tourism experience than the low-level SMC group (mean travel expectation M _high = 4.59, M _low = 2.97; F _[1,226] = 189.13, p = 0.000; mean travel experience M _high = 4.48, M _low = 2.96; F _[1,226] = 149.747, p = 0.000).

Model 6 in the PROCESS was used to test the mediating effect. Bootstrapping repeated sampling of 5000 times was applied. The results show that there is a chain mediation effect between SMC and TBW, and between tourism expectation and tourism experience.[β = 0.028; Lower Limit of the 95% Confidence Interval (LLCI = 0.004), Upper Limit of the 95% Confidence Interval (ULCI = 0.069)].

Given that participants in the perceived tourism experience were formed by experimental stimuli rather than real travel scenarios, this study verified the statistical association of the path, which is the order of SMC → tourism expectation → tourism experience → TBW. The mediated pathway serves only as an exploratory explanation for the effects of SMC on TBW.

We further examine the moderating role of CI, clarifying the boundary conditions of causal effects of SMC on tourism expectations. A2 (high SMC vs. low SMC) × 2 (high CI vs. low CI) two-way between-subjects design was adopted. Among them, the SMC level was randomized through experimental manipulation and cultural intelligence level was the inherent attribute of the subjects. The data were split at the median, separating the high CI group and the low CI group. Gender, age, education, monthly income and occupation were set as control variables.

148 participants were divided into high- and low-cultural intelligence groups based on the cultural intelligence scale. There was a significant difference in CI between the two groups (M _{high CI=4.58}, M _{low CI} = 3.46, F _[1,146]= 260.750, p < 0.001). Two-factor contrast analysis results indicated that CI has a significant moderating effect on the relationship between SMC and tourism expectation (F _[1,146]= 12.888, p < 0.001). The results of simple effects analysis indicated that CI significantly moderated the relationship between SMC and tourism expectations. At low CI levels, the high-level SMC group had significantly higher travel expectations than the low-level SMC group [M _high=4.37, Standard Deviation (SD) _high = 0.41; M _low = 3.55, SD _low = 0.97; t = 5.342, p < 0.001]. At high CI levels, there was no statistically significant difference in travel expectations between the high-level and low-level SMC groups (M _high = 4.48, SD _high = 0.59; M _low = 4.47, SD _low = 0.54; t = 0.058, p = 0.954).

It should be noted that because CI is a non-manipulable individual variable, the above results only reflect the statistical interaction between CI level and SMC effect, providing directional support for subsequent experimental research on manipulating CI.

Based on symbolic interaction theory and embodied cognitive theory, this study developed an integrated model to examine how SMC influences TBW through the chain mediation of tourism expectations and tourism experiences, with cultural intelligence (CI) as a moderator.

First, SMC has a significant positive impact on TBW, supporting prior research (Liu, 2024). SMC conveys systematic destination symbols – cultural, emotional and informational, e.g. scenic introductions, food recommendations (Culler, 1981). These immediate, interactive and diverse symbols accelerate decision-making, converting potential tourists into actual visitors and enhancing TBW.

Second, structural equation modeling and experimental methods show that tourism expectation and tourism experience are not significant as independent mediators, but their chain mediation is significant. Expectation provides an explanatory framework for experience, while experience validates expectation; they form an inseparable psychological sequence. SMC activates symbolic expectations, which are then carried into the embodied destination experience. Tourists constantly compare expectations with perception: when experience matches or exceeds expectations, positive evaluations arise; when it falls short, negative evaluations follow. Only through this comparison and verification does initial symbolic expectation translate into behavioral willingness (e.g. revisit, recommend, premium payment). Thus, TBW is not a direct response to expectation or experience alone, but to the cognition of consistency between them. The impact of SMC is strongest when its intended effects are verified by actual experience.

Third, demographic findings indicate that male tourists show stronger additional tourism willingness. Tourists with lower education levels are less influenced by SMC. For example, farmers show lower TBW and CI than other occupational groups.

Fourth, CI negatively moderates SMC's influence on tourism expectations. For low-CI tourists, simple, intuitive visual and emotional symbols are more accessible, so easily understood, emotionally rich SMC generates higher expectations. In contrast, high-CI tourists, with richer cultural symbol knowledge, rely less on SMC; if SMC emphasizes emotional resonance without deep interpretation, they develop lower expectations (Ng, 2007). Considering empirical evidence that CI is a critical factor in personal attributes influencing tourist destination alternatives (Earley, 2003), future research should explore the cognitive pathway linking CI between SMC and TBW.

This study adds value to knowledge of tourist behavior in destination marketing. First, it expands symbolic interaction and embodied cognition theories by integrating SMC, CI and TBW into a comprehensive framework – the symbol-embodied interaction model (Figure 7). Existing symbolic interaction theory focuses on how symbols create meaning in interpersonal interaction but neglects the cognitive shift from remote symbol reception to on-site physical participation. Embodied cognition theory emphasizes physical presence but rarely addresses how pre-formed symbolic expectations shape experience interpretation. Our model distinguishes the departure stage (SMC symbol stimulation of expectation) from the embodied stage (sensory verification of expectation). TBW arises only from comparing and calibrating these two stages, not from either alone. The model follows the core logic of combining meaning-construction and behavioral guidance (symbolic interaction) with sensory interaction and cognitive formation (embodied cognition). The integrated model illustrates the mechanism as follows:

TBW results from continuous, multi-sensory interaction between the tourist and destination symbols, verifying prior expectations. Through destination marketing symbolism, tourists engage in symbolic imagination (e.g. imagining themselves in a Tang-era cityscape). Indirect symbols (e.g. videos of others in Tang costume) trigger simulated physical reactions (excitement, desire to try), strengthening future travel intentions. On-site, visitors interact with direct symbols multi-dimensionally and through multiple senses, engaging in embodied experiences like role-playing and consumption. Comparing first-hand sensory information (touching sculptures, seeing costumed tourists) with pre-symbolic imagination may confirm, subvert, or modify expectations. When tourism experience successfully integrates symbolic meaning with self-perception through physical practice, deep positive emotions attach to destination symbols, increasing revisit and recommendation likelihood.

In the symbol-embodied interaction model, tourism expectation and experience have strict order, functional differentiation and psychological dependence. Expectation precedes experience temporally and presets cognitive evaluative criteria, acting as a reference benchmark. Experience without expectation lacks a comparison framework; experience provides comparative material. Without experience, expectation remains imaginary and TBW does not emerge. Sensory inputs (e.g. a crowd) can be interpreted as lively (festive expectation) or too noisy (quiet expectation). Thus, an irreversible order exists: comparison benchmark → comparative material → evaluation result (TBW).

Second, while existing research shows SMC dimensions directly contribute to TBW (e.g. re-visits, likes, recommendations), evidence on how SMC attributes influence TBW through expectations and experiences is lacking. This study proposes “expectation-experience coherence as cognition” as the actual psychological carrier of mediation. In social-media-driven travel decisions, the real mediating variable is not expectation or experience alone, but the outcome of their comparison. When SMC's expected effects are verified in embodied experience, TBW (recommend, revisit, pay premium) is stronger. Thus, “expectation-experience coherence as cognition” is the core explanatory variable.

Third, CI negatively moderates SMC's impact on tourism expectations. This appears counter-intuitive (high-CI people should use information better). Likely, high-CI individuals demand deeper, authentic cultural symbols; simplified, emotional, visual SMC lowers expectations because they interpret it as signaling “over-commercialization” or “cultural shallowness”. In the symbol-embodied interaction model, CI can be redefined from general “cross-cultural competence” to an index of symbolic critical distance. Although SMC can activate TBW, higher-CI tourists have stronger symbolic critical distance and lower expectations; only in-person embodied experience drives their committed TBW.

The chain mediation finding (SMC → expectation → experience → TBW, with single mediations non-significant) offers a key practical insight: simply raising expectations or improving on-site experience is insufficient to generate TBW. Consistency between expectation and experience is crucial.

First, expectation calibration, not maximization, should guide SMC design. Current practice often posts idealized destination images to maximize expectations. But the chain mediation logic shows that excessively high expectations unmet by field experience widen the expectation-experience gap, weakening positive mediation. Marketers should moderately retain authentic details in SMC (e.g. reasonable crowd levels and seasonal climate) so tourists form realistic expectations. When on-site experience aligns with or slightly exceeds expectations, the explanatory framework of expectation functions fully, enhancing TBW.

Second, marketers can design verifiable symbols in SMC (e.g. specific photo spots and local gathering places). When tourists successfully reproduce these on-site, expectations are instantly verified, generating positive evaluation and reinforcing TBW. This transforms abstract expectations into performable tasks. Given non-significant single mediation, two marketing pitfalls should be avoided: overinvesting in artificial, idealized content while neglecting live-experience content; and improving on-site experience without posting information that manages expectations. Marketers should shift from simple destination exposure to strengthening expectation-experience coherence as cognition.

Third, for low-CI tourists, marketers can use prominent visual symbols and attention-grabbing gimmicks to enhance emotional impact, reducing cognitive obstacles. For higher-CI tourists, who are more rational and have strong cross-cultural adaptability, promotion content should feature well-crafted cultural symbols, deep cultural interpretation (historical background, architectural details, intangible heritage stories), high-quality interactions and tailored strategies – not simple, intuitive posters.

Fourth, TBW is particularly characteristic of males, while lower-education groups pay less attention to SMC and prefer offline communication. Destinations can identify male tourists, encourage them to share travel experiences (e.g. earning electronic medals for completing routes and points for sharing strategies) and leverage their high willingness for secondary SMC dissemination. For low-education tourists, provide cost-effective products and offline touchpoints (community travel promotions, referral rewards for friends and family).

Finally, marketers should enhance SMC skills and employ big data to target active tourists. Destinations should build emotional connections through virtual and real experiences and strengthen on-site relationships.

Notwithstanding the foregoing, this research also has limitations that must be improved upon in future research. First, the sample was drawn from Xi'an Grand Tang Mall, China. Findings need cross-context validation in other countries. Second, factors such as free tickets, ticket prices and social media influencers may impact SMC's effect on TBW and should be explored. Third, although CI was included as a moderator, other mediators and moderators at different levels may have been missed. Future research can further examine boundary conditions. Fourth, the experimental method is limited by the sample and measurement tools, particularly regarding the causal direction of dependent variables. Future longitudinal experiments or empirical sampling methods can provide more robust causal evidence of mediation.