2026 Volume 11 Issue 3
Creative Commons License

TikTok Short-Form Content and Gen Z Community Tourism Intentions in Ho Chi Minh City


, , ,
  1. Faculty of Business Management, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam.
Abstract

Community tourism, a relatively new form of tourism, is currently receiving significant attention from government agencies. In parallel, industry's potential tourists, acting as 'digital natives,' search, research, and consult destinations primarily through social media platforms, with TikTok being the most prominent. The study employed PLS-SEM with a sample of 354 respondents using an online questionnaire. The results indicate that Ethical Value influences Community-Based Tourism Intention through the mediating roles of Perceived Ease of Use, Perceived Usefulness, Time Distortion, and Attention Focus. Conversely, Perceived Ease of Use does not mediate the relationship between Aesthetic Value, Play Value, and Community-Based Tourism Intention. This research opens a new direction by examining how content on social media platforms provides value to users. It also offers practical management implications to help community tourism destination administrators and content creators develop deeper strategies for the TikTok platform, specifically, and social media in general.


Keywords: Community tourism, Holbrook's typology of consumer value, Video marketing, Tourism intention.

Introduction

Five years after the COVID-19 pandemic, daily life has undergone a substantial transformation as activities have gradually shifted from face-to-face interactions to online environments. The ubiquitous image of individuals constantly using smartphones has become commonplace, reflecting deep integration of digital technology into everyday life. Social networking platforms are increasingly perceived as “living” environments; notably, 55% of Asian respondents reported feeling more connected to others through social media. In Vietnam, this digital expansion is evident with approximately 72.7 million internet users. The most widely used platforms include Facebook, YouTube, Instagram, WhatsApp, and TikTok. Despite being a relatively late entrant, TikTok has rapidly gained global prominence, reaching 1.59 billion users and placing Vietnam among the top ten countries with the highest TikTok usage, at approximately 40.9 million users.

The rapid growth of TikTok reflects a broader shift in content consumption behavior from long-form to short-form media. As noted by (Newman, 2010), watching short videos resembles “snacking culture,” where users consume content in brief intervals suited to modern lifestyles. Research by (Omar & Dequan, 2020) indicates that TikTok usage is driven by participatory behaviors such as liking, commenting, and sharing, alongside content consumption and the desire to archive personal moments. Moreover, TikTok videos are typically characterized by low production costs but high virality (Wang et al., 2023), with influencers and celebrities playing a significant role in enhancing user engagement (Duan, 2023). The platform’s mechanism encourages collective participation (Lartey, 2024) and acts as an effective complementary channel within the broader social media ecosystem (Fan & Hemans, 2022).

Beyond entertainment, TikTok has also profoundly influenced consumer behavior in the tourism sector, which is becoming a crucial economic driver in Vietnam. The country is renowned for its rich natural landscapes, favorable climate, diverse cuisine, and the hospitality of its people. In 2024, domestic tourist arrivals reached approximately 110 million, suggesting an average of around one trip per person annually, while international arrivals totaled 14 million. There is a growing interest among tourists in community-based tourism that focuses on cultural and culinary experiences (Anggara et al., 2025; Oktaviani et al., 2025; Wulandari, 2025). In this context, TikTok has emerged as a key platform influencing travel behavior, as users increasingly shift from traditional search tools such as Google, Facebook, and YouTube to TikTok for travel inspiration.

Empirical studies confirm that TikTok content significantly affects tourists’ travel intentions (Maharani & Effendi, 2025). Short, visually engaging videos serve as powerful marketing tools that directly influence service selection decisions (Vequist, 2025). Furthermore, TikTok’s algorithm-driven personalization enhances accessibility to tourism products and services (Nguyen, 2021), while short videos have been recognized as highly effective for destination marketing (Cao et al., 2021). These findings highlight the substantial impact of TikTok’s concise and engaging content on travel decision-making processes.

However, despite growing recognition of this impact, research on the role of short-form content—particularly on TikTok—in shaping community-based tourism intentions remains limited. In Vietnam, (Tri Thong et al., 2023) explored the influence of social media on domestic travel intentions but reported a relatively low explanatory power (R² = 50.2%) and did not incorporate mediating variables. Similarly, (Ánh et al., 2026) examined Gen Z’s social media behavior without focusing on specific usage characteristics. International studies, such as (Liu et al., 2023; Liu et al., 2024), are largely limited to China, where platform dynamics differ from TikTok. Other research has overlooked key mediating factors like perceived usefulness and time distortion (Yue & Li, 2024), or ignored platform-specific characteristics (Shuvo & Ahmed, 2025). Additionally, (Wang et al., 2022) emphasized the need for further research on short-form content and tourism intention.

Therefore, a significant research gap persists regarding the impact of TikTok short-form content on community-based tourism intention. To address this, the present study integrates the Technology Acceptance Model (TAM), Holbrook’s Typology of Consumer Value, and Flow Theory to examine the influence of TikTok content on Gen Z’s intention to engage in community tourism in Ho Chi Minh City. This study aims to contribute theoretically and provide practical implications for content creators and tourism managers in developing effective engagement strategies.

Theoretical Framework and Research Hypotheses

The Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), developed by (Davis et al., 1989), is widely recognized as a fundamental framework for predicting users’ acceptance and use of technology. It explains that individuals’ behavioral intentions are influenced primarily by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), which reflect the effectiveness and effortlessness of a system (Leong, 2003; Sukackė, 2019; Bazine, 2025). Prior studies confirm TAM as one of the most reliable models in information system research (Haryani et al., 2014; Ayeh, 2015; Balouchi et al., 2017). It has been validated across tourism and social media contexts, particularly in explaining travel planning behavior (Mariani et al., 2019; Singh & Srivastava, 2019; Cheunkamon et al., 2020).

Holbrook's Typology of Consumer Value

Holbrook’s Typology of Consumer Value (Holbrook, 1999) conceptualizes consumer value as a multidimensional construct combining material and experiential aspects (Weibel et al., 2012). It includes utilitarian, hedonic, social, and cognitive values (Holbrook, 1994), and distinguishes between extrinsic (tangible) and intrinsic (subjective, experiential) value dimensions.

Self – Oriented and Other - Oriented

Self-oriented Values: At their core, self-oriented values revolve around personal needs, wants, and aspirations. They encompass the individual satisfactions that consumers expect or experience from a product or service. Other-oriented Values: In contrast, other-oriented values shift the focus to considerations beyond the self. They involve awareness of and responsiveness to the needs and wants of others, reflecting the social or altruistic dimension in a consumer’s value calculus (Holbrook, 1994).

Active and Reactive

Active value arises when consumers intentionally engage with a product or service to seek and obtain benefits, while reactive value occurs when benefits are passively experienced without deliberate effort (Holbrook, 1994). Tourism, particularly hotel experiences, is ideal for examining this typology due to its multidimensional nature and high involvement (Al-Sabbahy et al., 2004; Gallarza & Gil, 2008; Wu & Liang, 2009). Holbrook’s framework has been widely validated in tourism contexts (Gallarza et al., 2017).

Flow Experience

Flow Experience Theory, proposed by (Csikszentmihalyi, 1990), describes a state of deep engagement where individuals feel fully immersed and intrinsically motivated. This state combines enjoyment, cognitive efficiency, and happiness (Moneta & Csikszentmihalyi, 1996), often causing time distortion and intense focus (Rodríguez-Sánchez et al., 2008; Li, 2021), characterized by key dimensions such as enjoyment, interactivity, and attention.

Ethical Value

Ethical values are the core principles that guide the beliefs, actions, and priorities of an individual or a profession. They are generally regarded as preparation guidelines for operating within the professional working environment (Shahriari et al., 2012). (Hợi, 2007) defines the ethical values within each person as those principles chosen and evaluated by individuals; they are considered to be actions that hold positive social significance, are endorsed by one's conscience, and are praised by public opinion.

In the business context, ethical values translate into managerial behaviors that significantly impact employee satisfaction and organizational outcomes (Lather, 2009; Salikhova et al., 2023; Nyamagoud et al., 2024). In the healthcare context, particularly nursing, ethical values are crucial for nurses to handle difficult situations and make informed decisions while maintaining professional integrity (Manfredini et al., 2024; Haddad & Geiger, 2025; Petronis et al., 2025). (Shuvo & Ahmed, 2025) found that ethical values influence a component of the Theory of Planned Behavior, specifically the attitude variable. However, research directly examining the effect of ethical values on the two core TAM constructs, Perceived Usefulness and Perceived Ease of Use, is currently scarce. Given the context of digital technology and short-video platforms like TikTok, where ethical values can be conveyed through tourism messages, local culture promotion, or proper behavioral codes, exploring the relationship between ethical values and TAM presents a novel and promising avenue for research.

H1a: Ethical Value perceived by users from TikTok's short-form content positively influences the Perceived Ease of Use of the TikTok platform.

H1b: Ethical Value perceived by users from TikTok's short-form content positively influences the Perceived Usefulness of the TikTok platform.

Aesthetic Value

Aesthetic value refers to the appreciation derived from consumption experiences independent of functional benefits (Perry, 2013). It shapes first impressions through visual elements like layout and color (Park & Gretzel, 2007) and enhances perceived usability (Ben-Bassat et al., 2006; Li & Yeh, 2010), although findings remain inconsistent (Hamborg et al., 2014).

H2a: Aesthetic Value perceived by users from TikTok's short-form content positively influences the Perceived Ease of Use of the TikTok platform.

H2b: Aesthetic Value perceived by users from TikTok's short-form content positively influences the Perceived Usefulness of the TikTok platform.

Play Value

Play Value is an intrinsic dimension of value, derived from the enjoyment or pleasure gained from using a product or service. It reflects the consumer’s pursuit of delight, enjoyment, and emotional satisfaction (Holbrook, 1999). This play value is often culturally determined, being shaped by social, historical, educational, economic, and political factors. This is evident in Bangladesh, where parents view play as purely recreational, while educators recognize its learning benefits (Chowdhury & Rivalland, 2012; Muthanandam et al., 2024).

(Agarwal & Karahanna, 2000) propose a psychological mechanism: that enjoyable technologies are perceived as more useful because users are not consciously aware that they are also being more efficient and productive. In the context of mobile online commerce, features such as attractiveness and liveliness positively influence Play Value. This Play Value, in turn, has a strong positive impact on Perceived Value, an extension that encompasses Perceived Usefulness (Hong & Na, 2008; Matute-Vallejo & Melero-Polo, 2019; Çınaroğlu et al., 2023).

H3a: Play Value perceived by users from TikTok's short-form content positively influences the Perceived Ease of Use of the TikTok platform.

H3b: Play Value perceived by users from TikTok's short-form content positively influences the Perceived Usefulness of the TikTok platform.

Perceived Ease of Use

Perceived Ease of Use substantially affects an individual's attitude through two main psychological mechanisms: self-efficacy and instrumentality (Davis, 1985). (Venkatesh, 2000) defines PEOU as initial perceptions shaped by general beliefs, including self-efficacy, facilitating conditions, enjoyment, and other factors. These perceptions are subsequently adjusted based on actual experience and objective usability. Research has indicated that the Perceived Ease of Use (PEOU) of emerging technologies has a significant impact on the sense of immersion (Zhou, 2013). (Hsu, 2010) In his study, he confirmed that PEOU acts as an antecedent to flow in the context of video games. Conversely, (Yang et al., 2022) found that the flow experience in online tourism boosts tourists' Perceived Usefulness and Perceived Ease of Use, thereby establishing a bidirectional relationship where technology acceptance factors influence flow states and vice versa.

H4a: The Perceived Ease of Use of TikTok short-form video content positively influences Time Distortion.

H4b: The Perceived Ease of Use of TikTok short-form video content positively influences Attentional Focus.

Perceived Usefulness

Perceived Usefulness refers to the extent to which a technology enhances performance, efficiency, and convenience, thereby increasing adoption intention (Chesney, 2006; Muñoz-Leiva et al., 2017). It also positively influences flow experiences in tourism contexts and virtual environments, reinforcing its critical role in shaping user behavior (Natarajan et al., 2017; Xie & Yuan, 2021).

H5a: Perceived Usefulness of TikTok short-form content positively influences Time Distortion.

H5b: Perceived Usefulness of TikTok short-form content positively influences Attentional Focus.

Time Distortion

Time Distortion is a core characteristic of the Flow State (Csikszentmihalyi, 1990). One hypothesis suggests that when our attention is fully focused on a task, the brain devotes fewer resources to tracking time. This results in the registration of fewer time signals,leading us to underestimate the actual elapsed time (Rutrecht et al., 2021). Consistent with this, research on virtual reality (VR) indicates that a successful VR experience often results in the user losing their sense of time (Yang et al., 2022).

In the context of tourism, when tourists experience a state of flow, they tend to perceive that time passes more quickly. A highly immersive virtual experience that leads to time distortion can create positive memories and emotions, thereby promoting intentions to visit the destination in reality (Wei et al., 2019). From a commercial standpoint, when tourists experience flow, they are inclined to extend their length of stay, express an intention to return, and recommend the destination to others (Coffey & Csikszentmihalyi, 2016).

H6: Time Distortion positively influences the Community Tourism Intention of Gen Z in Ho Chi Minh City.

Attention Focus

Attention Focus is defined as the state in which an individual's attention and engagement are fully directed to the task or activity at hand. It is also described as a high level of concentration and focus on the present moment(Te Brömmelstroet et al., 2022). This state of high concentration renders the experience more effective and enjoyable, resulting in positive attitudes and a desire to repeat the experience (Wang & Lee, 2020). Research by (Zhu & Yang, 2023) on users' intention to continue searching for travel information in virtual communities found that both attention focus and time distortion had a significant impact on continuance intention. Furthermore, attention focus has been shown to positively affect the overall experience of computer users and their intention for repeated system use (Novak & Hoffman, 1997).

H7: Attention Focus positively influences the Community Tourism Intention of Gen Z in Ho Chi Minh City.

Community Tourism Intention

Customer intention towards products and services is the result of the satisfaction perceived by customers with the offerings provided by the service providers (Ratnasari et al., 2020). Travel intention is generally defined as the attitude of tourists towards a specific destination (Chen & Tsai, 2007). Tourist behavioral intention is crucial for tourism managers because this intention significantly impacts the number of tourist arrivals (Ratnasari et al., 2020).

Based on the preceding hypotheses, the author proposes the following research model (Figure 1):

 

Figure 1. Research model

Materials and Methods

This study employs both qualitative and quantitative methods to ensure theoretical depth and the accuracy of the model during its conceptualization, development, and validation process. The author utilized SPSS 20 for descriptive statistical analysis and SmartPLS 4 for PLS-SEM analysis. Secondary data was derived from pre-existing research. Primary data was collected by designing and administering a survey. The author conducted exploratory interviews with a group of eight individuals to assess the model's conceptual relevance and the measurement properties of the observed variables.

The author employed a non-probability sampling method, specifically, quota sampling, conducted from August 21, 2025, to November 10, 2025. A total of 354 samples were collected via online questionnaires from Gen Z individuals at schools, parks, and shopping centers (such as Emart and Lotte) in Ho Chi Minh City.

Research Process

The study employed a survey distributed online via social media platforms such as Facebook, Zalo, and Microsoft Teams using a Google Forms link and QR code to enhance efficiency. The questionnaire included three sections: screening questions, demographic information, and measures of community tourism intention. Based on (Hair et al., 2019), a minimum sample of 145 was required; however, 377 responses were collected to ensure reliability, with 354 valid samples retained. Reliability and validity were assessed using Cronbach’s Alpha, Composite Reliability, R², f², and Q² following established thresholds (Cohen, 2013; Comrey & Lee, 2013; Hair, 2014).

Measurement Scale

The study adopts measurement scales from established research sources. The model includes eight constructs with 29 observed variables, measured using a five-point Likert scale from strongly disagree to strongly agree. Specifically, Ethical, Aesthetic, Play Value, and Community Tourism Intention are adapted from Shuvo and Ahmed (2025), while TAM-related constructs are adopted from Liang et al. (2025).

Results and Discussion

Descriptive Statistics

The sample is concentrated among respondents born between 2002 and 2004, accounting for over 52%, with 2003 being the largest group. This suggests strong interest in community tourism among Gen Z, particularly individuals around age 22 who are transitioning into the workforce and gaining financial independence.The sample of 354 respondents is relatively balanced in gender, with females accounting for 50.8% (180) and males 49.2% (174), ensuring objectivity. Regarding occupation, full-time employees dominate (53.4%), followed by students (33.6%), reflecting core Gen Z characteristics. In terms of income, most respondents earn 3–7 million VND (41.2%), indicating high price sensitivity. TikTok usage is frequent, with 71.8% using it daily, highlighting its strong presence in Gen Z’s daily life for both entertainment and information purposes.

Measurement Model

Composite Reliability values (0.912–0.944) meet recommended thresholds (Fornell & Larcker, 1981; Hair, 2014), indicating strong reliability without redundancy. Additionally, all AVE values exceed 0.5, confirming adequate convergent validity, meaning each construct explains at least 50% of the variance in its observed indicators (Table 1)

Table 1. Cronbach’s Alpha and CR

 

Cronbach’s Alpha

rho_a

rho_c

AVE

AF

0.908

0.908

0.935

0.784

AV

0.879

0.887

0.925

0.805

CI

0.904

0.905

0.933

0.777

EV

0.897

0.897

0.928

0.763

PEOU

0.927

0.927

0.944

0.773

PU

0.859

0.862

0.914

0.780

PV

0.857

0.880

0.912

0.775

TD

0.875

0.878

0.923

0.800

 

The inter-construct correlation coefficients presented in Table 2 are all smaller than the square root of the Average Variance Extracted (AVE) for the respective constructs. This finding confirms adequate discriminant validity among the constructs within the proposed model.

Table 2. Fornell-Larcker

 

AF

AV

CI

EV

PEOU

PU

PV

TD

AF

0.885

             

AV

0.223

0.897

           

CI

0.624

0.189

0.881

         

EV

0.479

-0.069

0.420

0.874

       

PEOU

0.555

-0.011

0.502

0.566

0.879

     

PU

0.557

0.384

0.468

0.370

0.198

0.883

   

PV

0.078

-0.035

0.076

-0.059

-0.057

0.243

0.880

 

TD

0.542

0.148

0.595

0.437

0.545

0.481

0.099

0.895

 

Structural Model

The author conducted the Bootstrapping procedure using a subsample size of 5,000.

Table 3. Assessment of the Structural Model

Standardized Path Coefficient

Sample Mean

Standard Deviation

T-Statistic

P-Value

Conclusion

AF -> CI

0.427

0.427

0.042

10.207

< 0.001

Accepted H7

AV -> PEOU

0.027

0.027

0.045

0.602

0.547

Not Supported H2a

AV -> PU

0.422

0.422

0.043

9.770

< 0.001

Accepted H2b

EV -> PEOU

0.566

0.566

0.036

15.904

< 0.001

Accepted H1a

EV -> PU

0.416

0.417

0.038

11.032

< 0.001

Accepted H1b

PEOU -> AF

0.463

0.463

0.034

13.703

< 0.001

Accepted H4b

PEOU -> TD

0.468

0.468

0.039

12.114

< 0.001

Accepted H4a

PU -> AF

0.466

0.465

0.036

13.090

< 0.001

Accepted H5b

PU -> TD

0.389

0.389

0.038

10.202

< 0.001

Accepted H5a

PV -> PEOU

-0.022

-0.024

0.044

0.505

0.614

Not Supported H3a

PV -> PU

0.282

0.383

0.040

7.003

< 0.001

Accepted H3b

TD -> CI

0.364

0.363

0.042

8.603

< 0.001

Accepted H6

R2

R2PEOU = 0.135; R2PU = 0.379; R2AF = 0.514; R2TD = 0.439; R2CI = 0.479

Q2

Q2PEOU = 0.309; Q2PU = 0.370; Q2AF = 0.297; Q2TD = 0.232; Q2CI = 0.219

f2

f2AF ->CI = 0.248; f2AV -> PEOU = 0.001; f2Av ->PU = 0.288; f2EV -> PEOU = 0.468; f2EV -> PU = 0.279; f2PEOU -> AF = 0.426; f2PEOU -> TD = 0.376; f2PU -> AF = 0.431; f2PU -> TD = 0.260; f2PV -> PEOU = 0.001; f2PV -> PU = 0.128; f2TD -> CI  = 0.180

 

The results of the hypothesis testing indicate that most proposed relationships in the research model are statistically significant. Specifically, Ethical Value has a strong positive impact on both Perceived Ease of Use and Perceived Usefulness, thereby supporting H1a and H1b. Similarly, Aesthetic Value and Play Value significantly influence Perceived Usefulness, confirming H2b and H3b; however, their effects on Perceived Ease of Use are not statistically significant, leading to the rejection of H2a and H3a. Furthermore, both Perceived Ease of Use and Perceived Usefulness positively affect Attention Focus and Time Distortion, supporting H4a, H4b, H5a, and H5b. In turn, these flow-related factors significantly enhance Community Tourism Intention, confirming H6 and H7. Overall, the findings demonstrate that TAM constructs and experiential factors play a crucial role in shaping tourists’ behavioral intentions, although some value dimensions show limited influence on ease of use (Table 3).

The findings indicate that all Adjusted R² values exceed the recommended thresholds (Falk & Miller, 1992; Hair, 2014), while Q² values are above zero, confirming the model’s predictive relevance (Evermann & Tate, 2016; Yanti & Endri, 2024). Most relationships demonstrate acceptable effect sizes (f² ≥ 0.02) and statistical significance (p < 0.05), except for AV → PEOU and PV → PEOU, which are insignificant and negligible (Cohen, 2013). Additionally, the indirect effect analysis shows that most hypotheses are supported at the 5% level, although several are rejected due to p-values exceeding 0.05.

First, this study contributes to theory by extending the integration between Holbrook’s Consumer Value Typology and the Technology Acceptance Model (TAM), thereby offering a novel interdisciplinary framework. While prior research by (Shuvo & Ahmed, 2025) primarily examined consumer values as antecedents of emotional and attitudinal responses, this study advances the literature by demonstrating that ethical considerations are embedded within digital content and can directly influence users’ technological perceptions. Specifically, the findings reveal a significant relationship between Ethical Value and both Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), thereby enriching the explanatory power of TAM in digital content contexts.

Second, the study identifies a significant indirect effect of Ethical Value on Community Tourism Intention through sequential mediators, including PEOU, PU, Time Distortion, and Attention Focus. This extended mediation mechanism has not been previously addressed in the literature. The confirmation of H1a and H1b represents a notable theoretical advancement, as it positions consumer value—traditionally linked to satisfaction or intention—as a direct antecedent of core TAM constructs. This suggests that in short-form content environments such as TikTok, content characteristics can directly shape users’ perceptions of the technological platform, rather than merely influencing downstream behavioral outcomes.

Third, Aesthetic Value demonstrates a complex pattern of influence on Community Tourism Intention through mediated pathways. While its positive effect on Perceived Usefulness is supported, its impact on Perceived Ease of Use is not significant, leading to the rejection of H10a and H10c but supporting H10b and H10d. This finding contradicts earlier studies (Ben-Bassat et al., 2006; Sonderegger & Sauer, 2010) but aligns with (Katz & Berman, 2011) and partially with (Cyr et al., 2006), reinforcing the nuanced and context-dependent role of aesthetics in digital environments.

Finally, Play Value influences Community Tourism Intention indirectly through TAM and flow-related constructs, although its direct effects on PEOU and PU are not consistently supported. While H13c and H13d are confirmed, H13a and H13b are rejected, reflecting inconsistencies with prior findings but partial alignment with (Yoon et al., 2013). Notably, the mediation results (H14a, H14b) highlight that when TikTok is perceived as easy to use, users allocate greater cognitive resources to content consumption, enhancing Attention Focus and immersion. This flow experience facilitates emotional engagement and strengthens positive attitudes toward destinations, ultimately promoting Community Tourism Intention.

Conclusion

The study has significantly broadened the understanding of community tourism an area prioritized for development by the government but not yet widely adopted by the younger generation of tourists in the Vietnamese context. The research results provide clear evidence of the positive impact of various values on community tourism  intentions. Ultimately, this study contributes a robust research model on the topic of tourism, specifically focusing on the CBT segment, to the existing body of knowledge.

Furthermore, in this research, the author also proposes managerial implications aimed at fostering community tourism intentions among Gen Z in Ho Chi Minh City. These implications are specifically targeted towards content creators, managers of CBT destinations, and local government authorities.

Theoretical Contributions

The study has contributed significant findings and knowledge regarding the factors influencing community tourism intention, specifically:

First, the study successfully developed and validated a comprehensive integrated model, connecting three theoretical frameworks: Holbrook's Typology of Consumer Value, the Technology Acceptance Model (TAM) and Flow Theory. This contribution fills a theoretical gap by demonstrating a clear causal mechanism: values derived from content do not directly influence Flow, but rather indirectly through shaping the core attributes of the TAM model.

Second, the research successfully decoded the mediating role of the Technology Acceptance Model's constructs within this context. The results confirm that Perceived Usefulness acts as the most critical mediating mechanism, serving as a successful bridge for all types of values to influence Flow. Conversely, Perceived Ease of Use is shown to be a selective mediator, effectively bridging only Ethical Value (EV) but failing to mediate the relationships for Aesthetic and Play Value.

Third, the study provides novel empirical evidence affirming that Ethical Value is a crucial antecedent of the TAM model. The results demonstrate that Gen Z perceives ethical content as useful because it aligns with their goals for meaningful travel, and they also perceive the information gathering process as easier due to the trustworthiness that authenticity provides.

Practical Contributions

This study extends theory by integrating Holbrook’s Consumer Value Typology with the Technology Acceptance Model (TAM), showing that Ethical Value directly influences both Perceived Ease of Use and Perceived Usefulness. It also reveals a novel indirect effect of Ethical Value on Community Tourism Intention through PEOU, PU, Time Distortion, and Attention Focus. Aesthetic Value only impacts Perceived Usefulness, highlighting its contextual role in digital environments. Meanwhile, Play Value affects intention indirectly via TAM and flow factors. Importantly, when TikTok is easy to use, users become more immersed, enhancing attention and emotional engagement, which ultimately strengthens Community Tourism Intention.

Limitations and Future Research Directions

This study has several limitations, including the use of non-probability sampling, limiting representativeness, and data collection restricted to Ho Chi Minh City, reducing generalizability. The focus on Gen Z also narrows applicability. Future research should expand samples, include diverse regions and generations, and re-examine relationships across different platforms.

Acknowledgments: This study was supported by the Industrial University of Ho Chi Minh City.

Conflict of Interest: None

Financial Support: None

Ethics Statement: This study was conducted in accordance with ethical principles governing research with human participants. Participation was voluntary, and all data were collected anonymously. No formal ethics committee approval was required for this type of survey-based research under the institutional guidelines of the Industrial University of Ho Chi Minh City.

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. doi:10.2307/3250951

Al-Sabbahy, H. Z., Ekinci, Y., & Riley, M. (2004). An investigation of perceived value dimensions: Implications for hospitality research. Journal of Travel Research, 42(3), 226–234. doi:10.1177/0047287503258841

Anggara, B., Martina, C. D., Muharis, M., & Sirajuddin, M. (2025). Enhancing tourist loyalty: The impact of marketing mix strategies on revisit intention from a tourist perspective in Sade Village. Jurnal Manajemen Pelayanan Hotel, 9(1), 150–164.

Anh, N. T. P., Khoi, B. H., Thu, N. Q., & Ghi, T. N. (2026). The role of the level of artificial intelligence adoption on business model innovation, sustainable competitive advantage, and firm performance: Integrating the TOE framework and dynamic capabilities theory. Green Technologies and Sustainability, 100384.

Ayeh, J. K. (2015). Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173–180. doi:10.1016/j.chb.2014.12.049

Balouchi, M., Aziz, Y. A., Hasangholipour, T., Khanlari, A., Abd Rahman, A., & Raja-Yusof, R. N. (2017). Explaining and predicting online tourists' behavioural intention in accepting consumer generated contents. Journal of Hospitality and Tourism Technology, 8(2), 168–189.

Bazine, I.-D. (2025). Exploring the development of the Technology Acceptance Model (TAM): A chronological overview. International Journal of Research and Scientific Innovation, XII, 1643–1655. doi:10.51244/IJRSI.2025.120600138

Ben-Bassat, T., Meyer, J., & Tractinsky, N. (2006). Economic and subjective measures of the perceived value of aesthetics and usability. ACM Transactions on Computer-Human Interaction, 13(2), 210–234. doi:10.1145/1165734.1165737

Cao, X., Qu, Z., Liu, Y., & Hu, J. (2021). How the destination short video affects customers’ attitude: The role of narrative transportation. Journal of Retailing and Consumer Services, 62, 102672. doi:10.1016/j.jretconser.2021.102672

Chen, C.-F., & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions. Tourism Management, 28(4), 1115–1122. doi:10.1016/j.tourman.2006.07.007

Chesney, T. (2006). An acceptance model for useful and fun information systems. Human Technology, 2(2), 225–235. doi:10.17011/ht/urn.2006520

Cheunkamon, E., Jomnonkwao, S., & Ratanavaraha, V. (2020). Determinant factors influencing Thai tourists’ intentions to use social media for travel planning. Sustainability, 12, 7252. doi:10.3390/su12187252

Chowdhury, N. N., & Rivalland, C. (2012). Value of play as an early learning instrument in Bangladesh context: A sociocultural study. Australasian Journal of Early Childhood, 37(4), 115–122. doi:10.1177/183693911203700416

Çınaroğlu, M., Ahlatcıoğlu, E. N., Prins, J., & Nan, M. (2023). Psychological challenges in cancer patients and CBT impact. International Journal of Social Psychology Aspects in Healthcare, 3, 21–33. doi:10.51847/ZDLdztUSsw

Coffey, J., & Csikszentmihalyi, M. (2016). Finding flow as a tourist.

Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. doi:10.4324/9780203771587

Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press. https://www.taylorfrancis.com/books/mono/10.4324/9781315827506

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience.

Cyr, D., Head, M., & Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Information & Management, 43(8), 950–963. doi:10.1016/j.im.2006.08.009

Davis, F. (1985). A Technology Acceptance Model for empirically testing new end-user information systems.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982

Duan, J. (2023). Research on the development of China’s short video industry. Advances in Economics, Management and Political Sciences, 61, 96–101. doi:10.54254/2754-1169/61/20231047

Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565–4582. doi:10.1016/j.jbusres.2016.03.050

Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.

Fan, M., & Hemans, M. (2022). TikTok: How a Chinese video clip app became a popular and successful global brand (pp. 33–45). doi:10.1007/978-981-16-8074-8_4

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. doi:10.1177/002224378101800104

Gallarza, M. G., & Gil, I. (2008). The concept of value and its dimensions: A tool for analysing tourism experiences. Tourism Review, 63(3), 4–20. doi:10.1108/16605370810901553

Gallarza, M. G., Arteaga, F., Del Chiappa, G., Gil-Saura, I., & Holbrook, M. B. (2017). A multidimensional service-value scale based on Holbrook’s typology of customer value: Bridging the gap between the concept and its measurement. Journal of Service Management, 28(4), 724–762. doi:10.1108/JOSM-06-2016-0166

Haddad, L. M., & Geiger, R. A. (2025). Nursing ethical considerations. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK526054/

Hair, J. F. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage. https://books.google.com/books?id=IFiarYXE1PoC

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. doi:10.1108/EBR-11-2018-0203

Hamborg, K.-C., Hülsmann, J., & Kaspar, K. (2014). The interplay between usability and aesthetics: More evidence for the “what is usable is beautiful” notion. Advances in Human-Computer Interaction, 2014, 946239. doi:10.1155/2014/946239

Haryani, D., Septia, M. A., & Pujani, V. (2014, February 14–15). E-travel use in Padang: The role of enjoyment, perceived ease of use, and perceived usefulness. In Proceedings of the International Conference on Business, Management and Corporate Social Responsibility (ICBMCSR'14), Batam, Indonesia. doi:10.15242/ICEHM.ED0214037

Hợi, M. X. (n.d.). Giá trị đạo đức và sự biểu hiện của nó trong đời sống xã hội.

Holbrook, M. B. (1994). Axiology, aesthetics, and apparel: Some reflections on the old school tie. In Aesthetics of textiles and clothing: Advancing multi-disciplinary perspectives, 131–141.

Holbrook, M. B. (1999). Introduction to consumer value. In Consumer value. Routledge.

Hong, B.-S., & Na, Y.-K. (2008). The effect of perceived hedonic value, usefulness and ease of use on attitude toward using in internet shopping mall and purchase intention of fashion merchandise. Journal of the Korean Society of Clothing and Textiles, 32, 147. doi:10.5850/JKSCT.2008.32.1.147

Hsu, C.-L. (2010). Exploring the player flow experience in e-game playing. International Journal of Technology and Human Interaction, 6(2), 47–64. doi:10.4018/jthi.2010040104

Katz, A., & Berman, I. (2011). Designing an e-mail prototype to enhance effective communication and task management: A case study. Serdica Journal of Computing, 5, 39–64. doi:10.55630/sjc.2011.5.39-64

Lartey, S. (2024). The future influence of TikTok on evolving social media trends: A comprehensive analysis.

Lather, A. S. (2009). Measuring the ethical quotient of corporations: The case of small and medium enterprises in India. Forum on Public Policy. https://go.gale.com

Leong, L. (2003). Theoretical models in IS research and the Technology Acceptance Model.

Li, Y. (2021). The effect of TikTok on tourists’ travel intention. Frontiers in Economics and Management, 2(2), 176–186. doi:10.6981/FEM.202102_2(2).0022

Li, Y.-M., & Yeh, Y.-S. (2010). Increasing trust in mobile commerce through design aesthetics. Computers in Human Behavior, 26, 673–684. doi:10.1016/j.chb.2010.01.004

Liang, J., Zhao, L., Zhang, S., & Lu, L. (2025). How does immersive technology drive real tourism? SAGE Open, 15. doi:10.1177/21582440251330012

Liu, C., Jiang, M., & Muhammad, Z. A. (2024). The impact of TikTok short video factors on tourists’ behavioral intention. PLOS ONE, 19(12), e0315140. doi:10.1371/journal.pone.0315140

Liu, J., Wang, Y., & Chang, L. (2023). How do short videos influence users’ tourism intention? Frontiers in Psychology, 13. doi:10.3389/fpsyg.2022.1036570

Maharani, S. H., & Effendi, T. (2025). Driving travel intention through TikTok: A Generation Z perspective. Asian Journal of Management, Entrepreneurship and Social Science, 5(04), 159–171. doi:10.63922/ajmesc.v5i04.1472

Manfredini, M., Poli, P. P., Giboli, L., Beretta, M., Maiorana, C., & Pellegrini, M. (2024). Dental implant prognosis factors. Journal of Current Research in Oral Surgery, 4, 41–49. doi:10.51847/Gv4h1XyPfr

Mariani, M. M., Borghi, M., & Gretzel, U. (2019). Online reviews: Differences by submission device. Tourism Management, 70, 295–298. doi:10.1016/j.tourman.2018.08.022

Matute-Vallejo, J., & Melero-Polo, I. (2019). Understanding online business simulation games: The role of flow experience. Australasian Journal of Educational Technology, 35(3). doi:10.14742/ajet.3862

Moneta, G. B., & Csikszentmihalyi, M. (1996). The effect of perceived challenges and skills on the quality of subjective experience. Journal of Personality, 64(2), 275–310. doi:10.1111/j.1467-6494.1996.tb00512.x

Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use mobile banking apps. Spanish Journal of Marketing, 21(1), 25–38. doi:10.1016/j.sjme.2016.12.001

Muthanandam, S., Muthu, J., Babu, B. V., Rajaram, S., & Kengadharan, S. (2024). Oral precancer awareness among drivers. International Journal of Social Psychology Aspects in Healthcare, 4, 20–25. doi:10.51847/2JNlaeP6n5

Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding intention to use mobile shopping applications. Journal of Retailing and Consumer Services, 37, 8–22. doi:10.1016/j.jretconser.2017.02.010

Newman, M. (2010). New media, young audiences and discourses of attention. Media, Culture & Society, 32, 581–596. doi:10.1177/0163443710367693

Nguyen, T. V. (2021). Personalized content in approaching Vietnamese Gen Z customers. http://www.theseus.fi

Novak, T. P., & Hoffman, D. L. (1997). Modeling the structure of the flow experience among web users.

Nyamagoud, S. B., Swamy, A. H. V., Chacko, A., & James, J. (2024). Actinomycetoma of the left foot. Interdisciplinary Research in Medical Sciences, 4(2), 41–47. doi:10.51847/UcEjBW4qBs

Oktaviani, D., Sumiyarti, S., & Ratnawati, N. (2025). Household behavior in choosing travel destinations. Almana: Jurnal Manajemen Dan Bisnis, 9(1), 50–64.

Omar, B., & Dequan, W. (2020). Watch, share or create: The influence of personality traits and user motivation on TikTok mobile video usage. International Journal of Interactive Mobile Technologies, 14, 121. doi:10.3991/ijim.v14i04.12429

Park, Y. A., & Gretzel, U. (2007). Success factors for destination marketing websites. Journal of Travel Research, 46(1), 46–63. doi:10.1177/0047287507302381

Perry, R. B. (2013). Frontmatter. In Realms of value: A critique of human civilization (pp. i–vi). Harvard University Press. doi:10.4159/harvard.9780674430419.fm

Petronis, Z., Golubevas, R., Rokicki, J. P., Guzeviciene, V., Sakavicius, D., & Lukosiunas, A. (2025). Trigeminal neuralgia and neurovascular compression. Journal of Current Research in Oral Surgery, 5, 17–24. doi:10.51847/sptZWIrWeo

Ratnasari, R. T., Gunawan, S., Septiarini, D. F., Rusmita, S. A., & Kirana, K. C. (2020). Customer satisfaction between perceptions of environment destination brand and behavioural intention. International Journal of Innovation, Creativity and Change, 10(12), 472–487.

Rodríguez-Sánchez, A. M., Schaufeli, W. B., Salanova, M., & Cifre, E. (2008). Flow experience among ICT users. Psychological Reports, 102(1), 29–39. doi:10.2466/pr0.102.1.29-39

Rutrecht, H., Wittmann, M., Khoshnoud, S., & Igarzábal, F. A. (2021). Time speeds up during flow states: A study in virtual reality with the video game thumper. Timing & Time Perception, 9(4). doi:10.1163/22134468-bja10033

Salikhova, L. R., Khantueva, K. K., Magomedkerimova, N. N., Arganov, F. I., Tambieva, T. S., & Brodskaya, T. A. (2023). Evaluating hypoxia in pregnant women. Interdisciplinary Research in Medical Sciences, 3(1), 20–25. doi:10.51847/nsfOJO4IDe

Shahriari, M., Mohammadi, E., Abbaszadeh, A., Bahrami, M., & Fooladi, M. (2012). Perceived ethical values by Iranian nurses. Nursing Ethics, 19, 30–44. doi:10.1177/0969733011408169

Shuvo, M. I. M., & Ahmed, T. (2025). Sustainable clicks: Exploring Gen Z’s e-commerce engagement. Sustainable Futures, 10, 101147. doi:10.1016/j.sftr.2025.101147

Singh, S., & Srivastava, P. (2019). Social media for outbound leisure travel. Journal of Tourism Futures, 5, 43–61. doi:10.1108/JTF-10-2018-0058

Sonderegger, A., & Sauer, J. (2010). Influence of design aesthetics in usability testing. Applied Ergonomics, 41(3), 403–410. doi:10.1016/j.apergo.2009.09.002

Sukackė, V. (2019). Extending the original Technology Acceptance Model. Society. Integration. Education Proceedings, 5, 525. doi:10.17770/sie2019vol5.3798

Te Brömmelstroet, M., Nikolaeva, A., Mladenović, M., Milakis, D., Ferreira, A., Verlinghieri, E., Cadima, C., de Abreu e Silva, J., & Papa, E. (2022). Have a good trip! Expanding our concepts of the quality of everyday travelling with flow theory. Applied Mobilities, 7(4), 352–373. doi:10.1080/23800127.2021.1912947

Tri Thong, T., Tung, N., & Ba, N. (2023). Impacts of social media on domestic tourists’ travel intentions in Vietnam. Journal of Science Quy Nhon University, 17, 5–19.

Venkatesh, V. (2000). Determinants of perceived ease of use. Information Systems Research, 11(4), 342–365. doi:10.1287/isre.11.4.342.11872

Vequist, D. (2025). Health tourism facility use of digital marketing and AI. Visions in Leisure and Business, 27. doi:10.25035/visions.27.01.04

Wang, H., & Lee, K. (2020). Getting in the flow together. Sustainability, 12, 6853. doi:10.3390/su12176853

Wang, X., Yu, Y., Zhu, Z., & Zheng, J. (2022). Visiting intentions toward theme parks. Sustainability, 14, 12206. doi:10.3390/su141912206

Wang, Y., Mohamed Salim, N. A., Subri, S., Zhang, X., & Zhu, M. (2023). Features of TikTok viral video advertising. ICIMCIS 2023 Proceedings, 267–272. doi:10.1109/ICIMCIS60089.2023.10349038

Wei, W., Qi, R., & Zhang, L. (2019). Effects of virtual reality on theme park visitors’ experience. Tourism Management, 71, 282–293. doi:10.1016/j.tourman.2018.10.024

Weibel, D., Stricker, D., & Wissmath, B. (2012). Virtual learning centre in university lecture context. Interactive Learning Environments, 20(1), 77–87. doi:10.1080/10494821003755502

Wu, C. H.-J., & Liang, R.-D. (2009). Effect of experiential value on customer satisfaction. International Journal of Hospitality Management, 28(4), 586–593. doi:10.1016/j.ijhm.2009.03.008

Wulandari, S. A. (2025). Pengaruh lifestyle dan local food terhadap pemilihan destinasi wisata di Kota Bukittinggi dengan eating experience sebagai variabel mediasi (Studi pada wisatawan nusantara non-Minangkabau yang berasal dari luar Provinsi Sumatera Barat) (Master’s thesis, Universitas Andalas). Universitas Andalas Repository. http://scholar.unand.ac.id/503732/

Xie, S., & Yuan, Y. (2021). Online travel consumers’ brand loyalty and flow experience. E3S Web of Conferences, 253, 03043. https://www.e3s-conferences.org

Yang, C., Yan, S., Wang, J., & Xue, Y. (2022). Flow experiences and virtual tourism. Sustainability, 14(9), 5361. doi:10.3390/su14095361

Yanti, F., & Endri, E. (2024). Financial behavior and investment decisions. International Journal of Economics and Financial Issues, 14, 289–298. doi:10.32479/ijefi.16811

Yoon, G., Duff, B. R. L., & Ryu, S. (2013). Gamers just want to have fun? Journal of Applied Social Psychology, 43(9), 1814–1826. doi:10.1111/jasp.12133

Yue, R., & Li, J. (2024). Impact of short video content on tourism intentions. TourismSpectrum: Diversity & Dynamics, 1(1), 40–53. doi:10.56578/tsdd010104

Zhou, T. (2013). Flow experience and mobile TV adoption. Behaviour & Information Technology, 32(3), 263–272. doi:10.1080/0144929X.2011.650711

Zhu, Z., & Yang, K. (2023). Virtual community users’ travel information-seeking continuance intention. Journal of Systems and Information Technology, 25(1), 91–108. doi:10.1108/JSIT-07-2022-0178

 


How to cite this article
Vancouver
Nguyen TN, Vo KH, Tran TVT, Vu SV. TikTok Short-Form Content and Gen Z Community Tourism Intentions in Ho Chi Minh City. J Organ Behav Res. 2026;11(3):9-21. https://doi.org/10.51847/CMz3P1E6Wz
APA
Nguyen, T. N., Vo, K. H., Tran, T. V. T., & Vu, S. V. (2026). TikTok Short-Form Content and Gen Z Community Tourism Intentions in Ho Chi Minh City. Journal of Organizational Behavior Research, 11(3), 9-21. https://doi.org/10.51847/CMz3P1E6Wz
Related articles:
Most viewed articles:
Issue 4 Volume 11 - 2026