Lissa Rosdiana Noer*
| Fadila Isnaini | Prahardika Prihananto | Sri Yayu Ninglasari | Syazwan Ab Talib | Muhammad Alfarizi
© 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
The rapid digitalization of work and the ubiquity of social media have heightened technostress (TS) and social media stressors (SNS stressors) among Generation Z (Gen Z) workers, impacting mental well-being and sustainable lifestyle choices. This study aims to examine how TS and SNS stressors affect sustainable tourism behavioral intention (TBI) among Indonesian Gen Z workers, using the Theory of Planned Behavior (TPB) framework. A quantitative method was applied using a structured online survey targeting 217 formally employed Indonesian Gen Z workers (aged 18–27). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. Results indicate that TS and SNS stressors significantly influenced subjective norms (SN) and perceived behavioral control (PBC), but not attitude (AE). These two TPB constructs significantly predicted sustainable tourism intention. Mediation analysis revealed that PBC significantly mediated the relationship between TS and sustainable tourism intention, while both SN and perceived control mediated the effects of SNS stressors. AE did not serve as a significant mediator in either pathway. Theoretically, this study extends TPB by integrating modern digital stress dimensions, highlighting the primacy of social and control factors over attitudinal influences in Gen Z’s sustainable travel decisions. Practically, the findings inform tourism marketers, policymakers, and HR managers to design wellness-green tourism, digital detox packages, and workplace policies that address digital fatigue. Positioning tourism as a means of psychological recovery from digital overload is crucial for engaging the digital-native workforce.
Generation Z, psychological recovery, social media stressors, Theory of Planned Behavior, technostress, sustainable tourism intention, SDGs 3
The rapid advancement of digital technology and social media has transformed professional and personal life, especially for younger generations, the most active users. In work, digitalization boosts productivity and flexibility but increases time pressure, blurs work–life boundaries, and fosters technostress [1]. In personal life, intensive social media use is linked to anxiety, depression, and fear of missing out (FoMO) [2], with World Health Organization [3] reporting 11% of adolescents showing problematic use. In Indonesia, social media users exceeded 143.0–191.4 million in early 2025 [4], dominating Gen Z work norms through AI reliance and online collaboration [5]. Technostress (TS) involves techno-overload, techno-invasion, and uncertainty, while social media stressors (SNS stressors) include digital fatigue, FoMO, and social comparisons [6]. Both cause anxiety, depression, emotional fatigue, and sleep disorders while harming work–life balance [7, 8].
The relevance of digital stress to the tourism sector can be seen from the increase in travel intentions as a coping mechanism against psychological pressure due to digital fatigue. Tourism is an effective means of psychological escape to reduce the digital burden [9]. Trips that involve contact with nature or places with minimal digital connections have been shown to restore mental and physical well-being [10]. For young workers, the intensity of digital stress encourages them to seek out activities that support digital detoxification and emotional recovery. The intention to travel appears as a form of seeking tranquility and fulfilling the psychological need for relaxation, meaningful social interaction, and improved health [11]. Therefore, understanding the factors that influence travel intentions, such as digital stress levels, personal motivation, and expectations for the travel experience, is essential to developing a tourism promotion strategy that is relevant to the young working segment.
A review of recent empirical studies shows that mental health factors play an important role in shaping travel intentions. Motivation to escape, personal growth, and involvement in wholesome activities are strong drivers in fitness tourism [12, 13]. Nature-based tourism has also been shown to be effective in reducing symptoms of stress, depression, and anxiety, even though the effects are temporary [14]. Technology-free tourism offers relief from digital stress, facilitating mental recovery [15]. In addition, tourism with animal interaction also provides psychological benefits [16] and hot spring tourism improves psychological well-being and revisit intentions [17]. In the post-COVID-19 era, psychosocial needs and perception of health risks are the main determinants of travel intention [18, 19].
However, prior research highlights the existence of an attitude–behavior gap, particularly among Generation Z (Gen Z) in sustainability contexts. Although Gen Z frequently expresses strong pro-environmental attitudes and ethical concerns, these attitudes do not consistently translate into actual sustainable behaviors [20, 21]. This discrepancy may be driven by situational constraints, social influence, hedonic motivations, perceived behavioral control (PBC), and lifestyle convenience. In tourism, this gap is especially relevant because travel—while perceived as restorative—can simultaneously increase carbon emissions and consumption intensity [22, 23]. Therefore, examining how digital stress interacts with attitudes (AE), norms, and perceived control becomes crucial to understanding whether travel intention reflects genuine sustainable motivation or merely short-term psychological coping.
Although a number of studies have explored the relationship between digital stress and travel behavior, most are still general and have not integrated specific TS and SNS stressors in a single comprehensive theoretical framework. In addition, the Theory of Planned Behavior (TPB) approach, which is effective in explaining behavioral intentions, has not been widely applied to understand travel intentions in response to digital pressures, especially in the context of Gen Z workers in Indonesia. In fact, Gen Z is a digital native generation that is vulnerable to digital fatigue while also having great potential as a tourism actor. Therefore, this study is important to fill this gap by simultaneously examining the influence of TS, SNS stressors, and TPB factors (AE, subjective norms (SN), and perceptions of behavioral control) on tourism intentions. There are research questions proposed by this study, namely: How do TS and SNS stressors influence AE, SN, and PBC, and in turn shape tourism behavioral intentions among Indonesian Gen Z workers, including the mediating role of these psychological factors in the relationship between digital stressors and travel intentions?
This study aims to analyze the influence of TS and SNS stressors on the tourism behavioral intention (TBI) of Gen Z workers in Indonesia through the framework of the TPB, considering the mediating role of AE, SN, and perceptions of behavior control. Theoretically, the study expands the application of the SDGs by integrating two types of digital stress as predictors in the context of tourism, which are still rarely studied simultaneously. This contributes to the development of literature on digital psychology, tourism, and the behavior of the younger generation. Practically, the results of this research can be the basis for policy makers, tourism industry players, and work organizations to design tourism promotion strategies based on the psychological needs of young workers, including the development of digital detox tourism programs and work-life balance campaigns, in order to respond to the negative impact of digitalization on the welfare of the productive young generation.
2.1 Theoretical foundation: Theory of Planned Behavior
The TPB [24] is a theoretical model that explains how a person's intentions can predict their actual behavior. Three main components make up behavioral intentions: AE toward behavior, SN, and perceived control of behavior [24, 25]. The three influence each other in shaping the individual's tendency to act.
In the empirical study of tourism, the SDGs have become an important theoretical framework for understanding the intentions and behaviors of tourists. Research in the field of medical tourism shows that the three components of the SDG significantly influence travelers' decisions [26, 27]. In the case of sustainable tourism and slow tourism, AE towards the environment and perceptions of control over tourism choices are important indicators in tourists' decision-making behavior [28, 29].
The implementation of SDGs has also proven to be relevant among the younger and digital generations. TPB is used to describe the use of smartphones in the search for tourist information. Other studies show that SN and perceived control of behavior play a key role in the sustainable tourism behavior of younger generations [30] and in the intention of digital entrepreneurship [31].
Thus, TPB is not only relevant in explaining the behavior of tourists in general but also very effective in analyzing the behavioral dynamics of the younger generation influenced by digital technology and evolving social values.
2.2 Technostress in the digital work environment
TS is a negative psychological stress arising from the use of information and communication technology (ICT), characterized by anxiety, mental fatigue, and resistance to technology [32, 33]. The main dimensions of TS include techno-overload (excessive workload due to technology), techno-anxiety (technological anxiety), and digital fatigue [34]. Among young workers, TS is triggered by high job expectations and demands to stay connected (techno-invasion), difficulty understanding new technologies (techno-complexity), and anxiety about rapid technological changes (techno-uncertainty) [35, 36]. Lack of experience in managing digital dynamics also exacerbates this pressure. The impact includes psychological well-being disorders such as stress, anxiety, and emotional exhaustion, as well as decreased life satisfaction [37, 38]. TS encourages individuals to seek escape through leisure activities as compensation for cognitive fatigue and the role conflicts it causes [35, 37]. Stress due to techno-overload and techno-invasion increases the desire to achieve psychological recovery through relaxation and digital detoxification [39]. Therefore, TS is a strategic issue in the management of digital welfare of the young working generation.
Within the framework of the TPB, TS affects AE, SN, and perceptions of behavioral control towards psychological well-being activities such as leisure tourism and digital-free gaming. Alotaibi [39] and Liu and Hu [40] showed that TS decreases the intention to engage in digital learning due to negative perceptions of life balance and mental health. Sterling et al. [41] found that Digital Free Tourism (DFT) emerged as a planned response to the digital burden, driven by health and social connectedness motives, and strongly influenced by positive AE and PBC. In leisure gaming and light recreation studies, the intention to engage in these activities was shown to be more influenced by AE towards the benefits of relaxation than by technical constraints [42, 43].
Based on the empirical evidence above, this study predicts that TS affects the perception of tourism psychological recovery activities of Gen Z Workers in a planned and meaningful way.
H1. TS in Gen Z Workers has a significant positive effect on AE.
H2. TS in Gen Z Workers has a significant positive effect on SN.
H3. TS in Gen Z Workers has a significant positive effect on the PBC.
2.3 Social media stressors and digital overload
SNS stressors such as FoMO, comparative stress, and cyberfatigue are significant triggers for digital fatigue. FoMO arises from anxiety about being left behind in other people's social experiences displayed on social media, triggering overuse and productivity disruptions [44-46]. Meanwhile, comparative stress drives feelings of inferiority due to curated ideal content, exacerbating emotional exhaustion [47, 48]. Cyberfatigue, as a form of digital fatigue, is an accumulation of constant exposure to online social demands [49]. Social media algorithms also exacerbate the pressure by selectively presenting content to increase engagement, reinforce the cycle of anxiety and self-image [50]. Social pressure and the need to represent oneself ideally create identity conflicts that trigger psychological stress [51].
Interestingly, social media plays an ambivalent role in shaping travel desires. Highly aesthetically curated visual content—such as images of exotic destinations, unique experiences, and luxurious lifestyles—evokes tourist aspirations and the urge to experience the same [52]. However, constant exposure to this kind of content can also exacerbate digital overload, especially when individuals feel stuck in a routine and unable to embody that lifestyle [53]. As a result, a paradox emerges: the desire to explore increases, but with mental fatigue due to social expectations shaped by social media algorithms.
Kessling et al. [53] and Xiang et al. [54] revealed that acute stress increases cue-reactivity to social media, especially in individuals with problematic usage tendencies, which indicates the role of intention as a mediator. Meanwhile, gaming activities can function as a stress escape, but they are also at risk of becoming internet gaming disorder (IGD) if accompanied by a poor coping strategy [55]. Social support through shared "hangouts" has been shown to reduce social media fatigue and reinforce positive experiences in online gaming [56].
Based on the literature and empirical evidence above, this study predicts that social media digital stress affects the perception of tourism psychological recovery activities of Gen Z Workers in a planned and meaningful manner.
H4. SNS stressors in Gen Z Workers has a significant positive effect on AE.
H5. Social Networks Stressor in Gen Z Workers has a significant positive effect on SN.
H6. SNS stressors in Gen Z Workers has a significant positive effect on PBC.
2.4 Linking Theory of Planned Behavior constructs and Gen Z workers' tourism intention
The TPB is a powerful theoretical framework in explaining the tourism intentions of the younger generation, especially Gen Z. Three main constructs of the TPB—AE, SN, and perceptions of behavior control—show varying influences on Gen Z's travel intentions in various tourism contexts. AE towards travel experiences are consistently the strongest predictors in determining Gen Z's travel intentions, including towards eco-friendly hotels and low-carbon transportation [57, 58]. SN show mixed influences; significant in some studies, especially related to the influence of friends and family, but not universally consistent [30, 59]. Meanwhile, PBC shows an important role in the context of domestic tourism during the COVID-19 pandemic [60], but minimal in the context of international destinations [61].
The SDG model is expanded with additional dimensions such as environmental concern and perceived risk to improve its predictive capabilities further. For example, environmental values reinforce AE towards sustainable tourism [62]. While the perception of health risks decreases tourism intentions, it can be mediated by positive AE and behavioral control [58, 63]. Gen Z's distinctive characteristics, such as digital connectedness and environmental concern, make them responsive to digital marketing and sustainable tourism campaigns. However, the gap between intentions and actual behavior remains a challenge [64].
Therefore, the integration of TPB with value, risk, and motivation-based approaches is relevant to effectively understanding and optimizing the travel behavior of Gen Z Workers.
H7. AE in the TPB construct has a significant effect on Gen Z Workers' TBI.
H8. SN in the TPB construct have a significant effect on Gen Z Workers' TBI.
H9. PBC in the TPB construction has a significant effect on Gen Z Workers' TBI.
2.5 The mediating role of Theory of Planned Behavior
The TPB is not only used to predict tourist intentions and behavior. Still, it has also been widely applied as a mediating variable in various contemporary tourism studies. These studies show that AE, SN, and PBC not only act as direct predictors but also bridge the relationship between external constructs and actual intentions and behaviors. In the expanded TPB model, Soliman [64] showed that AE, SN, and PBC mediate the influence of travel motivation, eWOM, destination image, and familiarity on tourists' revisit intentions to Egypt. In the context of cycling tourism, Sann et al. [65] revealed that perceptions of sustained alternative attraction mediate actual intentions and behaviors, highlighting the importance of volitional and non-volitional processes. As a mediator, the TPB construct also connects various important variables. For example, Liu et al. [66] found that behavioral intentions mediated the influence of satisfaction on the actual behavior of international travelers. Meanwhile, destination imagery mediates the relationship between eWOM and tourism intentions [67, 68]. In the context of heritage tourism, environmental interpretations significantly strengthen the relationship between intentions and behaviors [69]. The expanded TPB model has been shown to have higher predictability than the original version, as shown in a study of Chinese students intending to go to Japan [70].
This study proposes the role of TPB construct mediation in explaining the relationship between digital stress and Gen Z workers' travel intentions, in order to expand understanding of the psychological mechanisms that influence the travel behavior of young generations in the digital era, which is full of technological and information overload pressure.
H10. AE mediates the relationship between TS and Gen Z Workers' TBI.
H11. SN mediates the relationship between TS and Gen Z Workers' TBI.
H12. PBC mediates the relationship between TS and Gen Z Workers' TBI.
H13. AE mediates the relationship between SNS stressors and Gen Z Workers' TBI.
H14. SN mediates the relationship between SNS stressors and Gen Z Workers' TBI.
H15. PBC mediates the relationship between SNS stressors and Gen Z Workers' TBI.
The series of 15 hypotheses that have been formed became the basis for the preparation of the research model in Figure 1.
Figure 1. Research model proposed
3.1 Research design and measurement
This research aims to examine the impact of TS and SNS stressors on sustainable tourism intentions, adopting the Theory of TPB as a theoretical framework. Toward that aim, this study seeks to understand how stress induced by technology and social media platforms affects the psychological mechanisms—such as AE, SN, and PBC—that influence Indonesian Gen Z workers’ intentions to travel. To achieve this, the research adopts a quantitative approach using a structured survey method. Data will be collected through an online questionnaire distributed to Indonesian Gen Z workers, defined as individuals born between 1997 and 2012 who are currently employed. The unit of analysis in this study is individual Indonesian Gen Z workers.
To measure the constructs in this study, a structured questionnaire was developed using multiple items adapted from validated scales in previous literature. TS was measured through 18 items encompassing work overload, invasion of personal life, complexity, insecurity, and uncertainty related to technology [71-74]. SNS stressors were assessed using 19 items capturing aspects such as technological burden, social overload, relational pressure, and cognitive strain arising from social media platforms [75-78]. AE toward travel was measured through 4 items reflecting respondents’ positive evaluation of travel experiences [23, 68, 79, 80]. SN were assessed using five items that captured perceived social pressure and support from friends and family regarding travel [23, 68, 79, 80]. PBC included three items related to respondents’ perceived ease or difficulty in engaging in tourism activities [23, 68, 79, 80]. Finally, TBI was measured with four items that reflect the intention to engage in future travel behaviors [63, 81-83]. All items were rated using a 5-point Likert scale.
3.2 Sample and data collection
This study focuses on Gen Z workers in Indonesia as the target population, referring to individuals born between 1997 and 2012. A purposive sampling technique was employed to select respondents based on the following inclusion criteria: (1) Indonesian citizenship, (2) aged between 18 and 27 at the time of data collection, (3) active users of social media, (4) formally employed in a company or public institution, and (5) having experience or intention to travel for recreational purposes or mental recovery. These criteria were established to ensure that respondents’ experiences were relevant to the study’s main theme concerning digital stress and tourism intention. To calculate the minimum number of participants, used G*Power software to perform a power analysis for multiple regression with an alpha level of 0.05, statistical power of 0.90, a medium effect size (0.15), and five predictors [84], producing a minimum requirement of 129 samples.
Table 1. Respondent characteristics
|
|
Total |
Percentage |
|
Age |
||
|
17-20 Years Old |
75 |
34.56% |
|
21-24 Years Old |
103 |
47.47% |
|
25-30 Years Old |
39 |
17.97% |
|
Gender |
||
|
Man |
40 |
18.43% |
|
Woman |
177 |
81.57% |
|
Last Education |
||
|
Vocational High School/High School |
64 |
29.49% |
|
Diploma |
22 |
10.14% |
|
Bachelor |
91 |
41.94% |
|
Postgraduate (Magister/Doctor) |
40 |
18.43% |
|
Censorship Level; Monthly Income (in Rupiah-Dollar) |
||
|
IDR 1,00,000 - IDR 2,999,999 (USD 65 - USD 193) |
22 |
10.14% |
|
IDR 3,000,000 - IDR 4,999,000 (USD 194 - USD 322) |
78 |
35.94% |
|
IDR 5,000,000 – IDR 6,999,999 (USD 323 - USD 451) |
66 |
30.41% |
|
IDR 7,000,000 – IDR 10,000,000 (USD 452 – USD 645) |
31 |
14.29% |
|
> IDR 10,000,000 (> USD 645) |
20 |
9.22% |
|
Domicile |
||
|
Sumatera |
50 |
23.04% |
|
Jawa |
103 |
47.47% |
|
Kalimantan |
24 |
11.06% |
|
Sulawesi |
13 |
5.99% |
|
Bali-Nusa Tenggara |
19 |
8.76% |
|
Maluku-Papua |
8 |
3.69% |
|
Types of Frequent Tours (More than 1) Total Visits: 289 Visits |
||
|
Recreational Tourism |
83 |
28.72% |
|
Cultural Tourism |
60 |
20.76% |
|
Nature Tourism |
75 |
25.95% |
|
Religious Tourism |
24 |
8.30% |
|
Educational Tourism |
32 |
11.07% |
|
Health Tourism |
15 |
5.20% |
Source: Author’s elaboration
Data collection used a hybrid strategy, deploying Google Forms as the survey tool. Enumerators shared the form both physically and through digital channels including WhatsApp, Instagram, and X (formerly known as Twitter), to ensure wide distribution across different regions and subgroups. The study adhered to ethical standards by providing respondents with clear information regarding the voluntary nature of participation, securing informed consent, and guaranteeing anonymity and confidentiality of their responses.
This study has successfully involved a total of 217 respondents who are Gen Z workers in Indonesia. Based on Table 1, the majority of respondents in this study were aged 21–24 years (47.47%), followed by the age group of 17–20 years (34.56%). Respondents were dominated by women (81.57%), and most had a Bachelor's degree (41.94%). Most respondents have a monthly income between IDR 3,000,000 and IDR 4,999,999 (35.94%). In terms of domicile, the most respondents came from Java (47.47%), followed by Sumatra (23.04%). In terms of the type of tourism that is most often carried out, recreational tourism occupies the highest position (28.72%), followed by natural tourism (25.95%) and cultural tourism (20.76%). These findings show that the majority of Gen Z Indonesian workers in the study are young women with a bachelor's education, with a middle income and a primary interest in leisure and nature tourism. This data is an important foundation in understanding the behavior of their travel intentions amid the pressure of technology and social media.
3.3 Data analysis technique
This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4 to analyze the data. PLS-SEM was selected due to its suitability for predictive research models, complex structural relationships, and when data distribution assumptions are not strictly met [85]. The analysis process followed a two-step approach: first, the measurement model was assessed to evaluate the reliability and validity of the constructs, including indicator loadings, composite reliability (CR), average variance extracted (AVE), and discriminant validity using the Fornell-Larcker criterion and HTMT ratio [86]. Second, the structural model was examined to test the hypothesized relationships among variables by analyzing path coefficients, t-values, and significance levels via bootstrapping with 5,000 resamples. The coefficient of determination (R²), effect sizes (f²), and predictive relevance (Q²) were also assessed to evaluate the model’s explanatory and predictive power [87]. This approach ensures robust and comprehensive evaluation of the research framework.
4.1 Measurement outer model
The measurement model in this study was assessed using indicators of reliability and validity, including Cronbach’s alpha (CA), CR, AVE, outer loadings, and multicollinearity via variance inflation factor (VIF), as shown in Table 2. Given the reflective nature of the constructs, model reliability was evaluated through CA and CR, both of which exceeded the recommended threshold of 0.7 [86]. The CA values ranged from 0.602 (TBI) to 0.946 (TS), and CR values ranged from 0.774 to 0.952, indicating adequate internal consistency across constructs.
Table 2. Convergent validity, reliability, and common method bias-variance inflation factor (CBM-VIF) results
|
Construct |
Outer Loadings |
AVE |
CA |
CR |
VIF |
|
Indicator |
|||||
|
Technostress (TS) |
|||||
|
Technological developments forced me to work much faster. |
0.729 |
0.525 |
0.946 |
0.952 |
2.289 |
|
I was forced by technology to do more work than I could handle. |
0.792 |
2.120 |
|||
|
I was forced by this technology to work on a very tight schedule. |
0.790 |
1.711 |
|||
|
I was forced to change my work habits in order to adapt to new technology. |
0.735 |
2.570 |
|||
|
My workload increases due to the high complexity of technology. |
0.755 |
2.803 |
|||
|
I spend less time with my family because of technology. |
0.779 |
3.039 |
|||
|
I still have to connect with work even while on vacation because of this technology. |
0.710 |
3.045 |
|||
|
I had to sacrifice my vacation time and weekends to keep up with new technologies. |
0.788 |
3.181 |
|||
|
I feel like my personal life is being disrupted by this technology. |
0.742 |
3.982 |
|||
|
I don't understand this technology enough to do my job well. |
0.760 |
3.605 |
|||
|
It took me a long time to understand and use the new technology. |
0.715 |
3.281 |
|||
|
I didn't have enough time to learn and improve my tech skills. |
0.708 |
4.663 |
|||
|
I feel like my colleagues understand computer technology better than I do. |
0.739 |
4.489 |
|||
|
I often feel like new technologies are too complicated for me to understand and use. |
0.713 |
2.457 |
|||
|
I have to update my skills so that they are irreplaceable constantly. |
0.794 |
2.336 |
|||
|
I felt threatened by colleagues with newer tech skills. |
0.764 |
2.631 |
|||
|
I don't share my knowledge for fear of being replaced. |
0.790 |
2.930 |
|||
|
There are always new developments in the technology I use. |
0.792 |
3.138 |
|||
|
SNS Stressor (STS) |
|||||
|
It took me a long time to understand and use social networking platforms. |
0.716 |
0.541 |
0.897 |
0.906 |
1.776 |
|
I don't have enough time to improve my tech skills in using social networking platforms. |
0.797 |
6.438 |
|||
|
Younger people are more adept at using social networking platforms than I am. |
0.766 |
6.321 |
|||
|
I often feel that social networking platforms are too complicated to use. |
0.745 |
4.135 |
|||
|
There are always new terms and conditions on social networking platforms. |
0.763 |
5.019 |
|||
|
In general, social networking platforms are constantly changing. |
0.710 |
3.142 |
|||
|
I communicate too much with my online contacts, even on vacation. |
0.743 |
3.217 |
|||
|
I feel like my personal life is being disrupted by social networking platforms. |
0.780 |
2.667 |
|||
|
I felt compelled to tell my friend about the news immediately. |
0.797 |
2.892 |
|||
|
I feel like I have to communicate regularly with friends. |
0.754 |
2.321 |
|||
|
I feel like my social status is always threatened by what I share online. |
0.775 |
1.654 |
|||
|
I feel pressured by friends to keep checking for their updates. |
0.713 |
2.522 |
|||
|
I don't share all my news so that I can stay more informed than my friends. |
0.776 |
1.946 |
|||
|
I feel like my friends don't share all their news either, to stay the ones who know the best. |
0.801 |
2.295 |
|||
|
I pay too much attention to the well-being of my friends on social networking platforms. |
0.725 |
3.178 |
|||
|
I'm too involved with the personal problems of friends on social networking platforms. |
0.807 |
3.705 |
|||
|
My sense of responsibility for my friends' online enjoyment is too great. |
0.751 |
2.953 |
|||
|
I care too much about my friends on social networking platforms. |
0.731 |
4.312 |
|||
|
I pay too much attention to my friends' uploads. |
0.732 |
4.015 |
|||
|
Attitude (AE) |
|||||
|
I feel that the journey is very interesting. |
0.817 |
0.621 |
0.795 |
0.867 |
1.948 |
|
I consider travel to be something valuable. |
0.845 |
2.021 |
|||
|
I believe that travel is meaningful. |
0.735 |
1.365 |
|||
|
I consider travel to be a fun activity. |
0.748 |
1.475 |
|||
|
Subjective Norm (SN) |
|||||
|
My friends/family often talk about travel. |
0.784 |
0.651 |
0.866 |
0.903 |
2.123 |
|
My friends/family love to travel. |
0.851 |
2.718 |
|||
|
My friends/family recommended that I go on a trip. |
0.850 |
2.366 |
|||
|
My friends/family believe that I should travel. |
0.748 |
2.131 |
|||
|
My friends/family are supportive of my travel plans. |
0.796 |
2.256 |
|||
|
Perceived Behavioral Control (PBC) |
|||||
|
I have enough time to travel. |
0.845 |
0.573 |
0.617 |
0.795 |
1.464 |
|
I have enough money to travel. |
0.839 |
1.433 |
|||
|
If I wanted to, I felt like I could travel. |
0.748 |
1.092 |
|||
|
Sustainable Tourism Behavioral Intention (TBI) |
|||||
|
I want a sustainable vacation. |
0.877 |
0.589 |
0.602 |
0.774 |
1.893 |
|
I want to increase the frequency of my vacation. |
0.847 |
1.881 |
|||
|
I want to extend the duration of my vacation. |
0.714 |
1.012 |
|||
|
I want to increase my vacation spending. |
0.709 |
1.201 |
|||
Convergent validity was confirmed with AVE values ranging from 0.525 to 0.651, exceeding the 0.5 threshold. Most outer loadings also surpassed 0.7, confirming good item reliability. Although some indicators slightly approached 0.7, they were retained due to strong theoretical grounding and overall construct reliability.
VIF values were examined to assess common method bias (CMB) and multicollinearity. All values were below the critical value of 5 and mostly below 3.3, indicating no serious CMB or multicollinearity issues [88]. Thus, the measurement model demonstrates satisfactory reliability, validity, and robustness.
Table 3. Discriminant validity
|
Fornell-Larcker Criterion |
||||||
|
AE |
PBC |
STS |
SN |
TS |
TBI |
|
|
AE |
0.888 |
|||||
|
PBC |
0.622 |
0.957 |
||||
|
STS |
0.735 |
0.632 |
0.784 |
|||
|
SN |
0.696 |
0.735 |
0.649 |
0.807 |
||
|
TS |
0.443 |
0.539 |
0.700 |
0.518 |
0.724 |
|
|
TBI |
0.550 |
0.775 |
0.573 |
0.689 |
0.582 |
0.699 |
|
Heterotrait-Monotrait Ratio (HTMT) |
||||||
|
AE |
||||||
|
PBC |
0.782 |
|||||
|
STS |
0.771 |
0.801 |
||||
|
SN |
0.828 |
0.758 |
0.654 |
|||
|
TS |
0.498 |
0.694 |
0.712 |
0.558 |
||
|
TBI |
0.775 |
0.748 |
0.758 |
0.718 |
0.726 |
|
Discriminant validity in this study was evaluated using two key methods: the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio (HTMT), as shown in Table 3. According to the Fornell-Larcker criterion, discriminant validity is established when the square root of AVE for each construct (diagonal values) is greater than its correlations with other constructs (off-diagonal values) [89]. All constructs in this study fulfill this condition—for example, the AVE square root for AE is 0.888, which is higher than its correlation with other constructs, such as STS (0.735) and SN (0.696).
Furthermore, the HTMT values for all construct pairs are below the conservative threshold of 0.85 [90], with the highest being 0.828 between AE and SN. This confirms that constructs are empirically distinct. Therefore, both Fornell-Larcker and HTMT results indicate that the measurement model possesses strong discriminant validity, supporting the model’s robustness and structural accuracy.
4.2 Goodness of Fit model analysis
Goodness of Fit (GoF) model analysis was assessed using several fit indices, including SRMR, d_ULS, d_G, Chi-square, and NFI, as presented in Table 4. The SRMR values for both the saturated (0.045) and estimated (0.058) models are below the 0.08 threshold [86], indicating a good model fit. The NFI values are 0.912 (saturated) and 0.894 (estimated), both exceeding the recommended minimum of 0.9 [86], suggesting acceptable model quality. Additionally, the Chi-square values and discrepancy measures (d_ULS and d_G) fall within acceptable ranges [91]. Overall, these results confirm that the model demonstrates good fit and structural adequacy.
Table 4. Fit model result
|
Fit Index |
Saturated Model |
Estimated Model |
|
SRMR |
0.045 |
0.058 |
|
d_ULS |
1.756 |
2.103 |
|
d_G |
1.223 |
1.450 |
|
Chi-square |
120.45 |
134.78 |
|
NFI |
0.912 |
0.894 |
4.3 Inner model structural
The inner model structural evaluation in PLS-SEM was conducted using effect size (f²), T-statistics (T), and p-values (P). All relationships in the model demonstrated statistically significant results, indicated by T-values > 1.96 and p-values < 0.05. These findings confirm that the hypothesized paths among constructs are supported [86]. Additionally, effect size (f²) values ranged from small to moderate, suggesting varying but meaningful influences among variables [87].
Based on Figure 2 and Table 5, the structural model analysis confirmed that most hypothesized relationships were statistically significant. TS significantly influenced AE (β = 0.139, p = 0.017), SN (β = 0.124, p = 0.047), and PBC (β = 0.190, p = 0.004), indicating its moderate effect, especially on control (f² = 0.784). Similarly, SNS Stressor had a strong and significant effect on AE (β = 0.832, p < 0.001), SN (β = 0.562, p < 0.001), and PBC (β = 0.499, p < 0.001), reflecting high predictive power.
Among the TPB components, SN (β = 0.255, p < 0.001) and PBC (β = 0.580, p < 0.001) significantly predicted TBI. However, AE (β = 0.012, p = 0.808) was not significant, indicating it does not directly influence intention in this model. These results highlight the stronger role of perceived social pressure and control over behavior compared to personal evaluation.
This study concludes that digital pressure in the form of TS and SNS stressors significantly affects the sustainable travel intention of Gen Z Indonesian workers, through the mediation of SN and PBC, but not through AE. These findings highlight the importance of social factors and perceptions of personal control in shaping travel intentions in response to digital fatigue. TS drives the need for psychological recovery through an increased sense of control over time and energy, while social media pressures reinforce the influence of collective norms and social expectations on sustainable travel decisions. By integrating the TPB with digital stress, the study expands the theoretical understanding of the travel behavior of the digital-native generation.
However, this study has some limitations. First, data were collected cross-sectionally, so it was not possible to capture the longitudinal dynamics of TS on tourism behavior. Second, the population is limited to Gen Z workers in Indonesia, which limits global generalizations, even though the theoretical approach is universal. Third, insignificant AE variables may be affected by social biases or dynamics of local cultural norms that are not directly measured. For further research, it is recommended that a longitudinal approach, cross-cultural population expansion, as well as the integration of other factors such as coping styles, digital literacy, and economic risk, be suggested in order to provide a more holistic understanding of digital-native tourism behavior in the face of technological pressure.
This research was funded by the Strategic Research Grant 2026 by Institut Teknologi Sepuluh Nopember with Contract Number 958/PKS/ITS/2026. We gratefully acknowledge this support.
[1] Laursen, C.S., Nielsen, M.L., Dyreborg, J. (2021). Young workers on digital labor platforms: Uncovering the double autonomy paradox. Nordic Journal of Working Life Studies, 11(4): 65-84. https://doi.org/10.18291/njwls.127867
[2] Sao, R., Chandak, S., Barhate, B., Mondal, S. (2024). Social media and Gen Z's mental well-being: Impact of excessive usage on anxiety, stress, and depression levels analysis. PURUSHARTHA-A Journal of Management, Ethics and Spirituality, 17(1): 23-38. https://doi.org/10.21844/16202117102
[3] World Health Organization. Teens, screens and mental health 2024. https://www.who.int/europe/news/item/25-09-2024-teens--screens-and-mental-health, accessed on Jul. 28, 2025.
[4] Data Reportal. Digital 2025: Indonesia — Global Digital Insights 2025. https://datareportal.com/reports/digital-2025-indonesia, accessed on Jul. 28, 2025.
[5] Kaniah, K., Budihastuti, E., Suharja, A.A., Maemunah, E., Parwati, S.A.P.E., Mulyanah, A., Widiastuti, R., Kusnanto, E.A. (2024). The utilization of social media and the reinforcement of national identity in generation Z. Southeast Asian Language and Literature Studies, 1(2): 27-32. https://doi.org/10.55981/SALLS.2024.7077
[6] Naga, J.F., Ebardo, R.A. (2025). Social network sites (SNS) an archetype of techno-social stress: A systematic review. Heliyon, 11(1). https://doi.org/10.1016/j.heliyon.2024.e41119
[7] Dragano, N., Barbaranelli, C., Reuter, M., Wahrendorf, M., Wright, B., Ronchetti, M., Buresti, G., Di Tecco, C., Iavicoli, S. (2018). Young workers’ access to and awareness of occupational safety and health services: Age-differences and possible drivers in a large survey of employees in Italy. International Journal of Environmental Research and Public Health, 15(7): 1511. https://doi.org/10.3390/ijerph15071511
[8] Mohanan, M., Shekhar, S.K. (2021). A study on the mediating effect of FoMO on social media (Instagram) induced travel addiction and risk taking travel behavioral intention in youth. Journal of Content, Community and Communication, 14(7): 57-67. https://doi.org/10.31620/JCCC.12.21/06
[9] Arenas-Escaso, J.F., Folgado-Fernández, J.A., Palos-Sánchez, P.R. (2024). Internet interventions and therapies for addressing the negative impact of digital overuse: A focus on digital free tourism and economic sustainability. BMC Public Health, 24(1): 176. https://doi.org/10.1186/s12889-023-17584-6
[10] Petrick, J.F., Markert, C., Sasangohar, F. (2021). Stress for success: Potential benefits of perceived and actual stress while cruising. Journal of Travel Research, 60(8): 1787-1801. https://doi.org/10.1177/0047287520966377
[11] Schmitt, J.B., Breuer, J., Wulf, T. (2021). From cognitive overload to digital detox: Psychological implications of telework during the COVID-19 pandemic. Computers in Human Behavior, 124: 106899. https://doi.org/10.1016/j.chb.2021.106899
[12] Gan, T., Zheng, J., Li, W., Li, J., Shen, J. (2023). Health and wellness tourists’ motivation and behavior intention: The role of perceived value. International Journal of Environmental Research and Public Health, 20(5): 4339. https://doi.org/10.3390/ijerph20054339
[13] Hudson, S., Thal, K., Cárdenas, D., Meng, F. (2017). Wellness tourism: Stress alleviation or indulging healthful habits? International Journal of Culture, Tourism and Hospitality Research, 11(1): 35-52. https://doi.org/10.1108/IJCTHR-09-2015-0111
[14] Avecillas-Torres, I., Herrera-Puente, S., Galarza-Cordero, M., Coello-Nieto, F., Farfán-Pacheco, K., Alvarado-Vanegas, B., Ordóñez-Ordóñez, S., Espinoza-Figueroa, F. (2025). Nature tourism and mental well-being: Insights from a controlled context on reducing depression, anxiety, and stress. Sustainability, 17(2): 654. https://doi.org/10.3390/su17020654
[15] Egger, I., Lei, S.I., Wassler, P. (2020). Digital free tourism–An exploratory study of tourist motivations. Tourism Management, 79: 104098. https://doi.org/10.1016/j.tourman.2020.104098
[16] Lin, G., Li, M. (2024). Animal psychotherapist: The potential impacts of animal-based tourism on mental health. Tourism Management, 105: 104959. https://doi.org/10.1016/J.TOURMAN.2024.104959
[17] Lin, C.H. (2013). Determinants of revisit intention to a hot springs destination: Evidence from Taiwan. Asia Pacific Journal of Tourism Research, 18(3): 183-204. https://doi.org/10.1080/10941665.2011.640698
[18] Aebli, A., Volgger, M., Taplin, R. (2022). A two-dimensional approach to travel motivation in the context of the COVID-19 pandemic. Current Issues in Tourism, 25(1): 60-75. https://doi.org/10.1080/13683500.2021.1906631
[19] Thao, V.T., Hüsser, A.P., Ohnmacht, T. (2022). A combined theory-based explanatory model for predicting tourists’ travel intentions during the COVID-19 pandemic: The role of individual’s assessment of the compliance with non-pharmaceutical interventions. Discover Psychology, 2(1): 36. https://doi.org/10.1007/s44202-022-00046-2
[20] Elkhwesky, Z., Abuelhassan, A.E., Elkhwesky, E.F.Y., Khreis, S.H.A. (2024). Antecedents and consequences of behavioural intention to use virtual reality in tourism: Evidence from Gen-Y and Gen-Z consumers in Egypt. Tourism and Hospitality Research, 24(4): 560-576. https://doi.org/10.1177/14673584231170576
[21] Van Ngo, Q. (2023). Research on the behavior of Gen Z on using of green tourism: The role of knowledge sharing. International Journal of Professional Business Review, 8(8): 55. https://doi.org/10.26668/businessreview/2023.v8i8.3172
[22] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2): 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
[23] Al-Suqri, M.N., Al-Aufi, A.S. (2015). Information Seeking Behavior and Technology Adoption: Theories and Trends: Theories and Trends. IGI Global.
[24] Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4): 314-324. https://doi.org/10.1002/hbe2.195
[25] Boguszewicz-Kreft, M., Kuczamer-Kłopotowska, S., Kozłowski, A., Ayci, A., Abuhashesh, M. (2020). The theory of planned behaviour in medical tourism: International comparison in the young consumer segment. International Journal of Environmental Research and Public Health, 17(5): 1626. https://doi.org/10.3390/ijerph17051626
[26] Cham, T.H., Lim, Y.M., Sigala, M. (2022). Marketing and social influences, hospital branding, and medical tourists' behavioural intention: Before-and after-service consumption perspective. International Journal of Tourism Research, 24(1): 140-157. https://doi.org/10.1002/jtr.2489
[27] Meng, B., Choi, K. (2016). Extending the theory of planned behaviour: Testing the effects of authentic perception and environmental concerns on the slow-tourist decision-making process. Current Issues in Tourism, 19(6): 528-544. https://doi.org/10.1080/13683500.2015.1020773
[28] Petrescu, D.C., Bran, F. (2020). The use of smartphone for the search of touristic information. An application of the theory of planned behavior. Economic Computation & Economic Cybernetics Studies & Research, 54(1): 125-140. https://doi.org/10.24818/18423264/54.1.20.09
[29] Fenitra, R.M., Tanti, H., Gancar, C.P., Indrianawati, U. (2021). Understanding younger tourist'intention toward environmentally responsible behavior. Geo Journal of Tourism and Geosites, 36: 646-653. https://doi.org/10.30892/GTG.362SPL12-694
[30] Çela, A., Mansi, E., Morina, F. (2025). Young adults’ pathway to digital entrepreneurship: From ideas to innovation. European Journal of Innovation Management, 28(7): 2909-2939. https://doi.org/10.1108/EJIM-04-2024-0486
[31] Tacy, J.W. (2016). Technostress: A concept analysis. On-Line Journal of Nursing Informatics, 20(2).
[32] Cooper, C., Brough, P., Anderson, V.L. (2024). Elgar Encyclopedia of Occupational Health Psychology. Edward Elgar Publishing. https://doi.org/10.4337/9781035313389.ch57
[33] Thurik, R., Benzari, A., Fisch, C., Mukerjee, J., Torrès, O. (2024). Techno-overload and well-being of French small business owners: Identifying the flipside of digital technologies. Entrepreneurship & Regional Development, 36(1-2): 136-161. https://doi.org/10.1080/08985626.2023.2165713
[34] Marchiori, D.M., Mainardes, E.W., Rodrigues, R.G. (2019). Do individual characteristics influence the types of technostress reported by workers? International Journal of Human–Computer Interaction, 35(3): 218-230. https://doi.org/10.1080/10447318.2018.1449713
[35] Yang, Y., Shamim, S., De Massis, A., Gao, D. (2025). Defensive routines as coping mechanisms against technostress: Roles of digital leadership and employee goal orientation. Technological Forecasting and Social Change, 216: 124143. https://doi.org/10.1016/j.techfore.2025.124143
[36] Ficapal-Cusí, P., Peñarroja, V., Enache-Zegheru, M., Salazar-Concha, C. (2025). Employee technostress: Analyzing the influence of sociodemographic characteristics on techno-stressors and their consequences. International Journal of Human–Computer Interaction, 41(2): 1309-1322. https://doi.org/10.1080/10447318.2024.2313926
[37] Ioannou, A. (2023). Mindfulness and technostress in the workplace: A qualitative approach. Frontiers in Psychology, 14: 1252187. https://doi.org/10.3389/fpsyg.2023.1252187
[38] Rahmi, K.H., Fahrudin, A., Supriyadi, T., Herlina, E., Rosilawati, R., Ningrum, S.R. (2025). Technostress and cognitive fatigue: Reducing digital strain for improved employee well-being: A literature review. Multidisciplinary Reviews, 8(12): 2025380. https://doi.org/10.31893/multirev.2025380
[39] Alotaibi, N.B. (2025). Exploring the impact of factors on mobile learning adoption and academic performance: A study of undergraduate students in Riyadh region universities, Kingdom of Saudi Arabia. Education and Information Technologies, 30(8): 11185-11221. https://doi.org/10.1007/S10639-024-13232-7/TABLES/5
[40] Liu, Y., Hu, H. (2021). Digital-free tourism intention: A technostress perspective. Current Issues in Tourism, 24(23): 3271-3274. https://doi.org/10.1080/13683500.2021.188356
[41] Sterling, H.M., Allan, B.A., Heesacker, M. (2020). Planning leisure: Helpful or harmful? Leisure/Loisir, 44(4): 469-488. https://doi.org/10.1080/14927713.2020.1815560
[42] Rhodes, R.E., Dean, R.N. (2009). Understanding physical inactivity: Prediction of four sedentary leisure behaviors. Leisure Sciences, 31(2): 124-135. https://doi.org/10.1080/01490400802685948
[43] Gopakumar, S., Dananjayan, M.P. (2024). FOMOphobia: The psychological drivers of OTT media consumption through social comparison, self-esteem, and anxiety. In The Rise of Over-the-Top (OTT) Media and Implications for Media Consumption and Production, pp. 161-184. https://doi.org/10.4018/979-8-3693-0116-6.ch010
[44] Tandon, A., Dhir, A., Talwar, S., Kaur, P., Mäntymäki, M. (2021). Dark consequences of social media-induced fear of missing out (FoMO): Social media stalking, comparisons, and fatigue. Technological Forecasting and Social Change, 171: 120931. https://doi.org/10.1016/j.techfore.2021.120931
[45] Rozgonjuk, D., Sindermann, C., Elhai, J.D., Montag, C. (2020). Fear of Missing Out (FoMO) and social media’s impact on daily-life and productivity at work: Do WhatsApp, Facebook, Instagram, and Snapchat Use Disorders mediate that association? Addictive Behaviors, 110: 106487. https://doi.org/10.1016/j.addbeh.2020.106487
[46] Fox, J., Moreland, J.J. (2015). The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Computers in Human Behavior, 45: 168-176. https://doi.org/10.1016/j.chb.2014.11.083
[47] Servidio, R., Soraci, P., Griffiths, M.D., Boca, S., Demetrovics, Z. (2024). Fear of missing out and problematic social media use: A serial mediation model of social comparison and self-esteem. Addictive Behaviors Reports, 19: 100536. https://doi.org/10.1016/j.abrep.2024.100536
[48] Jabeen, F., Tandon, A., Sithipolvanichgul, J., Srivastava, S., Dhir, A. (2023). Social media-induced fear of missing out (FoMO) and social media fatigue: The role of narcissism, comparison and disclosure. Journal of Business Research, 159: 113693. https://doi.org/10.1016/j.jbusres.2023.113693
[49] Metzler, H., Garcia, D. (2024). Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science, 19(5): 735-748. https://doi.org/10.1177/17456916231185057
[50] Hall, M., Caton, S. (2017). Am I who I say I am? Unobtrusive self-representation and personality recognition on Facebook. PLoS ONE, 12(9): e0184417. https://doi.org/10.1371/journal.pone.0184417
[51] Kim, J., Fesenmaier, D.R. (2017). Sharing tourism experiences: The posttrip experience. Journal of Travel Research, 56(1): 28-40. https://doi.org/10.1177/0047287515620491
[52] Mehataj, M., Kanimozhi, P., Ananth Kumar, T. (2024). Social media and its influence on travel decision- making. In Decoding Tourist Behavior in the Digital Era: Insights for Effective Marketing, pp. 135-166. https://doi.org/10.4018/979-8-3693-3972-5.ch006
[53] Kessling, A., Müller, A., Wolf, O.T., Merz, C.J., Brand, M., Wegmann, E. (2025). Effects of acute psychosocial stress on cue-reactivity, attentional bias and implicit associations in women with problematic social network use: An experimental study. Addiction, 120(10): 2067-2079. https://doi.org/10.1111/add.70099
[54] Xiang, H., Liu, L., Su, X., Yang, Y., et al. (2025). The association between a stress-is-enhancing mindset and internet gaming disorder was mediated by behavioral disengagement among medical undergraduate students: A multi-center survey in China. BMC Psychiatry, 25(1): 472. https://doi.org/10.1186/s12888-025-06910-4
[55] Dengah II, H.F., Snodgrass, J.G., Else, R.J., Polzer, E.R. (2018). The social networks and distinctive experiences of intensively involved online gamers: A novel mixed methods approach. Computers in Human Behavior, 80: 229-242. https://doi.org/10.1016/j.chb.2017.11.004
[56] Wu, H., Wang, W., Tao, Y., Shao, M., Yu, C. (2024). Understand the Chinese Z generation consumers’ green hotel visit intention: An extended theory of planned behavior model. Heliyon, 10(3): e25067. https://doi.org/10.1016/j.heliyon.2024.e25067
[57] Li, C., Huang, X. (2022). How does COVID-19 risk perception affect wellness tourist intention: Findings on Chinese generation Z. Sustainability, 15(1): 141. https://doi.org/10.3390/su15010141
[58] Phi, L.H., Duong, H.T. (2024). Factors affecting Generation Z’s intention of choosing a tourism destination. African Journal of Hospitality, Tourism and Leisure, 13: 486-493. https://doi.org/10.46222/ajhtl.19770720.532
[59] Kharuhayothin, T., Kitiwong, W., Chaitarin, W. (2024). Generation Z's COVID-19 risk perception and socially responsible behaviors influencing intentions to participate in the tourism stimulus campaign. Journal of Hospitality and Tourism Insights, 7(4): 1699-1719. https://doi.org/10.1108/JHTI-10-2022-0481
[60] Pan, S., Satchabut, T. (2022). Predicting Generation Z’s travel intention through structural equation modeling. GMSARN International Journal, 16(3): 232-239.
[61] Nguyen, T.V., Bui, T.Q.T., Bui, L.P. (2024). Integrating TPB, VAB, and generation theory in studying the green tourism behavior of Generation Z: A study in Vietnam. Geo Journal of Tourism and Geosites, 57: 1930-1940. https://doi.org/10.30892/gtg.574spl07-1360
[62] Hsieh, C.M., Park, S.H., McNally, R. (2016). Application of the extended theory of planned behavior to intention to travel to Japan among Taiwanese youth: Investigating the moderating effect of past visit experience. Journal of Travel & Tourism Marketing, 33(5): 717-729. https://doi.org/10.1080/10548408.2016.1167387
[63] Liu-Lastres, B., Bao, H., Cecil, A. (2025). Exploring gaps in young generations’ sustainable travel behavior: A mixed-methods study. Journal of Sustainable Tourism, 33(6): 1165-1180. https://doi.org/10.1080/09669582.2025.2453693
[64] Soliman, M. (2021). Extending the theory of planned behavior to predict tourism destination revisit intention. International Journal of Hospitality & Tourism Administration, 22(5): 524-549. https://doi.org/10.1080/15256480.2019.1692755
[65] Sann, R., Jansom, S., Muennaburan, T. (2024). An extension of the theory of planned behaviour in Thailand cycling tourism: The mediating role of attractiveness of sustainable alternatives. Leisure Studies, 43(1): 47-61. https://doi.org/10.1080/02614367.2023.2182346
[66] Liu, X., Li, J.J., Kim, W.G. (2016). The role of travel experience in the structural relationships among tourists’ perceived image, satisfaction, and behavioral intentions. Tourism and Hospitality Research, 17(2): 135-146. https://doi.org/10.1177/1467358415610371
[67] Bui, T.T.B. (2022). An evaluation of the new tourist behavior model based on the extended theory of planned behavior. Journal of Tourism, Heritage & Services Marketing, 8(2): 48-57. https://doi.org/10.5281/zenodo.7358733
[68] Torabi, Z.A., Hall, C.M., Tavakoli, M., Vahed, Z. (2025). Environmental interpretation and environmentally responsible behavior of tourists in heritage villages. International Journal of Environmental Science and Technology, 22(7): 5513-5528. https://doi.org/10.1007/s13762-024-06216-0
[69] Park, S.H., Hsieh, C.M., Lee, C.K. (2017). Examining Chinese college students’ intention to travel to Japan using the extended theory of planned behavior: Testing destination image and the mediating role of travel constraints. Journal of Travel & Tourism Marketing, 34(1): 113-131. https://doi.org/10.1080/10548408.2016.1141154
[70] Huang, Z., Tsai, H. (2025). Customer acceptance of guest room technologies: The moderating role of demographic factors and technostress. International Journal of Hospitality & Tourism Administration, 27(2): 385-412. https://doi.org/10.1080/15256480.2025.2481633
[71] Lee, S., Erdem, M., Anlamlier, E., Chen, C.C., Bai, B., Putney, L. (2023). Technostress and hotel guests: A mere hurdle or a major friction point? Journal of Hospitality and Tourism Management, 55: 307-317. https://doi.org/10.1016/J.JHTM.2023.04.008
[72] Tarafdar, M., Tu, Q., Ragu-Nathan, B.S., Ragu-Nathan, T.S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1): 301-328. https://doi.org/10.2753/MIS0742-1222240109
[73] Chen, S.X., Zhang, Y., Tse, S.W.T. (2024). Untangling the smart tourism technostress enigma: Assessing the effects of smart technology-related stressors on smart tourism avoidance behaviors in senior tourists. Journal of Travel & Tourism Marketing, 41(9): 1161-1176. https://doi.org/10.1080/10548408.2024.2404839
[74] Masood, A., Luqman, A., Feng, Y., Shahzad, F. (2022). Untangling the adverse effect of SNS stressors on academic performance and its impact on students’ social media discontinuation intention: The moderating role of guilt. Sage Open, 12(1): 21582440221079905. https://doi.org/10.1177/21582440221079905
[75] Park, J.E., Lee, E., Kim, J.Y., Koo, C. (2024). ‘Platform stress’: Exploring a new type of stress in the sharing economy. Current Issues in Tourism, 27(18): 2934-2948. https://doi.org/10.1080/13683500.2023.2243002
[76] Park, H.J., Park, B.G., Kim, Y.H. (2015). A study on the relationship among social support, self expression, tourism experience and tourism SNS use intention-moderating effect of tourism SNS usage purpose. Journal of Digital Convergence, 13(12): 105-115. https://doi.org/10.14400/JDC.2015.13.12.105
[77] Maier, C., Laumer, S., Weinert, C., Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25(3): 275-308. https://doi.org/10.1111/ISJ.12068
[78] Zhao, S., Liu, Y. (2023). Revenge tourism after the lockdown: Based on the SOR framework and extended TPB model. Journal of Travel & Tourism Marketing, 40(5): 416-433. https://doi.org/10.1080/10548408.2023.2256777
[79] Hsu, C.H., Huang, S. (2012). An extension of the theory of planned behavior model for tourists. Journal of Hospitality & Tourism Research, 36(3): 390-417. https://doi.org/10.1177/1096348010390817
[80] Chi, N.T.K., Phuong, V.H. (2022). Studying tourist intention on city tourism: The role of travel motivation. International Journal of Tourism Cities, 8(2): 497-512. https://doi.org/10.1108/IJTC-03-2021-0042
[81] Siddiqui, S., Hamid, S. (2023). Applying the extended theory of planned behaviour (TPB) to predict behavioural intention in the context of transformational tourism: A case of Nizamuddin shrine. International Journal of Religious Tourism and Pilgrimage, 11(2): 6. https://doi.org/10.21427/1K13-CS43
[82] Osiako, P.O., Szente, V. (2024). Behavioral intention in domestic heritage tourism—An extension of the theory of planned behavior. Sustainability, 16(2): 521. https://doi.org/10.3390/su16020521
[83] Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203771587
[84] Hair Jr, J.F., Matthews, L.M., Matthews, R.L., Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2): 107-123. https://doi.org/10.1504/IJMDA.2017.087624
[85] 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. https://doi.org/10.1108/EBR-11-2018-0203
[86] Streukens, S., Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6): 618-632. https://doi.org/10.1016/j.emj.2016.06.003
[87] Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (IJEC), 11(4): 1-10. https://doi.org/10.4018/ijec.2015100101
[88] Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.1177/002224378101800104
[89] Henseler, J., Ringle, C.M., Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1): 115-135. https://doi.org/10.1007/s11747-014-0403-8
[90] Hair, J.F., Astrachan, C.B., Moisescu, O.I., Radomir, L., Sarstedt, M., Vaithilingam, S., Ringle, C.M. (2021). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 12(3): 100392. https://doi.org/10.1016/j.jfbs.2020.100392
[91] Shmueli, G., Sarstedt, M., Hair, J.F., Cheah, J.H., Ting, H., Vaithilingam, S., Ringle, C.M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11): 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189