Next Article in Journal
Delphi Technique to Generate a Sustainable Development Index in Alternative Tourism: An Applied Case in Colombia
Previous Article in Journal
The Fun Factor: Unlocking Place Love Through Exceptional Tourist Experiences
Previous Article in Special Issue
K-Pop Demon Hunters and Digital Cultural Diplomacy: Measuring Brand Identity-Image Convergence in Animated K-Content
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tourism and Hospitality
Volume 6
Issue 5
10.3390/tourhosp6050247
tourismhosp-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Digital Pathways to Sustainability: Eco-Travel Apps and Gen Z’s Eco-Friendly Travel Behaviors

by
Zehra Saltik
1,
Orhan Uludag
2 and
Berislav Andrlić
3,*
1
Tourism Management, Faculty of Applied Sciences, İstanbul Bilgi University, İstanbul 34440, Turkey
2
Hospitality Management and Tourism School, Central Asian University, Tashkent 111121, Uzbekistan
3
Faculty of Tourism and Rural Development Pozega, Josip Juraj Strossmayer University of Osijek, 34000 Požega, Croatia
*
Author to whom correspondence should be addressed.
Tour. Hosp. 2025, 6(5), 247; https://doi.org/10.3390/tourhosp6050247
Submission received: 29 September 2025 / Revised: 4 November 2025 / Accepted: 11 November 2025 / Published: 18 November 2025
(This article belongs to the Special Issue Digital Transformation in Hospitality and Tourism)
Browse Figures
Versions Notes

Abstract

This study examines the interrelations among the perception of Gen Z towards tourism’s negative environmental impacts, adoption intentions of eco-friendly travel apps, and attitudes towards the value of these apps, as well as the mediating roles of adoption intention and attitude towards the value of eco-friendly travel apps in the relations between tourism’s perceived negative environmental impacts and sustainable travel behavior. Methodologically, this study extends the Theory of Planned Behavior by incorporating adoption intention and attitude towards eco-friendly travel apps as mediators between perception and sustainable behavior. This integrated model offers a novel application of TPB within digital sustainability contexts. This study reveals that awareness of tourism’s environmental consequences positively impacts the intention to use eco-friendly travel apps and fosters sustainable travel behaviors. These findings highlight and underscore the role of attitudes and technological adoption in sustainable tourism. This study offers some recommendations for future researchers to explore whether the current findings are consistent across different cultural contexts as well as for practitioners to make several practical recommendations to encourage sustainable travel behaviors among young travelers.

1. Introduction

In an era where environmental concerns are at the forefront of global discussions, the role of technology in promoting sustainable tourism has gained special and significant attention. Understanding the behavior and motivation of young travelers towards eco-friendly travel solutions is critical, as they represent a growing segment of the global tourism market (Clark & Nyaupane, 2023; Mansoor et al., 2025). Young individuals are often considered early adopters of technology, making them key players in the shift towards more sustainable travel behaviors using eco-friendly travel apps (Chin et al., 2024; Smith & Anderson, 2018). However, despite the growing availability of these applications, little is known about how young individuals perceive the negative environmental impacts of tourism and how this shapes their adoption intention and attitudes towards the value of these apps. This brings the primary question to the agenda: “how does young travelers’ perception of tourism’s environmental consequences affect their attitudes and intentions toward adopting eco-friendly travel applications?” The current study seeks to find the answer to this question.
The tourism industry, besides being beneficial for economies and cultural exchange, has been a major contributor to environmental degradation. Issues such as increased carbon emissions, resource depletion, and ecosystem destruction are well documented (Gössling & Peeters, 2015). To mitigate these effects, the integration of eco-friendly technologies in travel planning and execution has become a promising approach (Chakraborty, 2024; Erdogan et al., 2022; Gavrilović & Maksimović, 2018; Jasrotia & Roy, 2024). However, existing literature has predominantly focused on general attitudes towards sustainable tourism or government policies aimed at reducing tourism’s environmental impact (Boley et al., 2017). Few studies have explored how eco-friendly travel apps can serve as a bridge between awareness of environmental impacts and sustainable travel behavior, particularly among young individuals.
In recent years, researchers have focused more on studying a specific segment of tourist demand: “Youth”. Young travelers are considered “the emerging visitors in the tourism industry” (Pendergast, 2009). This market segment is significant not only because it is expanding but also because it symbolizes the future of tourism (Vukic et al., 2014). Generational theory, which considers demographic factors, plays a key role in population analysis by categorizing generations, associating each birth year with its corresponding age group (Pendergast, 2009). The generation, which now includes the young, is Generation Z (Gen Z), who were born from the mid-1990s to the early 2010s and who are supposed to be between 12 and 27 years old now. Growing up in the 2000s, Generation Z has been profoundly influenced by global challenges like climate change and resource scarcity, fostering their tendency toward sustainability and mindful consumption (Khalil et al., 2021; Wu et al., 2024). In connection with this, Fuentes-Moraleda et al. (2019) pointed out that younger consumers appear more understanding and are more supportive of sustainable travel initiatives.
From this scientific point of view, this study aims to fulfill the research gap by investigating the interrelation between the perceptions of tourism’s negative environmental impacts and their sustainable travel behaviors as well as analyzing the mediating roles of intention to adopt eco-friendly travel apps and attitudes toward the value of these apps in this relationship. Understanding this dynamic is crucial, as it can reveal how technology can drive environmentally responsible actions among younger travelers, a group that will increasingly shape tourism trends in the coming decades (Buffa, 2015; D’Arco et al., 2023; Zeng et al., 2022).
The studies regarding sustainable behaviors in the tourism context are based on several theories, including the theory of planned behavior (TPB) (Ajzen, 1985, 1991), cognitive dissonance theory (Festinger, 1957), and the value-belief-norm theory (Stern, 2000). This study is grounded in the Theory of Planned Behavior (TPB), which is one of the most prominent frameworks for understanding human behavior in relation to intention and action (Ajzen, 1991). The TPB is especially considered suitable for this study, as it enables a nuanced exploration of both psychological and behavioral dimensions included in eco-friendly travel app adoption, which cannot fully be identified by alternative models like the Technology Acceptance Model (TAM), which primarily focuses on ease of use and perceived usefulness. The TPB incorporates subjective norms (Ajzen, 1985, 1991), making it especially appropriate in the context of youth behaviors, where peer influence and social expectations are considered to strongly shape both attitudes and adoption intentions.
Previous studies have utilized the TPB to investigate technology adoption in various fields, including tourism and environmental sustainability. For instance, Lam and Hsu (2006) applied the TPB to explain tourists’ intention to engage in sustainable travel behaviors, while M. F. Chen and Tung (2014) employed it to assess pro-environmental behaviors, showing that attitudes toward sustainability positively affect individuals’ behavioral intentions. This theory is particularly relevant here, as it allows for an examination of how perceptions of the environmental consequences of tourism (attitudes) influence their intention to use eco-friendly travel apps and engage in sustainable travel behaviors. Additionally, TPB’s emphasis on intention aligns well with the study’s focus on adoption intentions and attitudes toward the value of these apps. Hence, in the current study, sustainable travel behavior represents tourists’ reported behaviors rather than their observed behaviors due to financial, ethical, and time challenges (Juvan & Dolnicar, 2016) and measures to what extent they agree on their willingness to be part of sustainable travel practices.
Depending on the points stated so far, this study has two major objectives: (i) “to examine whether young travelers’ perceptions of tourism’s negative environmental consequences impact their attitudes and intentions towards adopting eco-friendly travel apps, using the Theory of Planned Behavior as a conceptual framework” and (ii) “to determine the mediating role of eco-friendly travel app attitudes and adoption intentions in the relationship between environmental perceptions and self-reported sustainable travel behaviors”. This study is considered significant, as it is expected to have the potential to influence both the design of eco-friendly travel apps and policymaking. By understanding what drives young travelers to adopt these apps and engage in sustainable travel behavior, app developers can create more effective tools that align with user needs and values. Additionally, policymakers can leverage these findings to promote the use of technology in environmental conservation strategies targeted at youth.
Unlike other studies, this research distinguishes itself by examining how eco-friendly travel applications serve as a technological conduit for translating environmental awareness into concrete behavioral intentions among youth travelers. Its novelty lies in examining not only adoption intention but also perceived app value as a key attitudinal variable that remains underexplored in existing literature. Previous studies have not extensively examined the role of eco-friendly travel apps in shaping youth travel decisions, making this study both timely and relevant in the context of sustainable tourism development (Smith & Anderson, 2018; Gössling & Peeters, 2015). From this aspect, this study is expected to provide a fresh perspective upon how digital tools can transform environmentally responsible actions. This paper, respectively, reviews the literature to justify the hypotheses of the study, provides methodological information, puts forward the findings through analyzing the data, then discusses the findings, comparing them with previous relevant studies, and concludes the research, providing implications for both future researchers and practitioners in the field of tourism.

2. Theoretical Framework and Hypotheses Development

Tourism has been one of the largest and fastest-growing industries, along with the rapid developments and improvements in transportation technologies (Bratić et al., 2025). While the number of international tourists was twenty-five million in the 1950s, it increased thirtyfold, reaching 760 million fifty years later. According to statistics, tourism accounts for one in four of all new jobs created worldwide. This means that ten percent of jobs are globally linked with the tourism industry (Soja, 2022). Today, tourism is an important source of export and foreign currency for many countries. Tourism, which holds a significant share in the economy, owes much of its development to natural resources. Tourism and the natural environment mutually influence one another. Many types of tourism (such as coastal tourism, mountain tourism, winter tourism, and thermal tourism) develop based on natural resources (Pešić et al., 2025). Therefore, the emergence of tourist demand in a region depends on the presence of these elements. When physical environmental conditions become less favorable, the life cycle of a tourist destination nears its end (Gössling & Hall, 2006). Environmental resources provide one of the critical resources necessary for the creation of a tourism product. Compared to other economic sectors, tourism utilizes environmental resources to a greater extent. The use of these resources significantly supports a country’s economic and social development (Tuna, 2007). Therefore, it can be said that there is a delicate relationship between tourism and the environment, and tourism activities should be carried out in an environmentally sensitive manner.
Tourism and the environment, as could be cogitated, symbolize a mutual relationship. On one hand, tourism is an activity highly dependent on the environment, while on the other hand, it significantly affects the environment. It is a sector that both extensively utilizes the environment and is obliged to protect it. Tourism activities require natural resources more than social data. The relationship between tourism and the environment is vital, and for tourism to continue to exist, the environment must be sustained (Demir & Çevirgen, 2006). Although tourism was predominantly seen as an ‘environmentally friendly activity’ and a ‘smokeless industry,’ in later years, concerns began to emerge about possible ecological imbalances that could arise from tourism development. In the 1970s, with the expansion of tourism into new geographic areas and the visible emergence of its negative effects, the environmental impacts of tourism started to be questioned more frequently (Holden, 2016). As a result, the tourism–environment (natural) relationship, particularly the negative environmental impacts of tourism on sustainable consumer behavior, has been widely studied by tourism academics.
Increasing knowledge and concerns about the negative impacts of tourism on the environment have heightened the need for sustainable tourism development. Sustainable tourism emphasizes the industry’s needs and the sustainable use of resources. It is defined as a tourism model that encompasses the ethical aspects of the sustainability ideology (Saarinen, 2006). Sustainable tourism is not opposed to growth but argues that there are limits to growth. Therefore, it advocates not only for sustainable production but also for sustainable consumption. In the understanding of sustainable tourism, the environment is prioritized, and behaviors that reduce tourism’s negative environmental impacts are of great importance (Holden, 2016). This is because the efforts of tourism producers alone will be insufficient to achieve the goals of sustainable tourism. It is crucial that these efforts be supported by sustainable consumption behaviors. At this point, the equation of tourism, consumption, and sustainability becomes an important agenda and research topic.
Individuals participating in tourism activities generate impacts such as greenhouse gas emissions, energy consumption, and environmental damage through their travels. In the context of tourism, sustainable consumption is defined as individuals traveling on a more local scale using high-efficiency transportation methods and/or, more importantly, being willing to pay more to reduce the environmental impacts resulting from their travel (Hall, 2009). In tourism literature, a sustainable consumer is described with the concepts “environmentally conscious consumer”, “green consumer”, “responsible consumer”, and “eco-friendly consumer”, and sustainable consumer behavior emphasizes environmentally friendly travel experiences (Mehmetoglu, 2009). In the current study, sustainable consumer behavior is represented with “sustainable travel behavior”, which measures the willingness of individuals to mitigate the environmental impact of their travel.
There is a growing number of studies focusing on perceptions over environmental impacts of tourism (Hedlund, 2011; Mikayilov et al., 2019; Sroypetch et al., 2018; Xu & Hu, 2021). Along with the increasing environmental concerns, assessing perceived negative environmental impacts of tourism and the influence on sustainable attitudes, behavioral intentions, and behaviors has become an important research topic. While extant studies have extensively explored the interrelations between tourism, environmental sustainability, and consumer behavior, the literature reveals theoretical ambiguities and fragmented evidence concerning the mechanisms driving sustainable travel behavior. Earlier research predominantly delineates the environmental impacts of tourism or examines pro-environmental intentions in isolation. However, the existing literature lacks a systematic integration of attitudinal (e.g., value perception), intentional (e.g., adoption intention), and behavioral (e.g., sustainable travel actions) dimensions within a single model grounded in the Theory of Planned Behavior (TPB).
Despite the widespread application of the Theory of Planned Behavior (TPB), previous studies possess inconsistencies regarding the mediating influence of technological adoption and environmental attitudes in shaping sustainable behaviors. These theoretical controversies (particularly the inconclusive translation of ecological perceptions into tangible behavioral outcomes) underscore the need for a more comprehensive model that systematically unifies perceptual, cognitive, and behavioral constructs. Additionally, few studies have empirically validated the mediating mechanisms linking environmental attitudes, technological adoption, and sustainable behavior in tourism contexts. As a result, the present study addresses these theoretical and empirical gaps by developing and testing a TPB-based model that explains how travelers’ perceptions of eco-friendly travel apps shape their intentions and actual sustainable behaviors. This synthesis not only reconciles divergent findings across prior research but also provides a more holistic understanding of digital sustainability within contemporary tourism.
According to behavioral science, the perceptions, beliefs, attitudes, norms, behavioral intentions, willingness, etc., can be preconditions of actual behaviors, although they do not necessarily convert into actual behaviors (Budeanu, 2007). Accordingly, the environmental perception was found to be effective on environmentally friendly purchasing behavior (Laroche et al., 2001), green hotel choices (Han et al., 2010), energy-saving and carbon-reduction behavior (Qiao & Gao, 2017), and attitudes towards eco-labeled products (Fairweather et al., 2005). Another study (Mckercher et al., 2010) focused on assessing consumers’ awareness towards the relation between tourism and climate change and its impact on changing their vacation behaviors to reduce environmental impacts. Similarly, Xu and Hu (2021) examined the link between perceived environmental impacts of tourism and environmentally responsible behavior (ERB) from residents’ perspectives and revealed that the perceived negative environmental impacts of tourism statistically affected ERB. In line with the findings of previous studies and the theory of planned behavior, the following hypothesis is developed:
H1. 
The perceived negative environmental impacts of tourism are significantly linked with sustainable travel behavior.
In the current study, the behavioral intention is represented with “adoption intention of eco-friendly travel apps”, and attitude is represented with “attitude toward value of eco-friendly travel apps”. Both the theory of reasoned action (Ajzen & Fishbein, 1980) and the theory of planned behavior (Ajzen, 1985, 1991) assume that perception plays a significant role in shaping attitudes and behaviors. Behavioral intentions have been found effective on the selection of sustainable travel options (Mohaidin et al., 2017). On the other hand, attitudes are considered significant determining factors, which influence individuals to behave in a more environmentally friendly way and contribute to sustainable tourism achievement (Butnaru et al., 2022). Thus, intention and attitude are essential predictors and precursors to behaviors (Ajzen, 1985, 1991). As a result, investigating the interaction between perceptions over negative environmental impacts of tourism, the intention to adopt eco-friendly travel apps, and attitudes towards the value of these apps seems to be important. Thus, the following hypotheses are proposed:
H2. 
The perceived negative environmental impacts of tourism are significantly linked with the adoption intention of eco-friendly travel apps.
H3. 
The perceived negative environmental impacts of tourism are significantly linked with the attitude towards the value of eco-friendly travel apps.
Furthermore, technological factors provide benefits for tourism consumers to develop sustainable behaviors. There are empirical studies that found a nexus between sustainable tourism behavior/consumption and technology use (Christodoulides et al., 2012; Horng et al., 2022; Parra-López et al., 2011). Innovative environmental technologies are thought to have the potential to transform consumer behaviors to reduce the environmental impact of their travel. Adopting environmental technologies in the hotel industry was found to have a significant influence on pro-environmental behavior (Adeel et al., 2024). Accepting and adopting environmentally friendly technologies in tourism are seen as a way to protect the environment and involve consumers (travelers, tourists, etc.) in sustainable travel practices (Gavrilović & Maksimović, 2018). Based on the existing literature and in line with the theory, the following hypotheses are developed:
H4. 
The adoption intention of eco-friendly travel apps is significantly linked with the attitude towards the value of eco-friendly travel apps.
H5. 
The adoption intention of eco-friendly travel apps is significantly linked with sustainable travel behavior.
H6. 
The attitude towards the value of eco-friendly travel apps is significantly linked with sustainable travel behavior.
The mediating roles of adoption intention and attitude toward the value of eco-friendly travel apps in the relationship between tourism’s perceived negative environmental impacts and sustainable travel behavior can be effectively framed within the Theory of Planned Behavior (TPB) (Ajzen, 1991). According to behavioral sciences, pro-environmental perceptions and beliefs do not necessarily result in behaviors (Ajzen, 1985, 1991; Budeanu, 2007; Juvan & Dolnicar, 2016). Therefore, it is considered significant to analyze the mediating variables to better explain sustainable travel behavior. TPB posits that behavior is primarily influenced by intention, which is shaped by attitudes, subjective norms, and perceived behavioral control (Ajzen, 1991; Ajzen & Fishbein, 1980). From this point of view, perceptions of tourism’s environmental impacts may evoke a heightened sense of responsibility mediated by behavioral intention and attitude. Consequently, the following hypotheses are proposed:
H7. 
The perceived negative environmental impacts of tourism have a significant impact on sustainable travel behavior mediated by the adoption intention of eco-friendly travel apps.
H8. 
The perceived negative environmental impacts of tourism have a significant impact on sustainable travel behavior mediated by the attitude towards the value of eco-friendly travel apps.
All direct and indirect relationships are illustrated on Figure 1.

3. Materials and Methods

3.1. Research Method

The current study utilizes a quantitative research method and is explanatory (hypothesis-driven) research in nature. The explanatory approach is crucial for identifying and pinpointing the factors that lead to specific outcomes (Yin, 2018). Explanatory research focuses on examining the correlation between variables regarding a phenomenon and involves cause-and-effect relationships (Taherdoost, 2022). On the other hand, this study utilizes a survey as a data collection technique. A survey is a commonly used technique in social sciences, as it is simple to conduct, highly practical, and well-regarded (Taherdoost, 2021).
In this study, the perception of the negative environmental impacts of tourism serves as the independent variable, while adoption intention and attitudes towards the value of eco-friendly travel apps and sustainable travel behavior are positioned as dependent variables. Additionally, adoption intention and attitudes towards the value of eco-friendly travel apps serve as mediating variables. The scale to measure the perception over negative environmental consequences of tourism includes four items and was adopted from Landon et al. (2018). The three items to measure the adoption intention of eco-friendly travel apps were adopted from Ajjan and Hartshorne (2008) and Roca et al. (2006). The four items to measure the attitude towards the value of eco-friendly apps were adopted from Patterson and Spreng (1997) and Verhagen et al. (2012). The five items to measure sustainable travel behavior were adopted from Minton and Rose (1997) and were modified accordingly. The statements of the scale indicate the willingness to mitigate travel-based environmental impacts. All scale items can be found in Appendix A at the end of the article. The reliability and validity coefficients were proved by previous studies to be within accepted values. In addition, the survey included various variables concerning the demographics of young tourists (i.e., age, gender, monthly allowance/income, travel frequency, residence background, etc.).

3.2. Research Population and Sample

The current study is based on the perspectives of Gen Z. The participants within this age group are represented by young travelers between the ages 17 and 25 in the northern part of Cyprus. Utilizing a purposive sampling procedure, the survey was administered to different higher education institutions in Northern Cyprus across varying bachelor programs and education levels. A key inclusion criterion was prior use of or being familiar with at least one eco-friendly travel application.
To ensure this, participants were first presented with a short operational definition of ‘eco-friendly travel apps’ (i.e., mobile applications that promote sustainable tourism practices, such as offering carbon-offset options and public transport-focused planning). Concrete examples, including Bikemap, Ecosia, and BlaBlaCar, were listed in the survey to guide participant understanding. A screening question asked participants to name at least one eco-friendly app they had used or they knew. Only those who could identify an app consistent with the provided examples were included in the final sample. This approach aimed to minimize ambiguity and mitigate the risk of including participants influenced by greenwashing or misconceptions about app sustainability features.
The researchers implemented several measures, as recommended by Podsakoff et al. (2003), to mitigate the risk of common method bias in their study. Prior to administering the survey, ethical approval for this study was sought and obtained from the university’s ethics committee. The study received formal approval under the article number EKK23-24/015/01, ensuring that all research protocols adhered to the institution’s ethical guidelines and standards.
Furthermore, a cover letter was included with each survey form (online and face-to-face), clearly explaining the purpose of the study and the reason for data collection. The confidentiality and anonymity of the responses were emphasized, ensuring participants that the data would be used solely for academic purposes. The researchers explicitly stated that there were no right or wrong answers, encouraging participants to respond honestly and reducing potential bias. Additionally, participants were informed of their right to withdraw from the study at any point if they felt uncomfortable, further enhancing voluntary and unbiased participation. Finally, the constructs of the study were not identified in the questionnaire nor listed in accordance with the proposed model, with the use of varied scale formats to reduce bias. Additionally, a marker variable, theoretically unrelated to the main constructs, was incorporated into the data collection process as a method to further control for common method bias. By examining the correlations between responses to this marker variable and the primary variables of interest, researchers could identify potential response biases, such as social desirability or consistency effects. The absence of significant correlations between the marker variable and the key constructs indicated that common method bias did not significantly influence the results of the study.
The survey was disseminated between the 14th of October and the 4th of November 2024. The survey was collected using online and face-to-face methods. An average of 15 min was spent on each survey. The data collection process yielded 420 valid responses, representing an 84% response rate, comprising 138 online submissions and 282 face-to-face surveys.

4. Data Analysis and Findings

The data was analyzed using SPSS for version 23.0 and AMOS 24.0 programs. For example, the reliability of the scales was tested through “Reliability Analysis” on SPSS, and the “Confirmatory Factor Analysis” and path analyses were conducted on AMOS. In the first place, the frequency analysis of demographics was performed. Table 1 reveals that 52.1% of respondents are male, with 42.6% aged between 23 and 25 and 36.7% between 20 and 22. Additionally, more than half of the respondents have a monthly allowance/income under EUR 500. The table also indicated that the great majority of the respondents are always able to access the internet (63.1%) and feel very comfortable using mobile apps for traveling (47.9%). Additionally, almost 80% of respondents come from an urban residence background, and more than half of them (53.3%) travel only once within a year. Only a very small percentage of participants travel more than five times in a year (5.7%).
As illustrated in Table 2, the Adoption Intention of Eco-friendly Travel Apps (AIoEFTA) exhibits robust factor loadings, with standardized estimates between 0.838 and 0.910. Correspondingly, ATVoEFTA (Attitude Towards the Value of Eco-friendly Travel Apps) exhibits robust standardized estimates ranging from 0.796 to 0.875. Finally, Sustainable Tourism Behavior (STB) exhibits a little lower still acceptable range of standardized values. Two items from the Sustainable Travel Behavior (STB) scale were removed due to standardized factor loadings below the acceptable 0.50 threshold, likely because they reflected high-effort or cost-intensive behaviors less aligned with the travel realities of our Gen Z sample, and their removal improved model fit and preserved construct reliability and validity (Hair et al., 2010). The standardized estimates demonstrate that the measuring model has robust convergent validity across all constructs.
Table 3 displays the composite reliability (CR), Cronbach alpha reliability (α), average variance extracted (AVE), maximum shared variance (MSV), and maximum H reliability (MaxR(H)) for the principal constructs in this research: Perceived Negative Environmental Effects of Tourism (NEIoT), Intention to Adopt Eco-friendly Travel Apps (AIoEFTA), Attitude Towards the Value of Eco-friendly Travel Apps (ATVoEFTA), and Sustainable Tourism Behavior (STB).
All constructs exhibited robust internal consistency, with CR values between 0.779 and 0.905, surpassing the advised criterion of 0.70 (Hair et al., 2010). In a similar vein, Cronbach’s alpha values also exceed the threshold value of 0.70. The AVE values, indicating the variance captured by the constructs, satisfied the acceptable threshold of 0.50. The MaxR(H) values reinforce the constructs’ reliability, signifying that the indicators are dependable measurements of their corresponding latent variables. To further ensure the robustness of the measurement model, several complementary statistical tests were conducted. Harman’s single-factor test was used to assess the potential for common method bias. The unrotated factor solution revealed that the first factor accounted for 45.11% of the total variance, which is below the recommended threshold of 50%, indicating that common method bias is not a serious concern (Podsakoff et al., 2003).
Collinearity diagnostics were examined using the Variance Inflation Factor (VIF). All constructs showed VIF values ranging from 1.00 to 1.83, well below the critical value of 5 (Hair et al., 2010), confirming the absence of multicollinearity issues among the predictor variables. Furthermore, discriminant validity was further assessed using the Heterotrait–Monotrait Ratio (HTMT; Henseler et al., 2015). The computed HTMT values ranged from 0.40 to 0.85, all below the conservative threshold of 0.90, confirming satisfactory discriminant validity across all constructs. Combined with the Fornell–Larcker criterion and acceptable reliability indices, these results confirm the distinctiveness and adequacy of the measurement model constructs.
The correlation matrix, presented in Table 3, underscores notable links among the constructs. The square roots of the AVE values, presented on the matrix’s diagonal, exceed the construct intercorrelations, so demonstrating discriminant validity (Fornell & Larcker, 1981). Several significant relationships arise from the correlation analysis. NEIoT exhibited a positive correlation with AIoEFTA (r = 0.326, p < 0.001), ATVoEFTA (r = 0.369, p < 0.001), and STB (r = 0.413, p < 0.001). The findings indicate that persons who perceive the negative environmental effects of tourism are more inclined to embrace eco-friendly travel applications, possess favorable opinions towards the utility of these applications, and participate in more sustainable tourist practices. AIoEFTA exhibited a robust correlation with ATVoEFTA (r = 0.719, p < 0.001) and STB (r = 0.658, p < 0.001), suggesting that individuals with greater intentions to adopt eco-friendly travel applications are inclined to possess more favorable attitudes regarding the value of these applications and are more predisposed to participate in sustainable tourism practices. ATVoEFTA exhibited a positive correlation with STB (r = 0.657, p < 0.001), indicating that favorable perceptions of eco-friendly applications are connected with enhanced sustainable tourism behaviors.
Table 4 shows the model fit statistics for the suggested structural equation model. The fit indices indicate an exceptional model fit according to the established standards in the literature (Hu & Bentler, 1999; Kline, 2016). The chi-square statistic (CMIN) was 137.344, accompanied by 71 degrees of freedom (DF). The chi-square test is influenced by sample size, although the chi-square to degrees of freedom ratio (CMIN/DF) is more frequently employed to evaluate model fit. The CMIN/DF ratio of 1.934 is within the suggested range of 1 to 3, signifying a great fit (Kline, 2016). The Comparative Fit Index (CFI) was 0.981 and the Tucker–Lewis Index (TLI) was 0.975, surpassing the recommended threshold of 0.95, hence suggesting an exceptional fit. The Standardized Root Mean Squared Residual (SRMR), which quantifies the disparity between observed and predicted correlations, was 0.044, significantly lower than the threshold of 0.08, indicating an exceptional fit. The Root Mean Square Error of Approximation (RMSEA) was 0.047, below the recommended maximum of 0.06, indicating a close fit of the model to the data. The PClose value, which evaluates the null hypothesis that RMSEA is less than or equal to 0.05, was 0.635, exceeding the threshold of 0.05, so offering additional evidence that the model closely fits the data (Browne & Cudeck, 1993).
In the final stage, the hypotheses were tested. A statistical approach known as path analysis, which extends multiple regression techniques, was employed to clarify the relationships among the variables (see Figure 2). Table 5 illustrates the outcomes of the hypothesis testing, analyzing the direct links among the constructs. All proposed hypotheses were corroborated by statistically significant pathways (p < 0.01).
Hypothesis 1, which proposed that the Perceived Negative Environmental Impact of Tourism (NEIoT) positively affects Sustainable Tourism Behavior (STB), was corroborated with a beta coefficient of 0.172. H2 identified a significant influence of NEIoT on AIoEFTA (Adoption Intention of Eco-friendly Travel Apps), with a beta coefficient of 0.326, corroborating the hypothesis that persons who recognize more environmental consequences are more inclined to embrace eco-friendly travel applications.
In a similar vein, H3 was validated, indicating that NEIoT exerts a positive influence on ATVoEFTA (Attitude Towards the Value of Eco-friendly Travel Apps) with a beta coefficient of 0.151. H4 exhibited a robust positive relationship between AIoEFTA and ATVoEFTA (β = 0.669), signifying that increased adoption intention results in more favorable perceptions of the value of these applications. Furthermore, AIoEFTA exerted a positive impact on STB (β = 0.363), corroborating the theory that the intention to embrace eco-friendly applications leads to sustainable tourist behavior. Ultimately, ATVoEFTA was determined to have a substantial impact on STB (β = 0.333), indicating that a favorable disposition towards eco-friendly travel applications promotes more sustainable tourist behaviors.
In order to test Hypotheses 7 and 8, Hayes Macro Model 4 analysis was conducted. As illustrated in Table 6, Perceived Negative Environmental Impact of Tourism (NEIoT) was found to influence Sustainable Travel Behavior (STB) both directly and indirectly through two mediators: Adoption Intention of Eco-friendly Travel Apps (AIoEFTA) and Attitude Towards the Value of Eco-friendly Travel Apps (ATVoEFTA). Specifically, the partial mediation effect through AIoEFTA was 0.0711, and through ATVoEFTA it was 0.0896.
The total indirect effect was 0.1607, suggesting that a significant portion of NEIoT’s impact on STB is mediated via AIoEFTA and ATVoEFTA. Despite these strong mediation effects, NEIoT retained a significant direct effect of 0.1218 on STB, indicating partial rather than full mediation. Overall, the model explained a substantial 40.5% of the variance in STB. As a result, Hypotheses 7 and 8 are also supported.

5. Discussion

The present study contributes to extending the Theory of Planned Behavior (TPB) by incorporating technology adoption as a mediating mechanism and provides valuable insights into how youths’ perceptions of environmental impact, intentions to adopt eco-friendly travel apps, and attitudes towards these apps collectively shape their sustainable travel behaviors. Analyzing these factors through the Theory of Planned Behavior (TPB) framework helps clarify how awareness, technological adoption, and favorable attitudes towards sustainable travel apps are interlinked. The study highlights that technological engagement serves as a functional bridge between intention and behavior. This section discusses the implications of these findings in the context of previous research, revealing how perceptions, intentions, and attitudes can work together to encourage eco-friendly practices among young travelers. For clarity, the discussion is organized into key thematic areas, exploring each primary relationship in detail.

5.1. Seeing Tourism’s Impact, Choosing Sustainability

The analysis highlights a significant relationship between youths’ perceptions of tourism’s negative environmental impact and their sustainable travel behaviors (STB). While this study confirms the theoretical assumptions of the Theory of Planned Behavior (TPB) (Ajzen, 1991), the relatively modest beta coefficient for H1 suggests that perceived negative environmental impacts alone may not be a dominant driver of sustainable travel behavior among Gen Z. This highlights the complexity of environmental action, where awareness does not always lead to behavior. Nevertheless, this finding corroborates earlier research by Xu and Hu (2021), which emphasizes the predictive role of perceived environmental impacts in environmentally responsible behavior (ERB) in tourism settings.
Moreover, similar studies by Budeanu (2007) and Qiao and Gao (2017) illustrate that environmental awareness can motivate eco-friendly consumer behavior across multiple domains, including travel. These comparisons confirm that perceived environmental impacts can effectively drive sustainable behavior, underscoring the importance of educational efforts and awareness campaigns targeting environmental issues among young travelers.

5.2. Eco-Friendly Tools Inspiring Sustainable Attitudes and Behaviors

The findings also demonstrate a positive correlation between adoption intention of eco-friendly travel apps and both attitudes toward their value and sustainable travel behaviors (STB). The structural equation model reveals that those with higher intentions to use eco-friendly apps show more positive attitudes toward the apps’ value and are more likely to engage in sustainable travel behaviors. These findings are consistent with the broader literature, where behavioral intentions play a significant role in technology adoption and subsequent sustainable behaviors (Mohaidin et al., 2017; Butnaru et al., 2022).
Adopting eco-friendly apps appears to serve as a mechanism through which young travelers can translate environmental perceptions into action. This is supported by Gavrilović and Maksimović (2018), who highlight that eco-friendly technologies can positively impact consumer choices, especially when these technologies facilitate easier access to sustainable options. Moreover, this study extends the work of Adeel et al. (2024) and Christodoulides et al. (2012) by emphasizing that travel apps designed with sustainability features can be instrumental in promoting eco-friendly behavior. Thus, eco-friendly travel apps represent a valuable tool for encouraging sustainable travel practices among youth, especially given this demographic’s proficiency with mobile technology.
Furthermore, the study illustrates that attitudes toward the value of eco-friendly travel apps significantly affect sustainable travel behaviors (STB). These findings highlight that a favorable perception of eco-friendly apps goes beyond the intent to adopt; it directly influences whether individuals follow through with sustainable actions. Previous research on the TPB also underscores the importance of attitudes as a predictor of behavior (Ajzen, 1991).
Comparatively, studies on sustainable tourism behaviors (e.g., M. F. Chen & Tung, 2014; Han et al., 2010) also indicate that positive attitudes toward eco-labeled products or sustainable tourism choices are crucial for converting sustainable intentions into actual behavior. The present findings suggest that attitudes formed by using eco-friendly travel apps reinforce the likelihood of sustainable travel behavior, implying that these apps may play a critical role in reshaping youth travel habits toward more sustainable choices.

5.3. Sustainability Enhanced: The Mediating Power of Eco-Friendly Apps

The findings of this study underscore the pivotal roles of adoption intention and attitudes towards the value of eco-friendly travel apps in fostering sustainable travel behavior. The mediating effect of adoption intention between the perceived negative environmental impact of tourism and sustainable travel behavior aligns with previous studies highlighting technology’s role in promoting environmental responsibility (e.g., Balinska et al., 2021; D’Arco et al., 2023; Zhang et al., 2022). Similarly, attitudes toward the value of eco-friendly travel apps were found to mediate the relationship between the perceived negative environmental impact of tourism and sustainable travel behavior, supporting research emphasizing the significance of perceived utility and ethical consciousness in app adoption (e.g., S. Y. Chen, 2016; Prakhar et al., 2024). These results suggest that individuals who perceive tourism’s negative environmental consequences are more likely to adopt and value eco-friendly travel solutions, translating their awareness into actionable sustainable behavior. These mediators highlight the importance of not only designing user-friendly and effective travel apps but also educating users about their ecological benefits to bridge awareness and action. Future studies could explore further behavioral antecedents and app features that enhance this mediation effect.

6. Conclusions

This study provides a comprehensive examination of the interrelations between tourism’s environmental consequences, the adoption of eco-friendly mobile apps, the attitude towards the value of eco-friendly travel apps, and sustainable travel behavior, with a particular focus on young travelers. Tourism, as a sector, has long contributed to both positive cultural exchange and significant environmental challenges, including carbon emissions, resource depletion, and habitat destruction. As awareness of these consequences has grown, so too has the demand for technological solutions to mitigate tourism’s environmental impact. Eco-friendly travel apps represent one such solution, offering users ways to make sustainable choices more accessible and actionable. The current study shows that youth perceptions of tourism’s environmental impact positively influence their intention to adopt these apps, with strong attitudes toward the value of these apps reinforcing the likelihood of sustainable travel behavior. These findings underscore a valuable contribution to the literature on sustainable tourism and environmental technology adoption, highlighting the role of eco-friendly mobile applications as facilitators of positive environmental change. By linking environmental perceptions, adoption intentions, and attitudes with sustainable behavior, this study enriches the understanding of the psychological and behavioral mechanisms underlying sustainable tourism practices, particularly within the increasingly influential youth demographic. Therefore, it can be stated that this study uniquely contributes by positioning eco-friendly travel apps as significant mediators that translate environmental perception into action, addressing a gap in the current literature, which often overlooks the role of digital platforms in achieving sustainability. As a result, it is possible to make some theoretical and practical recommendations.

6.1. Implications

From a theoretical perspective, the finding relying on the significant but weak effect of perceived negative environmental impacts of tourism on sustainable travel behavior reinforces the need to expand the Theory of Planned Behavior (TPB) to explicitly incorporate technology adoption as a factor influencing sustainable travel behaviors. Given the positive influence of eco-friendly travel apps observed in this study, the TPB framework may benefit from an additional variable that represents technological adoption as a driver of pro-environmental behavior. This potential extension could provide a more comprehensive theoretical understanding of sustainable travel choices, as supported by findings from Mohaidin et al. (2017) on the role of technology in encouraging eco-friendly actions. Furthermore, future research should explore whether these findings are consistent across different cultural contexts, as cultural factors may influence attitudes and intentions related to eco-friendly technology adoption. Research by M. F. Chen and Tung (2014) suggests that cultural variations can play a substantial role in shaping consumer behavior, indicating that cross-cultural studies could broaden the theoretical application of these findings.
Furthermore, it is suggested that longitudinal studies be conducted to analyze the lasting impact of eco-friendly app adoption on sustainable travel behavior. The findings of this study provide a valuable snapshot, yet longer-term research could reveal whether initial adoption leads to sustained eco-friendly behavior over time, further enriching the literature on the role of environmental technology in tourism. By investigating these areas, future studies can advance theoretical knowledge and provide deeper insights into how technological and environmental factors intersect to influence sustainable travel practices.
In light of the findings, it is possible to make several practical recommendations to encourage sustainable travel behaviors among young travelers. Initially, governments, tourism boards, and educational institutions should invest in awareness campaigns targeting youth, highlighting the environmental impacts of tourism and the role of eco-friendly travel apps as solutions to mitigate these impacts. Studies by Qiao and Gao (2017) and Xu and Hu (2021) emphasize that awareness is an essential driver of eco-friendly behavior, particularly when combined with accessible solutions. By promoting the convenience and positive environmental impact of eco-friendly travel apps, such campaigns could significantly increase their adoption among young users. Additionally, app developers are encouraged to design platforms that directly incentivize sustainable choices. Features like loyalty points, discounts, or partnerships with environmentally conscious brands could reinforce sustainable behavior by rewarding eco-friendly choices. This approach aligns with the findings of Christodoulides et al. (2012), who note that rewards are effective motivators for shaping consumer behavior.
Collaboration between eco-friendly app developers and tourism service providers, such as green-certified hotels and electric vehicle rental companies, is also recommended. Through these partnerships, apps could offer users real-time sustainable options, expanding the range of eco-friendly choices available to them during their travel planning. This recommendation echoes the work of Gavrilović and Maksimović (2018), who underscore that the easier it is to access eco-friendly options, the more likely consumers are to choose them. Educational institutions can also play a vital role by integrating responsible tourism and sustainability into their curricula, especially within tourism and hospitality programs. Gössling and Peeters (2015) argue that structured educational interventions can foster long-term environmental responsibility, and this approach would empower the next generation of travelers to prioritize sustainable choices.

6.2. Limitations and Future Research Directions

This study presents valuable insights; however, there are some limitations that future research should address to enhance the generalizability and depth of findings. First, the study is geographically limited, with data collected from university students in the Turkish Republic of Northern Cyprus, which may affect the cultural generalizability of the findings. Second, the cross-sectional design provides a snapshot of attitudes, intentions, and behaviors at a single point in time, which does not capture the evolution of sustainable travel behavior over an extended period. Another limitation of this study lies in the reliance on self-reported app usage, which may be subject to recall bias or social desirability effects. Although participants were asked to name specific eco-friendly travel apps and were provided with concrete examples to standardize understanding, we acknowledge the possibility of misinterpretation or overreporting of sustainable behavior.
This study also lacks a key driver of Generation Z’s behavior: social influence, such as peer recommendations, online reviews, or influencer endorsements, which are particularly relevant for understanding the behavior of Generation Z. Gen Z is considered highly responsive to peer opinions, influencer endorsements, and social media trends, all of which can significantly shape both their attitudes toward new technologies and their willingness to adopt so-called “green” apps. Finally, although this study focused on youth perceptions, intentions, and behaviors, it did not account for detailed demographic variations, such as socioeconomic background or prior travel experience, which could influence sustainable travel choices. Addressing these limitations in future studies would strengthen the reliability and scope of research in this area.
In light of these limitations, several recommendations are made for future researchers. To broaden the generalizability of results, future studies should consider cross-cultural comparisons, exploring whether the relationships identified in this study hold across different regions and cultural backgrounds. Studies by M. F. Chen and Tung (2014) suggest that cultural factors can significantly influence attitudes and behavioral intentions, indicating that examining diverse cultural contexts could provide a richer understanding of eco-friendly app adoption and sustainable tourism behavior. Longitudinal research is also recommended to investigate whether adoption of eco-friendly travel apps leads to sustained changes in behavior over time, as this approach could capture the long-term impact of attitudes and intentions on travel behavior, addressing the limitation of the present study’s cross-sectional design. Furthermore, future research could enhance the rigor of this inclusion criterion by verifying actual app usage through digital logs, screen recordings, or passive data collection methods. Additionally, future studies may consider developing or adopting a validated scale to assess the perceived eco-friendliness of travel apps to further mitigate subjectivity.
Additionally, future researchers are suggested to incorporate constructs related to social influence into the Theory of Planned Behavior (TPB) framework, adding survey items on peer usage or perceived social approval, or conducting follow-up qualitative interviews to explore how digital communities reinforce sustainable app adoption. Examining specific demographic subgroups within the youth population, such as differences based on socioeconomic status, education level, and travel experience, could yield more nuanced insights. Research by Budeanu (2007) has shown that demographic variables play a crucial role in shaping environmental perceptions and sustainable behaviors, so targeting these factors could provide more precise data on what motivates different segments of youth to adopt sustainable travel practices. Furthermore, future researchers should consider the influence of app design and usability factors on adoption and behavior, as the user experience of eco-friendly apps may significantly impact their effectiveness. Findings from Adeel et al. (2024) emphasize the importance of user-centered design in green technology adoption, suggesting that incorporating features like carbon footprint calculators or eco-friendly travel recommendations could enhance sustainable choices.
Finally, examining the role of policy interventions on eco-friendly app adoption and sustainable travel behavior would provide useful insights into how external incentives or requirements could further encourage eco-friendly tourism. Policies such as subsidies for sustainable travel options, certifications for eco-friendly travel services, or mandates for green practices within the tourism sector could support the adoption of eco-friendly apps, as suggested by Han et al. (2010). Exploring these policy impacts would contribute valuable information on the potential for collaborative efforts between governments, industry stakeholders, and app developers to advance sustainable tourism.

Author Contributions

Conceptualization, Z.S., O.U., and B.A.; funding acquisition, B.A.; investigation, Z.S., O.U., and B.A.; methodology, Z.S. and O.U.; resources, Z.S. and O.U.; supervision, O.U. and B.A.; validation, O.U.; writing—original draft, Z.S., O.U., and B.A.; writing—review and editing, Z.S., O.U., and B.A. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Faculty of Tourism and Rural Development in Požega with funding number 001.

Institutional Review Board Statement

The study was approved by the Ethics Committee of the Cyprus International University (protocol code no: EKK23-24/015/01, date of approval: 29 October 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The measurements of the study.
Table A1. The measurements of the study.
Perceived Negative Environmental Impact of Tourism (Landon et al., 2018)
  • Tourism can cause pollution in local environment (esthetic pollution, noise pollution, soil pollution, etc.).
2.
Tourism can cause destruction of native species’ habitat. (i.e., destruction of biodiversity, negative impacts on animals).
3.
Tourism can cause waste generation (trash, sewage, etc.) coming from tourists.
4.
Tourism threatens water security and impacts water overuse.
Adoption intention of eco-friendly travel apps (Ajjan & Hartshorne, 2008; Roca et al., 2006)
  • I intend to use eco-friendly travel apps on a regular basis in the future.
2.
I intend to use eco-friendly travel apps in order to fulfill my responsibility to protect environment.
3.
I will strongly recommend others to adopt eco-friendly travel apps to contribute to environmental protection.
Attitude toward the value of eco-friends apps (Patterson & Spreng, 1997; Verhagen et al., 2012)
  • I consider that the functions of eco-friendly travel apps have much value for environmental protection.
2.
I consider that the efficiency of eco-friendly travel apps corresponds to environmental protection expectations.
3.
I consider that eco-friendly travel apps provide benefits for environmental sustainability.
4.
I consider to utilize eco-friendly travel apps because they contribute to environmental protection.
Sustainable travel behaviors (Minton & Rose, 1997)
  • I would be willing to sign a petition to support a travel-based environmental cause.
2.
I would be willing to join a group or club which is concerned with the environmental impact of travel.
3.
I would be willing to pay more taxes to support greater government control of tourism pollution.
4.
I would be willing to stop buying services from tourism companies guilty of polluting the environment even though it might be inconvenient for me.
5.
I would be willing to make personal sacrifices for the sake of slowing down travel-driven pollution even though the immediate results may not seem significant.

References

  1. Adeel, H. B., Raja, I. S., Muhammad, S., & Muhammad, Z. (2024). Adoption of environmental technologies in the hotel industry: Development of sustainable intelligence and pro-environmental behavior. Discover Sustainability, 5, 79. [Google Scholar] [CrossRef]
  2. Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2), 71–80. [Google Scholar] [CrossRef]
  3. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. B. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Springer. [Google Scholar]
  4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [CrossRef]
  5. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall. [Google Scholar]
  6. Balinska, A., Jaska, E., & Werenowska, A. (2021). The role of eco-apps in encouraging pro-environmental behavior of young people studying in Poland. Energies, 14(16), 4946. [Google Scholar] [CrossRef]
  7. Boley, B. B., McGehee, N. G., & Hammett, A. L. (2017). Importance-performance analysis (IPA) of sustainable tourism initiatives: The resident perspective. Tourism Management, 58, 66–77. [Google Scholar] [CrossRef]
  8. Bratić, M., Stanković, A. M., Pavlović, D., Pivac, T., Kovačić, S., Surla, T., Čerović, S., & Zlatanov, S. (2025). New era of tourism: Innovative transformation through industry 4.0 and sustainability. Sustainability, 17(9), 3841. [Google Scholar] [CrossRef]
  9. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage. [Google Scholar]
  10. Budeanu, A. (2007). Sustainable tourist behaviour—A discussion of opportunities for change. International Journal of Consumer Studies, 31(5), 499–508. [Google Scholar] [CrossRef]
  11. Buffa, F. (2015). Young tourists and sustainability. Profiles, attitudes, and implications for destination strategies. Sustainability, 7, 14042–14062. [Google Scholar] [CrossRef]
  12. Butnaru, G. I., Nita, V., Melinte, C., Anichiti, A., & Brînza, G. (2022). The nexus between sustainable behaviour of tourists from generation Z and the factors that influence the protection of environmental quality. Sustainability, 14, 12103. [Google Scholar] [CrossRef]
  13. Chakraborty, P. P. (2024). The role of technology in enhancing sustainable tourism practices: Innovations and impacts. In K. J. Jermsittiparsert, & P. Suanpang (Eds.), Special interest trends for sustainable tourism (pp. 195–230). IGI Global. [Google Scholar] [CrossRef]
  14. Chen, M. F., & Tung, P. J. (2014). Developing an extended theory of planned behavior model to predict consumers’ intention to visit green hotels. International Journal of Hospitality Management, 36, 221–230. [Google Scholar] [CrossRef]
  15. Chen, S. Y. (2016). Green helpfulness or fun? Influences of green perceived value on the green loyalty of users and non-users of public bikes. Transport Policy, 47, 149–159. [Google Scholar] [CrossRef]
  16. Chin, C. H., Wong, W. P. M., & Ngian, E. T. (2024). The behavioral intention of young travelers to use virtual reality technology in cultural tourism destinations: An application of technology acceptance model. Pakistan Journal of Commerce and Social Sciences (PJCSS), 18(3), 552–570. [Google Scholar] [CrossRef]
  17. Christodoulides, G., Michaelidou, N., & Argyriou, E. (2012). Cross-national differences in e-WOM influence. European Journal of Marketing, 46(11/12), 1689–1707. [Google Scholar] [CrossRef]
  18. Clark, C., & Nyaupane, G. P. (2023). Understanding millennials’ nature-based tourism experience through their perceptions of technology use and travel constraints. Journal of Ecotourism, 22(3), 339–353. [Google Scholar] [CrossRef]
  19. D’Arco, M., Marino, V., & Resciniti, R. (2023). Exploring the pro-environmental behavioral intention of generation Z in the tourism context: The role of injunctive social norms and personal norms. Journal of Sustainable Tourism, 33, 1100–1121. [Google Scholar] [CrossRef]
  20. Demir, C., & Çevirgen, A. (2006). Turizm ve çevre yönetimi [Tourism and environment management]. Nobel Publishing. [Google Scholar]
  21. Erdogan, S., Gedikli, A., Cevik, E. I., & Erdogan, F. (2022). Eco-friendly technologies, international tourism and carbon emissions: Evidence from the most visited countries. Technological Forecasting and Social Change, 180, 121705. [Google Scholar] [CrossRef]
  22. Fairweather, J. R., Maslin, C., & Simmons, D. G. (2005). Environmental values and response to ecolabels among international visitors to New Zealand. Journal of Sustainable Tourism, 13(1), 82–98. [Google Scholar] [CrossRef]
  23. Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press. [Google Scholar]
  24. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [CrossRef]
  25. Fuentes-Moraleda, L., Lafuente-Ibáñez, C., Muñoz-Mazón, A., & Villacé-Molinero, T. (2019). Willingness to pay more to stay at a Boutique hotel with an environmental management system. A preliminary study in Spain. Sustainability, 11(18), 5134. [Google Scholar] [CrossRef]
  26. Gavrilović, Z., & Maksimović, M. (2018). Green innovations in the tourism sector. Strategic Management, 23(1), 36–42. [Google Scholar] [CrossRef]
  27. Gössling, S., & Hall, M. C. (2006). An introduction to tourism and global environmental change. In S. Gössling, & M. C. Hall (Eds.), Tourism and global environmental change (pp. 1–33). Routledge. [Google Scholar]
  28. Gössling, S., & Peeters, P. (2015). Assessing tourism’s global environmental impact 1900–2050. Journal of Sustainable Tourism, 23(5), 639–659. [Google Scholar] [CrossRef]
  29. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Prentice Hall. [Google Scholar]
  30. Hall, C. M. (2009). Degrowing tourism: Décroissance, sustainable consumption and steady-state tourism. Anatolia, 20(1), 46–61. [Google Scholar] [CrossRef]
  31. Han, H., Hsu, L. T. J., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325–334. [Google Scholar] [CrossRef]
  32. Hedlund, T. (2011). The impact of values, environmental concern, and willingness to accept economic sacrifices to protect the environment on tourists’ intentions to buy ecologically sustainable tourism alternatives. Tourism and Hospitality Research, 11(4), 278–288. [Google Scholar] [CrossRef]
  33. 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, 115–135. [Google Scholar] [CrossRef]
  34. Holden, A. (2016). Environment and tourism. Routledge. [Google Scholar]
  35. Horng, J. S., Liu, C. H., Chou, S. F., Yu, T. Y., Fang, Y. P., & Huang, Y. C. (2022). Student’s perceptions of sharing platforms and digital learning for sustainable behaviour and value changes. Journal of Hospitality, Leisure, Sport & Tourism Education, 31, 100380. [Google Scholar] [CrossRef]
  36. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. [Google Scholar] [CrossRef]
  37. Jasrotia, A., & Roy, P. (2024). An insight into the behavior of tech-savvy millennial travelers: A global perspective. In S. W. Maingi, V. G. B. Gowreesunkar, & E. K. Maximiliano (Eds.), Tourist behaviour and the new normal (pp. 173–184). Springer. [Google Scholar] [CrossRef]
  38. Juvan, E., & Dolnicar, S. (2016). Measuring environmentally sustainable tourist behavior. Annals of Tourism Research, 59, 30–44. [Google Scholar] [CrossRef]
  39. Khalil, M., Septianto, F., Lang, B., & Northey, G. (2021). The interactive effect of numerical precision and message framing in increasing consumer awareness of food waste issues. Journal of Retailing and Consumer Services, 60, 102470. [Google Scholar] [CrossRef]
  40. Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford Press. [Google Scholar]
  41. Lam, T., & Hsu, C. H. C. (2006). Predicting behavioral intention of choosing a travel destination. Tourism Management, 27(4), 589–599. [Google Scholar] [CrossRef]
  42. Landon, A. C., Woosnam, K. M., & Boley, B. B. (2018). Modeling the psychological antecedents to tourists’ pro-sustainable behaviors: An application of the value-belief-norm model. Journal of Sustainable Tourism, 26(6), 957–972. [Google Scholar] [CrossRef]
  43. Laroche, M., Bergeron, J., & Barbaro-Forleo, G. (2001). Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing, 18(6), 503–520. [Google Scholar] [CrossRef]
  44. Mansoor, M., Jam, F. A., & Khan, T. I. (2025). Fostering eco-friendly behaviors in hospitality: Engaging customers through green practices, social influence, and personal dynamics. International Journal of Contemporary Hospitality Management, 37(5), 1804–1826. [Google Scholar] [CrossRef]
  45. Mckercher, B., Prideaux, B., Cheung, C., & Law, R. (2010). Achieving voluntary reductions in the carbon footprint of tourism and climate change. Journal of Sustainable Tourism, 18(3), 297–317. [Google Scholar] [CrossRef]
  46. Mehmetoglu, M. (2009). Predictors of sustainable consumption in a tourism context: A CHAID approach. In J. S. Chen (Ed.), Advances in hospitality and leisure (pp. 3–23). Emerald Group Publishing Limited. [Google Scholar] [CrossRef]
  47. Mikayilov, J. I., Mukhtarov, S., Mammadov, J., & Azizov, M. (2019). Re-evaluating the environmental impacts of tourism: Does EKC exist? Environmental Science and Pollution Research, 26(19), 19389–19402. [Google Scholar] [CrossRef]
  48. Minton, A. P., & Rose, R. L. (1997). The effects of environmental concern on environmentally friendly consumer behavior: An exploratory study. Journal of Business Research, 40(1), 37–48. [Google Scholar] [CrossRef]
  49. Mohaidin, Z., Wei, K. T., & Murshid, M. A. (2017). Factors influencing the tourists’ intention to select sustainable tourism destination: A case study of Penang, Malaysia. International Journal of Tourism Cities, 3(4), 442–465. [Google Scholar] [CrossRef]
  50. Parra-López, E., Bulchand-Gidumal, J., Gutiérrez-Taño, D., & Díaz-Armas, R. (2011). Intentions to use social media in organizing and taking vacation trips. Computers in Human Behavior, 27(2), 640–654. [Google Scholar] [CrossRef]
  51. Patterson, P. G., & Spreng, R. A. (1997). Modelling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: An empirical examination. International Journal of Service Industry Management, 8(5), 414–434. [Google Scholar] [CrossRef]
  52. Pendergast, D. (2009). Getting to know the Y generation. In P. Benckendorff, G. Moscardo, & D. Pendergast (Eds.), Tourism and generation Y (pp. 1–15). CAB International. [Google Scholar] [CrossRef]
  53. Pešić, A. M., Brankov, J., & Moreira, C. O. (2025). Sustainable tourism and use of natural resources—Contemporary practices and management challenges. Sustainability, 17(6), 2383. [Google Scholar] [CrossRef]
  54. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. [Google Scholar] [CrossRef]
  55. Prakhar, P., Jaiswal, R., Gupta, S., & Tiwari, A. K. (2024). Electric vehicles in transition: Opportunities, challenges, and research agenda—A systematic literature review. Journal of Environmental Management, 372, 123415. [Google Scholar] [CrossRef]
  56. Qiao, G., & Gao, J. (2017). Chinese tourists’ perceptions of climate change and mitigation behavior: An application of norm activation theory. Sustainability, 9(8), 1322. [Google Scholar] [CrossRef]
  57. Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696. [Google Scholar] [CrossRef]
  58. Saarinen, J. (2006). Traditions of sustainability in tourism studies. Annals of Tourism Research, 33(4), 1121–1140. [Google Scholar] [CrossRef]
  59. Smith, A., & Anderson, M. (2018, March 1). Social media use in 2018. Available online: https://www.pewresearch.org/internet/2018/03/01/social-media-use-in-2018/ (accessed on 23 September 2024).
  60. Soja, I. (2022, October 7). 10% of jobs are worldwide connected to the tourism industry—What does that mean? Available online: https://www.solimarinternational.com/10-of-jobs-are-worldwide-connected-to-the-tourism-industry-what-does-that-mean/ (accessed on 30 September 2024).
  61. Sroypetch, S., Carr, N., & Duncan, T. (2018). Host and backpacker perceptions of environmental impacts of backpacker tourism. Tourism and Hospitality Research, 18(2), 203–213. [Google Scholar] [CrossRef]
  62. Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407–424. [Google Scholar] [CrossRef]
  63. Taherdoost, H. (2021). Data collection methods and tools for research; a step-by-step guide to choose data collection technique for academic and business research projects. International Journal of Academic Research in Management (IJARM), 10(1), 10–38. [Google Scholar]
  64. Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, andmixed method research, their applications, types, and limitations. Journal of Management Science & Engineering Research, 5(1), 53–63. [Google Scholar] [CrossRef]
  65. Tuna, M. (2007). Turizm, çevre ve toplum [Tourism, environment and society]. Detay Publishing. [Google Scholar]
  66. Verhagen, T., Feldberg, F., van den Hooff, B., Meents, S., & Merikivi, J. (2012). Understanding users’ motivations to engage in virtual worlds: A multipurpose model and empirical testing. Computers in Human Behavior, 28(2), 484–495. [Google Scholar] [CrossRef]
  67. Vukic, M., Kuzmanovic, M., & Stankovic, M. K. (2014). Understanding the heterogeneity of generation y’s preferences for travelling: A conjoint analysis approach. International Journal of Tourism Research, 17(5), 482–491. [Google Scholar] [CrossRef]
  68. Wu, P., Tang, T., Zhou, L., & Martínez, L. (2024). A decision-support model through online reviews: Consumer preference analysis and product ranking. Information Processing & Management, 61(4), 103728. [Google Scholar] [CrossRef]
  69. Xu, S., & Hu, Y. (2021). How do residents respond to negative environmental impacts from tourism? The role of community participation in empowering residents’ environmentally responsible behavior. International Journal of Tourism Research, 23(6), 1099–1111. [Google Scholar] [CrossRef]
  70. Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage Publications. [Google Scholar]
  71. Zeng, S., Tanveer, A., Fu, X., Gu, Y., & Irfan, M. (2022). Modeling the influence of critical factors on the adoption of green energy technologies. Renewable and Sustainable Energy Reviews, 168, 112817. [Google Scholar] [CrossRef]
  72. Zhang, F., Litson, K., & Feldon, D. F. (2022). Social predictors of doctoral student mental health and well-being. PLoS ONE, 17(9), e0274273. [Google Scholar] [CrossRef]
Tourismhosp 06 00247 g001
Figure 1. Research Model.
Figure 1. Research Model.
Tourismhosp 06 00247 g001
Tourismhosp 06 00247 g002
Figure 2. Results of structural equation modeling.
Figure 2. Results of structural equation modeling.
Tourismhosp 06 00247 g002
Table 1. Respondents’ profile.
Table 1. Respondents’ profile.
Category Frequency (N = 420)Percentage (%)
GenderFemale21952.1
Male20147.9
Age17–198720.7
20–2215436.7
23–2517942.6
Educational levelPrep school20.5
First year8119.3
Second year10424.8
Third year10424.8
Fourth year12930.7
Monthly allowance/incomeUnder EUR 500 22052.4
Between EUR 500 and 1000 16138.3
Over EUR 1000 399.3
Frequency of
access to the internet		Rarely61.4
Sometimes409.5
Frequently10926.0
Always26563.1
Being comfortable
with using
mobile apps
for travel planning		Very uncomfortable71.7
Somewhat uncomfortable112.6
Neutral10124.0
Somewhat comfortable10023.8
Very comfortable20147.9
Travel frequency
within a year		Only once22453.3
Between 2–517241.0
More than 5245.7
Residence
background		Urban residence background33379.3
Rural residence background8720.7
Source: Author’s work; N = sample size.
Table 2. Confirmatory factor analyses.
Table 2. Confirmatory factor analyses.
FactorIndicatorEstimateSEZpStand. Estimate
NEIoTNEIoT10.9500.050618.8<0.0010.808
NEIoT20.9200.051317.9<0.0010.782
NEIoT30.9050.051117.7<0.0010.776
NEIoT40.8320.051416.2<0.0010.727
AIoEFTAAIoEFTA10.8450.040920.6<0.0010.838
AIoEFTA20.9220.039523.4<0.0010.910
AIoEFTA30.8880.043220.6<0.0010.841
ATVoEFTAATVoEFTA10.9520.043022.2<0.0010.875
ATVoEFTA20.8400.039821.1<0.0010.849
ATVoEFTA30.8780.042620.6<0.0010.836
ATVoEFTA40.8060.042319.1<0.0010.796
STBSTB10.9050.047719.0<0.0010.842
STB20.8210.051116.1<0.0010.732
STB50.6930.053612.9<0.0010.623
Notes: NEIoT = Perceived Negative Environmental Impact of Tourism; AIoEFTA = Adoption Intention of Eco-friendly Travel Apps; ATVoEFTA = Attitude Towards Value of Eco-friendly Travel Apps; STB = Sustainable Tourism Behavior, MSV: Maximum Shared Variance; MaxR(H): Maximum H Reliability; CR: Composite Reliability; AVE: Average Variance Extracted. Source: Authors’ work.
Table 3. Correlations, reliability, and validity.
Table 3. Correlations, reliability, and validity.
CRAVEMSVMaxR(H)NEIoTAIoEFTAATVoEFTASTB
NEIoT0.8560.5980.1710.8590.774
AIoEFTA0.8980.7460.5160.9050.326 ***0.863
ATVoEFTA0.9050.7050.5160.9080.369 ***0.719 ***0.840
STB0.7790.5440.4330.8090.413 ***0.658 ***0.657 ***0.738
(α) 0.860.900.900.76
Notes: NEIoT = Perceived Negative Environmental Impact of Tourism; AIoEFTA = Adoption Intention of Eco-friendly Travel Apps; ATVoEFTA = Attitude Towards Value of Eco-friendly Travel Apps; STB = Sustainable Tourism Behavior, MSV: Maximum Shared Variance; MaxR(H): Maximum H Reliability; CR: Composite Reliability; AVE: Average Variance Extracted, *** p < 0.001. Source: Author’s work.
Table 4. Model fit statistics.
Table 4. Model fit statistics.
MeasureEstimateThresholdInterpretation
CMIN137.344----
DF71----
CMIN/DF1.934Between 1 and 3Excellent
CFI0.981>0.95Excellent
TLI0.975>0.95Excellent
SRMR0.044<0.08Excellent
RMSEA0.047<0.06Excellent
PClose0.635>0.05Excellent
Notes: CMIN = Chi-square, DF = Degrees of Freedom, CFI = Comparative Fit Index, TLI = Tucker–Lewis Index, SRMR = Standardized Root Mean Squared Residual, RMSEA = Root Mean Square Error of Approximation, PClose = Close Fit for the Model, Source: Author’s work.
Table 5. Hypotheses testing.
Table 5. Hypotheses testing.
Hypothesesβp-ValueDecision
Direct effects
H1: NEIoT → STB0.172<0.01Supported
H2: NEIoT → AIoEFTA0.326<0.01Supported
H3: NEIoT → ATVoEFTA0.151<0.01Supported
H4: AIoEFTA → ATVoEFTA0.669<0.01Supported
H5: AIoEFTA → STB0.363<0.01Supported
H6: ATVoEFTA → STB0.333<0.01Supported
Notes: β = Beta coefficient; NEIoT = Perceived Negative Environmental Impact of Tourism; AIoEFTA = Adoption Intention of Eco-friendly Travel Apps; ATVoEFTA = Attitude Towards Value of Eco-friendly Travel Apps; STB = Sustainable Tourism Behavior. Source: Author’s work.
Table 6. Indirect effects.
Table 6. Indirect effects.
EffectSELLCIULCIResult
TOTAL0.16070.02710.10900.2143
AIoEFTA0.07110.01760.03840.1068Supported
ATVoEFTA0.08960.02050.05290.1319Supported
Notes: AIoEFTA = Adoption Intention of Eco-friendly Travel Apps; ATVoEFTA = Attitude Towards Value of Eco-friendly Travel Apps; SE: Standard Error; LLCI: Lower Level Confidence Interval; ULCI: Upper Level Confidence Interval. Source: Authors’ work.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Saltik, Z.; Uludag, O.; Andrlić, B. Digital Pathways to Sustainability: Eco-Travel Apps and Gen Z’s Eco-Friendly Travel Behaviors. Tour. Hosp. 2025, 6, 247. https://doi.org/10.3390/tourhosp6050247

AMA Style

Saltik Z, Uludag O, Andrlić B. Digital Pathways to Sustainability: Eco-Travel Apps and Gen Z’s Eco-Friendly Travel Behaviors. Tourism and Hospitality. 2025; 6(5):247. https://doi.org/10.3390/tourhosp6050247

Chicago/Turabian Style

Saltik, Zehra, Orhan Uludag, and Berislav Andrlić. 2025. "Digital Pathways to Sustainability: Eco-Travel Apps and Gen Z’s Eco-Friendly Travel Behaviors" Tourism and Hospitality 6, no. 5: 247. https://doi.org/10.3390/tourhosp6050247

APA Style

Saltik, Z., Uludag, O., & Andrlić, B. (2025). Digital Pathways to Sustainability: Eco-Travel Apps and Gen Z’s Eco-Friendly Travel Behaviors. Tourism and Hospitality, 6(5), 247. https://doi.org/10.3390/tourhosp6050247

Article Metrics

Back to TopTop