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Article

Bridging the Attitude–Behavior Gap in Sustainable Tourism: An Extended TPB Model of Green Hotel Purchase Intentions

by
Arthur Araújo
1,2,*,
Isabel Andrés Marques
2,3,
Lorenza López Moreno
4 and
Patricia Carrasco García
4
1
Intrepid Lab, Universidade Lusófona, Rua de Augusto Rosa 24, 4000-098 Porto, Portugal
2
Centre for Transdisciplinary Development Studies (CETRAD), Rua de Augusto Rosa 24, 4000-098 Porto, Portugal
3
Instituto Politécnico de Gestão e Tecnologia (ISLA), Escola Superior de Gestão, Rua Diogo de Macedo, nº192, 4400-107 Vila Nova de Gaia, Portugal
4
Facultad de Ciencias Económicas y Empresariales, Universidad de Granada, Campus Universitario de Cartuja, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Tour. Hosp. 2025, 6(4), 215; https://doi.org/10.3390/tourhosp6040215
Submission received: 27 August 2025 / Revised: 22 September 2025 / Accepted: 2 October 2025 / Published: 15 October 2025
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Abstract

The awareness of tourism’s environmental impact has increased interest in sustainable alternatives such as green hotels, yet tourists often fail to translate pro-environmental attitudes into action, reflecting the attitude–behavior gap. This study extends the Theory of Planned Behavior (TPB) by incorporating Environmental Knowledge and Climate Change-Related Risk Perceptions (CC-RRPs) as background factors and testing their effects on Green Hotel Purchase Intentions (GHPIs) among Spanish travelers. Data from 1442 respondents were analyzed using covariance-based Structural Equation Modeling (SEM) with bootstrapped mediation testing. Results show that In-Group Norms are the strongest predictor of GHPIs, followed by Eco-Hotel Attitudes, while Perceived Behavioral Control (PBC) has a weaker but significant effect. Environmental Knowledge predicts all three mediators, and CC-RRPs predict Attitudes and Norms but not PBC. Crucially, both antecedents affect GHPIs only indirectly, supporting a mediation-based framework. These findings clarify the distinct roles of Environmental Knowledge as a cognitive antecedent and CC-RRPs as cognitive–affective evaluations that motivate attitudes and norms, while also highlighting the centrality of social influence in a Southern European context. Beyond theoretical contributions, the results underscore the importance of trust and authenticity: addressing greenwashing through transparent communication and credible certification frameworks is essential to ensure sustainable hospitality choices.

1. Introduction

The tourism industry plays a key role in the global economy, accounting for about 10% of global GDP in 2024, with even higher contributions in many countries (WTTC, 2025). However, its environmental and social impacts are also substantial. Resource-intensive operations often result in pollution (Pásková et al., 2024), high carbon emissions (Yang & Jia, 2022), waste generation (Jones et al., 2014), and social challenges such as gentrification (Lo & McKercher, 2023). Within the tourism system, hotels are particularly impactful due to their intensive water and energy use. Their hedonic nature—luxury amenities tied to customer experience—exacerbates ecological pressure amid growing global travel demand. At the same time, travelers are increasingly aware of these impacts and express willingness to make sustainable choices. Green hotels have thus emerged as a promising alternative, adopting practices such as eco-design, waste management, and sustainable food services (Solan et al., 2009).
Despite growing awareness, a persistent attitude–behavior gap remains: tourists often fail to translate pro-environmental attitudes into sustainable consumption choices (Gursoy et al., 2019). This gap challenges traditional rational-choice models and underscores the need to account for psychological, social, and affective factors. A widely used framework for addressing the role of these variables is the Theory of Planned Behavior (TPB) (Ajzen, 1991), but its explanatory power is limited in sustainability contexts. Prior studies suggest that environmental knowledge, climate change-related risk perceptions (CC-RRPs), and social identity-based norms may better capture the drivers of sustainable behavior (Barth et al., 2017; Masson et al., 2016). Yet these factors have rarely been systematically integrated into TPB. Moreover, existing work largely focuses on Anglo-Saxon and Asian contexts, leaving Southern European consumers underexplored, despite evidence that cultural values (e.g., collectivism in Spain) shape pro-environmental behaviors (de Mooij & Hofstede, 2010; Triandis, 1995).
Considering this background, the present study aims to answer the following research questions:
  • How do TPB constructs (Eco-Hotel Attitudes, subjective norms adapted as In-Group Norms, and PBC) influence Green Hotel Purchase Intentions (GHPIs) among Spanish travelers?
  • What role do Environmental Knowledge and CC-RRPs play as antecedents of GHPIs?
  • To what extent do indirect (mediated) effects enhance the explanatory power of TPB in the green hotel context?
The remainder of this article is structured as follows. Section 2 develops the theoretical framework by extending TPB with environmental knowledge, CC-RRPs, and In-Group Norms. Section 3 describes the methodology, including survey design, measurement, and data analysis. Section 4 presents empirical findings, while Section 5 discusses theoretical contributions to TPB, sector-specific insights for sustainable hospitality, practical implications for tourism stakeholders, limitations and avenues for future research. Finally, Section 6 summarizes the study’s conclusions.

2. Theoretical Overview and Hypotheses

TPB has been applied to measure tourists’ sustainable choices, not only during their trips but also their future everyday pro-environmental behavior as a result of their nature-based tourism experiences (Clark et al., 2019). Explorative studies such as Verma and Chandra (2018), have corroborated the applicability of the TPB model to predict consumers’ intention to stay in green hotels. In this context, especially when combined with background variables, the model can elucidate the reasons why tourists choose to stay in lodging facilities that minimize their environmental impact, such as through energy consumption (Tamuliene et al., 2024).
Building on this theory, we propose that Environmental Knowledge and Climate Change-Related Risk Perceptions (CC-RRPs) act as background or contextual antecedents, whose effects on GHPIs are mediated by TPB constructs: Attitudes (Eco-Hotel Attitudes), In-Group Norms, and PBC. We use In-Group Norms instead of traditional subjective norms because identity-based social influences have been shown to play a stronger role in shaping pro-environmental intentions. Prior research highlights that group identification amplifies pro-environmental choices (Liang et al., 2024; Masson et al., 2016), and that reference groups such as family and friends significantly affect green hotel purchase decisions (Ferreira et al., 2023; C.-P. Wang et al., 2023). This adaptation is particularly relevant in the Spanish context, where collectivist cultural traits and strong interpersonal relationships (de Mooij & Hofstede, 2010; Triandis, 1995) make in-group expectations a more effective predictor of behavior than generalized social pressure.
We draw support for this mediated structure from prior studies showing that knowledge and risk perceptions influence attitudes and norms, which then shape behavior, rather than only direct knowledge-behavior effects, as described in the following sub-sections.

2.1. Climate Change-Related Risk Perceptions (CC-RRPs)

Risk perceptions reflect people’s subjective judgements about potential harm or threats (Böhm & Pfister, 2011). In the context of climate change, van der Linden’s (2015) Climate Change Risk Perception Model (CCRPM) highlights that risk perceptions influence pro-environmental behavior indirectly through affective and normative processes. On the tourism domain, previous studies show that tourists’ climate change concerns influence their attitudes toward low-impact products (Chapungu et al., 2024; Clemente et al., 2020; Petrović et al., 2023). Likewise, Barth et al. (2017) and Masson et al. (2016) demonstrate that perceived climate threats strengthen ingroup norms, increasing conformity and pro-environmental intentions. Additionally, several studies show that risk perceptions can improve perceived behavioral control by fostering self-efficacy and the belief that one can contribute to mitigation (van Valkengoed et al., 2024; Xie et al., 2019). More recent research also finds that climate change risk perceptions enhance engagement in pro-environmental behaviors through psychological mediators rather than direct effects (Shershunovich, 2025).
Based on these contributions:
H1. 
CC-RRPs are positively associated with Eco-Hotel Attitudes, which mediate their effect on GHPIs.
H2. 
CC-RRPs are positively associated with In-Group Norms, which mediate their effect on GHPIs.
H3. 
CC-RRPs are positively associated with PBC, which mediates their effect on GHPIs.

2.2. Environmental Knowledge

Environmental knowledge is a background factor that shapes the cognitive foundations of sustainable decisions (Gautam, 2020). While some studies within the tourism domain (Gautam, 2020; Li & Wu, 2020) and in other contexts (Paço & Lavrador, 2017; Ünal et al., 2018) pointed to mixed or weak direct effects, others suggest that its role is largely mediated by psychological and social processes. For instance, Duke (2010) and Gaffney et al. (2023) show that environmental knowledge is internalized through ingroup feedback processes, making group norms a crucial mediator. Within tourism research, Balaskas et al. (2025) confirmed that knowledge significantly predicts intention only via affective and normative mediators. This reinforces results from previous studies in nature tourism (Zheng et al., 2018), as well as in the specific context of green hotels. Among the latter, research shows that environmental knowledge strengthens favorable attitudes by enhancing customers’ perceptions of perceived green knowledge (Sultana et al., 2022), therefore hotels that transparently communicate their environmental policies may strengthen customers’ trust (Balaji et al., 2019). Environmental knowledge has also been shown to enhance perceived green benefits (Tan, 2022), which in turn foster favorable attitudes, ultimately enhancing patronage intentions to green hotels.
Based on these contributions:
H4. 
Environmental Knowledge is positively associated with Eco-Hotel Attitudes, which mediate its effect on GHPIs.
H5. 
Environmental Knowledge is positively associated with In-Group Norms, which mediate its effect on GHPIs.
H6. 
Environmental Knowledge is positively associated with PBC, which mediates its effect on GHPIs.
Research has also increasingly linked environmental knowledge with higher risk perception of climate change (Diakakis et al., 2021; Lin & Wang, 2023). Tourists who understand the causes and consequences of climate change are more likely to perceive it as a personal and societal threat, which in turn influences their attitudes and group norms. However, this influence typically unfolds indirectly. That is, knowledge strengthens CC-RRPs, which then shape mediators such as attitudes and norms (Zacher et al., 2023; Zheng et al., 2018).
Based on these contributions:
H7. 
Environmental Knowledge is positively associated with CC-RRPs, which then indirectly influence GHPIs via TPB constructs.
The conceptual model, encompassing all the hypothesized relationships, is graphically represented in Figure 1.

3. Materials and Methods

The present study proposes and tests a causal model of the antecedents of tourists’ Green Hotel Purchase Intentions in the context of the climate crisis, encompassing Eco-Hotel Attitudes, In-Group Norms and PBC as direct predictors, and Environmental Knowledge CC-RRPs as indirect predictors. To this end, data were collected from a sample of Spanish travelers.

3.1. Questionnaire Development

The data was gathered using a survey questionnaire designed based on insights from prior research studies. GHPIs were assessed through five items adapted from Awuni and Du (2016), Chen and Tung (2014), J. Wang et al. (2018), and more recently applied to the context of tourists’ green purchasing behavior by Nekmahmud et al. (2022). Environmental Knowledge items were drawn from a combination of studies employing it as a predicting variable of green brand purchase (Ahmad, 2016), recycling (Sidique et al., 2010), visiting green hotels (S. Wang et al., 2018), and tourists’ general environmental behavior (Gautam, 2020). Climate Change-Related Risk Perceptions were measured using items adapted by Atzori et al. (2019), originally developed by Lorenzoni et al. (2006), and recently applied to the context of green tourism consumption by de Araújo et al. (2025). Eco-Hotel Attitudes were measured using five hotel-specific items adapted from Castellanos-Verdugo et al. (2016). In-Group Norms were evaluated using three items drawn from Li and Wu (2020). Lastly, PBC was measured through items adapted from C. Wang et al. (2018). The reliability of all measurement scales was confirmed prior to further analysis, with each scale achieving a Cronbach’s Alpha (CA) above 0.80.
The items’ wording was adjusted to ensure the semantic meaning to Spanish-speaking respondents. Originally written in English, the items were translated into Spanish using the reverse translation method, involving bilingual researchers who were native Spanish speakers and fluent in English. All items were operationalized as statements, and participants indicated their level of agreement using a 7-point Likert scale, ranging from 1 (“Totally disagree”) to 7 (“Totally agree”).
Since both predictor and criterion variables were collected using the same research instrument, several strategies were implemented to reduce common method bias—particularly issues of consistency motif, acquiescence and social desirability bias (SDB). First, participant anonymity was ensured, a practice known to help reduce both general common method bias (Podsakoff et al., 2003) and SDB specifically (Larson, 2019). Additionally, as suggested by Podsakoff et al. (2003), question order was counter-balanced—all items sets that did not require a specific introduction were included in a single section, and presented in a random order unique to each respondent. To further minimize bias, item wording was carefully revised based on expert feedback to reduce ambiguity. Finally, bi-polar numeric scales were avoided (Tourangeau et al., 1991), with all items measured through 7-point Likert-type scales. The questionnaire can be viewed in the following link: https://forms.gle/K3wYLMJSVAwyon5v5 (accessed on 1 October 2025).

3.2. Data Collection Procedures

Before launching the main data collection, a pre-test was conducted with a smaller group of Spanish travelers to identify and address any potential issues. Following the necessary adjustments, the final questionnaire was distributed via Google Forms to a convenience sample of Spanish residents who had traveled at least once in the previous twelve months. Data was collected between April and May 2023 through travel-related social media groups and marketing survey platforms. To align with the study’s aim of evaluating participants’ environmental knowledge, climate change-related risk perceptions, eco-hotel attitudes, in-group norms, perceived behavioral control and intentions to purchase green hotel services, respondents’ prior knowledge about sustainability, sustainable tourism, or climate change was intentionally not assessed. Similarly, the survey was not shared on groups focused on sustainability topics to avoid bias. Informed consent was obtained at the start of the questionnaire: the introduction outlined the study’s purpose and explained how the data would be used, following with a consent question. Only participants who agreed proceeded to the full questionnaire. In total, 1442 valid responses were gathered. The raw data used in this study is available from the authors upon request.
Table 1 presents an overview of the sample’ demographic characteristics. Over half of the respondents are female (63.3%), hold a University degree (58.6%) and fall within the 18–24 age range (58.5%). Regarding monthly income, the most represented category is that of those who earn less than €1000 (21.9%), followed by those earning between €1000 and €1500 (20%).

3.3. Data Analysis Procedures

The data analysis began with an Exploratory Factor Analysis (EFA), which served the purpose of measure purification (Vieira, 2011). This was followed by a Confirmatory Factor Analysis (CFA) conducted in AMOS, to evaluate the model’s dimensionality, convergent validity and reliability. Since AMOS does not provide all the necessary indicators, a separate discriminant validity analysis—as well as some additional reliability indicators calculation—was performed in Microsoft Excel, using the output from AMOS. Before running the CFA, the data underwent a screening process that included checks for missing values, outliers, collinearity and distributional issues. The final structural model was then tested using covariance-based SEM with the Maximum Likelihood Estimation (MLE) method.
In addition to the main constructs, demographic controls (gender, age, education, and monthly income) were included as exogenous predictors of Eco-Hotel Attitudes, In-Group Norms, PBC, and GHPIs. Prior to estimation, multicollinearity was tested using Variance Inflation Factors (VIFs) in IBM SPSS Statistics 25. All predictors and controls had VIF values below 5, well under the threshold for concern (Hair et al., 2014), indicating no serious multicollinearity issues.
For robustness, a multi-group analysis was conducted using age as the grouping variable (≤24 years vs. >24 years). Age was selected because it is a relevant factor in the context of green consumption. The cut-off was chosen both to optimize balance between group sizes and to roughly separate Generation Z respondents from the rest of the sample, as they are the group that could most likely differ from others in terms of green hotel consumption. Generation Z travelers have been found to be emotionally inclined to choosing socially responsible destinations and behaving in an environmentally responsible manner as tourists (Chang et al., 2024). Following the recommended invariance testing procedure (Byrne, 2001; Cheung & Rensvold, 2002), first configural invariance (same factor structure specified in all groups) was tested, followed by metric invariance (equality of factor loadings), and structural invariance (equality of structural paths). Model comparisons were evaluated using χ2 difference tests and ΔCFI, with invariance supported if ΔCFI ≤ 0.01.
To test the hypothesized mediation effects, a bootstrapping procedure was applied in AMOS. Following recommendations by Preacher and Hayes (2008), Baron and Kenny (1986) and Zhao et al. (2010), indirect, direct, and total effects were estimated using 5000 bootstrap samples with bias-corrected 95% confidence intervals. Mediation was considered significant if the confidence interval for the indirect effect did not include zero. This approach provides a more robust test of mediation than traditional methods and is widely recommended for SEM.
Methodological details and corresponding results are discussed in the next sections.

4. Results

4.1. Measurement Model Assessment

For the assessment of the measurement model, EFA–Principal Component Analysis, with Varimax rotation, was employed. The values of Bartlett’s test of sphericity (p = 0.000) and Kaiser–Neyer–Olkin measure of sampling adequacy (KMO = 0.944) show that factor analysis is adequate for exploring the data. The initial EFA suggested that one item of Perceived Behavioral Control (“I prefer to book environmentally friendly hotels”) should be excluded, as it loaded together with the items of GHPIs. The subsequent EFA rendered a factor solution that matched exactly to the latent variables within the proposed theoretical model and explained 84% of the total variances, which is a great value according to Hair et al. (2014). Moreover, no item presents a particularly low communality (the lowest is 0.698—I can pay a slightly higher price to stay in an environmentally friendly hotel; in Perceived Behavioral Control), and each item loads significantly onto its respective factor. Table 2 presents the descriptive statistics for each item.
Once the measurement model was assessed, the model validation was carried out using Anderson and Gerbing’s (1988), two-step approach. In this context, the sample was divided randomly into two parts, where each corresponds to approximately 50% of the total observations: the calibration and validation samples. The first step, the CFA, was then carried out through the MLE approach and using the calibration sample. Before such step was carried out, however, a data screening process needed to be undertaken.

4.2. Data Screening

The data was collected via Google forms and the questions that measured the model items were all mandatory. In this context, missing values were not an issue, but this same procedure can lead to outliers coming from incoherent responses. Those were examined through a Mahalanobis distance analysis, which however, suggested that outliers were not an issue for model assessment purposes, that is, they did not cause significant changes in model fit and path estimates.
The next aspect to be screened was collinearity. In this context, Tolerance values and VIFs of the independent and control variables were assessed. For all independent variables, Tolerance values were higher than 0.1 and VIFs were lower than 5, which indicates that collinearity is not a problem (Hair et al., 2014).
Next, Skewness and Kurtosis were observed. Skewness values vary between −1.727 (Countries around the world must take action to combat climate change—CC-RRPs) and 0.187 (I have enough information to identify and consume environmentally friendly hotel services.—PBC). Moreover, all items except those measuring PBC (which were very much normally distributed, with skewness values between 0.074 and 0.187) had negative skewness levels, particularly those measuring CC-RRPs, which were particularly skewed toward the higher end of the scale. Nevertheless, all items are within Kline’s (2015) recommended interval, between −2 and 2. Regarding kurtosis values, those range between 1.750 (also “Countries around the world must take action to combat climate change”—CC-RRPs) and −1.203 (I can pay a slightly higher price to stay in an environmentally friendly hotel.—PBC). That is, the items of some latent variables are quite peaked, but they are all within the recommended threshold.
After excluding missing and extreme values and addressing any potential issues with collinearity or unusual data distributions that could interfere with MLE, the model was evaluated in terms of its dimensionality, convergent validity, reliability and discriminant validity.

4.3. Dimensionality, Convergent Validity, Reliability, and Discriminant Validity Tests

Regarding model fit, the overall statistics support the unidimensionality of the constructs. Although the chi-square (X2) value is significant (p = 0.000)—a common outcome with large sample sizes—Bollen (1989) emphasizes the importance of considering the chi-square to degrees of freedom (X2/df) ratio. For this model, the ratio is 2.964, which falls within the recommended range of 2 to 3 suggested by Cote et al. (2001). Additionally, key indices, namely the Goodness of Fit Index (GFI = 0.916), the Comparative Fit Index (CFI = 0.972), the Tucker–Lewis Index (TLI = 0.966), and the Normed Fit Index (NFI = 0.958) are all above the threshold suggest by Tabachnick and Fidell (2007). The only index slightly below it is the Adjusted Goodness of Fit Index (AGFI = 0.892), which is, however, in a very close vicinity. Finally, the Root Mean Square Error of Approximation (RMSEA = 0.053), based on Hu and Bentler’s (1999) criteria, reinforced the evidence of a strong model fit. Considering the reported model fit results, in addition to the fact that each item loaded significantly onto only one factor, the data provides solid evidence for the unidimensionality of the factors.
Each item showed strong and significant loadings on its intended construct, with all factor loadings exceeding the 0.50 threshold recommended by Hair et al. (2014). Additionally, all t-values were statistically significant at the 99.9% confidence level (p ≤ 0.001), indicating that each item meaningfully contributes to measuring its respective factor. Lastly, all Average Variance extracted (AVE) values surpassed 0.50, further supporting the convergent validity of the constructs (Malhotra, 1996).
The CA values for all constructs exceed 0.80, and except for PBC, they exceed 0.90. Therefore, they are all well above the commonly accepted threshold of 0.70, indicating strong scale reliability (Nunnally, 1978). To further confirm reliability, the Composite Reliability (CR) values also surpass the recommended minimum of 0.70 (Bagozzi & Yi, 1988)—in fact, they also all surpass 0.80, and with the exception of PBC, 0.90—and for each construct, Maximum Reliability (H) (MaxR(H)) is greater than the corresponding CR, as advised by prior research (Hancock & Mueller, 2001; Raykov, 1997). A summary of the dimensionality, convergent validity, and reliability findings is presented in Table 3.
At last, discriminant validity was evaluated. For each construct, AVE is higher than the Maximum Shared Variance (MSV) and the Average Shared Variance (ASV). Additionally, the square root of each construct’s AVE is greater than its correlation with other constructs in the model. According to Fornell and Larcker (1981), these conditions provide sufficient evidence of discriminant validity. A summary of discriminant validity results—including AVE, MSV, ASV and the factor correlation matrix with AVE square roots on the diagonal—is presented in Table 4.

4.4. Hypothesis Testing

The structural model was evaluated using AMOS with the confirmation sample to examine the proposed mediated relationships between CC-RRPs, Environmental Knowledge, the TPB constructs (Eco-Hotel Attitudes, In-Group Norms, and PBC), and GHPIs.
Results confirm that Eco-Hotel Attitudes, In-Group Norms, and PBC are significant predictors of GHPIs, with In-Group Norms exerting the strongest effect (β = 0.459, p ≤ 0.001), followed by Eco-Hotel Attitudes (β = 0.347, p ≤ 0.001), and PBC, which although smaller, remains significant (β = 0.078, p = 0.020), albeit only at the 95% confidence interval. Together, these three mediators explain over half of the variance in GHPIs (R2 = 0.516).
Regarding the antecedents, CC-RRPs significantly predict Eco-Hotel Attitudes (β = 0.619, p ≤ 0.001) and In-Group Norms (β = 0.183, p ≤ 0.001), while their effect on PBC is non-significant (β = −0.001, p = 0.987). These offer initial support for H1 and H2 but not for H3. Environmental Knowledge exerts significant effects on all three TPB mediators: Eco-Hotel Attitudes (β = 0.273, p ≤ 0.001), In-Group Norms (β = 0.531, p ≤ 0.001), and Perceived Behavioral Control (β = 0.740, p ≤ 0.001). These results provide strong initial support for H4, H5, and H6. In addition, Environmental Knowledge positively predicts CC-RRPs (β = 0.367, p ≤ 0.001), initially supporting H7. Table 5 summarizes SEM results and Figure 2 depicts the structural model as tested on AMOS.
A bootstrapping procedure (5000 samples, bias-corrected) confirmed that CC-RRPs significantly influenced GHPIs (β = 0.299, 95% CI [0.241, 0.307]) indirectly via Eco-Hotel Attitudes (β = 0.370, 95% CI [0.307, 0.435]) and In-Group Norms (β = 134, 95% CI [0.088, 0.190]). These results corroborate H1 and H2. Similarly, Environmental knowledge had significant indirect effects on GHPIs (β = 0.506, 95% CI [0.455, 0.557]) through Eco-Hotel Attitudes (β = 0.312, 95% CI [0.254, 0.377]), In-Group Norms (β = 0.407, 95% CI [0.351, 0.466]) and PBC (β = 0.378, 95% CI [0.314, 0.441]). These results corroborate H4, H5, and H6. In both cases, direct paths to GHPIs were non-significant, supporting full mediation.
Bootstrapping also confirmed that Environmental Knowledge had an indirect effect on GHPIs via CC-RRPs and subsequent TPB mediators. Specifically, the paths Environmental Knowledge → CC-RRPs → Eco-Hotel Attitudes → GHPIs (β = 0.173, 95% CI [0.122, 0.230]) and Environmental Knowledge → CC-RRPs → In-Group Norms → GHPIs (β = 0.113, 95% CI [0.075, 0.160]) were significant. The CC-RRPs → PBC path was non-significant, and thus Environmental Knowledge did not affect GHPIs through this channel. Together, these results support H7. Table 6 summarizes the direct, indirect and total effects, while Table 7 summarizes the hypothesis testing results.

4.5. Controlled Model and Multi-Group Analysis

When including demographic controls (gender, age, education, income), the pattern of results remained unchanged. In the controlled model, Eco-Hotel Attitudes (β = 0.346, p < 0.001) and In-Group Norms (β = 0.452, p < 0.001) continued to be the strongest predictors of GHPIs, and Perceived Behavioral Control remained a smaller but significant predictor (β = 0.082, p < 0.015). Bootstrapped indirect effects (5000 samples, bias-corrected) for Environmental Knowledge (indirect β = 0.499, 95% CI [0.438, 0.560]) and CC-RRPs (indirect β = 0.301, 95% CI [0.233, 0.371]) remained significant, supporting the mediation hypotheses after adjustment for controls. Adding controls produced a small increase in explained variance for GHPIs (ΔR2 = 0.001), indicating modest direct contributions of demographics while leaving the substantive mediation findings intact.
Multi-group analysis confirmed that the proposed model was stable across age groups (≤24 years vs. >24 years). The configural model provided acceptable fit, and constraining factor loadings to equality (metric invariance) did not significantly reduce model fit (ΔCFI = 0.001). Similarly, constraining structural paths (structural invariance) showed negligible change in fit indices (ΔCFI = 0.002). Although equality constraints on residuals produced a significant χ2 (p < 0.001), the change in CFI was at the 0.01 threshold. Taken together, these results indicate that the factor structure, measurement properties, and structural relationships are invariant across both age groups, supporting the robustness and generalizability of the findings. Table 8 summarizes multi-group analysis results.

5. Discussion

The present study examined the predictors of GHPIs among Spanish travelers using an extended TPB. By incorporating Environmental Knowledge and CC-RRPs as background variables, we focused on their indirect effects through TPB constructs. The findings indicate that In-Group Norms and Eco-Hotel Attitudes are the strongest direct predictors of GHPIs, while PBC shows a weaker but still significant effect. Environmental Knowledge significantly predicts all three mediators, whereas CC-RRPs significantly predict Eco-Hotel Attitudes and In-Group Norms but not PBC. Moreover, both Environmental Knowledge and CC-RRPs affect GHPIs only indirectly, supporting a mediation-based framework.
Consistently with TPB (Ajzen, 1991), Attitudes, Norms, and PBC all contributed to explaining GHPIs. Among these, In-Group Norms emerged as the most influential factor, suggesting that Spanish travelers’ sustainable choices are strongly shaped by family, friends, and social groups. This result is particularly meaningful given Spain’s cultural profile: compared to other Western European countries, Spain exhibits stronger collectivist traits (Triandis, 1995), and consumer behavior is highly influenced by interpersonal relationships (de Mooij & Hofstede, 2010). The finding that In-Group Norms outweighed Attitudes and PBC therefore reflects not only the explanatory power of TPB but also the cultural context of the sample, where social identity and relational expectations carry significant weight in shaping decisions. Attitudes toward eco-hotels also had a strong effect, reinforcing that positive evaluations of sustainable practices remain central to behavioral intentions. In contrast, PBC displayed only a modest effect, a result that has been observed in previous pro-environmental behavior studies where external constraints limit PBC (van Valkengoed et al., 2024).
The findings clarify the debated role of environmental knowledge. Although prior research shows inconsistent direct effects (Ünal et al., 2018; Paço & Lavrador, 2017), the present study’s results demonstrate that knowledge is highly relevant when considered as a background factor. It exerts strong indirect effects on GHPIs through Attitudes, Norms, and PBC, aligning with studies that emphasize its influence on trust, perceived benefits, and green product evaluations (Zheng et al., 2018; Sultana et al., 2022; Tan, 2022). Moreover, knowledge positively shapes CC-RRPs, reinforcing its role as a cognitive antecedent of risk perception.
CC-RRPs were found to significantly shape Attitudes and In-Group Norms but had no significant effect on PBC. This suggests that risk awareness motivates emotional and normative responses but does not necessarily increase individuals’ sense of control over their actions. This pattern is consistent with the CCRPM (van der Linden, 2015) and studies highlighting that risk perceptions operate primarily through concern and social influence rather than efficacy (Xie et al., 2019). Thus, CC-RRPs play a vital role in strengthening collective norms and pro-environmental attitudes but not perceived behavioral capacity.

5.1. Theoretical Contributions

This study contributes to the literature in three ways. First, it advances TPB by integrating knowledge- and risk-based antecedents and testing them through mediated pathways rather than as direct predictors, addressing the attitude–behavior gap in sustainable consumption. Second, it highlights the centrality of In-Group Norms in explaining pro-environmental intentions in Southern European consumers, expanding research that has focused mainly on Anglo-Saxon and Asian contexts. Third, it demonstrates the importance of treating Environmental Knowledge as a cognitive foundation and CC-RRPs as cognitive–affective evaluations, both indirectly shaping intentions through TPB constructs. Finally, by explicitly linking Spain’s collectivist cultural traits to the strength of In-Group Norms, the study contributes to cross-cultural extensions of TPB, showing how cultural context influences the relative weight of its predictors.

5.2. Practical Implications

The results provide several implications for practitioners and policymakers in the hospitality sector. First, the strong influence of In-Group Norms suggests that marketing strategies should emphasize peer influence and social proof, for example, by showcasing guest testimonials, encouraging online sharing of sustainable experiences, or promoting community engagement initiatives. Hoteliers can also leverage transparent communication of their environmental practices to reinforce favorable attitudes and build consumer trust.
At the same time, the issue of greenwashing poses a significant barrier. Consumers’ trust in eco-claims depends heavily on the perceived authenticity and credibility of the information provided. To strengthen this trust, hoteliers should go beyond generic claims and clearly communicate measurable sustainability actions, while prominently displaying third-party certifications. Retailers and operators can contribute by educating consumers on how to recognize credible certifications and avoid misleading claims.
Policymakers, on the other hand, bear primary responsibility for creating the institutional frameworks needed to combat greenwashing effectively. This includes setting clear standards for eco-labels, regulating certification bodies, and ensuring transparency and accountability in the certification process. Such efforts would provide consumers with reliable signals of authenticity, reduce information asymmetry, and reinforce the social and normative drivers of pro-environmental choices identified in this study. By ensuring credibility at the systemic level, policymakers can amplify the effectiveness of industry and consumer initiatives in promoting sustainable hospitality.

5.3. Limitations and Future Research

Despite its contributions, the present study is not without limitations. First, it focused on Spanish travelers, which may limit generalizability to other cultural contexts. Future research should conduct cross-cultural comparisons to test whether the salience of In-Group Norms persists in less collectivist societies. Second, although multiple demographic controls were included, other situational factors such as travel purpose or trip length could also influence intentions. Finally, while this study examined mediated pathways, future research could incorporate moderators (e.g., environmental involvement, personal values) to further unpack heterogeneity in pro-environmental decision-making.

6. Conclusions

This study examined Spanish travelers’ intentions to stay in green hotels by extending TPB with two background factors: Environmental Knowledge and CC-RRPs. The findings show that both antecedents influence GHPIs indirectly through TPB constructs, rather than directly. Among these mediators, In-Group Norms emerges as the strongest predictor, followed by Eco-Hotel Attitudes, while PBC exerts only a modest effect.
These results underscore the importance of treating Environmental Knowledge as a cognitive foundation that enhances attitudes, norms, and PBC and CC-RRPs as cognitive–affective evaluations that motivate attitudes and social influence. This mediation-based perspective provides a clearer understanding of the mechanisms through which antecedents shape sustainable tourism intentions and helps explain the persistent attitude–behavior gap in sustainable tourism choices.
The study makes three contributions: it advances TPB by integrating knowledge- and risk-based antecedents into a mediation framework; it highlights the central role of In-Group Norms in collectivist contexts such as Spain; and it clarifies the distinct pathways through which cognitive (knowledge) and cognitive–affective (risk perception) factors shape sustainable consumption intentions. By showing that Spain’s cultural profile amplifies the role of In-Group Norms, the study also contributes to cross-cultural applications of TPB, illustrating how cultural context conditions the relative importance of its predictors.
For practitioners, the findings suggest that peer influence, transparent communication, and enabling conditions are key levers for strengthening demand for sustainable hospitality. For researchers, the study highlights the value of mediation-based models and calls for cross-cultural comparisons and the inclusion of moderators to further understand variability in pro-environmental choices. Finally, the findings point to the critical role of trust and authenticity in consumer decision-making. Addressing greenwashing through credible certification frameworks and transparent communication is essential to ensure that pro-environmental attitudes and norms translate into actual behavioral change.

Author Contributions

Conceptualization, A.A., I.A.M., L.L.M. and P.C.G.; methodology, A.A., L.L.M. and P.C.G.; software, A.A.; validation, A.A.; formal analysis A.A.; investigation, L.L.M. and P.C.G.; resources, L.L.M. and P.C.G.; data curation, A.A., L.L.M. and P.C.G.; writing—original draft preparation, A.A. and I.A.M.; writing—review and editing, A.A.; visualization, A.A.; supervision, A.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the Regulation (EU) 2016/679 of the European Parliament and of the Council states, on section 162, (https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02016R0679-20160504, accessed on 22 September 2025).

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in the 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.

Abbreviations

The following abbreviations are used in this manuscript:
AGFIAdjusted Goodness of Fit Index
ASVAverage Shared Variance
AVEAverage Variance extracted
CC-RRPsClimate Change-Related Risk Perceptions
X2chi-square
X2/dfchi-square to degrees of freedom (ratio)
CCRPMClimate Change Risk Perception Model
CFIComparative Fit Index
CRComposite Reliability
CFAConfirmatory Factor Analysis
EFAExploratory Factor Analysis
GFIGoodness of Fit Index
KMOKaiser–Neyer–Olkin
MLEMaximum Likelihood Estimation
MaxR(H)Maximum Reliability (H)
MSVMaximum Shared Variance
NFINormed Fit Index
PBCPerceived Behavioral Control
RMSEA Root Mean Square Error of Approximation
SEStandard Error
R2Squared Multiple Correlations
Std. BetaStandardized Beta
SEMStructural Equations Modelling
TBPTheory of Planned Behavior
TLITucker–Lewis Index
VBNValue–Belief–Norm
WTTCWorld Travel and Tourism Council

References

  1. Ahmad, A. (2016). Consumer’s intention to purchase green brands: The roles of environmental concern, environmental knowledge and self expressive benefits. Current World Environment, 10, 879–889. [Google Scholar] [CrossRef]
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [CrossRef]
  3. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. [Google Scholar] [CrossRef]
  4. Atzori, R., Fyall, A., Tasci, A. D. A., & Fjelstul, J. (2019). The role of social representations in shaping tourist responses to potential climate change impacts: An analysis of Florida’s coastal destinations. Journal of Travel Research, 58, 1373–1388. [Google Scholar] [CrossRef]
  5. Awuni, J. A., & Du, J. (2016). Sustainable consumption in chinese cities: Green purchasing intentions of young adults based on the theory of consumption values. Sustainable Development, 24(2), 124–135. [Google Scholar] [CrossRef]
  6. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. [Google Scholar] [CrossRef]
  7. Balaji, M. S., Jiang, Y., & Jha, S. (2019). Green hotel adoption: A personal choice or social pressure? International Journal of Contemporary Hospitality Management, 31(8), 3287–3305. [Google Scholar] [CrossRef]
  8. Balaskas, S., Yfantidou, I., & Komis, K. (2025). From awareness to action: Modeling sustainable behavior among winter tourists in the context of climate change. Psychology International, 7, 72. [Google Scholar] [CrossRef]
  9. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. [Google Scholar] [CrossRef]
  10. Barth, M., Masson, T., Fritsche, I., & Ziemer, C.-T. (2017). Closing ranks: Ingroup norm conformity as a subtle response to threatening climate change. Group Processes & Intergroup Relations, 21(3), 497–512. [Google Scholar] [CrossRef]
  11. Bollen, K. A. (1989). Structural equations with latent variables. Wiley. [Google Scholar]
  12. Böhm, G., & Pfister, H.-R. (2011). Tourism in the face of environmental risks: Sunbathing under the ozone hole, and strolling through polluted air. Scandinavian Journal of Hospitality and Tourism, 11(3), 250–267. [Google Scholar] [CrossRef]
  13. Byrne, B. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Lawrence Erlbaum Associates. [Google Scholar]
  14. Castellanos-Verdugo, M., Vega-Vázquez, M., Oviedo-García, M. Á., & Orgaz-Agüera, F. (2016). The relevance of psychological factors in the ecotourist experience satisfaction through ecotourist site perceived value. Journal of Cleaner Production, 124, 226–235. [Google Scholar] [CrossRef]
  15. Chang, J. Y.-S., Lim, X.-J., Luo, X., Cheah, J.-H., Morrison, A. M., & Hall, C. M. (2024). Modelling generation Z tourists’ social responsibility toward environmentally responsible behaviour: The role of eco-travel cravings. International Journal of Tourism Research, 26(4), e2695. [Google Scholar] [CrossRef]
  16. Chapungu, L., Maoela, M. A., Mashula, N., Kunene, H., & Nhamo, G. (2024). Climate change perceptions and experiences from tourists visiting popular destinations in South Africa. Cogent Social Sciences, 10(1), 2407024. [Google Scholar] [CrossRef]
  17. 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]
  18. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. [Google Scholar] [CrossRef]
  19. Clark, E., Mulgrew, K., Kannis-dymand, L., & Schaffer, V. (2019). Theory of planned behaviour: Predicting tourists’ pro-environmental intentions after a humpback whale encounter. Journal of Sustainable Tourism, 27(5), 649–667. [Google Scholar] [CrossRef]
  20. Clemente, F., Lopes, A., & Ambrósio, V. (2020). Tourists’ perceptions on climate change in Lisbon region. Atmosphere, 11, 297. [Google Scholar] [CrossRef]
  21. Cote, J., Netemeyer, R., & Bentler, P. (2001). Structural equations modeling—Improving model fit by correlating errors. Journal of Consumer Psychology, 10(1–2), 87–88. [Google Scholar]
  22. de Araújo, A. F., Andrés-Marques, I., & López Moreno, L. (2025). No planet-B attitudes: The main driver of gen Z travelers’ willingness to pay for sustainable tourism destinations. Sustainability, 17, 847. [Google Scholar] [CrossRef]
  23. de Mooij, M., & Hofstede, G. (2010). The Hofstede model: Applications to global branding and advertising strategy and research. International Journal of Advertising: The Quarterly Review of Marketing Communications, 29, 85–110. [Google Scholar] [CrossRef]
  24. Diakakis, M., Skordoulis, M., & Savvidou, E. (2021). The relationships between public risk perceptions of climate change, environmental sensitivity and experience of extreme weather-related disasters: Evidence from Greece. Water, 13, 2842. [Google Scholar] [CrossRef]
  25. Duke, C. C. (2010). Social identity and the environment: The influence of group processes on environmentally sustainable behaviour. University of Exeter. [Google Scholar]
  26. Ferreira, S., Pereira, O., & Simões, C. (2023). Determinants of consumers’ intention to visit green hotels: Combining psychological and contextual factors. Journal of Vacation Marketing, 31(3), 535–548. [Google Scholar] [CrossRef]
  27. 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]
  28. Gaffney, A. M., Souter, S. S., Jung, J., & Crano, W. D. (2023). Contexts and conditions of outgroup influence. Psychology of Language and Communication, 27(1), 173–198. [Google Scholar] [CrossRef]
  29. Gautam, V. (2020). Examining environmental friendly behaviors of tourists towards sustainable development. Journal of Environmental Management, 276, 111292. [Google Scholar] [CrossRef]
  30. Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. [Google Scholar] [CrossRef]
  31. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson. [Google Scholar]
  32. Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future (pp. 195–216). Scientific Software International. [Google Scholar]
  33. 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]
  34. Jones, P., Hillier, D., & Comfort, D. (2014). Sustainability in the global hotel industry. International Journal of Contemporary Hospitality Management, 26(1), 5–17. [Google Scholar] [CrossRef]
  35. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar]
  36. Larson, R. B. (2019). Controlling social desirability bias. International Journal of Market Research, 61, 534–547. [Google Scholar] [CrossRef]
  37. Li, Q., & Wu, M. (2020). Tourists’ pro-environmental behaviour in travel destinations: Benchmarking the power of social interaction and individual attitude. Journal of Sustainable Tourism, 28(9), 1371–1389. [Google Scholar] [CrossRef]
  38. Liang, L., Xia, J., Wu, G., & Li, T. (2024). Do tea-drinking habits affect individuals’ intention to visit tea tourism destinations? Verification from the Chinese cultural background. Journal of China Tourism Research, 20(4), 772–795. [Google Scholar] [CrossRef]
  39. Lin, C.-H., & Wang, W.-C. (2023). Impacts of climate change knowledge on coastal tourists’ destination decision-making and revisit intentions. Journal of Hospitality and Tourism Management, 56, 322–335. [Google Scholar] [CrossRef]
  40. Lo, J. S. K., & McKercher, B. (2023). Tourism gentrification and neighbourhood transformation. International Journal of Tourism Cities, 9(4), 923–939. [Google Scholar] [CrossRef]
  41. Lorenzoni, I., Leiserowitz, A., De Franca Doria, M., Poortinga, W., & Pidgeon, N. F. (2006). Cross-national comparisons of image associations with “global warming” and “climate change” among laypeople in the United States of America and Great Britain1. Journal of Risk Research, 9(3), 265–281. [Google Scholar] [CrossRef]
  42. Malhotra, N. K. (1996). Marketing research: An applied orientation. Prentice Hall. [Google Scholar]
  43. Masson, T., Jugert, P., & Fritsche, I. (2016). Collective self-fulfilling prophecies: Group identification biases perceptions of environmental group norms among high identifiers. Social Influence, 11(3), 185–198. [Google Scholar] [CrossRef]
  44. Nekmahmud, M., Ramkissoon, H., & Fekete-Farkas, M. (2022). Green purchase and sustainable consumption: A comparative study between European and non-European tourists. Tourism Management Perspectives, 43, 100980. [Google Scholar] [CrossRef]
  45. Nunnally, J. (1978). Psychometric theory (2nd ed.). McGraw-Hill. [Google Scholar]
  46. Paço, A., & Lavrador, T. (2017). Environmental knowledge and attitudes and behaviours towards energy consumption. Journal of Environmental Management, 197, 384–392. [Google Scholar] [CrossRef] [PubMed]
  47. Pásková, M., Štekerová, K., Zanker, M., Lasisi, T. T., & Zelenka, J. (2024). Water pollution generated by tourism: Review of system dynamics models. Heliyon, 10(1), e23824. [Google Scholar] [CrossRef]
  48. Petrović, M. D., Milovanović, I., Gajić, T., Kholina, V. N., Vujičić, M., Blešić, I., Đoković, F., Radovanović, M. M., Ćurčić, N. B., Rahmat, A. F., Muzdybayeva, K., Kubesova, G., Koshkimbayeva, U., & Dávid, L. D. (2023). The degree of environmental risk and attractiveness as a criterion for visiting a tourist destination. Sustainability, 15, 14215. [Google Scholar] [CrossRef]
  49. Podsakoff, P. M., Mackenzie, S. B., Lee, J., & 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]
  50. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. [Google Scholar] [CrossRef]
  51. Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21, 173–184. [Google Scholar] [CrossRef]
  52. Shershunovich, Y. (2025). Climate risk perception as a catalyst for pro-environmental behavior. Discover Sustainability, 6(1), 902. [Google Scholar] [CrossRef]
  53. Sidique, S. F., Joshi, S. V., & Lupi, F. (2010). Factors influencing the rate of recycling: An analysis of Minnesota counties. Resources, Conservation and Recycling, 54(4), 242–249. [Google Scholar] [CrossRef]
  54. Solan, P., Legrand, W., & Chen, J. (2009). Sustainability in the hospitality industry—Principles of sustainable operations. Elsevier. [Google Scholar]
  55. Sultana, N., Amin, S., & Islam, A. (2022). Influence of perceived environmental knowledge and environmental concern on customers’ green hotel visit intention: Mediating role of green trust. Asia-Pacific Journal of Business Administration, 14(2), 223–243. [Google Scholar] [CrossRef]
  56. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson Education. [Google Scholar]
  57. Tamuliene, V., Diaz-Meneses, G., & Vilkaite-Vaitone, N. (2024). The cultural roots of green stays: Understanding touristic accommodation choices through the lens of the theory of planned behavior. Sustainability, 16, 9947. [Google Scholar] [CrossRef]
  58. Tan, L. L. (2022). Understanding consumers’ preferences for green hotels—The roles of perceived green benefits and environmental knowledge. Journal of Hospitality and Tourism Insights, 6(3), 1309–1327. [Google Scholar] [CrossRef]
  59. Tourangeau, R., Rasinski, K. A., & D’Andrade, R. (1991). Attitude structure and belief accessibility. Journal of Experimental Social Psychology, 27(1), 48–75. [Google Scholar] [CrossRef]
  60. Triandis, H. C. (1995). Individualism and collectivism (1st ed.). Routledge. [Google Scholar] [CrossRef]
  61. Ünal, A. B., Steg, L., & Gorsira, M. (2018). Values versus environmental knowledge as triggers of a process of activation of personal norms for eco-driving. Environment and Behavior, 50(10), 1092–1118. [Google Scholar] [CrossRef]
  62. van der Linden, S. (2015). The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. Journal of Environmental Psychology, 41, 112–124. [Google Scholar] [CrossRef]
  63. van Valkengoed, A. M., Perlaviciute, G., & Steg, L. (2024). From believing in climate change to adapting to climate change: The role of risk perception and efficacy beliefs. Risk Analysis: An Official Publication of the Society for Risk Analysis, 44(3), 553–565. [Google Scholar] [CrossRef]
  64. Verma, V. K., & Chandra, B. (2018). An application of theory of planned behavior to predict young Indian consumers’ green hotel visit intention. Journal of Cleaner Production, 172, 1152–1162. [Google Scholar] [CrossRef]
  65. Vieira, A. L. (2011). Interactive LISREL in practice: Getting started with a SIMPLIS approach. Springer. [Google Scholar]
  66. Wang, C., Zhang, J., Yu, P., & Hu, H. (2018). The theory of planned behavior as a model for understanding tourists’ responsible environmental behaviors: The moderating role of environmental interpretations. Journal of Cleaner Production, 194, 425–434. [Google Scholar] [CrossRef]
  67. Wang, C.-P., Pong, P., Wong, W., & Wang, L. (2023). Consumers’ green purchase intention to visit green hotels: A value-belief-norm theory perspective. Frontiers in Psychology, 14, 1139116. [Google Scholar] [CrossRef]
  68. Wang, J., Wang, S., Xue, H., Wang, Y., & Li, J. (2018). Green image and consumers’ word-of-mouth intention in the green hotel industry: The moderating effect of Millennials. Journal of Cleaner Production, 181, 426–436. [Google Scholar] [CrossRef]
  69. Wang, S., Wang, J., Wang, Y., Yan, J., & Li, J. (2018). Environmental knowledge and consumers’ intentions to visit green hotels: The mediating role of consumption values. Journal of Travel & Tourism Marketing, 35(9), 1261–1271. [Google Scholar] [CrossRef]
  70. WTTC. (2025). Travel & tourism economic impact research (EIR). Available online: https://wttc.org/Research/Economic-Impact (accessed on 8 August 2025).
  71. Xie, B., Brewer, M. B., Hayes, B. K., McDonald, R. I., & Newell, B. R. (2019). Predicting climate change risk perception and willingness to act. Journal of Environmental Psychology, 65, 101331. [Google Scholar] [CrossRef]
  72. Yang, G., & Jia, L. (2022). Estimation of carbon emissions from tourism transport and analysis of its influencing factors in Dunhuang. Sustainability, 14, 14323. [Google Scholar] [CrossRef]
  73. Zacher, H., Rudolph, C. W., & Katz, I. M. (2023). Employee green behavior as the core of environmentally sustainable organizations. Annual Review of Organizational Psychology and Organizational Behavior, 10, 465–494. [Google Scholar] [CrossRef]
  74. Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. [Google Scholar] [CrossRef]
  75. Zheng, Q. J., Xu, A. X., Kong, D. Y., Deng, H. P., & Lin, Q. Q. (2018). Correlation between the environmental knowledge, environmental attitude and behavioral intention of toursits for ecotourism in China. Applied Ecology and Evironmental Research, 16(1), 51–62. [Google Scholar] [CrossRef]
Tourismhosp 06 00215 g001
Figure 1. Extended TPB with Background Variables.
Figure 1. Extended TPB with Background Variables.
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Figure 2. Structural model tested on AMOS.
Figure 2. Structural model tested on AMOS.
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Table 1. Sample characterization.
Table 1. Sample characterization.
N (1442)n%
Gender (N = 1442)
 Female91363.3
 Male49134.0
Formal education (N = 1437)
 No formal education70.5
 Primary School553.8
 Secondary School35724.8
 University degree84258.6
 Master’s degree or PhD17612.2
Age (N = 1437)
 18 to 24 years old84058.5
 25 to 34 years old1248.6
 35 to 44 years old815.6
 45 to 54 years old18212.7
 55 to 64 years old17612.2
 65 years or older342.4
Monthly family income (N = 1386)
 Less than 1000 euros30321.9
 1000 to 1500 euros27720.0
 1501 to 2000 euros24417.6
 2001 to 2500 euros19313.9
 2501 to 3000 euros14110.2
 More than 3000 euros22816.5
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
ItemMeanSD
Climate Change-Related Risk PerceptionsI believe that governments should take action against climate change.6.031.667
I believe that all citizens have a responsibility to act against climate change.6.031.708
Countries around the world must take action to combat climate change.5.821.740
I am convinced that human activities are one of the main causes of climate change.5.681.728
I believe that climate change will harm me and my family.5.621.813
I’m willing to sacrifice some of my comfort to stop climate change (for example, using less water, electricity and gas).5.351.728
Green Hotel PurchaseI’ll endeavour to book eco-friendly hotels when I’m travelling.4.781.855
I’ll stay in hotels that are considered less harmful to the environment.4.941.784
I plan to choose environmentally friendly hotels when travelling.4.471.855
I’m willing to choose environmentally friendly hotels when I travel.4.961.795
I will avoid staying in hotels that are potentially harmful to tourist sites.5.131.818
Environmental KnowledgeI know of actions that can mitigate the negative impact of hotels on animals and plants.4.331.861
I know of actions that can mitigate water pollution by hotels.4.221.877
I am aware of actions that can mitigate the impact of hotels on destination populations.4.341.886
I know of actions that can mitigate the negative impact of hotels on the environment.4.231.915
Eco-Hotel AttitudesThe role of sustainable hotel management goes beyond the economic function.5.501.681
Sustainable tourist destinations must limit the volume of visitors in order to preserve their cultural identity.5.481.720
Part of the revenue generated by tourism should finance the environmental and cultural conservation of the destination.5.431.659
Sustainable hotels can improve the personal development of visitors.5.671.694
Sustainable hotels must avoid interfering with the environment and the quality of life in destinations.5.551.748
In-Group NormsMy family and friends expect me to stay in environmentally friendly hotels.3.961.998
People who are important to me believe that I should stay in environmentally friendly hotels.4.121.949
The people I care about are happy if I choose sustainable hotels.4.331.976
Perceived Behavioral ControlI have enough information to locate environmentally friendly hotels.3.821.892
I have enough information to identify and consume environmentally friendly hotel services.3.721.855
I can pay a slightly higher price to stay in an environmentally friendly hotel.3.831.927
Table 3. Confirmatory Factor Analysis Results.
Table 3. Confirmatory Factor Analysis Results.
ItemStandard BetaSEt-Valuep
Climate Change-Related Risk Perceptions→I believe that governments should take action against climate change.0.896
→I believe that all citizens have a responsibility to act against climate change.0.8930.02146.632***
→Countries around the world must take action to combat climate change.0.9020.02836.082***
→I am convinced that human activities are one of the main causes of climate change.0.8890.02934.904***
→I believe that climate change will harm me and my family.0.8040.03428.315***
→I’m willing to sacrifice some of my comfort to stop climate change (for example, using less water, electricity and gas).0.7650.03525.905***
Green Hotel Purchase Intentions→I’ll endeavor to book eco-friendly hotels when I’m travelling.0.942
→I’ll stay in hotels that are considered less harmful to the environment.0.9110.01951.039***
→I plan to choose environmentally friendly hotels when travelling.0.9090.02342.969***
→I’m willing to choose environmentally friendly hotels when I travel.0.9070.02342.661***
→I will avoid staying in hotels that are potentially harmful to tourist sites.0.8310.02633.456***
Environmental Knowledge→I know of actions that can mitigate the negative impact of hotels on animals and plants.0.886
→I know of actions that can mitigate water pollution by hotels.0.9180.03330.988***
→I am aware of actions that can mitigate the impact of hotels on destination populations.0.8670.02540.035***
→I know of actions that can mitigate the negative impact of hotels on the environment.0.8970.03529.674***
Eco-Hotel Attitudes→The role of sustainable hotel management goes beyond the economic function.0.869
→Sustainable tourist destinations must limit the volume of visitors in order to preserve their cultural identity.0.8220.03526.72***
→Part of the revenue generated by tourism should finance the environmental and cultural conservation of the destination.0.8260.02931.419***
→Sustainable hotels can improve the personal development of visitors.0.8390.03427.643***
→Sustainable hotels must avoid interfering with the environment and the quality of life in destinations.0.7940.03625.721***
In-Group Norms→My family and friends expect me to stay in environmentally friendly hotels.0.933
→People who are important to me believe that I should stay in environmentally friendly hotels.0.9280.02343.244***
→The people I care about are happy if I choose sustainable hotels.0.8890.02538.578***
Perceived Behavioral Control→I have enough information to locate environmentally friendly hotels.0.861
→I have enough information to identify and consume environmentally friendly hotel services.0.9840.03730.793***
→I can pay a slightly higher price to stay in an environmentally friendly hotel.0.6960.04618.494***
Constructs’ Convergent Validity and Reliability CACRMaxR(H)AVE
Climate Change-Related Risk Perceptions0.9460.9440.9510.739
Green Hotel Purchase0.9580.9550.9610.811
Environmental Knowledge0.9470.9400.9420.796
Eco-Hotel Attitudes0.9210.9170.9190.689
In-Group Norms0.9410.9410.9430.841
Perceived Behavioral Control0.8430.890.9720.731
Model fit statistics:X2/df = 2.964; GFI = 0.916; AGFI = 0.892; CFI = 0.972; TLI = 0.966; NFI = 0.958;
RMSEA = 0.053
*** Significant at the 0.001 level (two-tailed).
Table 4. Discriminant Validity Results.
Table 4. Discriminant Validity Results.
AVEMSVASV123456
(1) In-Group Norms0.8410.4540.3370.917
(2) Climate Change-Related Risk Perceptions0.7390.6180.2920.4560.860
(3) Green Hotel Purchase0.8110.4540.3430.6740.5730.901
(4) Environmental Knowledge0.7960.4540.3260.6200.4540.5290.892
(5) Eco-Hotel Attitudes0.6890.6180.3520.5390.7860.6320.5540.830
(6) Perceived Behavioral Control0.7310.4540.2610.5930.3170.5050.6740.3790.855
Table 5. Structural Equations Modelling (SEM) Results.
Table 5. Structural Equations Modelling (SEM) Results.
Std. BetaSEp
Eco-Hotel Attitudes→Green Hotel Purchase Intentions0.3470.040***
In-Group Norms→Green Hotel Purchase Intentions0.4590.032***
Perceived Behavior Control→Green Hotel Purchase Intentions0.0780.0330.020
Climate Change-Related Risk Perceptions→Eco-Hotel Attitudes0.6190.031***
Climate Change-Related Risk Perceptions→In-Group Norms0.1830.041***
Climate Change-Related Risk Perceptions→Perceived Behavior Control−0.0010.0340.987
Environmental Knowledge→Eco-Hotel Attitudes0.2730.027***
Environmental Knowledge→In-Group Norms0.5310.041***
Environmental Knowledge→Perceived Behavior Control0.7400.036***
Environmental Knowledge→Climate Change-Related Risk Perceptions0.3670.035***
Squared Multiple Correlations (R2): Climate Change-Related Risk Perceptions = 0.135; Eco-Hotel Attitudes = 0.582; In-Group Norms = 0.387; Perceived Behavioral Control = 0.547; Green Hotel Purchase Intentions = 0.516.
*** Significant at the 0.001 level (two-tailed).
Table 6. Summary of direct, indirect and total effects.
Table 6. Summary of direct, indirect and total effects.
PredictorMediator(s)Direct Effect
on GHPIs		Indirect Effect
on GHPIs		Total Effect
on GHPIs
Eco-Hotel Attitudes—0.347 ***—0.347 ***
In-Group Norms—0.459 ***—0.459 ***
Perceived Behavioral Control—0.078 *—0.078 *
CC-RRPsAttitudes, Norms, PBCn.s.0.299 ***0.299 ***
Environmental KnowledgeAttitudes, Norms, PBCn.s.0.506 ***0.506 ***
Notes: *** p ≤ 0.001; * p ≤ 0.05; n.s. = non-significant.
Table 7. Summary of hypotheses testing.
Table 7. Summary of hypotheses testing.
HypothesisPath TestedResult
H1CC-RRPs → Eco-Hotel Attitudes → GHPIsSupported
H2CC-RRPs → In-Group Norms → GHPIsSupported
H3CC-RRPs → Perceived Behavioral Control → GHPIsSupported
H4Environmental Knowledge → Eco-Hotel Attitudes → GHPIsSupported
H5Environmental Knowledge → In-Group Norms → GHPIsSupported
H6Environmental Knowledge → Perceived Behavioral Control → GHPIsNot supported
H7Environmental Knowledge → CC-RRPs → Attitudes/Norms/PBC → GHPIsSupported
Table 8. Summary of multi-group analysis results.
Table 8. Summary of multi-group analysis results.
Modelχ2dfpΔCFIInterpretation
Configural invariance20,0545640.000—Acceptable fit; baseline model
Metric invariance (weights)15.841200.7260.001Factor loadings invariant
Structural invariance (paths)38.407300.1400.002Structural paths invariant
Structural covariances38.544310.1650.002Covariances invariant
Structural residuals51.232360.0480.003Residuals: ΔCFI < 0.01, invariant
Measurement residuals195.20162<0.0010.010Strict invariance not required
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MDPI and ACS Style

Araújo, A.; Marques, I.A.; Moreno, L.L.; García, P.C. Bridging the Attitude–Behavior Gap in Sustainable Tourism: An Extended TPB Model of Green Hotel Purchase Intentions. Tour. Hosp. 2025, 6, 215. https://doi.org/10.3390/tourhosp6040215

AMA Style

Araújo A, Marques IA, Moreno LL, García PC. Bridging the Attitude–Behavior Gap in Sustainable Tourism: An Extended TPB Model of Green Hotel Purchase Intentions. Tourism and Hospitality. 2025; 6(4):215. https://doi.org/10.3390/tourhosp6040215

Chicago/Turabian Style

Araújo, Arthur, Isabel Andrés Marques, Lorenza López Moreno, and Patricia Carrasco García. 2025. "Bridging the Attitude–Behavior Gap in Sustainable Tourism: An Extended TPB Model of Green Hotel Purchase Intentions" Tourism and Hospitality 6, no. 4: 215. https://doi.org/10.3390/tourhosp6040215

APA Style

Araújo, A., Marques, I. A., Moreno, L. L., & García, P. C. (2025). Bridging the Attitude–Behavior Gap in Sustainable Tourism: An Extended TPB Model of Green Hotel Purchase Intentions. Tourism and Hospitality, 6(4), 215. https://doi.org/10.3390/tourhosp6040215

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