Passenger Acceptance of Clean Energy Technology in Thai Airlines: Driving Sustainable Development (SDG 7 & 13)

The aviation industry plays a critical role in economic development, tourism, international trade, and global connectivity. However, aviation is also a significant source of greenhouse gas emissions, particularly carbon dioxide (CO₂), contributing to climate change and increasing pressure on the industry to transition toward more sustainable forms of air transportation [1]. As a result, reducing aviation-related emissions has become an important priority for international aviation organizations, governments, and airlines. The International Civil Aviation Organization (ICAO) has established a long-term global aspirational goal of achieving net-zero carbon emissions from international aviation by 2050 [2]. This objective is also consistent with the broader sustainability agenda of the United Nations, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) [3].

Clean energy technologies have emerged as important pathways for reducing the environmental impacts of aviation. These technologies include Sustainable Aviation Fuel (SAF), electric aircraft, hydrogen-powered aircraft, and other renewable energy solutions [4]. Among these alternatives, SAF has received considerable attention because it can potentially reduce lifecycle greenhouse gas emissions while being compatible with existing aircraft and aviation infrastructure. In Thailand, the transition toward cleaner aviation has increasingly attracted the attention of policymakers, airlines, energy producers, and other aviation stakeholders. Initiatives related to SAF production and the use of SAF blends in commercial aviation demonstrate growing efforts to support the transition toward lower-carbon air transportation [5].

However, technological development and investment alone cannot guarantee the successful adoption of clean energy technologies in aviation. Passenger acceptance is an important demand-side factor because passengers may influence airlines’ market decisions through their travel preferences, willingness to support sustainable practices, and intention to choose airlines that demonstrate environmental responsibility [6]. Even when clean energy technologies are technically feasible, their broader implementation may be constrained if passengers perceive them as providing limited benefits, creating inconvenience, or failing to meet their expectations regarding safety, service quality, and travel experience. Understanding the factors that shape passenger acceptance is therefore essential for supporting the transition toward sustainable aviation [7].

Previous studies have identified several factors that influence technology acceptance and behavioral intention. The Technology Acceptance Model (TAM) emphasizes the importance of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in explaining users’ acceptance of technology. The Theory of Planned Behavior (TPB) highlights the roles of attitude and Subjective Norms (SN) in shaping behavioral intention [8]. In addition, the Unified Theory of Acceptance and Use of Technology (UTAUT) identifies Facilitating Conditions (FC) as an important factor associated with technology adoption. These theoretical perspectives provide complementary explanations of how technological perceptions, individual evaluations, social influences, and enabling conditions may affect passenger acceptance of clean energy technologies [9].

Although research on sustainable aviation and environmentally responsible consumer behavior has expanded considerably, empirical evidence concerning passenger acceptance of clean energy technologies remains relatively limited in developing-country contexts. In particular, there is a lack of empirical research examining Thai airline passengers’ acceptance and intention to support clean energy technologies [10]. Furthermore, relatively few studies have integrated TAM, TPB, and UTAUT to provide a comprehensive explanation of passenger acceptance within the context of clean energy adoption in aviation. Addressing this gap is important because passenger perceptions and behavioral intentions may differ across national and institutional contexts [11].

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Figure 1. Comparison of CO₂ emissions per passenger among different transportation modes over a distance of one kilometer

As shown in Figure 1, CO₂ emissions per passenger vary considerably across different transportation modes, highlighting the environmental implications of transportation choices and the importance of promoting cleaner and more sustainable transportation technologies.

Therefore, this study develops an integrated conceptual framework based on TAM, TPB, and UTAUT to examine the factors influencing passenger Acceptance and Intention to Use (AI) clean energy technologies in Thai airlines. Specifically, the study examines five factors: PU, PEOU, Attitude (ATT), SN, and FC. The study also examines AI as the outcome variable [12]. By examining these relationships, the study seeks to provide empirical evidence that can contribute to a better understanding of passenger support for sustainable aviation and inform airlines, policymakers, and other aviation stakeholders in promoting the adoption of clean energy technologies and advancing SDG 7 and SDG 13 [13, 14].

3.1 Clean energy technology and sustainable aviation

The aviation industry plays a critical role in global connectivity and economic development, but it is also an important source of greenhouse gas emissions [15]. Consequently, the transition toward more sustainable aviation has become a major priority for airlines, governments, and international aviation organizations. Clean energy technologies, including Sustainable SAF, electric aircraft, hydrogen-powered aircraft, and renewable energy systems, have been identified as important technological pathways for reducing the environmental impacts of aviation [16]. The development and implementation of such technologies are also closely associated with the achievement of SDG 7 and SDG 13.

Although technological development is essential for sustainable aviation, successful implementation also depends on the acceptance and behavioral intentions of passengers [17]. Passengers are not necessarily directly involved in the technical operation of clean energy technologies; however, their perceptions of environmental benefits, usefulness, convenience, and the sustainability practices of airlines can influence their willingness to support airlines adopting such technologies. Previous research has demonstrated that consumers tend to show stronger support for environmentally responsible products and services when they perceive clear environmental and functional benefits [18]. Similarly, research on green initiatives in aviation suggests that passengers’ knowledge, attitudes, and perceptions toward environmental practices are relevant to their willingness to support sustainable aviation initiatives.

Therefore, understanding passenger acceptance requires an integrated perspective that considers technological perceptions, individual attitudes, social influences, and the conditions that facilitate adoption [19]. To address these dimensions, the present study integrates constructs derived from the TAM, the TPB, and the UTAUT.

3.2 Theoretical foundations: Technology Acceptance Model, Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology

The TAM developed by Davis [18] is one of the most widely used theoretical frameworks for explaining individuals’ acceptance of technology. TAM proposes that users’ technology acceptance is primarily determined by PU and PEOU [19, 20]. PU refers to the extent to which an individual believes that using a particular technology will provide meaningful benefits, whereas PEOU reflects the extent to which the technology is perceived as requiring little effort. These two constructs have been consistently applied to explain technology adoption across different contexts. Overall, perceptions of usefulness and ease of use remain important determinants of users’ acceptance and behavioral intentions.

The TPB provides a complementary behavioral perspective. It suggests that behavioral intention is influenced by attitude, Subjective Norm (SN), and perceived behavioral control [21]. Attitude represents an individual’s favorable or unfavorable evaluation of a behavior, while SN reflects perceived social pressure from important individuals or groups. TPB has been widely applied to explain environmentally responsible behaviors, including green consumption, renewable energy adoption, and sustainable transportation choices. In the context of sustainable aviation, these dimensions are particularly relevant because passengers’ decisions to support airlines adopting clean energy technologies may depend not only on technological benefits but also on their personal environmental attitudes and perceived expectations from others [22].

The UTAUT extends technology acceptance research by incorporating technological, social, and organizational conditions. Among its major constructs, FC refers to the extent to which individuals perceive that the necessary resources, infrastructure, and organizational support are available for technology use [23]. Although UTAUT was originally developed in the information technology context, its underlying logic can be applied to emerging sustainable technologies where adoption depends partly on technological availability, infrastructure readiness, institutional support, and enabling policies [24].

Accordingly, the present study combines the complementary strengths of TAM, TPB, and UTAUT. TAM contributes PU and PEOU; TPB contributes ATT and SN; and UTAUT contributes FC. Together, these constructs provide a multidimensional framework for explaining passengers’ AI clean energy technologies in the airline industry. This theoretical integration is consistent with the framework presented in the study, which examines technological, psychological, social, and facilitating factors simultaneously [25].

3.3 Perceived Usefulness and Acceptance and Intention to Use

PU refers to the extent to which individuals believe that a technology provides meaningful benefits or improves the outcomes associated with its use [26]. Within the context of clean energy technologies in aviation, PU can be understood as passengers’ perceptions that technologies such as SAF, electric aircraft, or hydrogen-powered aircraft can generate environmental and operational benefits. These benefits may include reductions in greenhouse gas emissions, improved environmental performance, and contributions to sustainable aviation.

The importance of PU is particularly evident when consumers evaluate environmentally oriented innovations. Innovations perceived to provide clear relative advantages are more likely to be adopted [27]. Perceived benefits are also important in explaining environmentally responsible consumption behavior. In the aviation sector, passengers may not possess detailed technical knowledge regarding clean energy technologies, but they can evaluate whether such technologies appear beneficial for environmental protection and the future sustainability of aviation. Previous research has also indicated that environmental and corporate social responsibility initiatives can influence customer responses toward airlines [28]. Therefore, when passengers perceive clean energy technologies as useful for reducing environmental impacts and improving the sustainability of aviation, they are expected to demonstrate stronger acceptance and intention to support airlines adopting such technologies. Based on TAM and previous empirical evidence, the following hypothesis is proposed:

H1: PU positively influences AI clean energy technologies.

3.4 Perceived Ease of Use and Acceptance and Intention to Use

PEOU refers to the extent to which individuals perceive that a technology can be used or implemented without requiring substantial effort. In the context of aviation, this construct is particularly relevant because passengers generally expect technological innovations to maintain or improve convenience, comfort, safety, and service quality. Although clean energy technologies may involve sophisticated technical systems, passengers primarily evaluate their implications from the perspective of the travel experience [29].

Venkatesh and Davis [30] emphasized that technologies perceived as easier to use are more likely to generate favorable acceptance and behavioral intentions. From the passenger perspective, the adoption of clean energy technologies should not create additional inconvenience in processes such as booking, check-in, boarding, or the provision of in-flight services. If passengers perceive that the transition toward cleaner aviation can occur without negatively affecting their travel experience, their resistance to technological change may be reduced [31].

Previous research on technology acceptance has consistently identified ease of use as an important factor influencing users’ behavioral responses. Accordingly, passengers who perceive clean energy technologies as practical, convenient, and compatible with their travel experience are expected to exhibit stronger acceptance and intention to support airlines implementing such technologies [32].

Thus, the following hypothesis is proposed:

H2: PEOU positively influences AI clean energy technologies.

3.5 Attitude and Acceptance and Intention to Use

ATT refers to an individual’s favorable or unfavorable evaluation of a particular behavior or object. Within TPB, ATT is a fundamental determinant of behavioral intention because individuals who evaluate a behavior positively are more likely to intend to perform or support that behavior. In sustainable aviation, ATT reflects passengers’ overall evaluations of airlines adopting environmentally responsible and clean energy technologies [33].

Positive attitudes toward environmental sustainability can encourage consumers to support green products, services, and organizational practices. Attitude is an important factor in explaining green consumption behavior. Environmental responsibility is also relevant to customer responses in the airline context. More specifically, passengers’ knowledge and attitudes toward green initiatives in aviation are important, as favorable passenger perceptions can contribute to stronger support for sustainable aviation practices [34]. Clean energy technologies are closely associated with environmental responsibility and climate action. When passengers believe that such technologies can reduce greenhouse gas emissions and contribute to a more sustainable aviation system, they are likely to develop more favorable evaluations toward airlines adopting them. Consequently, a positive ATT should increase passengers’ willingness to support, choose, and recommend airlines that implement such technologies [35].

Therefore, the following hypothesis is proposed:

H3: ATT positively influences AI.

3.6 Subjective Norms and Acceptance and Intention to Use

SN represents an individual’s perception of whether important people or social groups expect them to engage in a particular behavior. In the context of sustainable aviation, SN reflects the influence of family members, friends, colleagues, and wider society on passengers’ willingness to support airlines adopting clean energy technologies.

Environmental behavior is not determined solely by individual preferences. Social expectations and perceptions of what constitutes responsible behavior can also shape behavioral intentions. Social norms are relevant to environmentally responsible consumption and underscore the importance of social and psychological factors in explaining green purchase intentions through the TPB framework [36]. The role of SN may be particularly important for sustainable aviation because environmental responsibility has increasingly become a socially recognized expectation. Passengers may perceive supporting environmentally responsible airlines as socially desirable, particularly when sustainability issues are communicated through public campaigns, social media, and organizational communication. In addition, recommendations and opinions from people within passengers’ social networks may strengthen their willingness to choose or support airlines adopting clean energy technologies [37].

Consistent with TPB and UTAUT, social influence is therefore expected to contribute positively to passenger acceptance and behavioral intention. Accordingly, the following hypothesis is proposed:

H4: SN positively influences AI clean energy technologies.

3.7 Facilitating Conditions and Acceptance and Intention to Use

FC refers to the extent to which individuals believe that the necessary resources, infrastructure, technical support, and institutional arrangements are available to enable the adoption or use of a technology [38]. In sustainable aviation, FC encompasses the availability of appropriate infrastructure, technological readiness, institutional support, and government policies that enable airlines to implement clean energy technologies.

Unlike conventional consumer technologies that can often be adopted directly by individual users, clean energy technologies in aviation require substantial organizational and infrastructural support. The implementation of SAF, electric aircraft, hydrogen-powered aircraft, and related renewable energy systems depends on technological infrastructure, regulatory frameworks, investment, supply chains, and institutional coordination. Consequently, passengers’ perceptions of whether the aviation system is sufficiently prepared to implement such technologies may influence their confidence and willingness to support their adoption [39].

UTAUT identifies FC as an important component of technology adoption because users are more likely to accept technologies when they perceive that adequate resources and support are available. In the aviation context, visible evidence of infrastructure readiness, government support, and airline commitment may therefore reduce uncertainty and increase passengers’ confidence in clean energy technologies [40].

Accordingly, passengers who perceive strong FC are expected to demonstrate greater acceptance and intention to support airlines adopting clean energy technologies. Therefore, the following hypothesis is proposed:

H5: FC positively influences AI clean energy technologies.

3.8 Acceptance and Intention to Use clean energy technologies

AI represents passengers’ willingness to support, choose, and recommend airlines that utilize clean energy technologies. Behavioral intention is a central outcome in both technology acceptance and behavioral theories [41]. TAM emphasizes the role of users’ perceptions of technological benefits and ease of use in shaping acceptance, while TPB explains intention through ATT and SN. UTAUT further emphasizes the importance of social influence and FC in technology-related behavior.

In sustainable aviation, acceptance extends beyond direct use of a particular technology because passengers may not directly operate or interact with technologies such as SAF or hydrogen propulsion. Instead, acceptance can be expressed through willingness to support airlines implementing clean energy technologies, preference for airlines adopting environmentally responsible practices, and willingness to recommend such airlines. This perspective is appropriate for examining passenger responses to technological transitions that are primarily implemented by airline operators rather than individual consumers [42].

The literature therefore suggests that passenger acceptance of clean energy technologies is a multidimensional outcome influenced by technological perceptions, individual attitudes, social expectations, and enabling conditions. Integrating these perspectives provides a more comprehensive explanation of passenger behavioral intentions toward sustainable aviation [43].

3.9 Research framework and hypotheses development

Based on the reviewed literature, the present study integrates TAM, TPB, and UTAUT to explain passenger AI clean energy technologies in Thai airlines. The framework includes PU and PEOU from TAM, ATT and SN from TPB, and FC from UTAUT. The dependent construct is AI.

The integration of these theories is appropriate because passenger acceptance of clean energy technologies is unlikely to be explained by a single theoretical perspective. Technological perceptions capture the expected benefits and convenience of clean energy technologies, while ATT and SN represent psychological and social determinants of behavioral intention. FC complements these factors by addressing the infrastructure and institutional environment required for successful implementation. The resulting framework therefore provides an integrated explanation of passenger acceptance of clean energy technologies within the Thai aviation context. The proposed conceptual framework and the relationships among the study constructs are presented in Figure 2.

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Figure 2. Conceptual framework

3.10 Research hypotheses

H1: PU has a significant positive effect on AI.

H2: PEOU has a significant positive effect on AI.

H3: ATT has a significant positive effect on AI.

H4: SN has a significant positive effect on AI.

H5: FC has a significant positive effect on AI.

5.1 Respondent characteristics

A total of 400 valid questionnaires were retained for the final analysis after data screening and eligibility assessment. Table 1 presents the demographic characteristics of the respondents. The gender distribution was relatively balanced, with male and female respondents each accounting for 47.8% of the sample, while 4.5% identified as other genders. Regarding age, respondents aged 21–30 years represented the largest group (46.3%), followed by those below 20 years (41.0%). Thus, 87.3% of the respondents were below 31 years of age, indicating a substantial concentration of younger participants. In terms of monthly income, 72.8% reported earning less than 15,000 THB, while 13.8% earned 15,001–30,000 THB, 9.5% earned 30,001–50,000 THB, and 4.0% earned more than 50,000 THB. With respect to air travel frequency during the previous 12 months, 73.3% had traveled by air one to two times, followed by 13.0% who had traveled three to five times, 8.5% who had traveled six to ten times, and 5.3% who had traveled more than ten times. These characteristics indicate that the sample was concentrated among younger and lower-income respondents with relatively limited annual air travel frequency. Therefore, the findings should be interpreted as reflecting the perceptions of the surveyed sample rather than being generalized unconditionally to the entire population of Thai airline passengers.

Table 1. Demographic profile of respondents (N = 400)

Note: Percentages may not total exactly 100% due to rounding.

5.2 Descriptive statistics

Descriptive statistics were calculated for the six latent constructs. As shown in Table 2, all constructs received relatively high mean scores, ranging from 3.999 to 4.252 on the five-point Likert scale. AI recorded the highest mean score (M = 4.252, SD = 0.599), followed by ATT (M = 4.240, SD = 0.597) and PU (M = 4.157, SD = 0.647). SN had a mean of 4.086 (SD = 0.630), while FC recorded a mean of 4.018 (SD = 0.674). PEOU had the lowest mean score (M = 3.999, SD = 0.637), although it remained above the midpoint of the measurement scale. Overall, the descriptive results indicate favorable perceptions and relatively high acceptance of clean energy technologies among the respondents.

Table 2. Descriptive statistics of the latent constructs

5.3 Measurement model assessment

The measurement model was evaluated using SmartPLS 4.1.1.8 before assessing the structural relationships. The assessment examined indicator reliability, internal consistency reliability, convergent validity, discriminant validity, and collinearity. As shown in Table 3, all outer loadings ranged from 0.849 to 0.907, exceeding the commonly accepted threshold of 0.70. Cronbach’s alpha values ranged from 0.842 to 0.876, while composite reliability (rho_C) ranged from 0.905 to 0.924. The AVE values ranged from 0.760 to 0.802, exceeding the minimum criterion of 0.50. These findings demonstrate satisfactory indicator reliability, internal consistency reliability, and convergent validity for all six constructs.

Table 3. Measurement model: Outer loadings, reliability, and convergent validity

Table 4. Discriminant validity: Fornell–Larcker criterion

Note: Bold diagonal values represent the square root of AVE. ATT = Attitude, FC = Facilitating Conditions, PEOU = Perceived Ease of Use, PU = Perceived Usefulness, SN = Subjective Norms, AI = Acceptance and Intention to Use.

As shown in Table 4, the Fornell–Larcker criterion supports the discriminant validity of the constructs, with the square root of AVE for each construct exceeding its correlations with the other constructs. The cross-loading results presented in Table 5 further support discriminant validity. Each indicator demonstrated its highest loading on the construct to which it was theoretically assigned. For example, AI1, AI2, and AI3 loaded 0.906, 0.875, and 0.905, respectively, on AI, while their cross-loadings on the other constructs were lower. The same pattern was observed for the indicators of PU, PEOU, ATT, SN, and FC. Therefore, the indicators demonstrated satisfactory construct-specific loading patterns.

Table 5. Discriminant validity: cross-loadings

Note: Bold values represent the highest loading for each indicator. ATT = Attitude, FC = Facilitating Conditions, PEOU = Perceived Ease of Use, PU = Perceived Usefulness, SN = Subjective Norms, AI = Acceptance and Intention to Use.

Table 6. Collinearity statistics (VIF)

Note: ATT = Attitude, FC = Facilitating Conditions, PEOU = Perceived Ease of Use, PU = Perceived Usefulness, SN = Subjective Norms, AI = Acceptance and Intention to Use.

Collinearity was assessed using the Variance Inflation Factor (VIF). As shown in Table 6, all indicator-level VIF values ranged from 1.866 to 2.623, which were below the conservative threshold of 3.00. These results indicate that collinearity did not represent a critical problem in the measurement model.

5.4 Structural model assessment

Following confirmation of the reliability and validity of the measurement model, the structural model was assessed using PLS-SEM in SmartPLS 4.1.1.8. The assessment focused on the coefficient of determination (R²), effect size (f²), path coefficients, and statistical significance obtained through bootstrapping.

The endogenous construct, AI, produced an R² value of 0.760. This indicates that PU, PEOU, ATT, SN, and FC collectively explained 76.0% of the variance in AI. The remaining 24.0% was attributable to factors outside the proposed model. As presented in Table 7, the effect-size results indicate that ATT had the largest effect on AI (f² = 0.167), followed by PU (f² = 0.057), SN (f² = 0.047), PEOU (f² = 0.012), and FC (f² = 0.012). Thus, among the predictors included in the model, ATT demonstrated the greatest substantive contribution to explaining AI.

The effect-size results indicate that ATT had the largest effect on AI (f² = 0.167), followed by PU (f² = 0.057), SN (f² = 0.047), PEOU (f² = 0.012), and FC (f² = 0.012). Thus, among the predictors included in the model, ATT demonstrated the greatest substantive contribution to explaining AI.

Table 7. Coefficient of determination and effect size

Note: ATT = Attitude, FC = Facilitating Conditions, PEOU = Perceived Ease of Use, PU = Perceived Usefulness, SN = Subjective Norms, AI = Acceptance and Intention to Use.

Table 8. Partial Least Squares Structural Equation Modeling (PLS-SEM) model fit

As shown in Table 8, the estimated model produced an SRMR of 0.045 and an NFI of 0.857. The d_ULS and d_G values were 0.339 and 0.336, respectively, while the chi-square value was 1200.611. These indices are reported as supplementary indicators of model fit, while the primary evaluation of the PLS-SEM model is based on the quality of the measurement model and the explanatory and predictive assessment of the structural model. Model fit was additionally assessed using PLS-SEM fit indices. The estimated model produced an SRMR of 0.045 and an NFI of 0.857. The d_ULS and d_G values were 0.339 and 0.336, respectively, while the chi-square value was 1200.611. These indices are reported as supplementary indicators of model fit, while the primary evaluation of the PLS-SEM model is based on the quality of the measurement model and the explanatory and predictive assessment of the structural model.

5.5 Hypothesis testing

The proposed hypotheses were tested using the bootstrapping procedure in SmartPLS 4.1.1.8. The results are presented in Table 9. Four of the five hypothesized relationships were statistically significant at the 0.05 level.

ATT had the strongest positive effect on AI, with a path coefficient of β = 0.363, a t-value of 7.380, and p < 0.001. Therefore, H3 was supported. PU also had a significant positive effect on AI (β = 0.212, t = 4.480, p < 0.001), supporting H1. SN significantly and positively influenced AI (β = 0.202, t = 3.980, p < 0.001), supporting H4. PEOU had a smaller but statistically significant positive effect on AI (β = 0.098, t = 2.187, p = 0.029), supporting H2.

Table 9. Results of hypothesis testing

In contrast, FC did not have a statistically significant effect on AI (β = 0.091, t = 1.862, p = 0.063). Although the estimated coefficient was positive, the p-value exceeded the 0.05 significance threshold. Therefore, H5 was not supported. These results demonstrate that the statistically significant structural predictors of passenger acceptance in the present model were ATT, PU, SN, and PEOU, whereas FC did not demonstrate a statistically significant direct effect.

5.6 Common method bias assessment

Because all constructs were measured using a single self-reported online questionnaire, the possibility of common method bias (CMB) was considered. Procedurally, the questionnaire was administered anonymously, participation was voluntary, and respondents were informed that their responses would be treated confidentially. The measurement items were presented as statements using a consistent five-point Likert scale, without requesting personally identifiable information. The original study documentation confirms the use of voluntary participation, electronic informed consent, confidentiality, and the absence of personally identifiable information.

However, a statistical CMB test is not available in the SmartPLS outputs provided for this revision. Therefore, a Harman’s single-factor test or an alternative statistical assessment should be conducted using the respondent-level dataset before this subsection is finalized. A numerical CMB result should not be inserted without an actual statistical output.

This study examined the factors influencing passengers’ acceptance and intention to support clean energy technologies adopted by airlines in Thailand by integrating the TAM, TPB, and UTAUT. The findings provide empirical evidence that passenger acceptance of clean energy technologies is shaped primarily by psychological, technological, and social factors. Among the five proposed predictors, ATT had the strongest positive effect on AI, followed by PU, SN, and PEOU. In contrast, FC did not demonstrate a statistically significant direct effect on passenger acceptance.

The strongest effect of ATT indicates that passengers’ favorable evaluations of clean energy technologies are particularly important in encouraging their willingness to choose, support, and recommend airlines adopting such technologies. PU also demonstrated a significant positive effect, suggesting that communicating the environmental and operational benefits of clean energy technologies can strengthen passenger acceptance. In addition, the significant effects of SN and PEOU indicate that social expectations and perceptions of maintaining convenience and travel experience also contribute to acceptance. These findings support the relevance of TAM and TPB in explaining passenger responses to sustainable aviation technologies. The non-significant effect of FC, meanwhile, suggests that infrastructure, institutional support, and technological readiness may be perceived primarily as responsibilities of airlines, governments, airports, and other aviation stakeholders rather than as direct determinants of passengers’ behavioral intentions. This distinction is particularly relevant because passengers generally do not directly operate technologies such as SAF, electric aircraft, or hydrogen-powered aircraft.

The structural model explained 76.0% of the variance in AI (R² = 0.760), demonstrating substantial explanatory power. The overall findings therefore support the use of an integrated TAM–TPB–UTAUT framework for examining passenger acceptance of clean energy technologies in the Thai aviation context. The results also extend the application of technology acceptance perspectives to sustainable aviation, where acceptance represents not only direct technology use but also passengers’ willingness to support, choose, and recommend airlines that adopt environmentally responsible technologies.

From a practical perspective, airlines should prioritize strategies that strengthen positive passenger attitudes, communicate credible environmental and operational benefits, and reinforce social support for sustainable aviation. At the same time, policymakers and aviation stakeholders should continue developing the infrastructure, regulatory mechanisms, resources, and institutional support required for the implementation of clean energy technologies. Such coordinated efforts are relevant to the broader transition toward sustainable aviation and the advancement of SDG 7 and SDG 13.

Overall, the study concludes that passenger acceptance provides an important demand-side foundation for the transition toward cleaner aviation in Thailand. However, positive passenger intentions alone cannot ensure technological implementation or emissions reduction. Sustainable aviation ultimately requires alignment among passenger acceptance, technological development, airline strategies, infrastructure readiness, and institutional policy. The present findings provide a basis for airlines and policymakers to place greater emphasis on the passenger perspective while continuing to address the structural conditions necessary for long-term clean energy adoption in aviation.