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13 November 2025

Consumer Cost Perception and Its Role in Hydrogen Vehicle Adoption †

Department of Leadership and Marketing, Kautz Gyula Faculty of Business and Economics, Széchenyi István University, 9026 Györ, Hungary
†
Presented at the Sustainable Mobility and Transportation Symposium 2025, Győr, Hungary, 16–18 October 2025.
Eng. Proc.2025, 113(1), 65;https://doi.org/10.3390/engproc2025113065
This article belongs to the Proceedings The Sustainable Mobility and Transportation Symposium 2025
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Abstract

The widespread adoption of hydrogen-powered vehicles as a sustainable mobility solution depends on several factors, among which consumer acceptance is particularly important. This study examined how cost perception, demographic characteristics, and environmental attitudes influence the intention to adopt hydrogen-powered vehicles. The quantitative study (n = 1330) was conducted using an online questionnaire in Hungary. The results were verified by linear regression conducted in three steps, depending on the set of dependent variables chosen. The results showed that while cost perception alone has a moderate effect on acceptance, environmental attitudes have a strong and significant explanatory power. Acceptance is not merely an economic decision, but a value-based one closely linked to individuals’ environmental worldviews. The results highlight that the acceptance of hydrogen-based mobility is primarily a value-based decision, and that strengthening environmental commitment is key to promoting it.

1. Introduction

Sustainability is the principle of meeting current needs without compromising the ability of future generations to meet theirs [,]. It encompasses environmental, social, and economic dimensions, while some argue that cultural, technological, and political factors are also relevant [,]. Sustainable mobility can be understood as society’s ability to meet its transport needs without compromising fundamental human and ecological values []. This concept stems from the broader context of sustainable development []. Sustainable mobility, therefore, encompasses not only environmental considerations but also integrates social and economic dimensions in a complex manner. In parallel, automotive manufacturers are increasingly striving to improve the environmental performance of their supply chains through process optimization and green technologies, which can lead to up to 40% gains in energy efficiency and 30% reductions in emissions. Sustainable mobility is increasingly focused on alternative fuel vehicles (AFVs) to reduce greenhouse gas emissions and reliance on fossil fuels. Among the most promising options are electric vehicles, hydrogen fuel cell vehicles, and biofuel vehicles. Hydrogen, in particular, has favorable characteristics as a vehicle fuel due to its high energy content and clean emissions. However, the challenges associated with its production and storage can limit its widespread application [,]. Environmental concerns play a significant and positive role in the acceptance of low-emission vehicles, such as electric and hybrid models, primarily through shaping consumer attitudes, intentions and behaviour. Numerous studies confirm that environmental awareness directly increases the intention to adopt such vehicles [,,]. However, the effectiveness of environmental attitudes becomes truly significant when coupled with appropriate policy measures and incentives. According to numerous studies, strict environmental regulations, financial incentives and well-designed support schemes significantly reinforce the behavioural impact of environmental concerns [,,,,]. In practice, high environmental awareness alone is often not enough: financial, technical, or infrastructural barriers—such as the price of vehicles or a lack of charging facilities—hinder acceptance even among environmentally conscious individuals [,,]. This highlights the need for complex, system-level support in addition to environmental attitudes, including the involvement of market actors, education, and long-term regulatory stability.
Consumer acceptance of hydrogen-based mobility solutions, in particular, depends on psychological, social, economic, and infrastructural factors. Key factors for acceptance include perceived benefits, trust, awareness, affordability, and infrastructure availability. Addressing these factors is essential to promote widespread adoption. The global fleet of hydrogen-powered passenger cars is still relatively small, with around 51,437 registered vehicles worldwide. Approximately 90% of this fleet is concentrated in just four countries—South Korea, the United States, Japan, and Germany—with South Korea considered the leader in applying this technology [,]. The hydrogen refuelling infrastructure is similarly concentrated: there are currently 729 refuelling stations worldwide, most located in Japan. This geographical concentration clearly shows that hydrogen mobility is still in its infancy and that there are significant regional differences in the pace of development and deployment [].
The acceptance of the technology is primarily influenced by the benefits (e.g., environmental impact, health benefits, independence from centralised networks) and risks (e.g., safety, cost) that consumers attribute to hydrogen technology. These assessments are often accompanied by emotional reactions, which also shape attitudes [,,,,,]. Low levels of public awareness and a lack of knowledge about hydrogen are significant barriers; however, education and environmental awareness are positively associated with acceptance [,]. Costs and their perceived affordability continue to play a key role in decision-making; the promise of economic benefits can also be an incentive [,]. Adequate infrastructure, especially the availability of charging stations, is also essential for acceptance, as it directly influences the technology’s usability []. Social and psychological factors also influence acceptance. Environmental attitudes and a positive attitude towards clean energy increase the likelihood of acceptance []. While educational attainment consistently affects acceptance, other demographic variables such as age, gender, and income have a less clear impact []. Among Hungarian studies, Makkos-Káldi et al. [] also emphasize that in consumer behavior—especially among older generations—health and tradition play a decisive role, highlighting the importance of generational value preferences in decision-making. Similarly, Eisinger et al. [] demonstrate that regular, sustainability-oriented decision-making processes lead to significantly lower long-term costs and environmental impacts, underscoring the broader societal benefits of consistent, value-based choices.
Based on the literature, it can be concluded that the acceptance of hydrogen-based transport is a multidimensional process influenced by perceived benefits and risks, trust, awareness, economic factors, and the availability of infrastructure. In order to ensure successful deployment, it is essential to expand the knowledge base of the population, build trust, and communicate in a targeted manner, which can only be effective in conjunction with a supportive regulatory framework and accessible infrastructure.
This study surveys the attitudes of Hungarian consumers. The factors influencing acceptance, which are examined in the rest of the study, were selected based on the literature.

2. Materials and Methods

The research used a quantitative methodological approach and a structured questionnaire to examine consumer attitudes towards hydrogen-powered vehicles. Data was collected online in Hungary using a snowball sampling method. The sample size is 1330 respondents. The questionnaire measured acceptance intention (ADP), cost perception (COS), and environmental attitude (ENV) based on validated scales [], as well as additional dimensions such as safety perception, risk perception, and social influence.
This study focused on the relationship between cost perception and acceptance intention, as well as the role of demographic characteristics and environmental attitudes. The analysis was guided by the following research question: RQ1: To what extent do perceived costs, demographic characteristics, and environmental attitudes influence the acceptance intention of hydrogen-powered vehicles?
The data analysis included descriptive statistics, correlation analysis, and a linear regression conducted in three consecutive models with different sets of independent variables.

3. Results and Discussion

Below are the main empirical results of the questionnaire survey and their theoretical and practical interpretation. The demographic characteristics of the respondents are described as a first step in the analysis.
Table 1 summarises the demographic characteristics of the respondents participating in the survey. A total of 1330 respondents completed the questionnaire during the survey. The gender distribution of the sample can be considered balanced: 52.6% of respondents were female and 44.7% were male. The remaining 2.7% identified as other gender identities or did not wish to answer this question. The generational distribution predominates Generation Z (1996–2010), accounting for 63.5% of the total sample. Generation Y (1982–1995) accounted for 16.8%, while Generation X (1961–1981) accounted for 17.7%. Baby Boomers and the Silent Generation accounted for only 2.1% of the sample, indicating a strongly youthful composition of respondents. Based on the respondents’ subjective income classification, the majority (59.9%) classified their situation as average. The proportion of those with lower incomes was 22.5%, while those with incomes above average accounted for 17.6%. Regarding the highest level of education, 7.1% of respondents had only primary education, while the majority (62.1%) reported secondary education. 30.9% of respondents had higher education.
Table 1. Demographics.
These data show that the majority of survey participants are younger, have an average income level, and have at least secondary education, which is an important factor in interpreting and generalising the results.
Table 2 presents descriptive statistics of respondents’ attitudes toward hydrogen fuel cell vehicles (HFCVs) and their perceptions of cost. Among the attitude-related items (ADP1–ADP4), the highest mean value was observed for the statement “I believe that innovation gives me more control over my daily life” (M = 3.61, SD = 1.15), indicating a generally positive perception of innovation in everyday life. However, this openness did not consistently translate into enthusiasm for hydrogen vehicles, as the mean values for direct engagement with HFCVs (ADP2: M = 3.02, ADP3: M = 2.92) and willingness to pay more for environmentally friendly alternatives (ADP4: M = 2.79) were notably lower, reflecting a more reserved or cautious attitude toward adoption. Regarding cost perception (COS1–COS4), the highest agreement was with the statement “HFCVs are more expensive than conventional vehicles” (COS1: M = 3.83, SD = 1.10), suggesting that price is widely perceived as a key barrier. The perceived high service charges (COS3: M = 3.58) and the cost of hydrogen fuel (COS2: M = 3.23) were also acknowledged, though slightly less. Interestingly, the item referring to HFCVs having low maintenance cost per km (COS4) yielded a moderate mean (M = 3.46), indicating ambiguity or mixed perception regarding operational expenses. The relatively consistent medians and modes (mostly centered around 3 or 4) across items indicate a central tendency toward moderate agreement, with standard deviations ranging from 1.10 to 1.29, reflecting moderate response variability. While participants express general openness to innovation, they remain hesitant regarding hydrogen vehicle adoption, especially when higher costs are involved. During the analysis, two constructs—intention to adopt hydrogen-powered vehicles and cost perception—were operationalised using several questions with similar content. The research aimed to analyse these concepts, so it was methodologically justified to transform individual item groups into composite scales. Composite scales allow us to measure multidimensional attitudes more reliably, stably, and comprehensively, reducing the potential distorting effect of individual items. Furthermore, aggregating scales are statistically advantageous because they simplify examining relationships and effects, especially in correlation and regression analyses. The Cronbach’s alpha coefficient was used to examine the internal consistency of the multi-item constructs in the questionnaire, namely acceptance intention (ADP1–ADP4) and cost perception (COS1–COS4). The aim was to verify that the items belonging to each scale reliably measure a common underlying concept. Based on the results, the reliability of the acceptance intention scale is adequate (α = 0.787), while that of the cost perception scale can be considered particularly good (α = 0.835). These values exceed the generally accepted threshold of 0.7 in the literature [], so it is considered justified to average the items and treat them as composite scale variables in further analyses in the case of the ADP and COS scales. After confirming the internal consistency of the scales, the next step in the research was to examine the relationships between the variables, starting with the calculation of Pearson’s correlation between the composite scales. The aim was to explore the extent and direction of the relationship between cost perception and the intention to adopt hydrogen-powered vehicles.
Table 2. Mean, Median, Mode, and Std. Dev. of Variables.
Table 3 shows that Pearson’s correlation coefficient was used to examine the relationship between the two composite scale variables—acceptance intention (ADP_MEAN) and cost perception (COS_MEAN). The results show that there is a positive, weak to moderate, statistically significant relationship between the two variables (r = 0.291, p < 0.001). This means that respondents who expect higher costs associated with the use of hydrogen-powered vehicles also tend to show greater willingness to accept them. At first glance, this result may seem to contradict the preliminary assumption that high perceived costs reduce technology acceptance. However, this positive correlation may indicate that certain respondents are willing to accept higher costs in order to achieve a more sustainable form of transport. In order to better understand the impact of cost perception on acceptance intentions and to explore the possible distorting role of demographic factors, we apply a linear regression model with control variables in the next step. The low correlation may raise further questions. In this case, various demographic or attitudinal factors may significantly impact the model. The question may arise as to whether demographic factors such as generation, or even environmental orientation may influence the intention to adopt new technologies. Therefore, a linear regression analysis was conducted in three consecutive models to examine the impact of cost perception on acceptance in greater depth. The models aimed to explore the extent to which factors related to cost perception, demographic characteristics, and environmental attitudes contribute to explaining the variance in acceptance intention (ADP_MEAN). The models were constructed according to the general-to-specific principle.
Table 3. Relationship between two composite scale variables.
In the first and most comprehensive model, all three predictor blocks were included: perceived cost (COS_MEAN), demographic variables (generation, income, education), and the environmental attitude composite scale (ENV_MEAN). This model performed the best among all, showing a significant and meaningful improvement over the more parsimonious models (R2 = 0.177; F(5, 1323) = 56.957; p < 0.001). The explained variance increased notably, underlining the added value of including environmental attitudes. Correlation analysis also confirmed that environmental attitude has a moderately strong positive relationship with acceptance intention (r = 0.387; p < 0.001). The results highlight that environmental commitment is the most substantial predictor of openness toward sustainable mobility alternatives, such as hydrogen-powered vehicles.
In the second model, the environmental attitude composite scale was excluded, and only cost perception and three demographic variables—generational affiliation, income level, and educational attainment—were included as predictors. The model proved to be significant (R2 = 0.097; F(4, 1324) = 35.639; p < 0.001), and the fit remained acceptable, though slightly weaker than the full model. The explained variance decreased by 8 percentage points compared to the previous model, indicating the loss of explanatory power due to the omission of environmental attitudes. Demographic factors provided statistically detectable but only a modest amount of additional explanatory value. This result suggests that the intention to adopt is not primarily determined by generation, income, or education alone, at least not directly or within the scope of the current measurement framework.
In the third and most parsimonious model, only the cost perception composite scale (COS_MEAN) was included as an independent variable. The analysis showed that cost perception was a statistically significant, though limited, explanatory factor (R2 = 0.085; F(1, 1327) = 123.520; p < 0.001). This means that perceived cost explained approximately 8.5% of the variance in the dependent variable. The model’s standard error was 0.903, indicating moderate estimation accuracy. These results suggest that while cost perception plays a role in shaping acceptance intention, it alone cannot sufficiently account for consumer openness toward hydrogen-powered vehicles. Moreover, the effect of perceived cost may be embedded in broader attitudinal or contextual factors, as revealed in the subsequent models.
The comparison of the three regression models confirms that the inclusion of environmental attitudes substantially enhances explanatory power. While perceived cost and demographic factors contributed modestly to the variance in acceptance intention, the addition of environmental attitudes proved to be the most influential factor. This supports the final model as the most robust and theoretically grounded representation of consumer decision-making regarding hydrogen vehicle adoption. Accordingly, the structure of the first, most complex, final model is presented below in the form of a standardised regression equation:
Adoption_Intention = 0.130⋅Perceived cost + 0.107⋅Income + 0.048⋅Education − 0.018⋅Generation + 0.330⋅Environmental attitude + ε
The equation fits well with the hypothetical structure. It confirms the assumption that the influence of environmental values can be interpreted as a determining factor in the acceptance of hydrogen-powered vehicles. This result is strongly supported by previous research [,,], which identified environmental concern and ecological values as key drivers in the adoption of sustainable energy technologies, including hydrogen vehicles.

4. Conclusions

This study examined how cost perception factors influence the intention to adopt hydrogen-powered vehicles and how this effect changes when different demographic background variables and environmental attitudes are taken into account.
The study’s results indicate that the acceptance of hydrogen-powered vehicles stems from a complex, multidimensional decision-making process, in which cost perception is only one factor. At the same time, environmental attitudes play a dominant role. The model’s results showed that environmental commitment is a more significant driver of acceptance than financial rationality, i.e., the value assessment for money. This finding can be used on several levels. On the one hand, the dominant logic of communication and incentive systems related to hydrogen technology, which has focused primarily on cost reduction, government subsidies, or long-term returns, is insufficient. The results suggest that consumer decision-making is value-based rather than purely economic, which must be considered in market introduction strategies. On the other hand, the predictive effect of environmental attitudes also indicates that hydrogen-based transport is a technological and social innovation requiring a cultural change. Technology acceptance is closely linked to an individual’s ecological worldview, which means that regulation and education cannot be limited to creating financial incentives. Instead, long-term social learning and awareness-raising mechanisms must be implemented to deepen ecological attitudes. Therefore, the results of this study are also relevant for planning sustainable mobility policies. At the same time, it can be argued that the acceptance of hydrogen-based transport may be socially selective, as higher-income respondents have shown a greater willingness to adopt it. If hydrogen technology becomes available only to higher-income groups, this observation risks social mobility and equality. Therefore, it would be advisable to introduce targeted support and access improvement measures that make sustainable technologies affordable for less affluent groups.
Ultimately, the study underscores that the adoption of hydrogen-powered vehicles is not merely an economic or technological issue, but a value-based decision that is shaped by both objective costs and subjective values. The scientific significance of the research lies in its contribution to a deeper understanding of the factors influencing consumer acceptance of hydrogen-powered vehicles, in particular by examining the relationship between cost perceptions and ecological attitudes. The results of the model provide novel empirical evidence that acceptance intentions are shaped not only by rational economic considerations but also by value-based attitudes. From a societal perspective, a key finding of the study is that the adoption of sustainable technologies cannot be achieved solely through market incentives. In the long term, conscious social learning processes, value-based communication, and measures to ensure targeted access are needed to make these innovations acceptable to broader social groups.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The author declares no conflicts of interest.

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