Exploring the Link Between Ride-Sharing Experience and Autonomous Vehicle Acceptance in the Context of Sustainable Mobility ^†

Abstract

Sustainable transportation systems are becoming an increasingly important issue around the world, especially with the advancement of urbanisation. Autonomous vehicles and ride-sharing services represent innovative mobility solutions that can improve not only the efficiency of transportation but also its environmental sustainability. The aim of this study is to examine Hungarian consumers’ attitudes toward ride-sharing and their acceptance of AVs, with a focus on whether there is a link between the two phenomena. The research is based on a nationally representative sample of 2000 respondents. Correlation analyses were performed based on the dimensions of technology acceptance models. Based on the results, a significant positive correlation can be demonstrated between the willingness to use ride-sharing services in the future and the openness towards AVs. Perceived usefulness and social influence showed the strongest relationship with intention of usage. The results contribute to the social acceptance of autonomous technologies and sustainable transport in Hungary.

1. Introduction

With the acceleration of global urbanisation and the sharp increase in the proportion of the urban population, the sustainability of transport systems has become a key challenge. According to UN projections, the proportion of the world’s population living in cities will increase from 56% in 2021 to 68% in 2050 [1]. This trend places enormous demands on urban mobility, as increasing travel demand leads to high energy consumption, severe traffic congestion, and significant greenhouse gas emissions. Transport currently accounts for around 23% of global emissions, and this figure could double by 2050 [2]. Recognising these negative externalities, the search for sustainable transport solutions that reduce environmental impact and improve urban quality of life has come to the fore worldwide. In line with this, new mobility services such as community (sharing-based) transport and vehicle technology innovations, including electric and autonomous vehicles, have been gaining momentum in recent years. Ride-sharing services such as Uber, Lyft and, in Europe, Bolt, are now widespread worldwide, and their user numbers are growing rapidly. This is happening in parallel with the revolution in autonomous vehicle technology, one of the main promises of which is a significant improvement in transport safety by eliminating human error. Shared services and autonomous technologies are becoming increasingly intertwined in urban mobility. In many future scenarios, shared autonomous vehicles (SAVs) are important players, combining the advantages of ride sharing and self-driving [3]. According to some studies, consumers would rather use autonomous vehicles as a service than owning them privately. Such integrated solutions can maximise the social benefits of autonomous technology; studies show that AVs alone have a limited impact on energy consumption and emissions, but when combined with new transport models based on sharing and electric propulsion, significant positive economic, environmental, and social impacts can be achieved [4]. However, to do all this, consumers must trust and be open to new technologies. The success of technological innovations is largely determined by their social acceptance. This is particularly true for autonomous vehicles, where mistrust of novelty can be a serious barrier to widespread adoption. Although experts estimate that self-driving vehicles could prevent up to 60% of road accidents, user intent remains low, with many considering the technology too risky and thus being reluctant to try or use it [3]. In Hungary, the above-mentioned trends have so far been limited; therefore, examining the domestic context is justified from both a scientific and practical point of view. Ride-sharing services are not very widespread in Hungary: the best-known international player, Uber, withdrew from the country in 2016 due to the regulatory environment and market conflicts, and since then similar services have only been available to a limited extent. Currently, mobile applications (e.g., Bolt) and community ride-sharing platforms are mainly based on taxi services, but forms of sharing economy transportation are less prevalent in everyday life than in many other countries. For example, a recent questionnaire survey conducted in the city of Győr found that respondents who already had prior knowledge of autonomous vehicles had a significantly more positive attitude toward them than those who did not [5].

The study aims to examine the extent to which consumers in Hungary are familiar with these technologies and services and their level of acceptance. Our goal is to gain a deeper understanding of the role social acceptance plays in its spread in Hungary and what measures must be taken to introduce and implement a more sustainable transport structure. What is new is that we are linking the two areas in a unique way: we are exploring the relationship between the shared mobility experience and the acceptance of technology. The social significance of the work lies in the fact that the results can help understand the expected reactions of the Hungarian public to the emergence of self-driving technologies, which can provide valuable information for decision-makers, urban planners, and service providers. By mapping user attitudes, we contribute to ensuring that the introduction of autonomous vehicles takes place responsibly, in line with the goals of building social trust and sustainable urban mobility.

2. Theoretical Background

2.1. Acceptance of Autonomous Vehicles in the Context of Sustainable Mobility

Sustainable mobility is receiving increasing attention at the global level, as the transport sector has a significant impact on the environment, particularly in terms of carbon dioxide emissions. The spread of AVs offers a promising solution for sustainable transport, as they can contribute to traffic optimisation, accident reduction, and improved fuel efficiency [6]. However, social acceptance of new technologies is a key factor in realising these potential benefits. The Technology Acceptance Model (TAM) and its subsequent developments [7], such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and the UTAUT2 models, play an important role in research on the acceptance of technological innovations [8]. These models typically identify five key factors that influence acceptance intention: perceived usefulness, perceived ease of use, trust, social influence, and behavioural intention. Numerous empirical studies have confirmed that the acceptance of AV technology is primarily determined by perceived usefulness and ease of use [9]. Users are more likely to accept a new technology if they perceive it to be useful and easy to learn. However, trust is a particularly critical factor in the case of AVs, as users must relinquish control to an automated system, which can lead to increased perceived risk [10]. This is supported by the research of Choi and Ji (2015) [11], who extended the TAM with a trust dimension and found that higher levels of trust positively influence perceived usefulness and intention to use. Further studies have also shown that social influence, that is, the opinions and behaviour of others, can also be a powerful driver of the adoption of AV technology [12]. If the environment shows a supportive attitude, this also has a positive effect on individual decision-making. At the same time, studies have shown that sustainability attitudes (e.g., environmental awareness, sensitivity to low emissions) also play a role in AV acceptance. Those who are committed to environmental protection are more likely to see self-driving vehicles as part of a sustainable future [13]. In addition, individual psychological factors such as innovation, optimism, and risk tolerance can also influence the acceptance of technological innovations.

2.2. The Role of Ride-Sharing Experience and Technology Acceptance Models

Ride-sharing has become a defining feature of urban transport today and could serve as a precursor to the acceptance of self-driving vehicles, especially shared autonomous vehicles (SAVs). Several studies have shown that positive experiences with ride-sharing services promote openness toward self-driving vehicles [14]. Those who regularly use apps such as Uber or Bolt are already accustomed to ordering rides digitally, travelling with unknown drivers and rating the experience, and are therefore more confident about future automated solutions [15]. TAM/UTAUT models are particularly well suited to examine the impact of ride-sharing experiences. Regular use of ride-sharing services increases perceived usefulness and ease of use of AVs, as users are already accustomed to organising their trips via digital platforms. Furthermore, concerns about safety and data privacy are reduced, increasing trust in the technology [3]. This is particularly important because trust, especially in system safety and algorithm reliability, remains one of the main barriers to AV acceptance. According to marketing research, ride-sharing experiences not only have a positive impact on individual TAM dimensions, but also reinforce purchase/usage intentions [16]. Users who have already accepted the logic of the sharing economy are more willing to try new technologies and are more receptive to solutions that offer convenience, efficiency and environmentally friendly operation. Therefore, TAM and UTAUT-based models are not only suitable for predicting the acceptance of AVs in the context of ride-sharing but can also be used to develop targeted marketing and communication strategies. For example, emphasised benefits (e.g., accident prevention), ease of use (e.g., intuitive control system), social acceptance (e.g., opinions of friends and family), and trust-building messages (e.g., data protection, system security) can strengthen user acceptance, especially among those who have already had positive experiences with ride-sharing services.

3. Materials and Methods

A questionnaire survey was conducted in Hungary in 2024 with 2000 respondents to examine the general attitude of Hungarian consumers towards ride sharing. The questionnaire is representative of the Hungarian population, so the results can be generalised to this area. This study analyses questions/statements related to the acceptance of autonomous vehicles and general attitudes toward ride sharing, but the questionnaire itself contained several other questions and covered other topics as well. IBM SPSS Statistics 29.0 statistical analysis software was used to analyse the data.

Demographic Data of the Respondents

Table 1. Demographic Characteristics of the Respondents.

Demography	Proportion
Gender
Male	50%
Female	50%
Age
18–25	14%
26–35	20%
36–45	23%
46–55	25%
56–65	18%
Education
Primary school	15%
High school without high school diploma	23%
High school diploma with high school diploma	36%
University	25%

shows the demographic characteristics of the sample of 2000 people. The gender ratio was equal, 50–50%. In terms of age, only adults were surveyed, so the sample consists of people over the age of 18, with the upper age limit being the retirement age of 65 in Hungary. The third demographic factor is educational attainment, with high school graduates making up the largest proportion of the sample at 36%. Twenty-five percent have a university degree and 23% have some form of vocational qualification.

4. Results and Discussion

The present study aimed to explore how Hungarian consumers perceive and relate to emerging mobility solutions, particularly ride-sharing services and autonomous vehicles. The results are presented in two sections: awareness and general openness toward ride-sharing services; attitudinal factors influencing the acceptance of AVs.

4.1. Ride-Sharing Attitude

The primary objective of this study was to examine Hungarian consumers’ attitudes towards ride-sharing services. In addition to being less environmentally burdensome than travelling by private car, ride-sharing also contributes to mobility. For this reason, our aim was to assess general awareness and attitudes towards ride sharing.

Figure 1 shows the percentage distribution of awareness of ride-sharing. The largest proportion, more than half of the participants (55%), have heard of this type of service but have never used it. Thirty-one percent of the sample had never heard of it, which is probably due to the fact that such services are not available in smaller villages and towns. In Hungary, ride-sharing is most accessible to the population of the capital, Budapest. Only 14% of the participants had used a ride-sharing service, which is relatively low.

Figure 2 shows how often the respondents used ride-sharing services in the past year. The data clearly show that the majority, 37.85% of respondents, did not use these services at all during the period in question. On the contrary, 16.32% tried the service at least once in the past year, while 13.54% used it 1–2 times every six months. More regular use is less common: 9.03% monthly, 8.68% every two weeks, 7.29% every two months, and only 3.82% weekly used ride-sharing solutions. Overall, although the availability and awareness of the service are gradually increasing in Hungary, regular use remains limited to a minority. However, the rate of occasional use is noteworthy, indicating openness to the technology and potential for future acceptance.

4.2. Acceptance of Autonomous Vehicles

In addition to ride-sharing, AVs can also contribute significantly to increased mobility in transportation. AVs enable people who are unable to drive for any reason (e.g., illness) to travel to more distant locations. This section describes the extent to which the factors examined contribute to the acceptance of AVs and whether there is a correlation between attitudes toward ride-sharing and attitudes toward AVs as drivers of urban mobility.

Table 2. Correlation Matrix of Technology Acceptance Dimensions Related to Autonomous Vehicles.

		PU_MEAN	PEU_MEAN	PT_MEAN	SI_MEAN	BIU_MEAN
PU_MEAN	Pearson Correlation	1
	Sig. (2-tailed)
	N	1859
PEU_MEAN	Pearson Correlation	0.635 **	1
	Sig. (2-tailed)	<0.001
	N	1793	1813
PT_MEAN	Pearson Correlation	−0.404 **	−0.282 **	1
	Sig. (2-tailed)	<0.001	<0.001
	N	1830	1787	1882
SI_MEAN	Pearson Correlation	0.690 **	0.480 **	−0.228 **	1
	Sig. (2-tailed)	<0.001	<0.001	<0.001
	N	1779	1737	1805	1820
BIU_MEAN	Pearson Correlation	0.753 **	0.563 **	−0.354 **	0.716 **	1
	Sig. (2-tailed)	<0.001	<0.001	<0.001	<0.001
	N	1739	1698	1762	1726	1767

** The correlation is significant at the 0.01 level (2-tailed).

shows the correlation analysis of the five dimensions examined in relation to AV acceptance.

The purpose of the research was to examine the psychological factors that determine the acceptance of AVs, with a particular focus on how these factors can contribute to the spread of sustainable forms of mobility in the future. Within this framework, we performed a correlation analysis between the dimensions of perceived usefulness (PU), perceived ease of use (PEU), perceived trust (PT), social influence (SI), and usage intention (BIU). Based on the analysis results, there is a very strong positive correlation between perceived usefulness and intention to use (r = 0.753; p < 0.001), indicating that the more individuals consider AVs useful for their own transportation needs, the more likely they are to plan to use them in the future. There is also a strong positive correlation between social influence and intention to use (r = 0.716; p < 0.001), i.e., individuals who consider the opinions of others important and feel that their environment accepts or supports the use of autonomous vehicles are also more likely to be open to using them. Perception of ease of use is moderately positively correlated with both dimensions of usefulness (r = 0.635; p < 0.001) and intention to use (r = 0.563; p < 0.001) dimensions. This suggests that those who believe that AVs are easy to learn to operate not only find them more useful but also show a greater willingness to use them. The results also confirm the well-known correlations of technology acceptance models (e.g., TAM). Perceived trust, which consists of items with opposite orientations, shows a negative correlation with the other dimensions examined. The strongest negative relationship is observed with the intention to use (r = −0.354; p < 0.001), which means that those who are more concerned about the safety, security of the system, or data protection of autonomous vehicles are less inclined to use them. A similarly negative, albeit weaker, correlation can be observed with perceived usefulness (r = −0.404; p < 0.001) and perceived ease of use (r = −0.282; p < 0.001), meaning that lack of trust reduces positive perceptions in general.

Table 3. Relationship between future use of ride-sharing services and intention to purchase AV.

		F01b_7
D08	Pearson Correlation	0.125 *
	Sig. (2-tailed)	0.034
	N	288

* Correlation is significant at the 0.05 level (2-tailed).

shows the results of a correlation analysis that focusses on the likelihood of using ride-sharing services in the future (variable: D08) and the intention to purchase Avs (F01b_7). The Pearson correlation coefficient allows to evaluate the linear relationship between two variables measured on an ordinal or interval scale. The correlation coefficient obtained was r = 0.125, which can be considered a weak positive relationship according to the interpretation framework in the literature. However, the relationship proved to be statistically significant at the significance level (p = 0.034), indicating that the observed correlation cannot be considered random. Based on the interpretation of the results, it can be said that respondents who are more likely to use ride-sharing services in the future also show a slightly higher willingness to purchase or try autonomous vehicles. This correlation supports the assumption that experience with sharing-based modes of transport and plans to use them in the future may have a positive impact on openness to new, technology-intensive transport solutions. However, the weak correlation also points to the fact that other factors, such as trust in technology, social influence, or personal competence, are likely to have a stronger influence on the acceptance of autonomous vehicles.

5. Conclusions

The aim of the study was to explore the experiences of Hungarian consumers with ride-sharing services and how these relate to the acceptance of AVs in the context of sustainable mobility. The research was based on a nationally representative questionnaire survey of 2000 people, in which we performed a quantitative analysis using SPSS software, based on the main dimensions of technology acceptance models (TAM and UTAUT), i.e., perceived usefulness, perceived ease of use, trust, social influence, and intention to use. The results show that the vast majority of respondents have not yet used a ride-sharing service, but awareness of the technology is growing. The intention of using ride sharing in the future is weak but significantly positively correlated with the intention of purchasing autonomous vehicles (r = 0.125; p = 0.034), suggesting that positive attitudes towards sharing-based mobility may also facilitate the acceptance of new technologies. According to the technology acceptance model, the intention to use autonomous vehicles was most strongly correlated with perceived usefulness (r = 0.753) and social influence (r = 0.716), while the lack of trust showed a negative correlation with all acceptance dimensions.

The novelty of the research lies in its unique combination of experiences related to ride sharing and the acceptance of autonomous vehicles in Hungary. Previous domestic research has not examined these two areas together, so this study fills an important empirical gap. However, the research has several limitations. On the one hand, the self-reported questionnaire methodology may distort the objectivity of the data, and on the other hand, attitudes towards AVs were largely assessed on theoretical grounds rather than through real-life experiences. Furthermore, due to its cross-sectional nature, causal conclusions can be drawn. The practical implications of the research are significant at several levels. An important message for service providers is that users’ acceptance of AVs strongly depends on perceived usefulness and social reinforcement, so marketing and communication strategies should focus on these aspects. For policymakers, the research highlights that encouraging the social acceptance of sharing-based mobility and autonomous technologies can facilitate the development of sustainable transport systems. Increasing social trust and experience is an essential step towards the future integration of AV technology.