Central Europe: Application of the Unified Theory of Acceptance and Use of Technology Framework

Introduction

In the last decade ride-hailing apps have gained huge popularity. Since then, the global ride-hailing market has become an integrated ecosystem with many players from all over the country and regions.

In particular, the number of ride-hailing apps incorporating traditional taxi-booking options has been increasing, blurring the distinction between conventional taxi services and modern ride-hailing platforms and enabling taxi drivers to operate through these platforms.

The integration makes it easier for passengers to book rides and traditional taxis using the same app, thereby widening user choice and convenience.

The aim of this study is to explore the key factors influencing Gen Z’s adoption and use of ride-hailing services in Central Europe, addressing a significant research gap where regional studies applying the latest version of the Unified Theory of Acceptance and Use of Technology UTAUT ( 1 ) remain absent. Using PLS-SEM, the research analyzes data from Slovak and Czech respondents collected between May and September 2024. By examining behavioral drivers and comparing cross-country differences, this study provides novel insights into ride-hailing adoption among young users, offering practical implications for service providers in the region.

Generation Z (Gen Z), typically defined as individuals born between the late 1990s and early 2010s, represents a dominant user group for digital services, particularly Mobility-as-a-Service (MaaS) and ride-hailing platforms—because of their early and frequent adoption of mobile technologies and strong preference for flexible, on-demand solutions ( 2 ). Unlike previous generations, Gen Z is more likely to delay car ownership and instead rely on shared transportation models that align with their values of convenience, connectivity, and sustainability ( 3 ). As their purchasing power increases and their influence on peer behaviors strengthens through social media and digital communication, understanding their technology adoption patterns is essential for forecasting the evolution of ride-hailing markets ( 4 ).

The adoption of ride-hailing services has been extensively researched in recent years, with various factors influencing its uptake. Studies have explored the influence of socio-demographic and locational characteristics, as well as the availability of intermediate public transport modes, on ride-hailing adoption ( 5 ). Loa et al. (2021) were interested in the influence of land use and transit network attributes on Gen Z and the relationship between that and the demand for public transit and ride-hailing services, providing a comprehensive understanding of their interplay ( 6 ). Moreover, Wang et al. have indicated in their research that ride-hailing can serve as a viable alternative to driving. Participants who used more ride-hailing tended to drive less, and longitudinally, an increase in ride-hailing usage was associated with fewer driving trips ( 7 ). Gomez et al. (2021) stated that young, well-educated and wealthy people who are familiar with new technologies will be more likely to use ride sharing services. Their research, conducted in Madrid, suggested that environmental consciousness plays an important role in the frequency of use of ride-hailing services ( 8 ).

Adoption and frequency of use of ride-hailing services have also been linked to socio-demographic and land use factors by Sikder (2019). These services are more likely to be adopted and used by people who work full time but on flexible schedules than by other workers. Individuals in households with insufficient vehicles, that is households with more workers than vehicles, are more likely to adopt and use these services than other individuals ( 9 ). Ling et al. (2023) examined key factors more from the personal point of view. Study showed that the most important factors in determining how much passengers use ride-hailing services is trust and loyalty. However, the frequency of use of ride-hailing was not significantly influenced by passengers’ perception of the Covid-19 pandemic ( 4 ).

A spirit of innovation has characterized the ride-hailing business. The promise of future improvements in services and sustainability is based on continued technological progress, for example AI driven routing and the adoption of electric cars ( 10 ). The model of ride-hailing has been successfully adapted by local and regional players to suit the diverse needs and regulatory environments in individual markets ( 9 ).

Not surprisingly, previous research has been mostly devoted to highly populated urban areas where the best known ride hailing companies are most represented. Higher density regions are also likely to have higher usage of ride-hailing within trip chains ( 11 ). The following overview summarizes selected studies dedicated to ride-hailing adoption in the last five years (2019–2024). Researchers examined determinates of acceptance in the following highly populated cities/regions across the world:

Given their high adoption of digital technologies and app-based services, Generation Z is likely to represent an important segment of users of these platforms. This generation plays a crucial role in the success of ride-hailing services given their unique characteristics and influence as digital natives. This cohort, born between the late 1990s and early 2010s, is highly tech-savvy, comfortable with mobile applications, and values convenience and speed in their daily lives ( 2 ). They are also early adopters of new technologies and often set trends that influence other demographics ( 13 ). Their reliance on shared and on-demand services aligns with the core value propositions of ride-hailing platforms, such as cost-effectiveness and environmental sustainability ( 14 ). Moreover, as they engage heavily on social media, their opinions and experiences can significantly shape public perceptions and drive adoption rates among peers and other generations, making them a critical demographic for ride-hailing companies to target and understand ( 15 ).

While previous studies have successfully explored ride-hailing adoption in various metropolitan areas across North America, Asia, and Western Europe ( 16 – 19 ), they often suffer from three major limitations. First, most are geographically concentrated in high-density, well-connected urban environments, making their findings less transferable to the Central European context, where mobility infrastructure and public transport integration differ significantly ( 20 ). Second, these studies primarily focus on the general adult population, with limited segmentation by age cohorts such as Generation Z, despite this group’s growing influence on mobility trends ( 21 ).

Third, although UTAUT and its extensions have been widely applied, few studies incorporate the most recent meta-UTAUT framework, which allows for a richer understanding of adoption behavior by integrating constructs like compatibility, personal innovativeness, and user education. By leveraging its constructs, the research captures the nuances of technological adoption among this demographic, highlighting both the opportunities and challenges for ride-hailing services aiming to cater to their preferences. The subsequent sections build on this foundation, detailing the methodological steps taken to ensure rigor and reliability while aligning with the unique characteristics and behaviors of this pivotal generational cohort. This approach bridges the theoretical background and the practical implications of the findings, setting the stage for meaningful insights and actionable recommendations.

Methodology

The research was conducted in five steps.

Step I: Definition of Research Method

The UTAUT is one of the most widely used theories in technology adoption research because of its comprehensive framework and adaptability across various contexts. It has been cited extensively in academic literature, with the original paper ( 22 ) accumulating over 36,000 citations on Google Scholar and the UTAUT 2 paper ( 23 ) surpassing 9,000 citations. Its popularity stems from its ability to integrate key constructs from multiple earlier models into a unified theory that explains user intentions and behaviors effectively.

Furthermore, the authors have continually updated the theory to keep it relevant, introducing UTAUT 2 in 2012 for consumer contexts and encouraging its application in emerging areas such as cultural studies and new technology types ( 23 ). This ongoing refinement ensures that the UTAUT remains a robust and evolving tool for understanding technology adoption in both academic and practical settings. Building on this foundation, the meta-UTAUT, published in 2022, incorporates additional predictors and moderating mechanisms drawn from a broad meta-analysis of technology adoption studies, making it a more comprehensive and flexible framework.

A revised meta-UTAUT framework extends the original theory by incorporating new endogenous mechanisms, such as technology compatibility, user education, personal innovativeness, and technology costs ( 1 ). The evolution of UTAUT theory is shown in Figure 1.

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Figure 1.

UTAUT development.

The UTAUT framework is highly applicable for examining the adoption of services like Uber or Bolt among Gen Z because of its ability to integrate various theoretical perspectives and adapt to diverse technological contexts. Its robust structure enables a nuanced analysis of the generational characteristics and external factors influencing the acceptance of innovative digital platforms.

While the original version of UTAUT has been applied to study ride-hailing services ( 16 , 24 , 25 ), the extended meta-UTAUT framework has not yet been utilized in this context, highlighting a significant research gap that offers opportunities for further exploration.

Step II: Definition of Research Model and Hypothesis Development

The development of a research model, rooted in the meta-UTAUT framework, provides the comprehensive theoretical underpinning, incorporating its core constructs to capture the multidimensional factors influencing user behavior and technology acceptance. However, given the context of ride-hailing applications, which are typically offered free of direct technological costs to users, the construct of technological costs was excluded. This adjustment ensures that the model remains contextually relevant while maintaining theoretical rigor, offering a tailored approach to understanding user adoption in this specific domain. The following is an overview of main factors with a brief explanation in the ride-hailing context:

Performance expectancy (PE) is a central construct in the model, representing the degree to which users believe that ride-hailing services will enhance their ability to complete transportation-related tasks efficiently ( 26 ). This construct consistently emerges as a strong predictor of behavioral intention, as users prioritize convenience, time savings, and reliability not only in transportation services ( 27 ).

The perceived ease of use of ride-hailing applications is captured in effort expectancy (EE). Platforms that offer intuitive interfaces, simple booking processes, and seamless navigation are more likely to be adopted ( 28 ). This factor is especially significant during initial interactions with the service, as users evaluate the app’s usability and the overall learning curve ( 29 ).

Social influence (SI) refers to the extent to which users perceive that their peers, family, or societal norms encourage the use of selected services ( 30 ). This construct is particularly relevant in urban settings or among younger people like Gen Z, where recommendations and social validation significantly shape behavioral intentions ( 28 ).

Facilitating conditions (FC) encompass the external resources and infrastructure that support the use of ride-hailing services ( 22 ). These include reliable internet access, mobile device compatibility, availability of payment methods, and access to customer support ( 16 ). Such factors play a critical role in enabling users to overcome potential technical or logistical barriers.

The enjoyment or satisfaction users derive from using ride-hailing apps is reflected in hedonic motivation (HM) ( 27 ). Beyond utility, features like in-app entertainment, gamified loyalty programs, or a premium ride experience can enhance user adoption by providing intrinsic enjoyment during the process ( 31 ).

Price value (PV) examines the perceived balance between the cost of using ride-hailing services and the benefits they provide. Affordable pricing models, promotional discounts, and transparent fare structures can positively influence user adoption by ensuring that the perceived benefits outweigh the costs ( 26 ).

Habit (H) refers to the extent to which users automatically rely on ride-hailing services based on past behavior ( 23 ). Regular users who have integrated these services into their daily routines, such as commuting or traveling to social events, are more likely to exhibit sustained usage ( 25 ).

In the context of ride-hailing, compatibility (CT) refers to the alignment of the application with users’ existing technological ecosystems, such as compatibility with various smartphone operating systems, seamless integration with other apps (e.g., navigation or payment platforms), and adaptability to diverse hardware capabilities ( 1 ). Technologies that fit smoothly into users’ current devices and digital environments are more likely to be adopted, as they reduce the friction associated with technological mismatches ( 32 ). It is based on the diffusion of innovations theory that early adopters or tech-savvy individuals are more likely to embrace services because of their openness to innovation and novel experiences ( 33 ). For Gen Z, compatibility is particularly salient, as this cohort relies heavily on interconnected digital services and expects new applications to function seamlessly within established app ecosystems ( 34 ). In such contexts, perceived compatibility may outweigh other innovation attributes, as adoption decisions are driven less by experimentation and more by the perceived fit with everyday digital routines.

Unlike compatibility, which reflects how well a specific service fits into users’ existing digital routines and technological environment, personal innovativeness (PI) captures a broader individual tendency to experiment with and adopt new technologies. In the ride hailing context, personal innovativeness captures an individual’s willingness to adopt and experiment with new transportation solutions ( 1 ). This trait is particularly significant for Gen Z, as they are often at the forefront of adopting emerging technologies and services. Their openness to innovation can drive the success of novel ride-hailing features, such as eco-friendly options, subscription models, or integrated mobility solutions ( 15 ).

User education (E) in the context of ride-hailing services refers to the digital literacy of users, which influences their ability to understand and navigate the application. Higher levels of education or familiarity with digital technologies can lower perceived effort, enhance trust in the platform, and facilitate faster adoption ( 35 ). This factor is particularly relevant in regions with varying levels of digital infrastructure and educational attainment ( 36 ).

Behavioral intention (BI) reflects the degree to which users are inclined to adopt and use ride-hailing services in the future. A strong behavioral intention indicates a higher likelihood of transitioning from initial interest to actual usage, making it a critical predictor in understanding adoption patterns ( 23 ).

Finally, behavioral use (BU) represents the actual engagement with ride-hailing services, encompassing the frequency and consistency of use ( 22 ). This construct captures how users integrate the service into their daily routines, influenced by both initial adoption drivers and sustained satisfaction with the platform ( 37 ).

Complete research model showing all constructs and the number of measurement indicators assigned to each construct is shown in Figure 2.

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Figure 2.

Research model with indicators (yellow).

Table 1 provides an overview of hypotheses derived from the model based on the constructs provided:

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Table 1.

Hypothesis Overview

Hypothesis	Statement
H1	Performance expectancy positively influences the intention to use ride-hailing services.
H2	Effort expectancy positively influences the intention to use ride-hailing services.
H3	Social influence positively influences the intention to use ride-hailing services.
H4	Facilitating conditions positively influence the actual use of ride-hailing services.
H5	Hedonic motivation positively influences the intention to use ride-hailing services.
H6	Price value positively influences the intention to use ride-hailing services.
H7	Habit positively influences the intention to use ride-hailing services.
H8	Habit positively influences the actual use of ride-hailing services.
H9	Compatibility positively influences the intention to use ride-hailing services.
H10	Education level positively influences the intention to use ride-hailing services.
H11	Facilitating conditions positively influence the intention to use ride-hailing services.
H12	Compatibility positively influences the actual use of ride-hailing services.
H13	Education level positively influences the actual use of ride-hailing services.
H14	Personal innovativeness positively influences the actual use of ride-hailing services.
H15	Personal innovativeness positively influences the intention to use ride-hailing services.
H16	The intention to use positively influences the actual use of ride-hailing services.

Step III: Data Collection and Sample Profile

The data collection process was conducted at the Technical University of Liberec and through partner institutions in the Czech Republic and Slovakia, with a deliberate focus on selecting members of Gen Z. This targeted approach ensured that the study captured insights from individuals born between 1997 and 2012, aligning with the research objectives to explore demographic and social characteristics within this generational cohort, specifically in relation to ride-hailing services.

Informed consent was obtained from all participants before their participation in the study. Participants were fully informed about the purpose of the research, the voluntary nature of their involvement, and their right to withdraw at any time without consequence. The full text of the consent reads as follows:

I have read and understood the information provided about this study. I am aware of the voluntary nature of my participation and my right to withdraw at any time without penalty. I understand that my responses will remain anonymous, and no personally identifiable information will be collected. I agree to participate in this study and provide my consent to the use of my responses for research purposes.

The minimum sample size was determined using the 10X rule, which requires at least ten observations per indicator or path in the model. In our case, with 16 paths in the model, this threshold was exceeded, demonstrating the ambitious nature of the study while ensuring robust data for analysis. Specifically, the sample sizes of 231 in the Czech Republic and 249 in Slovakia surpass the minimum requirement, significantly enhancing the reliability and validity of the study’s findings.

The 10X rule is often recommended for determining the optimal sample size in studies involving similar models, particularly to ensure valid interpretation of data. While this is a rule of thumb based on experience rather than precise measurement, it remains a topic of discussion among researchers.

Despite its limitations—such as not accounting for effect size, reliability, or the number of indicators (38)—it is still recommended by Hair et al. (2011) as a rough estimate for minimum sample size (39). Peng and Lai (2012) further emphasize its use only under conditions of significant effect strength and high reliability in measured items (40).

To support the appropriate calculation of sample size, an a priori power analysis was subsequently conducted assuming a medium effect size (f² = 0.15), α = 0.05, power = 0.80, and 11 predictors, indicating that approximately 123 respondents would be sufficient. Both national samples exceeded this threshold, suggesting that the study was appropriately planned.

A post-hoc power analysis was also performed based on the actual model complexity and observed effect sizes. The high number of predictors and the relatively small effect sizes resulted in statistical power below 0.40 in both samples. This result is not uncommon in exploratory studies and reflects the practical limitations of real-world data. Table 2 presents demographics for both research samples.

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Table 2.

Demographics

Demographics	Slovakia	Czech Republic
GEN Z (1997–2012)	249	231
Gender
Female	152	121
Male	93	107
Other/no answer	4	3
Population of respondent's place of residence
Up to 10 000 inhabitants	75	55
Up to 50 000 inhabitants	68	57
Up to 100 000 inhabitants	75	35
More than 100 000 inhabitants	15	84
Education
High school	165	179
Bachelor degree	82	32
Master degree	2	20

The final sample included 480 Gen Z respondents—249 from Slovakia and 231 from the Czech Republic. Gender distribution was balanced across both countries. In relation to the population of respondent's place of residence, Slovak participants were evenly spread across smaller towns, while Czech respondents were concentrated in cities over 100,000 inhabitants, suggesting a stronger urban orientation. Educational levels showed a similar structure in both samples, with high school students and graduates forming the largest group. While the Czech sample contained a higher proportion of respondents with a master's degree, the Slovak sample included a larger share of respondents with university education overall.

These demographic differences are relevant for interpreting behavioral intentions, particularly in relation to access to urban mobility services and digital readiness, which are often shaped by educational background and place of residence.

Results

The structural model, applied in both countries, was evaluated after confirming that the design measures were both reliable and valid (see Appendix A). Key aspects of the model, including path coefficients, their statistical significance, and relevance, were analyzed in detail. Figure 3 presents the standardized path coefficients (β) on the arrows and the R² values inside the constructs, valid for the Czech sample. This provides a clear visualization of the model’s explanatory power and the strength and direction of relationships.

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Figure 3.

Assessed structural mode (Czech Republic).

The R² values indicate the model’s strong explanatory power. Behavioral intention has an R² of 0.779, meaning 77.9% of its variance is explained by the predictors, highlighting their significant influence on the intention to use ride-hailing services. Behavioral use has an R² of 0.581, indicating that 58.1% of its variance is explained by behavioral intention and other factors, showing a moderate to high ability to predict actual usage behavior. These results confirm the model’s effectiveness in capturing key determinants of ride-hailing service adoption.

Table 3 summarizes the t-statistics and p-values for all hypothesized relationships, reflecting their significance levels for the Czech sample. Performance expectancy (t = 2.359, p < 0.05), effort expectancy (t = 2.187, p < 0.05), social influence (t = 4.205, p < 0.05), habit (t = 5.703, p < 0.05), and education (t = 3.151, p < 0.05) demonstrated significant positive effects on behavioral intention, supporting these hypotheses. Similarly, behavioral intention (t = 6.150, p < 0.05) and habit (t = 2.445, p < 0.05) significantly influenced behavioral use, supporting these relationships.

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Table 3.

Hypothesis testing (Czech Republic)

Hypothesis	t statistics	p-values	Result
PE -> BI	2.359	0.018	Supported
EE -> BI	2.187	0.029	Supported
SI -> BI	4.205	0.000	Supported
PV -> BI	0.874	0.382	Rejected
HM -> BI	0.420	0.674	Rejected
FC -> BI	0.448	0.654	Rejected
H -> BI	5.703	0.000	Supported
CT -> BI	1.747	0.081	Rejected
E -> BI	3.151	0.002	Supported
PI -> BI	0.874	0.382	Rejected
BI -> BU	6.150	0.000	Supported
FC -> BU	0.889	0.374	Rejected
H -> BU	2.445	0.015	Supported
CT -> BU	1.111	0.267	Rejected
E -> BU	0.160	0.873	Rejected
PI -> BU	1.082	0.279	Rejected

Note: PE = performance expectancy; EE = effort expectancy; SI = social influence; PV = price value; HM = hedonic motivation; FC = facilitating conditions; H = habit; CT = compatibility; E = education; PI = personal innovativeness; BI = behavioral intention; BU = behavioural use.

Conversely, price value, hedonic motivation, facilitating conditions and compatibility did not show statistically significant effects on behavioral intention or behavioral use, as their p-values exceeded 0.05. These results highlight that only select factors play a significant role in shaping behavioral intention and actual use in the context of ride-hailing services.

Figure 4 presents the standardized path coefficients (β) and R² values for the Slovak sample, shown using the same visual format.

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Figure 4.

Assessed structural mode (Slovakia).

The R² values in the Slovak sample reveal notable insights into the model’s explanatory strength. An R² value of 0.712 for behavioral intention indicates a high explanatory power, suggesting that 71.2% of the variance in behavioral intention is accounted for by the independent variables in the model. This signifies that the model is effective in identifying key predictors of behavioral intention within the Slovak context. The R² value for behavioral use, at 0.466, shows moderate explanatory power, with 46.6% of the variance explained. While lower than behavioral intention, it still reflects a satisfactory level of prediction for actual usage behaviors.

The outcomes for the Slovak sample (see Table 4) indicate that performance expectancy (t = 3.409, p = 0.001), social influence (t = 2.554, p = 0.011), habit (t = 3.829, p = 0.000), compatibility (t = 2.014, p = 0.044), and personal innovativeness (t = 4.884, p = 0.000) positively influenced behavioral intention. For behavioral use, habit (t = 5.304, p = 0.000) was identified as the only significant factor. The results emphasize that in the Slovak context, behavioral intention and actual use are primarily shaped by habitual usage patterns and selected motivational and social factors.

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Table 4.

Hypothesis testing (Slovakia)

Hypothesis	t statistics	p-values	Result
PE -> BI	3.409	0.001	Supported
EE -> BI	1.333	0.183	Rejected
SI -> BI	2.554	0.011	Supported
PV -> BI	1.515	0.130	Rejected
HM -> BI	0.112	0.911	Rejected
FC -> BI	0.454	0.650	Rejected
H -> BI	3.829	0.000	Supported
CT -> BI	2.014	0.044	Supported
E -> BI	0.414	0.679	Rejected
PI -> BI	4.884	0.000	Supported
BI -> BU	1.571	0.116	Rejected
FC -> BU	1.323	0.186	Rejected
H -> BU	5.304	0.000	Supported
CT -> BU	1.040	0.298	Rejected
E -> BU	0.753	0.452	Rejected
PI -> BU	1.050	0.294	Rejected

Note: PE = performance expectancy; EE = effort expectancy; SI = social influence; PV = price value; HM = hedonic motivation; FC = facilitating conditions; H = habit; CT = compatibility; E = education; PI = personal innovativeness; BI = behavioral intention; BU = behavioural use.

Discussion

The findings of this study cannot be directly compared with research specifically conducted in the Czech Republic and Slovakia, as no such studies currently exist. This absence highlights a significant research gap within the regional context. Thus, the results will be evaluated in relation to existing international studies.

The comparison of the Slovak and Czech samples reveals both commonalities and differences in the adoption of ride-hailing services among Gen Z, based on the hypothesized constructs. Across both countries, performance expectancy, social influence, and habit positively influenced the intention to use ride-hailing services. These findings indicate that Gen Z values the efficiency and functionality of these services, perceiving ride-hailing as practical and time-saving—a conclusion that aligns with Dias et al., who noted that performance expectations evolve with socio-demographic shifts and technological advancements ( 47 ).

Recommendations from peers and established habitual behavior are also important factors influencing the adoption of ride-hailing services. Similarly, Idug et al. explored how social identity factors, such as ethnicity, can affect user satisfaction and trust in ride-hailing services, suggesting that social dynamics are integral to user experiences and perceptions ( 48 ).

In our study, social influence showed a moderate effect overall, but it was slightly stronger in the Czech sample. This may indicate that Czech Gen Z users are more responsive to peer approval or social norms in the context of mobility adoption, where Slovak users rely more on individual preferences or practical considerations.

Habit showed significant influence in both countries, indicating that repeated use and routine integration of ride-hailing apps play a central role in shaping behavioral intention. As users become more familiar with ride-hailing platforms, their likelihood of continued use increases, reinforcing habitual engagement. Studies have shown that as users become more familiar with ride-hailing platforms, their likelihood of continued use increases ( 49 , 50 ).

Beyond habit, individual-level traits also played a role—particularly in Slovakia, where compatibility and personal innovativeness significantly shaped behavioral intention. These differences may reflect cultural variations, where Slovaks may place greater emphasis on how well the services align with their existing needs and personal openness to new technologies ( 51 ).

In contrast, in the Czech sample, education significantly influenced behavioral intention but not actual behavior. One possible interpretation is that while higher education may increase awareness and openness toward new mobility options, it does not necessarily translate into habitual use—potentially because of competing preferences, infrastructure constraints, or cost considerations. In Slovakia, compatibility and personal innovativeness showed strong influence on behavioral intention, but only limited impact on actual use. This discrepancy could indicate that while users perceive ride-hailing services as suitable and interesting, external factors such as availability, service coverage, or financial constraints may hinder regular usage.

The observed differences could be attributed to several factors, including the maturity and availability of ride-hailing services in each country, economic conditions, and cultural attitudes toward convenience and technology adoption ( 26 ). For instance, Slovakia’s smaller urban centers and different transportation infrastructure might account for the heightened importance of compatibility and education ( 52 ).

In contrast, the relationship between behavioral intention and actual use differs across the two countries. In the Slovak sample, behavioral intention showed a weaker and statistically non-significant relationship with actual use, suggesting that expressed intention does not always translate directly into behavior in this context. In contrast, in the Czech sample, this relationship was strong and statistically significant, indicating that behavioral intention translates more directly into actual use. Czech users, particularly in larger cities, may benefit from higher service availability and more developed mobility infrastructure, allowing behavioral intention to translate more easily into actual use. This contrast also suggests that intention alone may not predict behavior equally in different national contexts. In general, this finding is consistent with outputs from rural-focused studies, which show that behavioral intentions to adopt demand-responsive transport are strongly influenced by local infrastructure and access conditions ( 53 ). Research by Pešek and Kraft highlights that current travel behavior in metropolitan areas reflects a desire for more efficient transport solutions, which ride-hailing can provide ( 54 ). Taken together, these outputs provide a nuanced understanding of Gen Z’s ride-hailing behavior, revealing shared priorities but also context-specific differences that must be reflected in regional strategy and service design.

Conversely, factors such as price value, hedonic motivation, and facilitating conditions did not significantly affect behavioral intention or behavioral use in either country. These findings are consistent with Gen Z’s status as digital natives, who tend to perceive app-based services as intuitive, low-effort, and fairly priced, reducing the relevance of ease-of-use or enabling factors in their decision-making ( 34 ).

Gen Z users, often early adopters of digital platforms, tend to consider smartphone-based services like ride-hailing or other e-services as intuitive and low-effort by default. As a result, traditional barriers related to ease of use or enabling infrastructure become less relevant. Toyama et al. found similar patterns in studies on Mobility-as-a-Service adoption, where younger users expected seamless integration without the need for external facilitation ( 55 ).

Likewise, factors such as price value or hedonic motivation may hold reduced importance because of shifting consumer priorities among younger users. Djafarova and Bowes observed that Gen Z tends to favor services that align with their identity, values, and daily routines—such as sustainability, convenience, or personalization—over those that simply offer lower cost or entertainment value ( 56 ). This aligns with the results of this study, which indicate that for this cohort, habitual use, functional value, and ecosystem compatibility may be stronger predictors of behavior than traditional utilitarian or emotional drivers.

Overall, the comparison highlights that while Gen Z in both Slovakia and the Czech Republic values functionality, habit, and social influence in ride-hailing adoption, regional differences—such as infrastructure, education, and cultural attitudes—shape how these factors translate into behavior. Slovak users respond more to compatibility and personal innovativeness, while Czech users are more influenced by social norms but less consistent in actual use. These findings highlight the importance of country-specific strategies, reflecting differences in behavioral drivers between the Slovak and Czech samples.

Conclusion

This study sheds light on the behavioral factors influencing Gen Z’s adoption and use of ride-hailing services in Central Europe, utilizing the latest version of UTAUT framework and PLS-SEM methodology. Key findings reveal that performance expectancy, habit and social influence are significant predictors of behavioral intention across both samples. Conversely, factors such as hedonic motivation, price value, and facilitating conditions were not found to significantly affect behavior. The comparison between Slovak and Czech respondents highlighted similarities in behavioral patterns while also emphasizing regional differences in specific factor significance, providing valuable insights for tailoring strategies to local markets.

Gen Z is characterized by its digital fluency and reliance on peer networks, prioritizing services that seamlessly integrate into their routines and align with their social environments. Interestingly, the lack of influence from effort expectancy and price value suggests that these users consider ride-hailing services intuitive and fairly priced, minimizing the importance of ease of use and cost as decision-making factors. These insights highlight the importance of tailoring ride-hailing services to resonate with Gen Z’s expectations of convenience, connectivity, and lifestyle alignment. Attained outputs can assist ride-hailing providers, city planners, and mobility policymakers in aligning services with Gen Z’s behavioral drivers by focusing on routine-based use, digital integration, and peer influence. Recognizing cross-country differences further enables these stakeholders to adapt strategies to specific urban contexts and user expectations.

Despite its contributions, this study has several limitations. The exclusive focus on Gen Z, while offering a focused generational perspective, limits the generalizability of findings to other demographic groups. While the behavioral intention (BI) model shows relatively high explanatory power, as indicated by the R² values, post-hoc statistical power may still vary across individual path relationships depending on the assumed effect size. Consequently, some non-significant effects should be interpreted with caution, as future studies with larger samples could provide additional evidence about these relationships. The regional scope, restricted to Slovakia and the Czech Republic, may not fully capture broader Central European trends. Additionally, although the sample size exceeds the minimum required for PLS-SEM analysis, a larger and more diverse sample could further strengthen the reliability of the results.

The model was based solely on the UTAUT framework, and other potentially relevant constructs drawn from alternative theories—such as trust, privacy concerns, or perceived risk—were not included. These could be considered in future research to capture a more complete picture of ride-hailing behavior.

Future research should expand the demographic scope to include other cohorts, enabling a comparative analysis of behavioral patterns across age groups. Broader geographical coverage could also reveal cross-regional differences and provide a more comprehensive understanding of ride-hailing adoption across Central Europe or other regions. Finally, incorporating longitudinal data could help examine how these behavioral factors evolve over time, especially as ride-hailing services and user expectations continue to develop. By addressing these limitations, future studies can deepen our understanding of the dynamics shaping ride-hailing behavior in diverse contexts. Despite these limitations, the study provides novel comparative evidence on how core behavioral constructs function within Gen Z across two neighboring Central European countries—an area that has remained underexplored in existing literature.

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