A recent study published in the European Transport Research Review identifies six distinct attitudinal profiles among potential users of shared cargo bike systems in Budapest. The research, conducted by Ognjen Bobičić and Domokos Esztergár-Kiss, utilizes survey data from 941 respondents to segment the population based on their openness to adopting this mode of transport. By applying Latent Class Cluster Analysis, the authors aim to understand the heterogeneity of urban travelers’ attitudes, which is essential for designing targeted interventions that promote the uptake of shared cargo bikes in cities where car dependency remains high.
Shared cargo bikes are often viewed as a practical alternative for short-distance urban trips, particularly for last-mile delivery and household travel. However, their adoption across Europe remains limited and highly context-dependent. The study addresses a knowledge gap regarding pre-deployment attitudinal diversity, noting that most previous research has focused on existing users or operational outcomes rather than potential users before a system is introduced. In Budapest, a city with a population of approximately 1.7 million that already offers shared bicycles and e-scooters, shared cargo bikes are not yet available, making the study of pre-deployment attitudes particularly relevant for planning future mobility services.
The survey was administered online in November 2024 by a professional market research company using quota-based sampling to align with the age and gender distribution of the Budapest population. After excluding incomplete or invalid responses, the final dataset comprised 941 valid participants. The sample was nearly evenly split by gender, with 51.01% identifying as women and 48.99% as men. The most represented age group was 40 to 54 years old, accounting for 33.79% of respondents. The sample showed a higher share of university-educated individuals compared to the general population, with 45.38% holding a university degree, while 75.13% of participants were employed.
To measure attitudes, the researchers employed statements derived from the Diffusion of Innovation framework, which links perceived attributes of an innovation to readiness to adopt it. The survey included items reflecting five core constructs: relative advantage, compatibility, trialability, observability, and complexity. An Exploratory Factor Analysis was conducted to extract factors from these statements, resulting in a final solution with four interpretable factors: perceived values, compatibility, trialability, and observability. The relative advantage and ease of use constructs were not retained in the final factor structure due to insufficient representation, a limitation the authors attribute to the difficulty of evaluating efficiency without hands-on experience.
Using the factor scores as inputs, the researchers applied Latent Class Cluster Analysis to identify unobserved subgroups within the sample. The model with six classes was selected based on the Bayesian Information Criterion, which balances model fit and complexity. The six identified clusters are labeled disengaged, sceptics, indifferent, explorers, supporters, and enthusiasts. These profiles differ not only in their attitudinal scores but also in their socio-demographic composition and mobility identities, revealing a complex landscape of potential users rather than a single homogeneous group.
The disengaged group represents the largest share of the sample at 28.19%, followed closely by the sceptics at 25.19%. Both groups express limited perceived relevance of shared cargo bikes, with the disengaged showing strong resistance and the sceptics displaying a cautious or doubtful stance. These clusters are dominated by older respondents and include a high proportion of car drivers and public transport users. The indifferent cluster, accounting for 19.08% of the sample, falls in between, showing neither strong rejection nor clear support. This group is heavily composed of public transport users and has the highest share of respondents without a driver’s license, suggesting a passive position where adoption is unlikely unless clear benefits are demonstrated.
In contrast, the explorers, supporters, and enthusiasts represent the more positive segments, making up 10.00%, 8.92%, and 8.62% of the sample, respectively. The explorers see shared cargo bikes as potentially useful but remain cautious, while the supporters display consistently favorable attitudes and view the service as valuable and relatively easy to adopt. The enthusiasts are the smallest group but the most committed, strongly perceiving shared cargo bikes as compatible, valuable, and visible in everyday life. This group is younger, with a significant concentration of cyclists and multimodal users, aligning with the profile of early adopters who could help normalize the mode through peer-to-peer exposure.
The structural model identified mobility identity as the strongest differentiator across clusters, with cyclists more likely to belong to the positive clusters and car drivers predominantly found in the negative ones. Age also played a significant role, with older respondents concentrated in the disengaged and sceptic profiles, while younger adults were more present in the exploratory and supportive groups. However, the study notes that younger adults are not uniformly enthusiastic, with a sizeable portion falling into the indifferent category. This suggests that uptake may depend on reduced uncertainty and clearer evidence of practical value rather than a simple demographic divide.
The findings indicate that pre-deployment adoption potential is structured in distinct attitudinal profiles rather than a single average willingness. The disengaged and sceptics groups form a majority, implying that a large share of the population may remain resistant to shared cargo bikes. For these groups, messaging alone is unlikely to shift attitudes unless accompanied by changes that make the service feel feasible and relevant. The authors suggest that built environment and safety conditions are especially important, as perceived risk can outweigh functional benefits. In Budapest, this implies that shared cargo bike deployment should be coupled with protected-route continuity and safe junction treatments to reach these groups.
For the indifferent respondents, the most effective lever may be making benefits observable in everyday contexts. The authors suggest locating shared cargo bike facilities near trip generators, such as retail clusters or multimodal transfer points, and using short, structured demonstrations to connect the service to routine errands. For the explorers and supporters, adoption is sensitive to service performance, with availability, coverage, and ease of access being important factors. Designing a system with predictable availability and low-effort rental workflows is crucial to ensure that initial interest translates into actual adoption. The enthusiasts, while a small group, can support uptake through early pilot recruitment and informal promotion, helping to stabilize operations and increase visibility.
The study acknowledges several limitations, including the use of self-reported attitudes in a pre-deployment context, which can introduce hypothetical bias and an attitude-behavior gap. The quota-based online sampling limits the broader representativeness of the sample, and the measurement model required item reduction, leaving some Diffusion of Innovation constructs unretained. The authors note that the study does not provide a quantitative demand estimation and that translating attitudinal segments into expected uptake would require alternative methodological approaches. Future research should validate whether the identified profiles predict actual shared cargo bike uptake once the service becomes available and examine transitions between groups over time.
Despite these limitations, the study provides a replicable framework for planning emerging micromobility services. By combining Diffusion of Innovation-based attitudinal measurement with Latent Class Cluster Analysis, the researchers produced actionable and interpretable profiles that can serve as a baseline for monitoring change. The findings highlight that potential shared cargo bike users in Budapest cannot be treated as a single market, and that effective targeting should rely on aligning interventions with specific attitudinal barriers and readiness levels rather than demographic segmentation alone. This approach supports the design of sustainable mobility transitions by identifying who is ready, who is movable, and who is unlikely to respond without major shifts in perceived fit and value.
Subject of Research: Attitudinal profiles of potential shared cargo bike users in Budapest
Article Title: Attitudinal profiles of potential shared cargo bike users in Budapest
Article References: Bobičić, O., & Esztergár-Kiss, D. (2026). Attitudinal profiles of potential shared cargo bike users in Budapest. European Transport Research Review, 18(1), Article 68. https://doi.org/10.1186/s12544-026-00828-y
Image Credits: AI Generated
DOI: 10.1186/s12544-026-00828-y
Keywords: Attitudinal, profiles, potential, shared, cargo, bike, users, Budapest, scientific research
News Source: Denise Maddox. (November 9, 2026). Budapest Study Maps Six Attitudinal Groups Toward Shared Cargo Bikes. Scienmag.
