# Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers

Philip Fitschen, Katrin Merfeld, Jan Klein, Sven Henkel

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## To cite this version:

Philip Fitschen, Katrin Merfeld, Jan Klein, Sven Henkel. Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers. Transport Policy, 2024, 158, pp.104-111. 10.1016/j.tranpol.2024.09.010. hal-04840228

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Submitted on 21 May 2025

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# Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers

Philip Fitschen<sup>a</sup>, Katrin Merfeld<sup>b,a,*</sup>, Jan F. Klein<sup>c</sup>, Sven Henkel<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Urban transport

Transport policy

Shared mobility

Travel mode

Travel cost

## ABSTRACT

Reducing urban car traffic has become a global policy objective. A vital element in achieving this objective is the availability of shared mobility providers offering sustainable alternative travel modes such as micromobility, carsharing, and ridehailing. Despite policy and industry efforts, shared mobility options have failed to change the travel patterns of current car users. We explore this urban mobility challenge through an empirical study of 807 urban travelers. Our results show that car drivers estimate travel costs conceptually rather than rationally and consider both trip purpose and length. In particular, car users perceive longer and recreational trips as cheaper per distance driven than shorter and work-related trips. A segmentation analysis reveals that habitual and technology-averse car drivers are particularly susceptible to this skewed cost perception, unlike more frequent transit users. Embedding these results in a case study of current shared mobility options shows that these options are ill-adjusted to the preferences of car travelers but more attractive to current transit users, potentially leading to cannibalization. Based on these findings, we outline several strategies for mobility providers and policymakers to attract car drivers to shared mobility options, thereby contributing to solving the urban mobility challenge.

## 1. Introduction

Mitigating urban car traffic is a global policy objective aimed at minimizing congestion, pollution, and accidents (Stradling et al., 2000). A vital element in achieving this objective is the role of shared mobility providers offering sustainable alternative travel modes such as micro-mobility (e.g., Lime or Tier scooters), carsharing (e.g., ShareNow), or ridehailing (e.g., Uber). However, shared mobility solutions often fail to attract car users, the group that most urgently must change their travel mode choice for sustainable transport policies to be effective (Anable, 2005; Clewlow, 2016; Hoffmann et al., 2020).

A key driver of travel mode choices is trip costs (Ha et al., 2020; Hess, 2001). Despite the objective cost advantage of shared mobility solutions (Liljamo et al., 2020), creating cost-based incentives for car users to switch from personal cars to more sustainable shared mobility offers is challenging. One reason is that travel mode cost estimation is subjective, complex and lacks comparability (Litman, 2004; Vortisch and Chlond, 2015; Andor et al., 2020). For example, when estimating costs, car drivers subjectively decide whether they account for vehicle depreciation, insurance, or parking in addition to fuel and maintenance costs,

whereas public transit users need to allocate fixed payments for their subscriptions over their various trips (Krämer, 2017).

Although travel mode choice is a central discussion in transportation research and policy (e.g., Jochem et al., 2021; Paulsen et al., 2014), users' financial perspectives are scarce in existing research. Differences in perceived cost structures between cars and mobility alternatives can lead to a distorted mental image of the operating cost of cars. This discrepancy is relevant when marketing shared mobility solutions to car drivers, as they might perceive trip costs differently based on the purpose and length of inner-city trips (Beirão and Sarsfield Cabral, 2007; Klöckner and Friedrichsmeier, 2011). Thus, trip purpose and trip length might directly affect car users' reluctance to switch to more sustainable shared mobility solutions. This is particularly interesting for policymakers, city planners, and new mobility providers, as current marketing efforts focus on promoting leisure trips with shared mobility offerings. If car users' cost perception is especially distorted for these leisure trips, convincing them to switch travel modes will be more challenging. Consequently, current efforts to promote shared mobility and reduce car traffic may be ill-adjusted.

Against this backdrop, our objective is to explore how consumers

<footnote><sup>a</sup> Department of Marketing, EBS Universität für Wirtschaft und Recht, Wiesbaden, Germany</footnote>

<footnote><sup>b</sup> Utrecht University School of Economics, Kriekenpitplein 21-22, 3584 EC, Utrecht, the Netherlands</footnote>

<footnote><sup>c</sup> IESEG School of Management, Univ. Lille, CNRS, UMR 9221, LEM - Lille Economie Management, F-59000, Lille, France</footnote>

<footnote>* Corresponding author. Utrecht University School of Economics, Kriekenpitplein 21-22, 3584 EC, Utrecht, the Netherlands. E-mail address: k.merfeld@uu.nl (K. Merfeld).</footnote>
perceptions and travel mode choices affect their cost estimations in different urban travel settings. Subsequently, we seek to identify car user segments particularly prone to distorting cost estimations. Based on these findings, we assess the suitability of current strategies of shared mobility providers to attract car users toward more sustainable travel mode choices. Accordingly, we seek to answer the following research questions.

* **RQ1:** How do trip characteristics and travel mode influence cost estimations for urban trips that could potentially be served by shared mobility offerings?

* **RQ2:** Are certain traveler segments particularly sensitive to distorted travel cost estimations and thus need to be targeted differently by shared mobility offerings?

* **RQ3:** How do the strategies of current shared mobility providers match with these findings?

Answering these research questions, the paper contributes to transportation policy and practice research in the following ways. First, our findings add to the literature that investigates how car drivers perceive usage costs and how this influences their travel mode choice (Hoffmann et al., 2020). In particular, we extend the research on cost perceptions in travel by considering trip characteristics such as purpose and length (e.g., Andor et al., 2020; Krämer, 2017). We find that trip purpose and length distort cost perceptions—short leisure trips are seen as cheaper than long and work trips. This low-cost perception hinders the switch from personal cars to shared mobility offerings, which are predominantly advertised for leisure and short trips (Rayle, 2014). Second, we investigate whether this cost perception distortion applies to the entire population or to specific car user segments. Our segmentation analysis shows that habitual and technology-averse drivers are particularly susceptible to the skewed cost perception effect, whereas transit users and innovation-oriented car drivers behave more rationally. Thus, we contribute to research on mobility consumer clusters (Kandt et al., 2015; Molin et al., 2016) and shared mobility (Alonso-González et al., 2020) as well as on car user clusters (e.g., Anable, 2015). Third, we contrast our findings with shared mobility providers' pricing, marketing, and customer journey strategies. We show that shared mobility often misfits car travelers' preferences but appeals to current transit users, possibly leading to the cannibalization of sustainable mobility forms. We, therefore, contribute to the overarching discussion on the cannibalization of public transportation by shared mobility solutions and new mobility services (Basu and Ferreira, 2021).

## 2. Research background: Explaining travel mode choice through mental accounting

Empirical research on shared mobility highlights cost-related motives as a determinant of travel mode choice (Huang et al., 2023; Luo et al., 2023). In addition to economic drivers, a psychological perspective is emphasized: consumers seek to reduce the financial burdens of ownership when deciding between car ownership and shared mobility. Thus, economic drivers and perceived ownership costs emerge as key factors in the decision to adopt shared mobility solutions (Lamberton and Rose, 2012; Bardhi and Eckhardt, 2012; Schaefers et al., 2016). We build on these established economic and cost-related motives as key factors for the adoption of shared mobility solutions but extend this by investigating subjective perceptions of financial gains and losses to explore how consumers mentally budget their transportation costs.

The decision to use shared mobility often involves reallocating funds from traditional transportation categories (e.g., personal car, public transit) to a new category for shared mobility. Understanding how individuals mentally budget transportation costs can help explain their travel mode choices. Investigations on financial gains and losses are well-established in marketing research and economic theory, most prominently in the theory of mental accounting (Thaler, 1985; Heath

and Soll, 1996). Mental accounting rejects the standard economic theory of perfectly rational behavior, suggesting that humans behave differently based on the mental account (e.g., work vs. leisure) from which they are paying (Thaler, 1985). This behavior leads to the segregation of gains and losses into different mental accounts and can cause over- and underconsumption within accounts as well as self-contradictory behavior (Heath and Soll, 1996).

Previous research indicates that mental accounting influences travelers' decisions such as choosing toll roads (Bao et al., 2015), overcompensating high fixed car usage costs (Ho et al., 2018), and shifting transportation modes after purchasing train discount cards (Krämer, 2017). Building on this, we apply mental accounting theory to explain consumer reluctance to adopt socially desirable travel mode changes toward shared mobility solutions. We argue that infrequent, substantial payments from the “car ownership” account create a distorted mental image of car operating costs, especially compared with the visible costs of shared mobility offerings. Consequently, car drivers underestimate their travel costs without immediate education about those costs. In contrast, shared mobility providers confront users with the exact travel cost for each trip (Liljamo et al., 2020). These effects likely manifest in different cost estimates depending on trip characteristics. Rationally, consumers should not perceive varying costs for driving a kilometer based on trip purpose or length. However, considering mental accounting effects, trip purpose and length might lead to cost estimates being allocated to different accounts.

**Trip purpose.** Prior literature indicates that travel mode choice and responses to policy stimuli are influenced by trip purpose (Azari et al., 2013; Ashkrof et al., 2019). We focus on the distinction between work and leisure—the most common reasons for intra-city travel—that involve distinct mental accounts (Van Exel and Rietveld, 2009). Work trips—daily commutes between home and work—are well-studied in mode choice literature (Ha et al., 2020; Ko et al., 2019; O'Fallon et al., 2004). According to the habit discontinuity phenomenon (Haggar et al., 2019), a distinction exists between the initial travel mode decision and subsequent repetitive action. Travelers initially exert mental effort to decide on a mode for work trips, balancing comfort, reliability, speed, cost, and social and environmental concerns (Beirão and Sarsfield Cabral, 2007). Subsequently, the chosen mode becomes habitual, and alternatives are less considered, strengthening the initial choice. The chosen mode also influences purchase decisions for cars or bicycles (Dargay and Hanly, 2007; Zhao and Zhang, 2018). Leisure trips, in contrast, are less actively considered. Although their variance in terms of destination and timing is higher, mode choice is predicted by the preferred commuting mode (Haggar et al., 2019)—particularly among car owners, who tend to use their cars for leisure trips without considering other modes (Klöckner and Friedrichsmeier, 2011).

**Trip length.** For a rational decision-maker, trip length and total cost estimates should exhibit a direct proportional relationship; however, degressive and progressive effects might arise. Degressive trip cost effects—that is, longer trips are perceived as cheaper per kilometer—are intuitive for transit users, given the largely constant price of transit tickets within cities (Czerliński and Bańka, 2021). Similarly, car users tend to spread out fixed costs over a longer total distance, leading to a degressive perception of car costs (Krämer, 2017).

Drawing on these insights from extant research and informed by mental accounting theory, we investigate how travelers estimate the cost of different trips (considering trip purpose and length), derive behavioral clusters of car users based on this cost perception, and benchmark the findings against current shared mobility offerings and practices to derive strategies for mobility providers and policymakers to attract car drivers toward shared mobility options.

## 3. Empirical investigation to understand the urban mobility challenge

Our empirical investigation employs a mixed-method design that
integrates quantitative data with qualitative insights. First, we conducted an experiment with 807 participants to explore how travelers estimate costs for trips of varying purposes and lengths. Next, we analyzed the experimental data to identify behavioral clusters, providing a deeper understanding of participants' mode choices and cost perceptions. Finally, we contextualized these quantitative findings through a case study, comparing them with qualitative insights into the strategies of shared mobility providers. This mixed-method approach allows us to connect quantitative travel behavior data and cost estimates with real-world strategies, enabling us to assess how well these strategies align with car driver behavior and their potential to promote sustainable shared mobility alternatives.

**Experimental Study.** To investigate how travelers estimate the cost of different trips, we use an experimental approach, allowing us to manipulate trip purpose and trip length. We conducted an experimental study with 807 participants recruited from an online panel (mean age = 40.5 years, 48.5% men). The study followed a 2 (trip purpose: work vs. leisure) x 2 (trip length: 6 vs. 12 km) between-subject design. Participants were randomly assigned to one of the four conditions. We focused on participants over 18 years of age who were current users of cars or public transportation, representing the key target group for shared mobility solutions. Participants were residents of Germany, were recruited through a professional panel provider, and received monetary compensation. To ensure data quality, we employed attention checks as recommended by Abbey and Meloy (2017), which were implemented via scale selection criteria before and after the manipulation (e.g., one item instructed participants to “select strongly disagree in this line”). Of the 1311 responses, 356 were screened out for not meeting the sample criteria (car or transit user, over 18 years old, and/or resident in Germany) or failing the attention checks, resulting in a useable sample of 807 participants. Table 1 provides an overview of the sample demographics, which illustrates adequate demographic representation, with the main differences being a slightly higher education level and a higher population density in the living areas within our sample. Although this indicated an over-representation of urban participants, it aligns with our focus on urban travelers as the key target group of shared mobility solutions.

After the participants answered the initial screening questions, they were asked about their main travel mode (car or transit). They were then presented with a hypothetical inner-city trip using their preferred mode and were randomly assigned a trip purpose (work commute vs. leisure trip) and a trip length (6 km vs. 12 km, which is ±3 km the average trip length on public transit in a major German city; Steinmeyer and Herrmann-Fiechtner, 2017). For the presented trip, participants first estimated their travel costs (in euros/cents) without any contextual information. Next, we used an educational stimulus on cost elements adapted from Andor et al. (2020) and Krämer (2017). Participants were asked to select those trip cost elements from a multiple-choice list that they included in their calculation, including fixed costs (insurance,

<table><caption>**Table 1**
Sample demographics.</caption><thead><tr><th></th><th>Experiment sample</th><th>German population (Statista, 2020)</th></tr></thead><tbody><tr><td>Age (avg. years)</td><td>40.5</td><td>44.5</td></tr><tr><td>Gender ratio (% male)</td><td>48.5</td><td>49.3</td></tr><tr><td>Household income (avg. EUR)</td><td>46 685</td><td>46 559 (couple households)</td></tr><tr><td>Population density (avg. inhabitants/km² in postcode area)</td><td>2129</td><td>1623</td></tr><tr><td>Car ownership (% of households)</td><td>80.4</td><td>81</td></tr><tr><td>Education</td><td>14% high school, 36% vocational training, 50% university</td><td>20% high school, 52% vocational training, 28% university</td></tr></tbody></table>

Notes: Sample demographics are comparable to the German population.

monthly subscriptions) and variable costs (fuel, tickets). This stimulus and the selection of costs included required the participants to actively think about cost components. The participants could then revise their cost estimate based on the outlined components. Thus, participants either retained their original estimate or adjusted it, ensuring active engagement in cost estimation (Krämer, 2017; adapted from Andor et al., 2020). Based on Kandt et al. (2015), we also collected responses to seven constructs related to mode choice, habituality, and innovativeness in addition to the cost estimates (see Appendix for an overview).

**Cluster Analysis.** Building on the experimental data, we then investigated whether the effects are inherent to travelers who prefer cars as their mode of choice or whether certain car user segments are particularly prone to these distorted cost estimations, thereby requiring different targeting strategies by policymakers and shared mobility providers. To achieve this goal, we conducted a cluster analysis to gain insights into the different segments of car users. Conducting user segmentation based on data regarding mode choice preference and the exhibited degree of habituality and innovativeness in mode choice, we can classify multiple individuals as tangible “personas” that share underlying traits (Anable, 2005; Kandt et al., 2015). In line with the key group identified for addressing the urban mobility challenge, we focus the cluster analysis on the data provided by car drivers for short leisure and work trips (n = 278).

Following the procedure in similar studies using cluster analysis (e. g., Anable, 2005), we employed a two-stage approach for the cluster analysis. First, we used agglomerative hierarchical clustering to identify the data structure and determine the best-fitting number of clusters based on the dendrogram. Next, we conducted a K-means analysis and applied cross-validation procedures to ensure we selected the optimal number of clusters, ultimately leading to an eight-cluster solution. Finally, we assessed the cost discrimination effect for each cluster to identify those car user segments that are particularly prone to distorted cost estimations.

**Case Study.** To contextualize our quantitative findings with qualitative insights, we then adopted a case study approach to examine how current shared mobility strategies align with our quantitative findings on travel mode choice and cost estimation. This approach allows for an in-depth examination of how shared mobility providers currently market their offerings and how these strategies match with the key target groups of shared mobility providers. In this case study, we compare these elements with the preferences of the key target groups identified in our previous studies: cost-discriminating car users, cost-parity car users, and transit users. This comparative analysis aims to understand how well current shared mobility offerings meet the needs and perceptions of the identified car user segments and transit users.

The selected case involves prominent shared mobility providers in a major German metropolitan city, which was chosen based on the availability of various shared mobility services, user behavior patterns (i.e., public transportation and car use), and the integration of shared mobility options for commuting. These mobility services serve as embedded units of analysis (Yin, 2017). We selected well-established, access-based, and more sustainable services than car travel, such as van-based microtransit (e.g., BerlKönig and Clevershuttle), bike sharing (e.g., Mobike and Nextbike), and scooter sharing (e.g., Lime and Voi). This case thus provides a perspective on how shared mobility services are marketed in a target market. We conducted desk research on these providers, analyzing their offerings and sign-up procedures. In particular, we collected and analyzed information on pricing for a short trip (6 km), mission and marketing strategies, and customer journey (including booking channels and operating costs). To compare these offerings with the behavioral preferences of car users, we analyzed the price of sample trips and selected non-financial indicators by examining or requesting trips using the respective booking applications.
## 4. Results illustrating the urban mobility challenge

In line with our empirical approach and research goals, we first present the results from our experimental study and cluster analysis, followed by contextualizing these findings through our qualitative case study. The experimental data reveal a non-normal distribution, with distinct anchor points around even numbers and typical transit fares. Therefore, we employed non-parametric Kruskal–Wallis rank tests to analyze treatment effects, as they offer greater statistical power in such situations (Koschate-Fischer et al., 2012). The following results were obtained for car drivers versus transit users:

Car drivers (n = 530). The main effect of trip length on cost estimates is significant and negative: car drivers reacted to different trip lengths, with longer trips being perceived as cheaper per kilometer traveled (i.e., $M_{6km} = 34.2$ cents/km, $M_{12km} = 22.8$ cents/km; $p < .001$). For example, comparing short versus long leisure trips, we see a 27% reduction in the estimated cost by car drivers. The main effect of trip purpose is not significant ($p > .1$). However, we observed a significant interaction effect of trip purpose and length, with short leisure trips ($M_{6kmLeisure} = 30.4$ cents/km) being perceived as cheaper than short work trips ($M_{6kmLeisure} = 38.2$ cents/km). That is, the trip purpose significantly affects cost perception in the short trip condition ($p < .05$). The effect of the educational stimulus is significant for all groups ($p < .05$): for example, for short leisure trips, cost education increased the estimated cost by 29%.

Transit users (n = 277): The main effect of trip length on cost estimates is significant and negative: transit users reacted to different trip lengths, with longer trips being perceived as cheaper per kilometer traveled (i.e., $M_{6km} = 34.2$ cents/km, $M_{12km} = 22.8$ cents/km; $p < .001$). For example, comparing short versus long leisure trips, we see a 37% reduction in the estimated cost by car drivers. The main effect of trip purpose is not significant ($p > .1$), and no significant interaction effect is observed ($p > .1$). Cost education also did not show an effect ($p > .1$).

Fig. 1 illustrates the cost estimates for each group before and after the education stimulus.

Overall, this experiment illustrates that trip length has a strong degressive effect on travel cost estimation, meaning longer trips are perceived as cheaper per kilometer. This perception is rational for transit users, as single transit tickets within a city typically cost the same regardless of distance. However, it is irrational for car users to perceive that driving a kilometer costs different based on the trip's purpose or length, indicating a skewed cost perception. The educational stimulus (highlighting trip cost elements) significantly affects car users but not

transit users, further underscoring this distortion. Car drivers systematically underestimate their travel costs without immediate education on those costs, showing a non-rational and context-dependent evaluation of travel costs. In particular, short leisure trips are perceived as cheaper than long and work trips. Trip purpose also influences the cost estimates for car travelers in the short trip condition. Therefore, car travelers for short leisure and work trips, who directly compete with shared mobility services (Reck et al., 2021), are a key group for addressing the urban mobility challenge. We focused on this group in our further investigation when deriving behavioral clusters.

Building on the experimental data, we conducted a cluster analysis to investigate whether this effect is inherent to all travelers who prefer cars as their mode of choice or whether certain car user segments are particularly prone to these distorted cost estimations. We identified eight clusters of car users. Table 2 shows the clusters in the left column; the appendix outlines the mean factor scores on the variables used to derive the clusters and significant differences. Within each of the eight clusters, we measured the strength of the trip purpose treatment effect on car users for the cost estimates of the short trip (6 km) condition, which showed cost discrimination effects regarding trip purpose. We split the eight clusters into two groups according to effect strength: one showing cost discrimination (57% of car drivers, with a cost estimate difference of 12 cents/km, $p < .001$) and the other showing no discrimination on cost (43% of car drivers, with a cost estimate difference of 2.6 cents/km, $p > .1$). The cost-discriminating group reacted more strongly to the education stimulus (see Table 2).

Regarding the cluster-forming attributes, we find that two groups of car driver segments exhibit significantly different values. The cost-parity group shows a higher preference for transit ($p < .001$), bicycle riding ($p < .01$), and mobility technology ($p < .01$), indicating greater openness to alternative travel modes. In contrast, the cost-discriminating group is more passionate about car travel ($p < .001$) and slightly more competent with innovative car technologies (e.g., driver assistance systems; $p < .05$). However, the demographics of the two groups do not significantly differ ($p > .01$), suggesting that cost discrimination is driven by psychological predisposition rather than education, living circumstances, or experience. We further find that the cost-discriminating group, including the monomodal and car-passionate segments, is particularly sensitive to distorted cost estimations, an effect not observed in the more multimodal cost-parity group.

In a final step, we compared these results with the insights of our case

<table><caption>**Table 2**

Car driver clusters based on cost estimate difference for short trips.</caption><thead><tr><th>Cluster (# car drivers)</th><th>Delta in cost estimate per km, work vs. leisure trip</th><th>p-Value</th><th>Behavior group</th></tr></thead><tbody><tr><td>1 – Traditional car users (66)</td><td>33.2 – 26.3 = <strong>6.9</strong></td><td>0.007</td><td rowspan="4">Cost-discriminating group</td></tr><tr><td>8 – Car-focused individualists (47)</td><td>38.5 – 29.3 = <strong>9.2</strong></td><td>0.18</td></tr><tr><td>5 – Suburban bike enthusiasts (33)</td><td>61.2 – 31.4 = <strong>29.8</strong></td><td>0.22</td></tr><tr><td>7 – Transit users (8)</td><td>40.2 – 26.7 = <strong>13.5</strong></td><td>0.49</td></tr><tr><td>Group average (154)</td><td>40.3 – 28.3 = <strong>12.0</strong></td><td>0.001</td><td></td></tr><tr><td>6 – Green travelers (11)</td><td>31.3 – 16.7 = <strong>14.6</strong></td><td>0.52</td><td rowspan="2">Cost-parity group</td></tr><tr><td>2 – Multimodal mobility innovators (38)</td><td>37.1 – 30.9 = <strong>6.2</strong></td><td>0.79</td></tr><tr><td>4 – Suburban multimodals (39)</td><td>35.8 – 42.6 = <strong>-6.8</strong></td><td>0.92</td><td></td></tr><tr><td>3 – Pragmatic car users (36)</td><td>35.6 – 31.5 = <strong>4.1</strong></td><td>0.94</td><td></td></tr><tr><td>Group average (124)</td><td>35.8 – 33.2 = <strong>2.6</strong></td><td>0.80</td><td></td></tr></tbody></table>

Notes: Cost estimate difference per kilometer by car drivers in each cluster for short work and short leisure trips. The cost-discriminating group shows a pronounced difference between leisure and work trips, but this is less the case in the cost-parity group.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Trip cost estimates by travel mode (in cents/km)</figcaption></figure>

Note: Of the 807 participants, 530 indicated “car” as their primary transportation mode and 277 indicated “transit.” Group sizes for car users are $n_{6kmLeisure} = 140$, $n_{12kmLeisure} = 122$, $n_{6kmWork} = 138$, and $n_{12kmWork} = 130$.
study to identify whether shared mobility offerings align with the needs of these different groups. Regarding the shared mobility providers in our case, we first present our findings on pricing, marketing strategy, mission statements, and customer journey. Table 3 provides an overview of the different insights for each type of shared mobility service in our case. We then present a more detailed outline of the customer journey to illustrate the different adoption processes and barriers for shared mobility versus car and transit users in Table 4.

**Pricing.** We find that access-based transportation prices are significantly higher than the cost estimates for car and public transit. For the sample 6-km trip, scooter sharing (EUR 3.80–5.00) and microtransit (EUR 5.00–9.00) are relatively more expensive than the perceived cost of driving a car (EUR 1.56–2.42) and, to a lesser extent, public transit (EUR 2.82). Only bike sharing (EUR 1.00–1.50) has a trip cost comparable to the existing modes. None of the shared mobility offerings have a meaningful degressive component despite the strongly degressive cost estimates observed for the existing modes, making the new offerings even less competitive for longer trips. Fig. 2 provides an overview of the cost estimates for the different segment groups (based on Study 2) and the actual trip cost.

**Mission and Marketing Strategy.** We find that each of the shared mobility services analyzed in our case aim to reduce car travel. Despite having a variety of shared mobility offerings, the providers have remarkably similar mission statements. In line with the consumer zeitgeist, they employ societal marketing strategies, positioning themselves not merely as a new, more convenient way to travel around a city but as key players in reducing congestion and pollution. Their goal is to prevent car travel and extend public transit rather than replace it. This is evident in their mission statements. For example, *Tier Mobility* states: “Micro-mobility’s potential is to facilitate the movement of people in the first and last mile, to increase accessibility and affordability of mobility, to complement public transportation, and to incentivize people to use a mixture of on-demand mobility services rather than owning and using cars. Through this combination, we can steer the mobility sector to a more sustainable future” (*Tier Mobility, 2020*).

**Customer Journey and Associated Adoption Barriers.** Innovative services such as shared mobility face significant adoption challenges (e.g., Burghard and Dütschke, 2019), and user-friendly customer journeys are crucial for reducing barriers and encouraging usage. We compare customer journeys and the associated barriers between shared mobility services and existing transportation modes (public transit and car) along the customer journey (Lemon and Verhoef, 2016). Table 4 shows the customer journey for shared mobility versus car and transit use. Both shared mobility and public transit have a complex, digital, interaction-heavy pre-usage stage, unlike car travel. They confront users

<table><caption>**Table 3**
Comparison of access-based mobility offerings.</caption><thead><tr><th>Mobility offering</th><th>Microtransit</th><th>Micromobility with scooter</th><th>Micromobility with bicycle</th></tr></thead><tbody><tr><td>Examples in Germany</td><td>Berlkönig, Clevershuttle</td><td>Tier, Lime, Voi</td><td>Mobike, Nextbike</td></tr><tr><td>Marketing strategy, mission</td><td>Societal marketing, focus on reducing car traffic</td><td>Societal marketing, focus on providing alternative vehicles</td><td>Societal marketing, focus on improving the quality of city life</td></tr><tr><td>Booking channel</td><td>Smartphone application, booking required, integration in official transit planner</td><td>Smartphone application, can be unlocked without booking, partial integration in Google Maps</td><td>Smartphone application, can be unlocked without booking</td></tr><tr><td>Price for a 6-km trip</td><td>EUR 5.00–9.00</td><td>EUR 3.80–5.00</td><td>EUR 1.00–1.50</td></tr><tr><td>Operating area</td><td>Very limited, entertainment and central residential district</td><td>Limited, city center</td><td>Limited, city center</td></tr></tbody></table>

<table><caption>**Table 4**
Customer journey of shared mobility services versus car and transit usage.</caption><thead><tr><th>Journey stage</th><th>Shared mobility service</th><th>Car</th><th>Transit</th></tr></thead><tbody><tr><td>Pre-usage</td><td><ul><li>(A/D) Consider mobility service</li><li>(D) Open application</li><li>(D) Research availability and route</li><li>(D) Find expected cost</li><li>(D) Purchase service</li><li>(D) Locate vehicle</li><li>(A) Walk to vehicle</li><li>(A) Identify right vehicle</li></ul></td><td><ul><li>(A) Decide to use car</li><li>(A) Walk to car</li></ul></td><td><ul><li>(A/D) Decide to use transit</li><li>(D) Open transit application</li><li>(D) Research route and timetable</li><li>(D) Purchase ticket</li><li>(A) Walk to station</li><li>(A) Identify right line/service</li></ul></td></tr><tr><td>Usage</td><td><ul><li>(A) Board vehicle</li><li>If self-driven:<ul><li>(D) Enter destination in GPS</li><li>(A) Drive to destination</li><li>(A) Find parking</li></ul></li><li>If driver-operated:<ul><li>(A) Ride to destination</li><li>(A) Exit</li></ul></li><li>(D) End booking, rate experience</li><li>(D) Receive invoice</li><li>(A) Walk to destination</li></ul></td><td><ul><li>(A) Board car</li><li>(D) Enter destination in GPS</li><li>(A) Drive to destination</li><li>(A) Find parking</li><li>(A) Exit</li></ul></td><td><ul><li>(A) Board vehicle</li><li>(A) Ride to destination</li><li>(A) Change lines (if necessary)</li><li>(A) Exit</li></ul></td></tr><tr><td>Post-usage</td><td><ul><li>(D) End booking, rate experience</li><li>(D) Receive invoice</li><li>(A) Walk to destination</li></ul></td><td><ul><li>(A) Pay parking fees (if necessary)</li><li>(A) Walk to destination</li></ul></td><td><ul><li>(A) Walk to destination</li></ul></td></tr></tbody></table>

Notes: Shared mobility services more strongly resemble the transit model, especially in the pre-usage stage.

The initial setup stage (i.e., the purchase of the car, the registration for a mobility provider) is omitted because of the low frequency of this interaction. D = digital interactions; A = analog interactions.

with travel costs and require a conscious decision to pay. For example, *Tier Mobility* charges “1.00 € to unlock and 0.19 €/min” (*Tier Mobility, 2020*). The actual usage stage is simpler. Scooter- and bike-sharing services, requiring user operation, are more similar to car use, whereas driver-operated microtransit services resemble transit use. In the post-usage stage, shared services send an invoice, reinforcing the mental accounting of trip costs, whereas car users face immediate costs mainly when paying for parking; transit users do not experience this. In conclusion, the user experience of shared mobility differs significantly from both car and transit, especially in the pre-usage stage. This difference is more pronounced for car users, who must make multiple digital interactions and conscious decisions not required in car travel. Thus, the usage barriers for shared mobility are generally greater for car users than for transit users. Combining this with our finding that cost-discriminating car segments are less open to innovation, we conclude that cost-discriminating car users face the highest adoption barriers, whereas transit users face the lowest. Cost-parity car users fall in between.

Our combined findings from the quantitative and qualitative studies reveal a nuanced picture. While shared mobility services align well with leisure trips for transit users, they are poorly suited for car users in all cases, except when the cost-parity group is commuting for work. We thus identify a mismatch between the structure of shared mobility offerings and the cost perception and user experience of most car users. This match likely diminishes the appeal of these services for this group, limiting the substitution potential for car trips. Conversely, shared mobility options are more attractive for short trips by transit users, where costs are competitive and barriers to use are minimal. These insights suggest a misalignment between shared mobility offerings and the preferences of car travelers as well as highlight a potential risk of
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Cost estimates (black) versus actual trip cost (gray and white)</figcaption></figure>

Notes: Cost estimates (black bars) compared with the actual trip cost (gray and white bars, indicating range) of shared mobility offerings for a 6-km trip in Berlin (in EUR). Except for bike sharing, all shared mobility offerings are significantly more expensive than the cost estimates of driving a car.

cannibalization – where shared mobility attracts current transit users, potentially shifting them from eco-friendly public transportation to more individual and less sustainable shared mobility modes. Table 5 provides an overview of how well shared mobility modes align with the different target groups: transit users, cost-parity car drivers, and cost-discriminating car drivers.

## 5. Discussion & conclusion

Our findings suggest that car drivers estimate travel costs conceptually rather than rationally by considering both trip purpose and length. We find that trip purpose and length distort car users' cost perceptions—short leisure trips are seen as cheaper than long and work trips. This low-cost perception hinders the switch from cars to shared mobility offerings. Thus, our work complements previous research on transport policy and mobility archetypes (e.g., Anable, 2005; Kandt et al., 2015) by incorporating cost perception dimensions and exploring subjective cost elements in consumers' travel mode choice (Hoffmann et al., 2020). For future research, we highlight the relevance of mental accounting principles and suggest their in-depth assessment in mobility contexts. We also extend research on cost perceptions and fixed cost allocations in travel contexts by considering trip characteristics (e.g.,

<table><caption>**Table 5**

Fit of shared mobility modes to potential user groups by trip purpose/length.</caption><thead><tr><th></th><th>Transit users</th><th>Cost-parity car drivers</th><th>Cost-discriminating car drivers</th></tr></thead><tbody><tr><td>Short work trips</td><td>Good fit (+)<br>Comparable cost to the current mode<br>(+) Low adoption barriers</td><td>Reasonable fit (o)<br>Moderate cost increase over the current mode<br>(+) Active consideration of other travel modes<br>(o) Moderate adoption barriers</td><td>Limited fit (+)<br>Limited cost increase over the current mode<br>(-) Little consideration of other modes<br>(-) High adoption barriers</td></tr><tr><td>Short leisure trips</td><td>Perfect fit (+)<br>Comparable cost to the current mode<br>(+) Lowest adoption barriers</td><td>Limited fit (o)<br>Moderate cost increase over the current mode<br>(o) Moderate adoption barriers</td><td>No fit (-)<br>Strong cost increase over the current mode<br>(-) Least consideration of other modes<br>(-) Highest adoption barriers</td></tr><tr><td>Long trips (work and leisure)</td><td>No fit, because the costs are not competitive with any conventional alternative</td><td></td><td></td></tr></tbody></table>

Notes: Currently, short leisure trips by transit users are easily substituted, whereas the societally desirable substitution of car trips is less supported.

Andor et al., 2020; Krämer, 2017).

Our segmentation analysis shows that this cost perception distortion is not universal but specific to certain car user clusters, namely the cost-discriminating segments. These habitual and technology-averse car users are particularly susceptible to a skewed cost perception, whereas transit users and innovation-oriented car users (cost-parity segment) behave more rationally. These findings contribute to research on mobility consumer clusters in general (e.g., Molin et al., 2016), shared mobility (e.g., Alonso-González et al., 2020), and car user clusters (e.g., Anable, 2005). We also extend Kandt et al.'s (2015) typology by proposing two additional mobility clusters: suburban bike enthusiasts and suburban multimodals that fall into the cost-discriminating and cost-parity groups, respectively.

Finally, we contrast our findings with shared mobility providers' pricing, marketing, and customer journey strategies. We find that shared mobility services face greater usage barriers among car users than among transit users. Considering that the cost-discriminating group is less open to innovation, we propose that these car users face the highest adoption barriers, whereas transit users face the lowest, with cost-parity car users in between. This indicates that shared mobility offerings often misfit car travelers' preferences but appeal to transit users, possibly leading to the cannibalization of sustainable mobility forms. We thus contribute to the overarching discussion in transport policy on the cannibalization of public transportation through new mobility services (Storme et al., 2020; Young et al., 2020; Martin and Xu, 2022). These insights help explain usage data showing that micromobility is frequently used for short trips outside commuting hours and often complements or substitutes transit (Fearnley et al., 2020; Zou et al., 2020; Reck et al., 2021).

### 5.1. Implications for transport policymakers and shared mobility providers

Based on our findings, we derive several recommendations for transport policymakers and shared mobility providers to improve the acceptance of shared mobility solutions by car users, thereby reducing car traffic and addressing the urban mobility challenge.

**Increasing the Focus on Work Trips.** Considering the higher cost perceptions of work trips and greater conscious effort involved in deciding on a travel mode for commuting purposes, we suggest that shared mobility providers should aim to serve commuters. Furthermore, as work trips are the most common purpose for traveling, such efforts would substantially expand their addressed market. This focus requires a change in the providers' operating models, as residential areas and commuting destinations must be served and the service quality must be sufficiently high (e.g., predictable availability during peak hours, usability in inclement weather). Shared mobility providers could increase
attractiveness of commuting trips through targeted communication or cooperation with large employers and educators. Measures could also include subsidized rates on trips to and from selected destinations and campaigns targeting newly enrolled students or people who have recently moved to cities.

*Reducing the Gap in Cost Perception.* Based on our findings on cost estimations, we propose three strategies to make shared mobility offerings appear more financially competitive. First, mobility providers could expand their offerings with degressive components covering both distance and total usage to better align with perceived costs (e.g., package subscriptions, partial ownership models). However, marketing-wise, barriers to service trials must remain low, which may be possibly achieved through promotional activities by municipalities or providers (e.g., free rides for those considering giving up their cars). Second, policymakers should reduce the degressive nature of car usage costs by coupling taxes, fees, and operating costs with actual use instead of ownership. Research in Singapore shows that increasing fixed costs leads to overcompensation and increased vehicle kilometers traveled (Chu, 2015; Ho et al., 2018). Alternatively, increasing subsidies for non-car transportation modes might be a more feasible approach for policymakers.

Third, educating car users on the true operating costs of cars is promising owing to the structural underestimation of driving costs. For example, requiring insurers or car dealerships to provide full cost calculations (similar to fuel consumption and emission labels) could increase cost awareness and thus help to encourage the use of shared mobility services.

*Adopting Segment-Specific Marketing Techniques.* The cost-parity group, which is conscious of their travel expenses, will require a perceived non-financial advantage in adopting alternative travel modes as opposed to car usage. For this group, the qualitative aspects of the cost-benefit equation might be of greater significance, such as enhanced availability, comfort, and enjoyment of alternative modes, in addition to the measures that decrease the appeal of car usage, such as implementing speed limits, creating car-free zones, and imposing parking restrictions. Efforts to emphasize the societal benefits of reduced car usage may also be effective. Cost-discriminating car users, in contrast, are sensitive to both the financial and non-financial costs of switching travel modes. Providers of mobility services should concentrate their efforts on addressing these factors. This strategy necessitates offering competitive travel prices as a foundation while minimizing usage barriers along the customer journey, particularly during the pre-usage phase.

## 5.2. Limitations & avenues for future research

Our study has some limitations that provide grounds for future research. Our geographical scope is limited to the German market. Conducting the study in different cities or regions with varying transportation infrastructures, cultural attitudes toward mobility, and economic conditions might enrich the discussion and add more facets to the findings. We also analyzed a static scenario and did not account for effects over time. A longitudinal study of travel mode choice around shared mobility offerings could reveal whether introducing a shared travel mode or changing the marketing of a particular mode drives opportunistic uses by residents, thus altering their mobility behavior. Moreover, given the nature of the studied effect (Charness et al., 2012), we used a binary classification of cost discrimination, labeling user groups as cost-discriminating or not cost-discriminating. This approach might not capture all relevant variations in traveler behavior and preferences; additional insights into the diversity of urban travelers may thus be required. Moreover, observing and quantifying this cost discrimination effect at the individual level would be more informative. This data could then be used to build more precise clusters and improve the overall statistical power of mode choice models.

### CRediT authorship contribution statement

**Philip Fitschen:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Katrin Merfeld:** Writing – original draft, Supervision, Project administration, Conceptualization. **Jan F. Klein:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Formal analysis. **Sven Henkel:** Supervision, Resources, Conceptualization.

### Declaration of competing Interest

None

### Data availability

Data will be made available on request.

## Appendix. Mean factor scores on variables used to derive the clusters and significant differences

<table><thead><tr><th rowspan="3"></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr><tr><th>Traditional car users</th><th>Multimodal mobility innovators</th><th>Pragmatic car users</th><th>Suburban multimodals</th><th>Suburban bike enthusiasts</th><th>Green travelers</th><th>Transit users</th><th>Car-focused individualists</th></tr><tr><th>24%</th><th>14%</th><th>13%</th><th>14%</th><th>12%</th><th>4%</th><th>3%</th><th>17%</th></tr></thead><tbody><tr><td>Rail preference</td><td>-1.226<sup>2345678</sup></td><td>0.266<sup>13578</sup></td><td>-0.347<sup>1245678</sup></td><td>0.392<sup>13578</sup></td><td>0.879<sup>123468</sup></td><td>0.261<sup>13578</sup></td><td>0.847<sup>123468</sup></td><td>-0.896<sup>1234567</sup></td></tr><tr><td>Car preference</td><td>0.343<sup>345678</sup></td><td>0.459<sup>145678</sup></td><td>0.668<sup>14567</sup></td><td>-0.104<sup>123578</sup></td><td>-0.964<sup>1234678</sup></td><td>-0.302<sup>123578</sup></td><td>-1.296<sup>1234568</sup></td><td>0.863<sup>124567</sup></td></tr><tr><td>Mobility service preference</td><td>-0.856<sup>2345678</sup></td><td>1.268<sup>1345678</sup></td><td>-0.578<sup>1245</sup></td><td>0.808<sup>123678</sup></td><td>0.669<sup>123678</sup></td><td>-0.520<sup>1245</sup></td><td>-0.572<sup>1245</sup></td><td>-0.608<sup>1245</sup></td></tr><tr><td>Bike preference</td><td>-0.908<sup>234568</sup></td><td>0.679<sup>1345678</sup></td><td>0.364<sup>124578</sup></td><td>-0.483<sup>123567</sup></td><td>0.976<sup>1234678</sup></td><td>0.381<sup>1245678</sup></td><td>-0.844<sup>234568</sup></td><td>-0.427<sup>123567</sup></td></tr><tr><td>Innovativeness</td><td>-0.643<sup>2458</sup></td><td>1.141<sup>1345678</sup></td><td>-0.653<sup>2458</sup></td><td>0.479<sup>123567</sup></td><td>-0.042<sup>1234678</sup></td><td>-0.588<sup>2458</sup></td><td>-0.748<sup>2458</sup></td><td>0.710<sup>123567</sup></td></tr><tr><td>Habituality</td><td>-0.585<sup>2345678</sup></td><td>0.308<sup>146</sup></td><td>0.539<sup>146</sup></td><td>-0.263<sup>1235678</sup></td><td>0.360<sup>146</sup></td><td>-1.380<sup>1234578</sup></td><td>0.503<sup>146</sup></td><td>0.518<sup>146</sup></td></tr><tr><td>Urbanity</td><td>-0.239<sup>257</sup></td><td>0.182<sup>13</sup></td><td>-0.394<sup>2578</sup></td><td>-0.107<sup>57</sup></td><td>0.343<sup>13468</sup></td><td>-0.068<sup>57</sup></td><td>0.341<sup>1346</sup></td><td>-0.005<sup>35</sup></td></tr></tbody></table>

Note: Description of variables: rail, car, mobility service, and bike preference refer to the propensity and openness to use a travel mode, measured independently for each mode (Kandt et al., 2015); innovativeness refers to openness to novel technologies; habituality refers to behavior automaticity; and urbanity refers to a measure of population density and public transportation access. All variables (except urbanity) were measured on seven-point Likert scales. Urbanity was measured based on the harmonic mean of the standardized logarithm of population density and the standardized square root of the distance from the geographic center of the postcode area to the next long-distance railway station (based on Kandt et al., 2015).

Significance test: the superscripts indicate the cluster number against which the value is significant (based on ANOVA post hoc analysis).
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