Riding a bicycle is environmentally friendly and good for your health – so it’s no wonder that 64% of Germans own a bicycle (Statista, 2021a). The number of bicycles in Germany last year was higher than ever before at around 79.1 million (Statista, 2021b). However, if you don’t own a bike, or don’t have it with you at the moment, many German cities offer the option of renting one. So-called bike-sharing systems have become increasingly popular in recent years. In the city of Berlin, for example, there are more than 15,000 rental bikes (Technologiestiftung Berlin, 2019).
There are two general types of bike-sharing systems. The first one is station-based bike-sharing, where bicycles are picked up and returned at self-serving docking stations. The second model is free-floating. Here you can simply leave the bicycle at any place, for example, on the side of the road. Usually, you can rent the bike using an app after registering once. In addition to regular bicycles, there is an increasing number of e-bikes being integrated into bike-sharing systems (note: the correct term is ‘pedelcs’; however, this is rarely used among most users).
Bike-sharing systems have many advantages, such as offering a sustainable and affordable mobility alternative for residents and visitors. Also, bike-sharing systems can bridge existing gaps in the public transport network, such as covering the distance between a public transport station and a person’s home – the so-called last mile (Shaheen et al., 2010). However, before designing a bike-sharing system, it is important to examine the acceptance of (potential) users, including their expectations and potential usage.
In 2017, we conducted a first study, in which the acceptance of a potential e-bike sharing system was evaluated. In order to examine the acceptance, the Unified Theory of Acceptance and Use of Technology – Extended (UTAUT; Venkatesh et al., 2012) was used, in which the acceptance is captured as behavioural intention. Via an online survey, we gathered data of 372 participants (aged 18 to 29 years) answering several questions about their beliefs and attitudes towards e-bike sharing. In the subsequent analysis, we saw that the biggest influence on the acceptance of e-bike sharing was the performance expectancy, e.g. reaching the destination faster and being independent of the road traffic and the public transport. Further, the participant’s evaluation of the simplicity of using e-bike sharing had a huge influence on the acceptance. Moreover, social influence (participant’s belief how others think about e-bike sharing), pleasure-related motivation, and habit (if people already bike a lot) influenced the acceptance of e-bike sharing.
Based on these results, we worked out the most important levers for increasing the acceptance of e-bike sharing for two target groups, namely the high- and the low-interested people. Participants were asked how likely they would use e-bike sharing on a Likert Scale from 1 (extremely unlikely) to 7 (extremely likely). The high-interested group was made up of those who answered with 5-7, the low-interested group of those who answered with 1-3. Interestingly, the factors leading to a higher acceptance differed between these two groups. In the interested group (n = 112) the acceptance was higher when participants evaluated the use of e-bike sharing as joyful and fun. Further, the independence from road traffic, departure times of public transport, and the simplicity of using the sharing app led to a higher acceptance. Contrarily, participants mentioning a lower interest in using the e-bike sharing system (n = 201) showed a higher acceptance when realizing the environmental aspects, the faster arrival, and when thinking that peers would welcome the use of it. In sum, we have learned that potential users need to be addressed in a target group-specific way to enhance the use of the e-bike sharing system. While among already interested people emotional aspects enhance the acceptance, rational and social arguments can convince lower interested people.
To clarify the potential use of bike-sharing, we conducted a further survey (n = 101) in the city centre of Stuttgart in 2019. The sample consisted of 87% of people who were interested in bike-sharing but had never used it before (i.e., potential users). The remaining 13% of respondents were members of one or more bike share systems (i.e., actual users). Among other things, potential users were asked for which trips they would use bike-sharing, such as trips to work or for leisure activities. Actual users reported the trip purposes for which they were currently using bike-sharing. In addition, the survey investigated whether e-bikes were preferred for different trips over regular bicycles. Since there were only few people in the sample who used bike-sharing at the time, the data of potential and actual users were analysed together.
Here are our main findings:
- Around 50% of respondents would use bike-sharing for trips to leisure activities, such as driving to the gym or visiting friends, as a tourist, or for running private errands, such as visiting a doctor.
- Around 40% of respondents would use bike-sharing as a sport, at night times when there are rare or no public transport connections, or for travelling to public transport stations.
- Around 30% of respondents would use bike-sharing for shopping or commuting to work.
- E-bikes and regular bicycles are preferred for similar trip purposes. However, the respondents would prefer to travel a distance of 5km with e-bikes, and a distance of 2km with regular bicycles.
Differences of our results to findings of other studies (Buck et al., 2013; LDA Consulting, 2015; Mineta Transportation Institute, 2012) mainly consist of the comparatively low preference to use bike-sharing for commuting. However, previous studies largely focussed on actual bike-sharing users, whereas most respondents in the present study were only potential users. Therefore, user preferences might differ between early bike-sharing adopters and potential bike-sharing users. In addition, our study was conducted specifically for the Stuttgart area. The results for Stuttgart are not fully comparable with results from other metropolitan regions in Germany or worldwide.
What can we learn from these results?
One prominent implication of the results relates to the strategic placement of rental stations. Bike-sharing stations should be installed near sports and leisure facilities, such as fitness centres, restaurants, shopping facilities, tourist attractions, event locations, or other points of interest. Furthermore, it seems reasonable to place bike-sharing stations near public transport stations in order to make it easier to cover the last mile.
The studies also showed that cities need to think broadly about bike-sharing systems to meet the needs of different target groups, such as leisure users, tourists, and commuters. This may include special tariffs for tourists and casual users, the possibility to rent a bicycle spontaneously without registration, or a built-in navigation system with trip suggestions for tourists. Similarly, the communication and advertisement should be in a target-group specific manner, highlighting emotional aspects for convincing interested people and rational/social aspects for attracting lower-interested people.
References:
Buck, D., Buehler, R., Happ, P., Rawls, B., Chung, P. & Borecki, N. (2013). Are Bikeshare Users Different from Regular Cyclists? Transportation Research Record: Journal of the Transportation Research Board, 2387(1), 112–119. https://doi.org/10.3141/2387-13 
LDA Consulting (2015). 2014 Capital Bikeshare Member Survey Report. Retrieved August 10, 2019, from https://d21xlh2maitm24.cloudfront.net/wdc/cabi-2014surveyreport.pdf?mtime=20161206135936
Mineta Transportation Institute (2012). Public Bikesharing in North America: Early Operator and User Understanding. Retrieved August 13, 2019, from https://rosap.ntl.bts.gov/view/dot/24566
Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2143(1), 159–167. https://doi.org/10.3141/2143-20
Statista. (2021a). Jeder Zehnte besitzt ein E-Bike. Retrieved June 22, 2021, from https://de.statista.com/infografik/24784/umfrage-welche-fahrrad-typen-die-deutschen-besitzen/
Statista. (2021b). Anzahl der Fahrräder in Deutschland von 2005 bis 2020. Retrieved June 22, 2021, from https://de.statista.com/statistik/daten/studie/154198/umfrage/fahrradbestand-in-deutschland/
Technologiestiftung Berlin. (2019). Leihfahrräder in Berlin: Erste Auswertungen. Retrieved June 22, 2021, from https://lab.technologiestiftung-berlin.de/projects/bike-analysis/de/
Venkatesh, V. & Thong, James, Xu, Xin. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36 (1), 157–178.
