Adoption of Electric Bicycle for the Daily Commute: Investigating Behavioral Factors in a Pilot Study

Cycling as a way of commuting is becoming more attractive from the eyes of the public, employers, and policymakers. By offering the ability to go faster with less physical effort, electric bicycles (e-bikes) can motivate more people to cycle to work. While the numbers of e-bicycles sold in the Netherlands is increasing (BOVAG-RAI, 2017; Plazier, Weitkamp, & van den Berg, 2017b), to reduce the amount of cars in traffic there have been initiatives to help e-bikes penetrate the market faster. However, there is a lack of research on the behavioral process to guide intervention-designers on how to facilitate this process further. To build on this gap, a case study in taken on hand: the green mobility program of Delft University of Technology (TU Delft) to explore the research question: What explains the behavioral factors behind the adoption of e-bikes as the daily commute? Within the program, there is an e-bike pilot that allows people to replace their cars with an e-bike for two months. Using TU Delft's e-bike adaptors and triers as a sample, this thesis aims to answer what explains the behavioral factors behind the adoption of e-bikes as the daily commute. A theory is needed to explain the behaviour scientifically. Theory of Planned Behaviour (TPB) of Ajzen (1991) was chosen as it has been used in similar research before. This theory assumes that people's attitudes, perceived behavioral control (PBC) and subjective norms (SN) around the behaviour affects their intention, which affects the behaviour together with PBC. For this case, behaviour is defined as buying the e-bike, and an exploratory conceptual model is built. Surveys have been used to collect data that has been used for quantitative estimation of the model. Multiple regression and descriptive statistics have been used for the estimates. As the sample size was relatively small (n=75), and the survey did not cover all factors of the TPB, to support the quantitative part, interviews were conducted with pilot participants and current e-bikers. Triangulation of quantitative and qualitative methods allowed concluding the research question and supported each other's limitations. The results show that TPB can be used to explain people's intention to buy an e-bike is affected by their attitudes, perceived behavioral controls, and intentions that surrounds commuting with an e-bike. Factors such as time savings, prices, convenience, enjoyment, responsibilities, health, weather, and societal perceptions affect people's intentions to use an e-bike or not. The most important finding is that these intentions are mainly blocked by the financial barrier. Options such as leasing or financial subsidies can be explored to lower this barrier. These findings were mainly in line with previous work of other researchers, and they have several societal and scientific implications. First, as the results are in line with research from countries with different cycling maturities, it shows that Dutch cycling research can be relevant for other settings as well. Second, while it is not a full confirmatory model, it can be said that TPB can indeed explain adopting e-cycling as a habit. However, further research is needed with a larger sample and a broader range of questions. Third, findings indicate that pilots are successful in facilitating e-cycling as they can change attitudes and PBC of individuals and SN indirectly. Fourth, as the initial investment of an e-bike is relatively higher than of a regular bicycle, this price difference remains as one of the main reasons behind the gap between intention and behaviour as people are risk-averse. Options, such as leasing, which will be easier with upcoming regulations in Dutch law, can lower the threshold. Fifth, faster e-bikes, speed pedelecs could facilitate cycling more as they allow better time savings. In short, policy-makers and employers can use pilots and better policies to increase e-cyclists and lower drivers. Several gaps have been identified. Future researchers can look into these to generate better and more important results. Moreover, they can use the exploratory conceptual models that result from the research to build better surveys that use TPB to explain changes in commuting mode changes, even outside the domain of e-bikes. For practical purposes, this thesis has important outcomes for TU Delft. Firstly, many employees expect the university to take measures to give incentives for cycling financially, such as participating in the new tax scheme that makes leasing easier or adopting a bicycle plan again. Secondly, participants and employees generally saw the pilot positively, especially the way it was professionally organized. Thirdly, it is possible that promoting cycling is not enough to push people out of the cars, and additional car-demotivating actions should be put into place. Lastly, a faster e-bike option, speed pedelecs could get more people to leave their cars as time savings are higher. It also gives insights into the behavioral process of changing commuting mode, of which pilot designers and policymakers can utilize to design better interventions to motivate people to switch to e-bikes. To extend the knowledge in the area, researchers can firstly focus on longitudinal studies with larger sample sizes to further investigate the suitability of TPB to offer guidance for policy-makers. Second, they can investigate how different financial incentives affect people's intentions and behaviour. Third, speed pedelecs need more attention as they have different barriers and different motivators, at least in the Netherlands. Fourth, e-bike research and pilots in non-cycling mature contexts could be interesting as the impact can is larger. Fifth, other theories can be used in similar research as well to compare whether or not they fit better than TPB or not. In conclusion, this thesis fits adopting e-biking into a behavioral theory context and offers insights to those who work to increase the percentage, in a similar context to TU Delft or not.Complex Systems Engineering and Management (CoSEM