Investigating willingness to pay towards shared e-bikes: A comparison of methods

Abstract

Background: Bike-sharing systems (BSSs) are essential for mitigating carbon emissions associated with individual transportation and for enhancing spatial and mobility efficiency. Nonetheless, the full potential of BSSs remains untapped. The introduction of new services, such as rental e-bikes, presents the challenge of establishing optimal pricing strategies that balance revenue generation and market penetration. Different measurement approaches to find optimal prices exist. Our research addresses the willingness to pay (WTP) towards shared e-bikes among users and nonusers of BSSs in order to address user and non-users of BSSs. Furthermore, two separate measurement approaches are utilized for the purpose of a methodological comparison. Methods: Our research uses quantitative data from three surveys (n = 968) to calculate WTP of users and non-users of BSSs towards shared e-bikes. We employ van Westendorp’s Price Sensitivity Meter (PSM) and a multinomial logit (MNL) model based on discrete choice experiments (DCEs) to measure WTP and compare results. Results: We find a strong price sensitivity towards e-bike rentals among users and non-users of BSSs. WTPs for a 30-minute e-bike rental measured with PSM range from €1.98 to €2.95 for users and from €1.40 to €2.85 for non-users. Comparing users only, MNL measurements broadly correspond to the PSM results, offering revenue-maximizing prices from €2.08 to €2.65 and competitiveness-oriented prices from €1.52 to €2.76 depending on the choice scenario. However, MNL results start to diverge from PSM results when considering more variance in the modal choice share for BSS. Conclusion: Our findings suggest that users and non-users of BSSs derive utility from e-bike rental and can be addressed at comparable prices. However, pricing depends on strategic goals. Our comparison of methods when measuring WTP suggests an overall comparability of PSM and MNL and highlights possible synergies. We also highlight the need for future research considering alternative choice modelling approaches, optimizing PSM data collection and subgroup comparisons.