How to promote sustainable public bike system from a psychological perspective?

ABSTRACT

The public bike system (PBS) has been actively promoted worldwide for the last decade. This study tried to find out policy strategies for sustainable PBS implementation targeting on the city that is under consideration of introducing bike sharing scheme. For this, the authors considered some psychological factors that may make impacts on PBS user's attitudes and hypothesized especially that individual environmental concern refers to an attitude toward environmental issues, influence an increase of their perceived value of PBS. The Norm Activation Model (NAM) is used to measure the public's environmental concern incorporating norm activation. In addition, willingness to pay (WTP) method is adopted to investigate the value of PBS individuals. Structural equation modeling (SEM) revealed that environmental concern influenced people's perception of the value of PBS. Furthermore, the positive correlation between environmental concern and awareness of consequences on cycling is observed. The study verifies how people perceive the value of a bike sharing system and how often people using a bicycle are dependent on their environmental concern. In conclusion, authors discuss how PBS could be promoted sustainably by suggesting policy strategies to enhance the perceived value of PBS and to increase bicycle use.

KEYWORDS:

• Bike sharing
• cycling policy
• environmental concern
• public bike system
• willingness to pay

Funding

This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIP) (NRF-2010-0028693) (NRF-2014R1A1A3052320).

Notes

¹ Transport policy measures to reduce car use are referred to as “travel demand management” (TDM) measures. These are divided into “hard” and “soft” measures. Hard transport policy measures include physical improvements of infrastructure for public transport, increased costs for car use, and control of road space, while soft transport policy measures include methods for voluntary behavioral change, as well as psychological and behavioral strategies (Gärling et al., 2009; Bamberg et al., 2011).

² Goodness-of-Fit-Index (GFI): GFI varies from 0 to 1, but could theoretically yield meaningless negative values. By convention, GFI should be near to or greater than 0.9 for the model to be accepted. The model presented meets this criterion.Normed-fit index (NFI): NFI assesses the model by comparing the χ2 value of the model to the χ2 of the null model, with recommended values greater than 0.90 indicating a good fit. Comparative Fit Index (CFI): CFI assumes that all latent variables are uncorrelated (null/independence model) and compares the sample covariance matrix with this null model. As with the NFI, values for this statistic range between 0.0 and 1.0 with values closer to 1.0 indicating a good fit. (Hooper et al., 2008).