Combining SEM, fsQCA and BNs to Explore E-Bike Riders’ Helmet Wearing Intentions under the Impact of Mandatory Policies: An Empirical Study in Zhenjiang

Abstract

China has become the largest country for e-bikes in the past decade, leading to a surge in road-related accidents. Chinese provinces and cities have successfully formulated local regulations, which set fines for not wearing helmets levying on e-bike riders. Without considering psychological resistance which is universal under the premise of compulsory legislation, the legislation and enforcement activities cannot continuously promote electric bikers’ helmet use. This study aims to investigate the predictors that influence e-bike riders’ intention to wear helmets by designing a research methodology that incorporates the theory of planned behavior (TPB), the protection motivation theory (PMT), and the psychological reactance theory (PRT). A multi-method analytical approach, including structural equation modeling (SEM), fuzzy-set qualitative comparative analysis (fsQCA), and a Bayesian Network (BN) with a sample dataset of 846 respondents. SEM and fsQCA explored the intentions to wear helmets from linear and nonlinear perspectives. BN verifies the degree of influence between different configurations resulting from fsQCA, to predict which mental configurations could get the most significant impact on helmet use. A total of 11 configurations lead to high helmet-wearing intention, and 4 lead to low helmet-wearing intention. Policymakers could design safety policies on our research, such as driving licenses and special lectures for e-bike riders.