Determinants of switching behavior to wear helmets when riding e-bikes, a two-step SEM-ANFIS approach

Abstract

E-bikes have become one of China’s most popular travel modes. The authorities have issued helmet-wearing regulations to increase wearing rates to protect e-bike riders’ safety, but the effect is unsatisfactory. To reveal the factors influencing the helmet-wearing behavior of e-bike riders, this study constructed a theoretical Push-Pull-Mooring (PPM) model to analyze the factor’s relationship from the perspective of travel behavior switching. A two-step SEM-ANFIS method is proposed to test relationships, rank importance and analyze the combined effect of psychological variables. The Partial Least Squares Structural Equation Model (PLS-SEM) was used to obtain the significant influencing factors. The Adaptive Network-based Fuzzy Inference System (ANFIS), a nonlinear approach, was applied to analyze the importance of the significant influencing factors and draw refined conclusions and suggestions from the analysis of the combined effects. The PPM model we constructed has a good model fit and high model predictive validity (GOF = 0.381, R2 = 0.442). We found that three significant factors tested by PLS-SEM, perceived legal norms (β = 0.234, p < 0.001), perceived inconvenience (β = -0.117, p < 0.001) and conformity tendency (β = 0.241, p < 0.05), are the most important factors in the effects of push, mooring and pull. The results also demonstrated that legal norm is the most important factor but has less effect on people with low perceived vulnerability, and low subjective norms will make people with high conformity tendency to follow the crowd blindly. This study could contribute to developing refined interventions to improve the helmet-wearing rate effectively.