Increasing Bike-Sharing Users’ Willingness to Pay — A Study of China Based on Perceived Value Theory and Structural Equation Model

Abstract

Bike sharing, as an innovative travel mode featured by mobile internet and sharing, offers a new transport mode for short trips and has a huge positive impact on urban transportation and environmental protection. However, bike-sharing operators face some operational challenges, especially in sustainable development and profitability. Studies show that the customers’ willingness to pay is a key factor affecting bike-sharing companies’ operating conditions. Based on the theories of perceived value, this study conducts an empirical analysis of factors that affect bike-sharing users’ willingness to pay for bike-sharing through measurement scales, user surveys, and structural equation models. We designed a five-point Likert-type scale containing 11 latent variables affecting willingness to pay and a total of 34 measurement items. We investigate bike-sharing users in China’s first and second-tier cities, with a total of 502 participants. The results show that perceived value, payment awareness, trust, and environmental awareness constitute key factors that directly affect bike-sharing users’ willingness to pay. And perceived usefulness, perceived ease-of-use, perceived cost, and perceived risk indirectly affect bike-sharing users’ willingness to pay. However, we found no significant effects of perceived entertainment on perceived value or word of mouth on willingness to pay. Our results are expected to provide theoretical and practical implications for bike-sharing programs.

Adoption of Electric Motorcycles in Pakistan: A Technology Acceptance Model Perspective

“…The degree to which consumers value a product's offering will determine how widely that product is used [30]. PV is the understanding by the customer of the perceived interest in any action or item, taking into account the net benefits and costs of consumption, consistent with previous studies [35,36]. Considering the facts, the present study proposes that perceived value is an influencing factor for the intentions to use EMs and postulates Hypothesis 4.…”

“…Perceived value is a trade-off between the received benefits and sacrifices. Where perceived benefits relate to positive consequences brought by a specific product (feelings of usefulness, ease-of-use, and entertainment), and perceived sacrifices relate to what is given up to enjoy that product (risk, effort) [35]. Previous Studies indicate these elements of perceived benefits and perceived sacrifices can be direct factors affecting users' willingness to use products [36].…”

A Comparison in Travel Characteristics of Bike-Sharing between College Students and Office Workers Based on Theory of Planned Behavior

“…Among the variables that affect the travel intention of office workers, AT, SN, and PBC have a significant positive impact on TBI, with SN having the largest impact on TBI and PBC having the smallest impact on TB. Unlike college students, “media publicity” and “policy guidance” have a greater impact on the choice of BS travel by office workers [ 53 , 54 ], which may be due to the proportion of government staff among respondents. PBC has a positive impact on TBI, but its significance is lower than that of college students.…”

“…The results show that environmental protection awareness variables have a positive impact on public transport travel choice behavior. Some scholars have also explored the relationship between environmental awareness and BS usage and found that the willingness to use BS is enhanced by low-carbon travel and environmental awareness [ 54 , 55 ]. Therefore, this study includes EA as an expanded variable in the TPB structure.…”

“…Model test indicators contain absolute goodness-of-fit test indicators, including the root mean square of the approximation error (RMSEA) and goodness-of-fit index (GFI), value-added goodness-of-fit test indicators, including normative fitting index (NFI), comparative fitting index (CFI), value-added fitting index (IFI), and degree of freedom of adjustment goodness-of-fit index (AGFI). Among them, the requirements of each index are: the RMSEA should be less than 0.08, CFI, GFI, and IFI should be greater than 0.9, and the other indexes should be greater than or close to 0.85 [ 15 , 54 ]. As shown in Table 5 , all indicators meet the standard requirements, the fitness index of the model meets the test standard, and the standardized path coefficient can be analyzed [ 16 , 22 , 56 ].…”