Psychosocial factors influencing shared bicycle travel choices among Chinese: An application of theory planned behavior

Abstract

The worldwide rise of shared bicycle use has changed the way people travel. Here we analyze shared bicycle use from the perspective of the theory of planned behavior, and propose a model to investigate factors influencing shared bicycle usage in China. A total of 211 shared bicycle users selected from 28 provinces throughout China completed a self-reported survey. Structural equation modelling (SEM) was used to delineate the pathway from shared bicycle usage. The SEM model demonstrated that: (1) shared bicycle use intention was significantly associated with four variables, namely travel attitude(β = 0.491, t = 24.569), social norms(β = 0.149, t = 6.771), travel habits(β = 0.146, t = 7.226) and perceived behavioral control (β = 0.190, t = 11.110); (2) shared bicycle use behavior was significantly affected by shared bicycle use intention(β = 0.406, t = 15.936), and also by travel habits(β = 0.320, t = 11.921); (3) shared bicycle use behavior was also affected by demographic variables (gender, age) and situational factors (distance). The conclusions of this study provide useful data for operators of bicycle services and government policy makers.

Planned Behavior and Intention to Study Abroad Concerning Family Rally Relationship: Case in Vietnam

“…Subjective data has some inherent disadvantages that are hard to avoid in surveys [59]. Our data is collected over a single period of productional data do not allow a dynamic assessment of changes in students' intentions and related behaviours regarding their college admissions, which may affect their applicability [60]. The intentional sampling method has certain limitations, not fully reflecting population characteristics [61].…”

Travel Mode Choice Intention in Reducing Traffic Congestion in Kajang, Selangor

“…The discriminant validity was assessed using the Fornell and Larcker criteria, as well as through cross-loadings (Hussain et al, 2018;Hair et al, 2011). According to Xin et al (2019), the square root of the AVE for the measured variables should be larger than their correlation coefficients, suggesting a strong discriminant validity for the Fornell and Larcker criteria. To calculate cross-loadings, however, each indicator loading must be at their highest for their assigned sub-construct on the same row (Hair et al, 2011).…”

How to improve users’ intentions to continued usage of shared bicycles: A mixed method approach

“…The questionnaire method was used to validate the conceptual model ( Table 1). All of the measured items were taken from other published studies: service quality was adapted from Oghuma, et al [43]; system quality was from Chen [44]; perceived usefulness and perceived ease of use were taken from Ma, et al [4]; habit was adapted from Xin, et al [2]; customer satisfaction was derived from Zhang, et al [45]; and, finally, the measures of continued usage intention were adapted from Bhattacherjee [20]. The questionnaires gauged responses on a 7-point Likert scale.…”

“…We examined how factors personal to users, inherent in the system, weather and trip distance all affected the decisions of riders to continue bike sharing. Previously published research on bike-sharing mainly focused on factors influencing the choice of bicycles [1,2], the shared bike user's subjective wellbeing [4], the diffusion of public bike sharing systems [5], and bike sharing's impact on other types of transportation such as private cars [6]. Few studies have examined what aspects of the service most influenced the decisions of riders to stop or continue using bike-sharing.…”

“…A review of the existing literature on bike sharing, showed that studies mainly focused on factors influencing the choice of bicycle [1,2]; shared bike use behavior [3]; shared bike users' subjective well-being [4]; the diffusion of public bike sharing systems [5]; the influence of bike sharing on other transportation modes such as car use [6]; the impact of weather conditions on bike use [7]; the effects of the built environment on bike sharing demand [8]; bike share rebalancing strategies, patterns, and purposes [9]; bike share safety [10]; bike share demand [11]; impact of pricing and transit disruptions on bike sharing [12]; optimization models for bike-sharing problems with transshipment [13]; bike share stations [14]; bike sharing travel patterns [15]; and, the shared bike users' recommendations [16]. However, few studies have examined what controls the shared bike users' intentions to continued or discontinued usage of shared bikes.…”