Experimental Study on Shared Bike Use Behavior under Bounded Rational Theory and Credit Supervision Mechanism

Abstract

As a new travel model, the bike-sharing system (BSS) solves the ‘last kilometer’ problem and has developed rapidly for its convenience. However, many accompanying problems have emerged. In China, parking violation problems—such as severe traffic congestion—are caused by dock-less shared bikes. Furthermore, a large number of shared bikes have to be scrapped early for vandalism. As a special form of public good, bike-sharing also faces the dilemma of negative externalities. Seeking a solution, Mobike has conducted a credit supervision mechanism, which transfers the users’ different behavior to credits for user behavior regulation, but with unsatisfactory results. The goal of the paper is to test the validity of credit supervision mechanism from user’s perspective to regulate the abuse of sharing bike by simulating the use scenario of BSS in real life in a lab experiment based on induced value theory. The behavioral and pre- and post-experiment survey data were thoroughly analyzed. The results show that, within a negative context, the credit supervision system has a more significant effect on inducing proper user behavior, which improves after adding a real-time feedback mechanism. Finally, we provide effective suggestions to policy makers and shared bike companies for inducing positive user behavior.

Improving Cycling Behaviors of Dockless Bike-Sharing Users Based on an Extended Theory of Planned Behavior and Credit-Based Supervision Policies in China

“…are taking corresponding measures to force operators to strengthen the management of the bicycle-sharing systems through limits on the number of releasing bikes, real-name registration system for users, etc. Bicycle-sharing companies like Ofo and Mobike have also begun to apply electronic fence technology and to set up urban recommended parking stations to guide users to ride civilly (Yao et al, 2019). Unfortunately, illegal parking, traffic violations, vandalism, and theft still occur frequently (Shen et al, 2018).…”

Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era

“…The credit supervision mechanism may work with the condition that the parking behavior of cyclists can be effectively monitored and sanctioned [29]. Yao et al further demonstrated that the credit supervision mechanism was more appealing to attract cyclists to ride shared bikes when a negative credit is introduced [30]. The social interaction influences cyclists' bikesharing choice by inducing family members or close friends to make the same choices [31].…”

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

“…This paper provides an additional configural nuance in our understanding of the determinants of remixing that could have been only very partially intuited based on simple regression analyses. Most prior studies provided a good understanding of the underlying mechanisms of shared bike use [3,16], however, few studies have focused on the configural nuance and effective path of bike sharing users' intentions to continue. This paper further contributes to the existing data base by exploring six combinations of variables that are sufficient conditions for explaining users' continued usage intention.…”

“…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.…”