Evaluating the effectiveness of Didi ride-hailing security measures: An integration model

被引:29
|
作者
Jing, Peng [1 ]
Chen, Yuanyuan [1 ]
Wang, Xingyue [1 ]
Pan, Kewen [1 ]
Yuan, Daibiao [1 ]
机构
[1] Jiangsu Univ, Sch Automot & Traff Engn, Zhenjiang 212013, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Ride-hailing; Rectification of security measures; Perceived security; Security risk; Government credibility; Intention to use or reuse ride-hailing; TECHNOLOGY ACCEPTANCE MODEL; PLANNED BEHAVIOR; PUBLIC TRANSPORT; SHARING ECONOMY; PERCEIVED RISK; EXTENDED THEORY; FIT INDEX; SAFETY; INTENTION; TRAVEL;
D O I
10.1016/j.trf.2020.11.004
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Security is one of the most critical factors influencing individuals' mobility. Ensuring security along ride-hailing trips is also a fundamental challenge to service providers. After two cases of rape and homicide, Didi has rectified measures again to meet passengers' need for security. However, there are few scientific findings concerning the impact of Didi rectified measures on personal perception of security. This study aims to explore critical latent factors that affect individuals' intentions to use or reuse ride-hailing after the rectification of security measures. This paper examines individuals' usage intentions by integrating and expanding both the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). Research results suggest that perceived security, security risk, and government credibility are correlated with the intentions to use or reuse ride-hailing. Importantly, perceived security and security risk both have a direct impact on behavioral intentions from a different perspective. In contrast, government credibility has an indirect effect. Hence, a mediating effect test is conducted. Government credibility could indirectly influence behavioral intention by affecting trust. Finally, this study verifies that the effectiveness of security measures could be evaluated and improved by studying the influence of latent factors on the intentions to use or reuse ride-hailing. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:139 / 166
页数:28
相关论文
共 50 条
  • [41] An three-in-one on-demand ride-hailing prediction model based on multi-agent reinforcement learning
    Qiao, Shaojie
    Han, Nan
    Huang, Jiangtao
    Peng, Yuzhong
    Cai, Hongguo
    Qin, Xiao
    Lei, Zhengyi
    APPLIED SOFT COMPUTING, 2023, 149
  • [42] Exploring the spatially heterogeneous effect of the built environment on ride-hailing travel demand: A geographically weighted quantile regression model
    Liu, Fang
    Gao, Fan
    Yang, Linchuan
    Han, Chunyang
    Hao, Wei
    Tang, Jinjun
    TRAVEL BEHAVIOUR AND SOCIETY, 2022, 29 : 22 - 33
  • [43] Target encirclement of moving ride-hailing vehicle under uncertain environment: A multi-vehicle mutual rescue model
    Luo, Dongyu
    Wang, Jiangfeng
    Lu, Wenqi
    Chen, Lei
    Gao, Zhijun
    Dong, Jiakuan
    COMPUTERS & OPERATIONS RESEARCH, 2022, 146
  • [44] Sustainability of ride-hailing services in China's mobility market: A simulation model of socio-technical system transition
    Lee, Junmin
    Kim, Jiyong
    Kim, Hongbum
    Hwang, Junseok
    TELEMATICS AND INFORMATICS, 2020, 53
  • [45] Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty
    Megantara, Tubagus Robbi
    Supian, Sudradjat
    Chaerani, Diah
    SUSTAINABILITY, 2022, 14 (17)
  • [46] Analyzing the relationship between bus and ride-hailing use in a large emerging economy city: A bivariate ordered probit model application
    Ribeiro, Marcelle Dorneles
    Lucchesi, Shanna Triches
    Larranaga, Ana Margarita
    Lavieri, Patricia Sauri
    Cheng, Yu -Tong
    JOURNAL OF PUBLIC TRANSPORTATION, 2024, 26
  • [47] Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership
    Wang, Zhenbao
    Gong, Xin
    Zhang, Yuchen
    Liu, Shuyue
    Chen, Ning
    SUSTAINABILITY, 2023, 15 (06)
  • [48] DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data
    Xie, Yizhen
    Ni, Qichao
    Alfarraj, Osama
    Gao, Haoran
    Shen, Guojiang
    Kong, Xiangjie
    Tolba, Amr
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2020, 2020
  • [49] Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin-Destination Ride-Hailing Demand Prediction
    Lin, Hongyi
    He, Yixu
    Liu, Yang
    Gao, Kun
    Qu, Xiaobo
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2024, 16 (03) : 111 - 124
  • [50] A Spatiotemporal Bidirectional Attention-Based Ride-Hailing Demand Prediction Model: A Case Study in Beijing During COVID-19
    Huang, Ziheng
    Wang, Dujuan
    Yin, Yunqiang
    Li, Xiang
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (12) : 25115 - 25126