摘要
为了破解当前新业态下外卖骑手的危险骑行难题,降低由此引发的道路交通事故发生率,基于工作要求-资源模型,探究平台算法控制与外卖骑手危险骑行之间的关系。使用SPSS 20.0和MPLUS 7.0软件对376份外卖骑手样本数据进行分析,揭示算法行为约束、算法追踪评估、算法规范指导3种类型的算法控制对外卖骑手危险骑行的影响。结果表明:外卖骑手在骑行过程中普遍存在危险骑行行为,且男性、年轻、未婚、受教育程度低和收入水平低的骑手表现出更高的危险骑行发生率;感知算法行为约束对危险骑行具有正向影响,但感知算法追踪评估和算法规范指导均对危险骑行产生非线性的U型影响,感知算法追踪评估和算法规范指导过低或过高都会增加危险骑行。
In order to address the serious challenge of risky riding among take-away riders in the current new business paradigm and reduce the resulting traffic accident rates,the job demands-resources model was applied to explore the relationship between platform algorithm control and risky riding of take-away riders.Using SPSS 20.0 and MPLUS 7.0 software,a total of 376 samples of take-away riders were analyzed to reveal the effects of three types of algorithmic controls(behavioral constraints,tracking evaluation,and standardized guidance)on risky riding of take-away riders.The results indicated that there is a widespread prevalence of risky riding behaviors among take-away riders during their delivering process.Take-away riders who are male,young,unmarried,and have lower levels of education and income exhibit a higher prevalence of risky riding.Perceived algorithmic behavioral constraint has a positive effect on risky riding,while both perceived algorithmic tracking evaluation and algorithmic standardized guidance have a nonlinear U-shaped impact on risky riding,with either low or high levels of perceived algorithmic tracking evaluation and algorithmic standardized guidance increasing the likelihood of risky riding.
作者
吴金南
齐娟
刘林
WU Jinnan;QI Juan;LIU Lin(School of Business,Anhui University of Technology,Ma′anshan 243032,China;不详)
出处
《武汉理工大学学报(信息与管理工程版)》
2025年第2期245-251,共7页
Journal of Wuhan University of Technology:Information & Management Engineering
基金
国家自然科学基金项目(72304002)
安徽省高校优秀科研创新团队项目(2023AH010018)
安徽省优秀青年教师培育重点项目(YQZD2023028)。
关键词
工作要求-资源模型
外卖骑手
算法控制
危险骑行
job demands-resources model
take-away rider
algorithmic control
risky riding
