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摘要翻译：
本研究的目的是了解不同的行为考虑，支配人们选择从事人群航运市场。利用美国研究人员收集的新数据，我们建立了离散-连续模型。本文采用二值logit模型来估计群体托运人的工作意愿，并用一个普通的最小二乘回归模型来计算群体托运人对运输和交货时间的最大容忍度。在模型中引入选择性偏差项，修正了群体托运人的工作意愿与最大旅行时间容忍度之间的条件关系。研究结果表明，社会人口特征（如年龄、性别、种族、收入和教育程度）、运输货运经验和社交媒体使用次数对参与人群航运市场的决定有显著影响。此外，大众托运人支付期望被发现是合理的，并与时间价值的文献一致。本文的研究结果有助于众船公司识别和吸引潜在的托运人。此外，了解大众托运人的行为、看法、人口统计、薪酬期望，以及他们愿意在哪些情况下离开他们的路线，对于制定商业战略，如匹配标准和司机合作伙伴的薪酬计划，都是有价值的。
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英文标题：
《Selectivity correction in discrete-continuous models for the willingness
to work as crowd-shippers and travel time tolerance》
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作者：
Tho V. Le and Satish V. Ukkusuri
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
The objective of this study is to understand the different behavioral considerations that govern the choice of people to engage in a crowd-shipping market. Using novel data collected by the researchers in the US, we develop discrete-continuous models. A binary logit model has been used to estimate crowd-shippers' willingness to work, and an ordinary least-square regression model has been employed to calculate crowd-shippers' maximum tolerance for shipping and delivery times. A selectivity-bias term has been included in the model to correct for the conditional relationships of the crowd-shipper's willingness to work and their maximum travel time tolerance. The results show socio-demographic characteristics (e.g. age, gender, race, income, and education level), transporting freight experience, and number of social media usages significant influence the decision to participate in the crowd-shipping market. In addition, crowd-shippers pay expectations were found to be reasonable and concurrent with the literature on value-of-time. Findings from this research are helpful for crowd-shipping companies to identify and attract potential shippers. In addition, an understanding of crowd-shippers - their behaviors, perceptions, demographics, pay expectations, and in which contexts they are willing to divert from their route - are valuable to the development of business strategies such as matching criteria and compensation schemes for driver-partners.
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PDF链接：
https://arxiv.org/pdf/1810.00985
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