Selectivity correction in discrete-continuous models for the willingness to work as crowd-shippers and travel time tolerance

TL;DR

This study develops discrete-continuous models with selectivity correction to analyze factors influencing willingness to work as crowd-shippers and their travel time tolerance, based on novel US data.

Contribution

It introduces a selectivity-bias correction in discrete-continuous models for crowd-shipping behavior analysis, incorporating socio-demographics and behavioral factors.

Findings

Socio-demographics significantly influence willingness to participate.

Crowd-shippers' pay expectations align with existing value-of-time literature.

Model results aid in developing targeted crowd-shipping strategies.

Abstract

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.