What do you purchase online and who you are: Evidence from the 2018 California Survey of Emerging Transportation Trends 

Abstract
Title:
What do you purchase online and who you are: Evidence from the 2018 California Survey of Emerging Transportation Trends

Niloufar Yousefi, Corresponding Author
Institute of Transportation Studies, University of California, Davis
1605 Tilia Street, Davis, CA, 95616
Tell: 5309793051 Email: nyousefi@ucdavis.edu

Kailai Wang, Ph.D.
Assistant Professor of Supply Chain and Logistics Technology
Department of Construction Management, College of Technology,
University of Houston, 4730 Calhoun Road, Houston, TX 77004, USA
Email: kwang43@central.uh.edu

Giovanni Circella
Institute of Transportation Studies, University of California, Davis
1605 Tilia Street, Davis, CA, 95616
Tell: 530 752 1072 Email:gcircella@ucdavis.edu


ABSTRACT
This study contributes to the literature by analyzing the factors that influence consumer decisions to purchase different commodities online. As the development and growth of e-commerce activities continue, it is becoming more and more crucial for us to understand consumer preferences better. In this study, we analyze a research dataset collected in 2018 as part of the California Panel Study of Emerging Transportation Trends. Two state-of-the-art theories guide the execution of our research in terms of diffusion of innovations theory and efficient market hypothesis. We find that two hypotheses can explain the links between neighborhood characteristics and online shopping frequency for different types of commodities. Sociodemographic traits explain people's online purchasing behaviors significantly. This study also shows that people who drive more often are less interested in shopping online. The research results can provide valuable insights and help assess the impacts of e-shopping behaviors. Developing merchandised-based policies based on customer online shopping preferences is beneficial to a sustainable freight transport system.


Keywords:
E-commerce, Types of Merchandises, Spatial Attributes, Multivariate Ordered Probit Model