A large-scale study of online shopping behavior

Soroosh Nalchigar, Ingmar Weber, Parisa Lak, Ayse Bener

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The continuous growth of e-commerce has stimulated great interest in generating theories and models for online consumer behavior. While studies on online consumer behavior are widespread, research on relating Internet browsing activities to online shopping behavior are scarce. This paper provides an exploratory analysis on the relationship between online browsing habits and consumers' pre-shopping effort, as one of the indicators of shopping behavior. The data used in this study was extracted from 88,637 users with more than half a million shopping instances from two large online retailers, Amazon and Walmart. Our findings provide insights for scholars to form hypotheses and design models or theories to explain online consumer behavior. Practitioners may also use the results of this study to make strategic decisions.

Original languageEnglish
Title of host publicationProceedings of the 20th International Database Engineering and Applications Symposium, IDEAS 2016
PublisherAssociation for Computing Machinery
Pages290-295
Number of pages6
Volume11-13-July-2016
ISBN (Electronic)9781450341189
DOIs
Publication statusPublished - 11 Jul 2016
Event20th International Database Engineering and Applications Symposium, IDEAS 2016 - Montreal, Canada
Duration: 11 Jul 201613 Jul 2016

Other

Other20th International Database Engineering and Applications Symposium, IDEAS 2016
CountryCanada
CityMontreal
Period11/7/1613/7/16

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Keywords

  • Big data
  • Data science
  • Online consumer behavior

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Nalchigar, S., Weber, I., Lak, P., & Bener, A. (2016). A large-scale study of online shopping behavior. In Proceedings of the 20th International Database Engineering and Applications Symposium, IDEAS 2016 (Vol. 11-13-July-2016, pp. 290-295). Association for Computing Machinery. https://doi.org/10.1145/2938503.2938534