Effects of the User-Need State for Online Shopping: Analyzing Search Patterns

Hsin Kai Yu, I. Chin Wu

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

1 Citation (Scopus)

Abstract

With the fast growth of e-commerce and the emerging trend of “New Retail”—that is, online and offline integration—the important research issues are how to know the best ways to collect and analyze users’ search behaviors online for a streamlined shopping process. Accordingly, we proposed a search pattern analytical method to analyze users’ search behavior in the entire shopping process on the target website from the perspective of the users’ need states. We have focused on the recommendation functions (RFs) and the search functions on Taobao.com to evaluate the effectiveness of each RF to support the online shopping process in different user-need states, namely in a goal-oriented or an exploratory-based approach to online shopping. We first adopted zero-order state transition matrices and then used lag sequential analysis (LSA) to derive the significant repeating search patterns. The results show that the goal-oriented shoppers tend to search directly, whereas exploratory shoppers tend to explore the categories of products as their initial RFs. In addition, goal-oriented users have much more simple search paths compared to the exploratory-based users when engaged in online shopping. Furthermore, based on the results of the LSA, there are two typical search patterns for goal-oriented users and no search pattern for the exploratory ones. Interestingly, the results reveal that exploratory-based users are easily stimulated by context even if they have moved to specific stores. The aim of this research is to summarize users’ search paths and patterns with different need states to help the e-store design the website.

Original languageEnglish
Title of host publicationInformation in Contemporary Society - 14th International Conference, iConference 2019, Proceedings
EditorsMichelle H. Martin, Bonnie Nardi, Natalie Greene Taylor, Caitlin Christian-Lamb
PublisherSpringer Verlag
Pages554-561
Number of pages8
ISBN (Print)9783030157418
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event14th International Conference on Information in Contemporary Society, iConference 2019 - Washington, United States
Duration: 2019 Mar 312019 Apr 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11420 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Information in Contemporary Society, iConference 2019
CountryUnited States
CityWashington
Period19/3/3119/4/3

Keywords

  • Lag sequential analysis
  • Need state
  • Recommendation function
  • Search patterns

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yu, H. K., & Wu, I. C. (2019). Effects of the User-Need State for Online Shopping: Analyzing Search Patterns. In M. H. Martin, B. Nardi, N. G. Taylor, & C. Christian-Lamb (Eds.), Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings (pp. 554-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11420 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15742-5_53