Sequential analysis and clustering to investigate users’ online shopping behaviors based on need-states

作者：

Highlights：

• A sequential search pattern analysis and clustering approach is proposed to analyze consumers’ search behavior throughout the entire shopping process from the perspective of need-states.

• We adopt maximal repeat patterns and lag sequential analysis to analyze the sequence of search paths and significant search patterns.

• We identify four groups of consumers who browse for information, adopt recommendations, consult reviews, and conduct searches with different levels of need-states.

• Each group employs its own particular web features to facilitate the shopping process.

摘要

•A sequential search pattern analysis and clustering approach is proposed to analyze consumers’ search behavior throughout the entire shopping process from the perspective of need-states.•We adopt maximal repeat patterns and lag sequential analysis to analyze the sequence of search paths and significant search patterns.•We identify four groups of consumers who browse for information, adopt recommendations, consult reviews, and conduct searches with different levels of need-states.•Each group employs its own particular web features to facilitate the shopping process.

论文关键词：Clustering,Lag Sequential Analysis,Web Features, Need-states,Sequential Search Patterns

论文评审过程：Received 11 December 2019, Revised 11 April 2020, Accepted 6 June 2020, Available online 23 June 2020, Version of Record 23 June 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102323