TY - GEN
T1 - Using sequence analysis to classify web usage patterns across websites
AU - Jiang, Qiqi
AU - Phang, Chee Wei
AU - Tan, Chuan-Hoo
AU - Wei, Kwok Kee
PY - 2011
Y1 - 2011
N2 - This study applies sequence analysis to identify the distinct web browsing patterns based on 200 China users' 30-days web usage. Our results reveal four key, unique web navigation behavior categories, namely search-information browsing, social-information browsing, ecommerce-information browsing, and direct browsing. Of these, the ratio of ecommerce activities in the social-information cluster is higher than the others, with the exception of the ecommerceinformation cluster. To test the robustness of the proposed method based on our classification, we also summarize the characteristics of each category after they were segmented according to two demographic indicators, i.e. gender and occupation. Different online shopping behaviors are also discussed through the proposed classified groups. Complementing the extant methods which are based on within-website categorization of consumers, the demonstration of the sequence analysis application to e-commerce affords a deeper, integrated understanding of an individual's online activity and behavior (i.e., navigation across multiple websites). © 2012 IEEE.
AB - This study applies sequence analysis to identify the distinct web browsing patterns based on 200 China users' 30-days web usage. Our results reveal four key, unique web navigation behavior categories, namely search-information browsing, social-information browsing, ecommerce-information browsing, and direct browsing. Of these, the ratio of ecommerce activities in the social-information cluster is higher than the others, with the exception of the ecommerceinformation cluster. To test the robustness of the proposed method based on our classification, we also summarize the characteristics of each category after they were segmented according to two demographic indicators, i.e. gender and occupation. Different online shopping behaviors are also discussed through the proposed classified groups. Complementing the extant methods which are based on within-website categorization of consumers, the demonstration of the sequence analysis application to e-commerce affords a deeper, integrated understanding of an individual's online activity and behavior (i.e., navigation across multiple websites). © 2012 IEEE.
UR - https://www.scopus.com/pages/publications/84857973843
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84857973843&origin=recordpage
U2 - 10.1109/HICSS.2012.631
DO - 10.1109/HICSS.2012.631
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780769545257
SP - 3600
EP - 3609
BT - Proceedings of the Annual Hawaii International Conference on System Sciences
T2 - 2012 45th Hawaii International Conference on System Sciences, HICSS 2012
Y2 - 4 January 2012 through 7 January 2012
ER -