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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
eMarketer: Number of digital buyers worldwide from 2014 to 2021 (in billions). https://www.statista.com/statistics/251666/number-of-digital-buyers-worldwide/. Accessed 6 Sept 2018
Wolfinbarger, M., Gilly, M.C.: Shopping online for freedom, control, and fun. Calif. Manage. Rev. 43(2), 34–55 (2001)
Li, Y.M., Wu, C.T., Lai, C.Y.: A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis. Support Syst. 55(3), 740–752 (2013)
Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Disc. 5(1–2), 115–153 (2001)
Moe, W.W.: Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. J. Consum. Psychol. 13(1–2), 29–39 (2003)
Rohm, A.J., Swaminathan. V.: A typology of online shoppers based on shopping motivations. J. Bus. Res. 57(7), 748–757 (2004)
Kau, A.K., Yingchan, E.T., Ghose, S.: Typology of online shoppers. J. Consum. Mark. 20(2), 139–156 (2003)
Zhang, W., Wang, J., Xu, S.: The probing of e-commerce user need states by page cluster analysis-An empirical study on women’s clothes from Taobao.com. New Technol. Libr. Inf. Serv. 31(3), 67–74 (2015)
Janiszewski, C.: The influence of display characteristics on visual exploratory search behavior. J. Consum. Res. 25(3), 290–301 (1998)
Bakeman, R., Gottman, J.M.: Observing Interaction: An Introduction to Sequential Analysis. Cambridge University Press, New York (1999)
Sackett, G.P.: The lag sequential analysis of contingency and cyclicity in behavioral interaction research. In: Osofsky, J.D. (ed.) Handbook of Infant Development, pp. 623–649. Wiley, New York (1979)
Wildemuth, B.M.: The effects of domain knowledge on search tactic formulation. J. Am. Soc. Inform. Sci. Technol. 55(3), 246–258 (2004)
Borlund, P.: Experimental components for the evaluation of interactive information retrieval systems. J. Documentation 56(1), 71–90 (2000)
Borlund. P.: The IIR evaluation model: A framework for evaluation of interactive information retrieval systems. Inf. Res. 8(3) (2003). http://informationr.net/ir/8-3/paper152.html. Accessed 06 Sept 2018
Acknowledgments
This research was financially supported by the Ministry of Education (MOE) of Taiwan under Grant MOST 105-2410-H-003-153-MY3 & the Institute for Research Excellence in Learning Sciences of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the MOE in Taiwan are gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, HK., Wu, IC. (2019). Effects of the User-Need State for Online Shopping: Analyzing Search Patterns. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-15742-5_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15741-8
Online ISBN: 978-3-030-15742-5
eBook Packages: Computer ScienceComputer Science (R0)