Abstract
The aim of this research is to explore consumers’ shop** behavior on mobile apps and to mine different types of consumers. We have selected the mobile Taobao app as our research target and designed two simulated shop** tasks—goal-oriented shop** and exploratory-based shop**—based on consumers’ need-states to examine consumers’ shop** journeys. We have also investigated the effect of the time factor which has been regarded as an important incentive (i.e., saves time and is convenient) for consumers to shop by using mobile apps. We used a k-means clustering algorithm based on nine features of the interface of a mobile app to analyze types of consumers’ online shop** behaviors. Our results have shown that there are five types of consumers which are transitional-oriented, price-sensitive, recommendation-adopting, information-consuming, and comparing groups. Our results reveal that the time factor is a primary means to differentiate consumers’ mobile shop** behaviors instead of need-states. The most obvious difference between the five groups of consumers is that the transitional-oriented group spent the least time browsing information in the app whereas the information consumption group spent the most. Interestingly, both groups went to their shop** carts frequently and consequently made purchase decisions. While the comparing group spent a great deal of time on checking, comparing, and evaluating information, they seldom went to the shop** cart page. In conclusion, we have found that consumers’ shop** behavior on mobile apps is deeply affected by the factor of time and exhibited different shop** journeys among groups. Our findings can provide a reference for mobile retailers to refine their app interfaces and develop marketing strategies tailored to different types of consumers.
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
References
eMarketer: Mcommerce Forecast 2021:What driving growth and what it means for retail (2021). https://www.insiderintelligence.com/content/mcommerce-forecast-2021
Pantano, E., Priporas, C.V.: The effect of mobile retailing on consumers’ purchasing experiences: a dynamic perspective. Comput. Hum. Behav. 61, 548–555 (2016)
Almarashdeh, I., et al.: Search convenience and access convenience: The difference between website shop** and mobile shop**. In: Proceedings of International Conference on Soft Computing and Pattern Recognition, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-17065-3_4
Lemon, K.N.,Verhoef, P.C.: Understanding customer experience throughout the customer journey. J. Mark.: AMA/MSI Spec. 80(6), 69–96 (2016)
Rennie, A., Protheroe, J., Charron, C., Breatnach, G.: Decoding decision: Making sense of the messy middle. Think with Google (2020)
Dasgupta, S., Grover, P.: Impact of digital strategies on consumer decision journey: Special. Acad. Mark. Stud. J. 23(1), 1–14 (2019)
Rudkowski, J., Heney, C., Yu, H., Sedlezky, S., Gunn, F.: Here today, gone tomorrow? Map** and modelling the pop-up retail customer journey. J. Retail. Consum. Serv. 54, 101698 (2019)
Tupikovskaja-Omovie, Z., Tyler, D.J.: Experienced versus inexperienced mobile users: eye tracking fashion consumers’ shop** behaviour on smartphones. Int. J. Fashion Des. Technol. Educ. 15(2), 178–186 (2022)
Kuehnl, C., Jozic, D., Homburg, C.: Effective customer journey design: Consumers’ conception, measurement and consequences. J. Acad. Mark. Sci. 47(3), 551–568 (2019)
Wu, I.C., Yu, H.K.: Sequential analysis and clustering to investigate users’ online shop** behaviors based on need-states. Inf. Process. Manage. 57(6), 102323 (2020)
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. Library Inf. Serv. 31(3), 67–74 (2015)
Kau, A.K., Tang, Y.E., Ghose, S.: Typology of online shoppers. J. Consum. Mark. 20(2), 139–156 (2003)
Liu, Y., Li, H., Peng, G., Lv, B., Zhang, C.: Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market. Ann. Oper. Res. 233(1), 263–279 (2013). https://doi.org/10.1007/s10479-013-1443-z
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)
Laran, J., Janiszewski, C., Salerno, A.: Exploring the differences between conscious and unconscious goal pursuit. J. Mark. Res. 53(3), 442–458 (2016)
Borlund, P.: Experimental components for the evaluation of interactive information retrieval systems. J. Documentation 56(1), 71–90 (2000)
Manning, C.D., Raghavan, P., Schütze, H. Introduction to Information Retrieval. Cambridge University Press (2008)
Wu, I.C., Yu, H.K.: Mining types of online consumers by search patterns: the influence of need states and product familiarity. J. Library Inf. Sci. Res. 16(1), 77–123 (2021)
Acknowledgement
This research was supported by the Ministry of Science and Technology, Taiwan under Grant No. 108-2410-H-003-132-MY2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, IC., Yu, HK., Lien, SI. (2023). Types of Mobile Retail Consumers’ Shop** Behaviors from the Perspective of Time. In: Nah, F., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2023. Lecture Notes in Computer Science, vol 14038. Springer, Cham. https://doi.org/10.1007/978-3-031-35969-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-35969-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35968-2
Online ISBN: 978-3-031-35969-9
eBook Packages: Computer ScienceComputer Science (R0)