Abstract
E-commerce shop owners often want to attract user attention to a specific product to enhance the chances of sales, to cross-sell, or up-sell. The way of presenting a recommended item is as important as the recommendation algorithms are to gain that attention. In this study, we examined the following types of highlights: background, shadow, animation, and border, as well as the position of the item in a 5 × 2 grid in a furniture online store, and their relationships with user fixations and user interest. We wanted to verify the effects highlighting had on attracting user attention. Various levels of intensity were considered for each highlight: low, medium, and strong. Methods used for data collection were both implicit and explicit: eye tracking, tracking cart’s contents, and a supplementary survey. Experimental results showed that a low-intensity background highlight should be the first-choice solution to best attract user attention in the presented shopping scenario, resulting in the best fixation times and most users’ selections. However, in the case of the highest-intensity animations, highlighting seemed to have negative effects; despite successful attempts to attract eyesight and a long fixation time, users did not add the highlighted products to cart.
Funder
West-Pomeranian University of Technology Highfliers School
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference31 articles.
1. (2022, June 18). Statista-The Statistics Portal. Available online: https://www.statista.com/statistics/272391/us-retail-e-commerce-sales-forecast/.
2. Usability in E-Commerce Websites: Results of Eye Tracking Evaluations;Int. J. Comput. Syst. Eng.,2018
3. Abascal, J., Barbosa, S., Fetter, M., Gross, T., Palanque, P., and Winckler, M. (2015). Human-Computer Interaction—INTERACT 2015, Lecture Notes in Computer Science; Springer.
4. Huang, J., White, R., and Buscher, G. (2012, January 5–10). User see, user point: Gaze and cursor alignment in web search. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, TX, USA.
5. Castagnos, S., Jones, N., and Pu, P. (2010, January 26–30). Eye-tracking product recommenders’ usage. Proceedings of the Fourth ACM Conference on Recommender Systems-RecSys ’10, Barcelona, Spain.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献