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International Conference on Human-Computer Interaction

HCII 2021: HCI in Business, Government and Organizations pp 90–104Cite as

Users’ Reception of Product Recommendations: Analyses Based on Eye Tracking Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12783))

Abstract

Based on eye tracking technology, we study consumers’ overall attention to recommendations appearing at different time settings (i.e., early, mid, and late) and their attention to different information contained in each recommendation, such as recommendation signs, product descriptions, and reviews. By investigating consumers’ eye movement patterns and attention distributions on recommendations, we open the “black box” of why consumers’ reception to recommendations appearing at different time settings varies. The product preference construction literature and mindset theory help to explain why the early recommendations receive the most attention. The need for justification helps to explain why the late recommendations should receive more attention than the mid recommendations. Besides, the fact that not all information appearing in recommendations will receive every customer’s attention inspires a more efficient recommendation page design. By exploring the patterns of consumers’ attention to recommendations, we contribute to the accumulation of recommendation literature and provide guidance for the practice.

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Notes

  1. 1.

    Tugba Sabanoglu, “Net revenue of Amazon from 1st quarter 2007 to 4th quarter 2020,” Statista, February 11, 2021, accessed March 9, 2021 www.statista.com/statistics/273963/quarterly-revenue-of-amazoncom/

  2. 2.

    Daniel Faggella, “The ROI of recommendation engines for marketing,” Martech, October 30, 2017, accessed January 19, 2021, https://martechtoday.com/roi-recommendation-engines-marketing-205787

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Acknowledgment

This research is partially supported by the Hong Kong Research Grants Council and City University of Hong Kong (Project No. CityU 11504417/11507619/9360147), a Taiwan Yushan Scholar grant NTU-109V0701, and the National Natural Science Foundation of China (NSFC No. 71701043).

Author information

Authors and Affiliations

  1. City University of Hong Kong, Hong Kong, China

    Feiyan Jia & Choon Ling Sia

  2. University of Science and Technology of China, Hefei, People’s Republic of China

    Feiyan Jia

  3. Southeast University, Nanjing, People’s Republic of China

    Yani Shi

  4. National Taiwan University, Taipei, Taiwan

    Choon Ling Sia

  5. National University of Singapore, Singapore, Singapore

    Chuan-Hoo Tan

  6. Missouri University of Science and Technology, Rolla, USA

    Fiona Fui-Hoon Nah & Keng Siau

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  1. Feiyan Jia
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  2. Yani Shi
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  3. Choon Ling Sia
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  4. Chuan-Hoo Tan
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Correspondence to Feiyan Jia .

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Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Fiona Fui-Hoon Nah

  2. Missouri University of Science and Technology, Rolla, MO, USA

    Keng Siau

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Jia, F., Shi, Y., Sia, C.L., Tan, CH., Nah, F.FH., Siau, K. (2021). Users’ Reception of Product Recommendations: Analyses Based on Eye Tracking Data. In: Nah, F.FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2021. Lecture Notes in Computer Science(), vol 12783. Springer, Cham. https://doi.org/10.1007/978-3-030-77750-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-77750-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77749-4

  • Online ISBN: 978-3-030-77750-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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