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Relevance judgment: What do information users consider beyond topicality?

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  • Yunjie (Calvin) Xu
  • Zhiwei Chen

Abstract

How does an information user perceive a document as relevant? The literature on relevance has identified numerous factors affecting such a judgment. Taking a cognitive approach, this study focuses on the criteria users employ in making relevance judgment beyond topicality. On the basis of Grice's theory of communication, we propose a five‐factor model of relevance: topicality, novelty, reliability, understandability, and scope. Data are collected from a semicontrolled survey and analyzed by following a psychometric procedure. Topicality and novelty are found to be the two essential relevance criteria. Understandability and reliability are also found to be significant, but scope is not. The theoretical and practical implications of this study are discussed.

Suggested Citation

  • Yunjie (Calvin) Xu & Zhiwei Chen, 2006. "Relevance judgment: What do information users consider beyond topicality?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(7), pages 961-973, May.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:7:p:961-973
    DOI: 10.1002/asi.20361
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    Citations

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    Cited by:

    1. Juan Xie & Hongru Lu & Lele Kang & Ying Cheng, 2022. "Citing criteria and its effects on researcher's intention to cite: A mixed‐method study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1079-1091, August.
    2. Xiaoli Huang & Dagobert Soergel, 2013. "Relevance: An improved framework for explicating the notion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 18-35, January.
    3. Jingfei Li & Peng Zhang & Dawei Song & Yue Wu, 2017. "Understanding an enriched multidimensional user relevance model by analyzing query logs," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2743-2754, December.
    4. repec:jtr:journl:v:4:y:2012:i:1:p:12-37 is not listed on IDEAS
    5. Warinrampai Rungruangjit & Kitti Charoenpornpanichkul, 2022. "Building Stronger Brand Evangelism for Sustainable Marketing through Micro-Influencer-Generated Content on Instagram in the Fashion Industry," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
    6. Jie Lou & Yulin Fang & Kai H. Lim & Jerry Zeyu Peng, 2013. "Contributing high quantity and quality knowledge to online Q&A communities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 356-371, February.
    7. Nathalie Demoulin & Kristof Coussement, 2018. "Acceptance of text-mining systems: The signaling role of information quality," Post-Print hal-02111772, HAL.
    8. Xiuping Zhang & Jaewon Choi, 2022. "The Importance of Social Influencer-Generated Contents for User Cognition and Emotional Attachment: An Information Relevance Perspective," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    9. Alexandra Dumitrescu & Simone Santini, 2021. "Full coverage of a reader's interests in context‐based information filtering," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1011-1027, August.

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