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Citation behavior: Classification, utility, and location

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  • V. Cano

Abstract

This study tested empirically the citation behavior model of Moravcsik and Murugesan and examined the hypothesized relationships between three variables: reported citation type, reported utility level, and citation location. A group of elite scientists constituting an “invisible college” were asked to classify the references they had made in two of their recent papers following the model in question, and to judge the utility content of each reference cited. The response rate constituted 66% of a total of 42 questionnaires. A total of 344 references were examined. Some departures from the Moravcsik and Murugesan citation behavior model were found, as well as indications of complexities of both citation motivation and citation evaluation. Many citations were paired in categories presumed dichotomous by the model: 29 instances of cited documents were reported to have both a conceptual and an operational nature. Indeed, a document may contain many items of information that may be cited for a number of reasons. It is concluded that studies focusing on elements of information cited (coupled to their location parameters) as opposed to full citations, are needed to develop empirically based models reflecting the patterns of information use and the citation behavior of a scientific community. © 1989 John Wiley & Sons, Inc.

Suggested Citation

  • V. Cano, 1989. "Citation behavior: Classification, utility, and location," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(4), pages 284-290, July.
    • Handle: RePEc:bla:jamest:v:40:y:1989:i:4:p:284-290
    DOI: 10.1002/(SICI)1097-4571(198907)40:43.0.CO;2-Z
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    Cited by:

    1. Chi-Shiou Lin, 2018. "An analysis of citation functions in the humanities and social sciences research from the perspective of problematic citation analysis assumptions," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 797-813, August.
    2. Frederique Bordignon, 2022. "Critical citations in knowledge construction and citation analysis: from paradox to definition," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 959-972, February.
    3. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.
    4. Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
    5. Hu, Zhigang & Chen, Chaomei & Liu, Zeyuan, 2013. "Where are citations located in the body of scientific articles? A study of the distributions of citation locations," Journal of Informetrics, Elsevier, vol. 7(4), pages 887-896.
    6. Linhong Xu & Kun Ding & Yuan Lin, 2022. "Do negative citations reduce the impact of cited papers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 1161-1186, February.
    7. Lutz Bornmann & K. Brad Wray & Robin Haunschild, 2020. "Citation concept analysis (CCA): a new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. K," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1051-1074, February.
    8. Saeideh Ebrahimy & Farideh Osareh, 2014. "Design, validation, and reliability determination a citing conformity instrument at three levels: normative, informational, and identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 581-597, May.
    9. Stremersch, S. & Camacho, N.M.A. & Vanneste, S. & Verniers, I.W.J., 2014. "Unraveling Scientific Impact," ERIM Report Series Research in Management ERS-2014-014-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    10. Wei‐Min Fan & Wei Jeng & Muh‐Chyun Tang, 2023. "Using data citation to define a knowledge domain: A case study of the Add‐Health dataset," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 81-98, January.
    11. Dangzhi Zhao & Andreas Strotmann, 2020. "Deep and narrow impact: introducing location filtered citation counting," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 503-517, January.
    12. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    13. Liyue Chen & Jielan Ding & Vincent Larivière, 2022. "Measuring the citation context of national self‐references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 671-686, May.
    14. Fatemeh Ghaffari & Mark C. Wilson, 2023. "A model for reference list length of scholarly articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5335-5350, September.
    15. Lina Zhou & Uchechukwuka Amadi & Dongsong Zhang, 2020. "Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location," Information Systems Frontiers, Springer, vol. 22(1), pages 77-90, February.
    16. Binglu Wang & Yi Bu & Yang Xu, 2018. "A quantitative exploration on reasons for citing articles from the perspective of cited authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 675-687, August.
    17. Dangzhi Zhao & Andreas Strotmann, 2020. "Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 255-270, July.
    18. Frederique Bordignon, 2020. "Self-correction of science: a comparative study of negative citations and post-publication peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1225-1239, August.
    19. Boyack, Kevin W. & van Eck, Nees Jan & Colavizza, Giovanni & Waltman, Ludo, 2018. "Characterizing in-text citations in scientific articles: A large-scale analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 59-73.
    20. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    21. Teresa H. Jones & Claire Donovan & Steve Hanney, 2012. "Tracing the wider impacts of biomedical research: a literature search to develop a novel citation categorisation technique," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 125-134, October.
    22. Stremersch, Stefan & Camacho, Nuno & Vanneste, Sofie & Verniers, Isabel, 2015. "Unraveling scientific impact: Citation types in marketing journals," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 64-77.
    23. Emanuel Kulczycki & Marek Hołowiecki & Zehra Taşkın & Franciszek Krawczyk, 2021. "Citation patterns between impact-factor and questionable journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8541-8560, October.
    24. Zehra Taşkın & Umut Al, 2018. "A content-based citation analysis study based on text categorization," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 335-357, January.
    25. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.

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