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Collaboration and Cognitive Structures in Social Science Research Fields. Towards Socio-Cognitive Analysis in Information Systems

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  • Peter Mutschke

    (Social Science Information Centre)

  • Anabel Quan Haase

    (University of Toronto)

Abstract

Bibliographic information systems have to address the needs of users by providing “value-added-components.” For instance, users would benefit from knowing the social and cognitive structures of research fields. Research suggests that a relationship exists between actors' position in scientific networks and the innovativeness of themes they examine. The present study confirms and expands these results through a technique that relates the cognitive and social structures of a research field (socio-cognitive analysis). The results from two social science fields suggest that well-integrated actors are engaged in the consolidation of the mainstream, whereas new ideas are most likely to be introduced and pursued by social climbers, i.e., actors who are starting to form a social network of collaboration.

Suggested Citation

  • Peter Mutschke & Anabel Quan Haase, 2001. "Collaboration and Cognitive Structures in Social Science Research Fields. Towards Socio-Cognitive Analysis in Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 52(3), pages 487-502, November.
    • Handle: RePEc:spr:scient:v:52:y:2001:i:3:d:10.1023_a:1014256102041
    DOI: 10.1023/A:1014256102041
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    References listed on IDEAS

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    1. Loet Leydesdorff, 1997. "Why words and co‐words cannot map the development of the sciences," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(5), pages 418-427, May.
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    Cited by:

    1. Bulent Ozel, 2012. "Individual cognitive structures and collaboration patterns in academia," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 539-555, May.
    2. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    3. Tiwana, Birinder & Sarkar, Sudeshna, 2009. "Analyzing the Socio-Cognitive Structure of an Economic Research Community," MPRA Paper 22483, University Library of Munich, Germany.
    4. Peter Mutschke & Philipp Mayr, 2015. "Science models for search: a study on combining scholarly information retrieval and scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2323-2345, March.
    5. Peter Mutschke & Philipp Mayr & Philipp Schaer & York Sure, 2011. "Science models as value-added services for scholarly information systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 349-364, October.
    6. Jingjing Zhang & Yan Yan & Jiancheng Guan, 2015. "Scientific relatedness in solar energy: a comparative study between the USA and China," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1595-1613, February.
    7. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.

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