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Contextual productivity assessment of authors and journals: a network scientometric approach

Author

Listed:
  • Hiran H. Lathabai

    (University of Kerala)

  • Thara Prabhakaran

    (University of Kerala)

  • Manoj Changat

    (University of Kerala)

Abstract

Information networks, especially citation networks, have many proven and potential applications in scientometrics. Identification of productivity of authors and journals is one of the prime concern of analysts. While there are many indices to measure the productivity of author or journal, there is no known index to determine productivity with respect to a particular research context. A network scientometric approach is devised to address the identification of contextual productivity. Work-author and Work-journal affiliations modelled as 2 mode networks provide effective means to assess the productivity of authors and journals in a particular research context. In this work, weighted 2 mode networks are created for the analysis of affiliations networks such that weights reflect some citation characteristics of the works in their original citation network. A set of network indices are proposed for the assessment of contextual importance of authors and journals which are illustrated in the case study of Biotechnology for Engineering. Online databases and digital libraries can use these indices to gather insights about most productive authors and journals, along with the search results.

Suggested Citation

  • Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2017. "Contextual productivity assessment of authors and journals: a network scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 711-737, February.
    • Handle: RePEc:spr:scient:v:110:y:2017:i:2:d:10.1007_s11192-016-2202-0
    DOI: 10.1007/s11192-016-2202-0
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    Cited by:

    1. Hiran H. Lathabai & Thara Prabhakaran, 2023. "Contextual Ψ-index and its estimate for contextual productivity assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4875-4886, August.
    2. Myeongji Oh & Hyejin Jang & Sunhye Kim & Byungun Yoon, 2023. "Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2079-2104, April.
    3. Susan George & Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2020. "A framework towards bias-free contextual productivity assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 127-157, January.
    4. Thara Prabhakaran & Hiran H. Lathabai & Susan George & Manoj Changat, 2018. "Towards prediction of paradigm shifts from scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1611-1644, December.
    5. Hiran H. Lathabai & Susan George & Thara Prabhakaran & Manoj Changat, 2018. "An integrated approach to path analysis for weighted citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1871-1904, December.
    6. Hiran H. Lathabai & Abhirup Nandy & Vivek Kumar Singh, 2021. "x-index: Identifying core competency and thematic research strengths of institutions using an NLP and network based ranking framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9557-9583, December.
    7. Similo Ngwenya & Nelius Boshoff, 2022. "Different manifestations of ‘context’: examples from a bibliometric study of research in Zimbabwe in Southern Africa," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3911-3933, July.
    8. Ephrance Abu Ujum & Sameer Kumar & Kuru Ratnavelu & Gangan Prathap, 2021. "A new journal power-weakness ratio to measure journal impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9051-9068, November.

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