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Understanding scientific communities: a social network approach to collaborations in Talent Management research

Author

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  • Liliana Arroyo Moliner

    (ESADE Business & Law School)

  • Eva Gallardo-Gallardo

    (Universitat Politècnica de Catalunya-Barcelona Tech)

  • Pedro Gallo de Puelles

    (University of Barcelona)

Abstract

Research on talent management (TM) is an emerging field of study and little is known about the connections among authors in this research community. This paper aims at disclosing the dynamics in TM research by offering a detailed picture of its evolving collaboration networks. By means of social network analysis (SNA), we both show and explain the extent of collaboration, taking articles’ co-authorship as an indicator of collaboration. We graphically display how the network builds up throughout time, which has allowed us to examine its main structural characteristics. We analyze the contribution of individual researchers and identify key players in the research network and their characteristics. The co-authorship network is composed by loose and low-density collaborations, mainly consisting in two big components and surrounded by scattered and weak relationships. Two main research perspectives are built and consolidated through time, but they are missing the richness of exchanging ideas among different views. Our results complement recent studies on the dynamics of TM research by offering evidence on how and why collaboration among researchers shapes the current debates on the field. Some basic hypothesis about network indicators are also tested and provide further evidence for the SNA advancement. The findings can be of value in the design of strategies that might improve both system and individual performance.

Suggested Citation

  • Liliana Arroyo Moliner & Eva Gallardo-Gallardo & Pedro Gallo de Puelles, 2017. "Understanding scientific communities: a social network approach to collaborations in Talent Management research," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1439-1462, December.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:3:d:10.1007_s11192-017-2537-1
    DOI: 10.1007/s11192-017-2537-1
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    Cited by:

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    2. Jingbei Wang & Naiding Yang, 2019. "Dynamics of collaboration network community and exploratory innovation: the moderation of knowledge networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1067-1084, November.
    3. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Gregorio González-Alcaide & Héctor Pinargote & José M. Ramos, 2020. "From cut-points to key players in co-authorship networks: a case study in ventilator-associated pneumonia research," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 707-733, May.
    5. José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2018. "The Nobel Prize in Economics: individual or collective merits?," Boston College Working Papers in Economics 966, Boston College Department of Economics.

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