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

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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.

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Notes

  1. We also tested the Eigenvector to offer a wider perspective, but after some analysis and non-significant results, we decided to focus on degree, betweenness and closeness as the best indicators for the identification of key players.

  2. There was only one author for which was impossible to find trustworthy information.

  3. We also tested Eigenvector centrality, which measures popularity, weighting the importance of each author/node according to how ‘popular’ or ‘well connected’ their connections are. While eigenvalues allow for the identification of “popular” actors, they is not adding useful information regarding the identification of key players, because they also highlight those that despite not being central are connected to the referential ones.

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Acknowledgements

We are grateful to the Editor Wolfgang Glänzel and two anonymous reviewers for their helpful observations and suggestions on previous versions of this manuscript.

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Authors and Affiliations

  1. ESADE Business & Law School, Av. Pedralbes 60-62, 08034, Barcelona, Spain

    Liliana Arroyo Moliner

  2. Department of Management, Universitat Politècnica de Catalunya-Barcelona Tech, C. Jordi Girona, 1-3, 08034, Barcelona, Spain

    Eva Gallardo-Gallardo

  3. Department of Sociology, University of Barcelona, Av. Diagonal, 696, 08034, Barcelona, Spain

    Pedro Gallo de Puelles

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  1. Liliana Arroyo Moliner
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Correspondence to Liliana Arroyo Moliner.

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Arroyo Moliner, L., Gallardo-Gallardo, E. & Gallo de Puelles, P. Understanding scientific communities: a social network approach to collaborations in Talent Management research. Scientometrics 113, 1439–1462 (2017). https://doi.org/10.1007/s11192-017-2537-1

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