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

• Published in: Scientometrics Vol. 113; no. 3; pp. 1439 - 1462
• Main Authors: Arroyo Moliner, Liliana, Gallardo-Gallardo, Eva, Gallo de Puelles, Pedro
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2017; Springer Nature B.V; Elsevier B.V
• Subjects: Authoring; Cerca de talents (Treball); Co authorship; Collaboration; Computer Science; Consolidation; Human resource management; Information Storage and Retrieval; Library Science; Network analysis; Política científica; Researchers; Science and state; Social network analysis; Social networks; Social organization; Talent identification; Talent management; Xarxes socials; Collaboration networks; Social network analysis; Science mapping; Talent management; Co-authorship
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-017-2537-1

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.