Academic journal article Journal of Research Administration

Social Network Analysis to Evaluate an Interdisciplinary Research Center

Academic journal article Journal of Research Administration

Social Network Analysis to Evaluate an Interdisciplinary Research Center

Article excerpt


Despite this nation's potential to deliver the finest health care in the world, the translational blocks from basic science to human studies and from clinical research to practice and policy clearly "impede efforts to apply science to better human health in a expeditious fashion." Sung et al., 2003) One way to expedite the translation of research to health care delivery is through interdisciplinary research, which crosses the traditional boundaries of profession, department, or institution. Indeed, much has been written in recent years about the value of interdisciplinary collaboration, to the extent that it has become one of the academic bandwagons of the day, and the National Institutes of Health (NIH) has identified interdisciplinarity as an explicit priority in its recent Roadmap, a strategic plan for future funding priorities http://nihroadmap.

In a recent survey, more than 2,000 fulltime academic researchers ranked their collaborators above salary and job security as their highest priorities for job satisfaction (Grimwade & Park, 2003). Nevertheless, academic environments generally have established incentives for an entrepreneurial, independent approach to research. It has been suggested, in fact, that the academic culture hinders collaboration and, hence, slows translational research (Pober, Neuhauser, & Pober, 2001; Sung et al., 2003). Thus, data suggest that an interdisciplinary culture must be well planned and executed before success is possible. Despite this, there is little empirical evidence of a change in the traditional departmental academic systems and networks, with many initiatives identified as interdisciplinary actually being reconfigurations of traditional modes of multidisciplinary research (Rhoten & Parker, 2004).

The ultimate purpose of interdisciplinary research is to develop new knowledge or solve a relevant human problem by combining the skills and perspectives of multiple disciplines. This requires a realistic understanding of the nature of disciplinarity. Although academic disciplines are often thought of as "bodies of teachable knowledge" (Woollcott, 1979) or as "conceptually specific structures" (Robertson, Martin, & Singer, 2003), these dehumanized descriptions do not capture the entire domain.

Disciplines are also "organized social groups," "sets of social relationships" (Lattuca, 2002), and "isolated domains of human experience possessing its own community of experts" (Nissani, 1997). Many of the challenges inherent in interdisciplinary research emanate from the isolation of disciplinary experts, resulting in knowledge silos. Viewed in this way, accomplishing interdisciplinary research becomes, at least in part, an issue of social interaction and the creation of integrated social networks.

Social Network Analysis

Social network analysis involves a unique set of tools capable of revealing the patterns of human interactions. Social networking can be used to track the extent to which a network grows and also answer questions regarding how it grows: What is the disciplinary composition of the team? Is the team all connected or are there subgroups? Are there central players crucial for creating connections between people? Social network analysis can elucidate many patterns of team assembly, such as team size, membership composition, and tendency to repeat previous collaborations that can determine the performance of creative teams (Guimera, Uzzi, Spiro, & Amaral, 2005).

A "social network" is defined as a group of collaborating (or competing) entities that are related to each other (Aviv, Erlich, Ravaid, & Geva, 2003), Network methods focus on the relational linkages between entities (i.e., individuals or groups of individuals or "things," such as electronic message boards, citations, or computer stations), using techniques based on graph theoretic methods (Wasserman & Faust, 1994). …

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