Building research collaboration networks--an interpersonal perspective for research capacity building.

Author:Huang, Jun Song
Position:Abstract
 
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Introduction

The development of social network theories has revealed that social structure of relationships around a person, group, or organization affects beliefs and behaviors (Burt, Kilduff, & Tasselli, 2013). For example, in research on innovation diffusion, Ryan and Gross (1943) find that Iowa farmers' adoption of hybrid-seed corn was mostly influenced by their neighbors, even though the farmers first heard the innovation from commercial salesmen. Godley, Sharkey and Weiss (2013) demonstrate that office location is one of the strongest predictors of grant collaborations amongst neuroscientists within an institute. Rogers (2003) further points out that interpersonal linkages among individuals in a social system can influence the communication flow and promote the adoption and diffusion of innovations in the system.

Increasingly, researchers are working in collaborations to address complex research issues. Higher Education Institutes (HEIs) are giving incentives for their researchers to take part in international collaborative projects. Funding agencies also favors collaborative research because it can draw diverse expertise, promote creativity and innovation and therefore lead to scientific breakthroughs. Social networks have been the subject of both empirical and theoretical studies in the social sciences for at least 50 years but has only been recently applied to research collaborations (Godley, et al., 2013; Woo, Kang, & Martin, 2013).

Implicit in social network theory is the assumption that there are outcomes associated with the connections. It is the thesis of this paper that research collaboration networks derive benefits to higher education institutions (HEIs). This author argues that of two hypothetical

institutes (Figure 1), Institute B's intentional connections provide greater opportunity for research collaboration than does Institute A wherein the researchers work in isolation. The author further claims that Institute B has higher research capacity as compared to Institute A.

This paper will focus on three important topics. Are social network theories relevant to research management? Can research institutes be informed by social network theories to promote research collaborations? What limitations do social network theories have when applying to research collaborations? In addition, this paper seeks to provide a theoretical framework for the role of research administration and capacity building through social networks. By linking social network theories with research management, the paper hopes to make contribution to the theory and practice of research capacity building.

To anchor this paper theoretically, social network theories are briefly introduced in the next section. The section does not cover technical details of the social network theories and models. More in-depth review of the theories can be found in the literature of Social Network Analysis (SNA) (Woo, et al., 2013).

Social Network Theories

Social network theories form a major paradigm in contemporary sociology. The theories focus on how people, organizations or groups interact with others in social networks (Burt, et al., 2013). In this sociology paradigm, the social relationships are studied in terms of diagrams of social networks which constitute nodes (e.g., people) and ties (e.g., the relationships among people). The diagrams can be used to understand social capitals (Williams & Durrance, 2008), the advantage that an individual, cluster or a network may gain from social interactions as a result of their location in social networks (e.g., who they are connected with). Theories are developed to explain why people interact, how they interact, at what level of closeness and with what kind of outcome.

The study on social network diagrams has led to multiple theories on social networks. For example, when examining the process of job seeking, Granovetter (1973) identifies the strength of weak ties. He finds that job seekers tend to hear of job opportunities from people connected by weak ties (e.g., acquaintance that does not share many common friends, just like people in a social network that has loose connections among members), rather than by strong ties (e.g., close friends who are closely connected among each other, just like people in a social network that has dense and coherent connections among members). The example of weak/strong ties is illuminated in a social network diagram presented in Figure 2. Node E shares a weak tie with Node H and strong ties with Node F and G. Granovetter explains that weak ties can transmit information (such as job opportunity) from distant part of the social system. Thus people that have few weak ties are confined mostly to the local information of their close friends. Empirical studies (Ahuja, 2000; Mehra, Kilduff, & Brass, 2001) have also demonstrated that individuals with weak ties can bridge different clusters in a social network and gain significant advantage.

Social network theories have their limitations. These theories take a relational approach and emphasize primarily the properties of relations among individuals (Kadushin, 2011). One major critique is their lack of recognition of the properties of these individuals (Martin & Wellman, 2010), for example, individuals' agency and determination in seeking information in social networks. Without denying this limitation, this paper argues that social network theories have potential to inform research management in HEIs.

The rest of the paper is developed into five sections (i.e., Section Two to Section Six). Section Two highlights the importance of collaboration in research. The next section reviews the literature of research capacity building. It argues that research collaboration networks are not adequately recognized as a form of research capacity. The fourth section uses two network diagrams to illustrate that structures of research collaboration networks can have impact on research creativity and productivity at both individual and collective levels. It is then argued that research collaboration networks can make unique contributions to research capacity building. The fifth section refers to social network theories and presents three mechanisms for building research collaboration networks. By making reference to the mechanisms and empirical findings, the last section discusses three challenging issues in building research collaboration networks.

Collaboration is Important for Research

Research collaboration has gain attention in the past few decades (Bammer, 2008; Wray, 2006). The observed growth in co-authorship provides partial evidence for increased collaborations in research (Katz & Martin, 1997; Sooho & Bozeman, 2005).

Bukvova (2010) notes that there is no clear definition on research collaboration in the literature. Many forms of collaboration work, such as casual discussion on a research idea, are hard to be measured as evidence of collaboration. For the purpose of this paper, research collaboration is regarded as joint work between researchers in achieving research objectives. More specifically, the two main forms of research collaboration discussed in this paper are jointly conducting research projects (i.e., joint grantsmanship) and co-authoring publications.

There are at least four reasons for researcher to collaborate: the need to address complex research issues; the need for learning and productivity in research; the need to reduce research cost and the need for intellectual companionship.

First, collaboration is necessary for researchers to address complex research issues that otherwise cannot be addressed by individual researchers. Due to the increased specialization in science, there is a need for individual researchers to keep their own activities focused and specialized (Bukvova, 2010; Katz & Martin, 1997). Such focus and specialization would allow researchers to make significant knowledge advancement in their respective fields (Bukvova, 2010). While it is possible for individual researchers to learn all the knowledge and skills needed to solve a complex research problem, this learning process can be very time-consuming and may prohibit one from being specialized. Thus, researchers, when addressing complex problems, need to pool expertise together and obtain cross-fertilization through interdisciplinary collaborations (Johari, Zaini, & Zain, 2012).

Second, collaboration is important for researchers' sustainable development in knowledge creation. The United Nations Office for Sustainable Development (2012) points out that in a knowledge economy, knowledge and capacity may be replaced or refreshed at a very fast pace. Thus, continuous learning and knowledge transfer are critical for researchers to remain relevant in their respective fields in an ongoing knowledge creation process. Such learning and transfer may bring together researchers with culturally different ideas which create conditions for new knowledge creation. Thus, learning and transfer through collaborations not only lead to research productivity (as indicated by grantsmanship and publications, as a result of knowledge creation), but also help researchers to maintain their ability for sustainable development in a knowledge economy.

Third, collaboration may reduce research costs. Bukvova's (2010) review on research collaboration finds that experimentalists tend to collaborate more than theoreticians. In experimental research, the instrumentations required are getting increasingly complex. Scientific instrumentation costs have jumped considerably with the successive generations of technology. By working together in collaboration, research costs can be shared and research facilities can be better optimized and utilized.

Fourth, collaboration may enable intellectual companionship as well. The goal of research is to expand the boundaries of knowledge. As researchers are specialized and focused, their advancement at the frontier of each research field...

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