Research Methodology

3.1.1 Bibliographic and Co-citation

Figure 4 shows the essential different methodologies between Bibliographic coupling and Co-citation. When different authors cite one or more papers in common, papers are classified as bibliographically coupling. On the other hand, co-citation analysis is based primarily on identifying pairs of highly-cited papers. Bibliographic coupling, which is retrospective, is a fixed and permanent relationship, whereas co-citation, which is essentially a forward-looking perspective, changes as the interests and intellectual patterns of the field change (Garfield, 2001; Small, 1973).

Figure 5 Bibliographic Coupling vs. Co-Citation Source: Garfield E, 2001

Bibliographic Co-citation, which is a popular similarity measure used to establish a subject similarity between two items, has been used by a variety of researchers to establish relationships among documents. If item A and B of earlier literature are both cited by later literature, they could be said to be related to one another, even though they do not directly

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refer to each other. If item A and B are both cited by many other items, then they would have a stronger relationship. The more items they are cited, the stronger relationship between them (Martins B. Class Co-citation).

The Co-citation method is defined as the frequency with which two documents or authors are cited together by the later literature in the same work. The aim of the Co-citation is to identify groups of closely related documents which can be considered as belonging to the same “research front” (Small, 1973). The co-citation is an objective, powerful and widely used methodology to study the structure of scientific disciplines and main trends for the researchers. In general, two items are considered as being co-citation when they appear together in the same reference list of a subsequent article (Zhao, 2006).

Author co-citation analysis (ACA), which moved the unit of analysis from co-cited documents to co-cited oeuvres, began at Drexel University in 1979. Oeuvres that called by French is a body of writing by a personal, and not the person himself (Dictionary). Based on co-citation frequencies of association between authors, ACA could be able to reveal pattern. It makes a prospective methodology for understanding the evolution of an academic discipline (White & McCain, 1998). To analyze the intellectual structure of science studies that ACA has been widely used in bibliometric technique as an analytical method. It is also used by researchers to investigate various phenomena, apply to identify authors from the same or similar research fields (He & Cheung Hui, 2002). To use ACA methodology, many researchers have already addressed the development (Culnan, 1986; He & Cheung Hui, 2002;

White, 1990, 2003a, 2003b). It might contribute to the understanding of intellectual structure in the sciences and possibly in other areas to the extent that those areas rely on serial publications (White & Griffith, 1981).

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In order to study the intellectual structure of this research field and infer to some characteristics of the corresponding scientific community, ACA researchers can use co-citation counts as similarity measures, which represent the proximity between authors.

When we get the result of co-citation between authors, we can obtain the Pearson correlation, and use multivariate techniques and multidimensional scaling to analyze the data (White &

Griffith, 1981; White & McCain, 1998).

In our study, there are two reasons for using a bibliometrical method: the first is to present a complete picture of the theory, and the second is to be objective, and then we can adopt the co-citation methodology to carry on empirical analysis of Supply Chain Management.

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3.1.2 Social Network Analysis

A social network is a collection of people, each of whom is acquainted with some subset of the others. Such a network can be represented as a set of points (or vertices) denoting people, joined in pairs by lines (or edges) denoting acquaintance (Newman, 2001).Social network analysis (SNA) seeks to examine relationships ranging from single user, two-person ties, and small groups to the entire community network. Social network analysis approach can help to uncover the roles and importance of members in a community to understand the nature of a social network (Garton, Haythornthwaite, & Wellman, 1997).

A lot of work with social networks is primarily descriptive and/or exploratory, rather than confirmatory hypothesis testing. SNA focuses on using quantitative measurement to study the interaction among the members to profile the structure of the community and its members (Hanneman, 2001). Social network researchers measure network activity for a node by using the concept of degree where how many direct links between two or more nodes. The relationship between the centralities of all nodes can reveal much about the overall network structure (Orgnet.com). The definition of hub is a node in a graph with a high degree.

Sometimes a node is connected to every other node in the graph. K-core is used to identify the strong ties in a network or community, and it is a set of nodes in a graph that each node in the set is connected to at least k other nodes in the set (ref. Graph theory). The more ties an actor has then, the more power they (may) have (Swan, 2001).

Social networks have been the subject of both empirical and theoretical study in the social sciences for at least 50 years. Stanley Milgram conducted a famous early empirical study of the structure of social networks (Newman, 2001). At present, social network analysts use two kinds of tools from mathematics to represent information about patterns of ties among social actors: graphs and matrices. It usually provides both a visual and a mathematical analysis of human relationships.

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The theory of SNA emphasizes patterns of relationships among individuals and interprets the behavior of individuals in reference to their positions within social networks. A primary task of social network analysis is to use the international patterns among individuals to identify clusters or cliques within a network of individuals (Ennett & Bauman, 1993). A sub-graph that forms a clique is considered interesting because the high interconnectedness of the nodes suggests that the sub-graph forms an important structure.

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