A network can be defined as a particular type of relation that links a set of people or objects, called the nodes of the network (Wasserman and Faust 1994). A communication network is a structure that is built on the basis of communication relationships between individuals, groups, organizations or societies. A networks perspective is a distinct research tradition within the social and behavioural sciences because it is based on the assumption that the attitudes and behaviours of the nodes (be they individuals or collectives) are significantly enabled or constrained by the networks in which they are embedded.
Communication networks have been studied in various social systems and at differing levels of analysis. At the intrapersonal level, research has shown that individuals who have similar positions in the organization’s network are more likely to have similar cognitions about the firm’s products and processes (Krackhardt 1987; Pattison 1995; Walker 1985). In the area of interpersonal relations, researchers have shown that the degree of overlap between individuals’ networks positively influences the development and stability of romantic relationships (Bott 1971; Parks and Adelman 1983). Research has also shown that an individual’s interpersonal and community networks serve an important social support role in overcoming difficult life events (Albrecht and Adelman 1984; Wellman and Wortley 1990). In the context of small group behaviour, studies have shown that characteristics of the network’s structure have a significant impact on the performance and satisfaction of group members involved in problem solving (Collins and Raven 1969; Shaw 1964). Considerable research has been conducted on communication and information flow in production, innovation, and informal social networks within organizations. For instance, Monge et al. (1983) showed that proximity, propensity to communicate, and commitment to the organization were important antecedents to the involvement of individuals in organizational networks. Researchers have noted that individuals’ decisions to adopt new communication technologies are significantly predicted by the decisions of others in their communication network. Inversely, the adoption of new communication technologies significantly alters individuals’ communication networks (Burkhardt and Brass 1990; Contractor and Eisenberg 1990). Likewise, there is a rapidly growing literature on the communication linkages among inter-organizational systems (Eisenberg et al. 1983; Mizruchi and Schwartz 1987). For instance, Galaskiewicz and Burt (1991) report that corporate decisions about making a philanthropic donation to specific non-profit organizations are positively influenced by the decisions of other donating officers in their inter-organizational communication network. At the broader societal and cultural levels, Rogers and Kincaid (1981) describe the uses of network analysis for the diffusion of ideas. Typical of this approach is their analysis of the communication patterns for the diffusion of family planning among sixty-nine women in a Korean village. These examples all employ ‘people’ as the nodes of the communication network, but this need not be the case. For example, Rice et al. (1988) used communication network analysis to demonstrate the influence of citation patterns among core journals in the field of communication.
Monge (1987) notes that two major objectives of network analysis are network articulation and metrics. In network articulation, individuals are assigned to various network role classifications such as clique member, liaison, and isolate. Liaisons do not belong to particular groups but have information linkages with people in two or more groups; they frequently figure importantly in linking groups together. As the name implies, isolates have few, if any, connections within the network. Research has shown that liaisons, isolates, and group members have different characteristics and function quite differently in communication networks (Monge and Eisenberg 1987). Network metrics refers to quantitative indices of various aspects of the network. The most frequently studied indices include density and centrality (Knoke and Kuklinski 1982). The density of a network is the ratio of the number of observed links connecting nodes to the number of possible links between the nodes. A node is central in a network if it is connected to nodes that are not directly connected to each other.
Techniques for observing communication networks are many and varied (Monge and Contractor 1988). Often people are asked to recall their interactions with all other network members and to report the frequency, duration and importance of these contacts. In some studies, participants have been asked to keep diaries of their interactions during a given time period. In other research, participant-observers have logged the frequency of interactions among people in an organization. In one interesting series of studies, Milgram (1967) asked people to send a message to an unknown target by sending it through someone they knew personally. That process was repeated at each step until a person was eventually found who knew the target and delivered the message. An average of seven steps was required to reach the target. Bernard and Killworth (1980) collected data about the communication networks among ham radio operators by recording their public dialogue on the radio waves. Another example is the work of Rice (1995), who describes the use of computer-mediated communication systems to record the frequency and duration of communication linkages among individuals.
Analysis of communication network data, whether network articulation or metrics, is too unwieldy to be undertaken without a computer. While many network researchers write their own programs, there are a handful of network analysis and graphics packages available for use on microcomputers (for a review, see Wasserman and Faust 1994). The programs differ considerably in terms of the assumptions that they make about network data, objectives of the analysis, and computational algorithms.
Networks perspectives have emerged as an influential intellectual force in the social and behavioural sciences. While individual attributes (such as an individual’s gender, age and education) are important sources of explanation for human behaviour, a number of social theorists (e.g. Burt 1982; 1992) have argued that they are incomplete and, in some cases, misleading. These theorists suggest that there are group and social phenomena that cannot be explained by the attributes of its constituents. In such cases, a networks perspective with its focus on the relationships between the constituents offers unique insights into collective phenomena.
Peter R.Monge
University of Southern California
Noshir S.Contractor
University of Illinois at Urbana-Champaign
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