Homophily, Proximity, and Social Support Theories

This chapter discusses three families of theoretical mechanisms—homophily, proximity (physical and electronic), and social support—that have been identified by social scientists as important motivations for why we create, maintain, dissolve, and reconstitute our communication networks. While much of this research is conducted in nonorganizational settings, this chapter focuses on the theory and research that we consider to be most germane to communication and other organizational networks. Several researchers have attempted to explain communication networks on the basis of homophily, that is, the selection of others who are similar. Brass (1995a, p. 51) notes that “similarity is thought to ease communication, increase predictability of behavior, and foster trust and reciprocity.” Homophily has been studied on the basis of similarity in age, gender, education, prestige, social class, tenure, and occupation (Carley, 1991; Coleman, 1957; Ibarra, 1993b, 1995; Laumann, 1966; Marsden, 1988; McPherson & Smith-Lovin, 1987). Several lines of reasoning provide support for the homophily hypothesis. These fall into two general categories: the similarity-attraction hypothesis (Byrne, 1971) and the theory of self-categorization (Turner, 1987). The similarity-attraction hypothesis is exemplified in the work of Heider (1958) who posited that homophily reduces the psychological discomfort that may arise from cognitive or emotional inconsistency. Similarly, Sherif (1958) suggested that individuals were more likely to select similar others because by doing so they reduce the potential areas of conflict in the relationship. The theory of self-categorization (Turner & Oakes, 1986) suggests that individuals define their social identity through a process of self-categorization during which they classify themselves and others using categories such as age, race, gender. Schachter (1959) argued that similarity provided individuals with a basis for legitimizing their own social identity. The manner in which individuals categorize themselves influences the extent to which they associate with others who are seen as falling into the same category. It is easy to see that the theoretical mechanism by which homophily influences the likelihood of a communication relation is based on the similarity among specific attributes of the actors.

Exchange and Dependency Theories

Extensive research has been conducted that seeks to explain the emergence of networks based on exchange and dependency mechanisms. Social exchange theory, originally developed by Romans (1950, 1974) and Blau (1964), seeks to explain human action by a calculus of exchange of material or information resources. In its original formulation, social exchange theory attempted to explain the likelihood of a dyadic relationship based on the supply and demand of resources that each member of the dyad had to offer. Emerson (1962, 1972a, 1972b) extended this original formulation beyond the dyad, arguing that in order to examine the potential of exchange and power-dependence relationships, it was critical to examine the larger network within which the dyad was embedded. Since then several scholars have developed this perspective into what is now commonly referred to as network exchange theory (Bienenstock & Bonacich, 1992, 1997; Cook, 1977, 1982; Cook & Whitmeyer, 1992; Cook & Yamagishi, 1992; Markovsky, Wilier, & Patton, 1988; Skvoretz & Wilier, 1993; Wilier & Skvortez, 1997; Yamagishi, Gillmore, & Cook, 1988). Network exchange theory posits that the bargaining power of individuals is a function of the extent to which they are vulnerable to exclusion from communication and other exchanges within the network. The argument is that individuals forge network links on the basis of their analysis of the relative costs and returns in exchanging their investments with others in the network. This is in contrast with theories of self-interest where actors seek to maximize their individual investments independent of its exchange value. Likewise, individuals maintain links based on the frequency, the uncertainty, and the continuing investments to sustain the interaction. Location in the network may confer on some people an advantage over others in engaging in exchange relationships. Aldrich (1982) notes that this argument is at the core of several theories dealing with social exchange as well as resource dependence theories. Within organizations, network researchers have proposed a social exchange mechanism for the study of (1) power, (2) leadership, and (3) trust and ethical behavior. At the interorganizational level, researchers have (1) tested resource dependence theory, (2) examined the composition of corporate elites and interlocking board of directorates, and (3) sought to explain the creation, maintenance, and dissolution of interorganizational links.

Theories of Self-Interest and Collective Action

Many social theories are based on generative mechanisms that are directly relevant to the emergence and coevolution of human networks. Ironically, as Monge and Contractor (2001) demonstrate, many published network studies fail to acknowledge or explicitly identify the social theories and generative mechanisms that motivate their research. In much of the rest of this book we examine a number of social theories in order to identify their generative mechanisms. These mechanisms can be used in conjunction with others to populate the multitheoretical, multilevel framework for the realization of communication and other networks described in chapter 2. For example, the theory of social capital suggests that people who try to exploit social holes will do so by seeking to improve their structural autonomy. On the other hand, theories of social exchange suggest that individuals and organizations forge ties by exchanging material or information resources. Of course, it is quite possible that people do both at the same time, thus requiring a multitheoretical framework. If this were the case, we would develop multitheoretical hypotheses. These would predict that statistical p* analysis of observed networks would reveal significant components for structural autonomy, mutuality, and reciprocation. Further, we would expect that other possible network components, such as transitivity and cyclicality, which are generative mechanisms in other theories, would not be statistically significant in the realization of this particular observed network. In this chapter we examine theories of self-interest and theories of mutual interest, the latter sometimes called theories of collective action, in order to identify their theoretical mechanisms. The self-interest theories are the theory of social capital, specifically Burt’s theory of structural holes, and transaction cost economics. The theory of collective interest that we examine is public goods theory. Social theorists have long been fascinated by self-interest as a motivation for economic and other forms of social action (Coleman, 1986). Theories of self-interest postulate that people make what they believe to be rational choices in order to acquire personal benefits. The strong form of this theoretical mechanism, originally postulated by Adam Smith, is “rationality.” It stipulates that people attempt to maximize their gains, or equivalently, minimize their losses.

Communication and Knowledge Networks as Complex Systems

The concept of a system has a long and distinguished history in the social sciences. In fact, Mattelart and Mattelart (1998) claim that “the idea of society as an organism, that is, a whole composed of organs performing predetermined functions, inspired the earliest conceptions of a ‘science of communication’”. We begin this chapter with a brief historical overview of the major systems perspectives that have been utilized in social theory and research: structural-functionalism, cybernetics, and general systems theory. We then apply recent developments in complex systems theories to organizational networks. In doing so, we look at communication and knowledge networks from the perspective of agent-based modeling and self-organizing systems. Mattelart and Mattelart (1998) trace the early growth of systems thinking in the social sciences. Adam Smith’s (1776) classic work, The Wealth of Nations, postulated that a laissez-faire system, the division of labor, and channels of communication and transportation were crucial aspects of economic prosperity. The key to economic and therefore social success was the unrestricted circulation of messages, materials, and money through secure networks. According to Mattelart and Mattelart (1998), Francois Quesnay, a French physician and economist, published an economic chart (tableau economique) in 1758. “The chart offers a macroscopic vision of an economy of ‘flows’ in the form of a geometrical zigzag figure in which the lines expressing exchange between human beings and the land, as well as among the three classes making up society, cut across each other and become intertwined”. The Mattelarts note that Claude Henri de Saint Simon’s eighteenth-century work also applied systems theory to the concept of networks. Saint-Simons’s theory conceived of society as “an organic system, a bundle or fabric of networks.... He attributed strategic importance to the development of a system of communication routes”. Out of this background Herbert Spencer (1820–1903) developed the first integrated theory of society built on a direct analogy with biological systems.

Evolutionary and Coevolutionary Theories

Chapter 3 discussed the emergence of communication networks from the perspective of complexity theory. Specifically, we described complexity as a network of agents, each with a set of attributes, who follow rules of interaction, which produces emergent structure. Complexity arose from the fact that there were numerous agents with extensive relations. Some complex systems but by no means all, we argued, were self-organizing, meaning that they created and sustained internal structure in response to the flow of matter and energy around them. Some readers, particularly those with some familiarity with the complex adaptive systems literature, may have noticed that the discussion of complexity in chapter 3 did not include processes of adaptation, evolution, or coevolution. The reason for this is that it is possible to view these as theoretical mechanisms that operate in at least some complex, self-organizing systems, though not necessarily all. Thus, we have chosen to treat adaptation and the coevolutionary perspective as theoretical mechanisms in the same manner as the other theoretical mechanisms we have examined in chapters 5 through 8. In the present chapter we examine adaptive and coevolutionary processes as the basis for building MTML models of emergent communication networks that form the basis for organizational populations and communities. Modern interest in evolutionary theory as a basis for studying human social processes can be traced to the work of Amos Hawley (1950, 1968, 1986). Much of the interest in applying this perspective to studying organizational structures is credited to Donald Campbell (1965, 1974). Over much of his professional life Campbell explored the application of evolutionary theory to a wide array of sociocultural processes, including organizations (Baum & McKelvey, 1999). Campbell is perhaps best known throughout the social sciences for his work on experimental and quasi-experimental design (Campbell and Stanley, 1966; Cook & Campbell, 1980) and multimethod triangulation (Campbell & Fiske, 1959). Nonetheless, McKelvey and Baum (1999) point to Campbell’s enormous influence in organizational science via the early work of Aldrich (1972) on organizational boundaries, Weick’s (1979) formulation of an evolutionary model of organizing, Hannan and Freeman’s (1977, 1983) development of population ecology theory (and inertial theory), McKelvey’s (1982) work on organizational taxonomies, and Nelson and Winter’s (1982) evolutionary theory of economics.

Network Concepts, Measures, and the Multitheoretical, Multilevel Analytic Framework

This chapter begins with an overview of network analysis concepts and measures. Those readers who are new to the area, or who are familiar with the social theories described in this book but not with network analysis itself, should find a careful reading of the first section of this chapter to be essential in understanding the remainder of the chapter and book. Network analysis has become a fairly technical topic, and there are a number of concepts, measures, and analytic strategies that require careful explication. This section of the chapter should provide sufficient background in network analysis to enable an informed reading of the network literature. We hasten to emphasize, however, that it is only a brief introduction. Hence, like all other introductory materials, an attempt is made to trade-off conceptual rigor with simplicity. An extensive literature exists on network analysis, including several fine texts and a number of excellent review chapters. Those who wish to explore further the network analysis material presented in the first third of this chapter should consult the sources in the references that we have identified under “Relations in a World of Attributes.” Those who are more familiar with network analysis will find the first section of this chapter less important. A quick skim should provide ample insight into our selection and use of concepts and definitions. The second section introduces the MTML framework. It shows how various network properties at different levels of analysis can represent the generative mechanisms from different social theories. It also shows how combining theories can provide broader explanations of emergent networks than each theory can alone. As a part of that framework we introduce the statistical ideas pertaining to realizations of a graph and discuss p* analytic strategies and the PSPAR computer program that can be used to analyze relevant data. This section concludes with an extended presentation of the MTML model, which broadly classifies variables into endogenous and exogenous factors, each with multiple levels. Examples are provided for each of the ten classes of hypotheses generated by this framework.

Multitheoretical, Multilevel Models of Communication and Other Organizational Networks

In this book we have argued for a multitheoretical, multilevel approach to the study of communication and other forms of organizational and social networks. We began by exploring several problems within the existing corpus of network research. We then showed how the MTML model provides a network research strategy that resolves most of these problems. (For ease of presentation, this review of the essential arguments and social theories includes citations only to references that have not been cited in earlier chapters of this book.) The first problem is the fact that the vast majority of network research is atheoretical. One reason for this is that there are very few explicit theories of social networks. Another reason is that researchers are generally not cognizant of the relational and structural implications inherent in various social theories. Even research that does employ theory typically does so without much attention to the network mechanisms implicit in the theories. A second problem with network research is that most scholars approach networks from a rather myopic, single-level perspective, which is reflected in the fact that almost all published research operates at a single level of analysis. Thus, they tend to focus on individual features of the network such as density. For the most part, researchers tend to ignore the multiple other components out of which most network configurations are composed, structural components from multiple levels of analysis such as mutuality, transitivity, and network centralization. Employing single levels of analysis is not inherently wrong; it is simply incomplete. Importantly, these components suggest different theoretical mechanisms in the formation, continuation, and eventual reconfiguration of networks. Typically, better explanations come from research that utilizes multiple levels of analysis. The third problem centers on the fact that most network research focuses on the relatively obvious elementary features of networks such as link density and fails to explore other, more complex properties of networks such as attributes of nodes or multiplex relations. But the members of networks often possess interesting theoretical properties, which help to shape the configurations in which they are embedded, and networks are themselves often tied to other networks.

Contagion, Semantic, and Cognitive Theories

This chapter reviews theory and research that seeks to explain the emergence of communication networks based on individuals’ cognitions about other people and the relations among those individuals. Contagion theories seek to explain networks as conduits for “infectious” attitudes and behavior. Semantic theories attempt explanations on the basis of networks that map similarities among individuals’ interpretations. Theories of cognitive social structures examine cognitions regarding “who knows who” and “who knows who knows who,” while theories of cognitive knowledge structures examine cognitions of “who knows what” and “who knows who knows what.” Finally, cognitive consistency theories explain how networks are understood on the basis of individuals’ cognitions of consistency or balance in their networks. The remainder of this chapter discusses each of these areas and their extensions. Contagion theories are based on the assumption that the opportunities for contact provided by communication networks serve as a mechanism that exposes people, groups, and organizations to information, attitudinal messages, and the behavior of others (Burt, 1980, 1987; Contractor & Eisenberg, 1990). This exposure increases the likelihood that network members will develop beliefs, assumptions, and attitudes that are similar to those of others in their network (Carley, 1991; Carley & Kaufer, 1993). The contagion approach seeks to explain organizational members’ knowledge, attitudes, and behavior on the basis of information, attitudes, and behavior of others in the network to whom they are linked. Rogers and Kincaid (1981) refer to this as the convergence model of communication. Theories that are premised on a contagion model, at least in part, include social information processing theory (Fulk, Steinfield, Schmitz, & Power, 1987; Salancik & Pfeffer, 1978), social influence theory (Fulk, Schmitz, & Steinfield, 1990; see also Marsden & Friedkin, 1993), structural theory of action (Burt, 1982), symbolic interactionist perspectives (Trevino, Lengel, & Daft, 1987), mimetic processes exemplified by institutional theories (DiMaggio & Powell, 1983; Meyer & Rowan, 1977), and social cognitive and learning theories (Bandura, 1986). Fulk (1993) notes that these constructivist perspectives “share the core proposition that social and symbolic processes produce patterns of shared cognitions and behaviors that arise from forces well beyond the demands of the straightforward task of information processing in organizations”.

Computational Modeling of Networks

Computer simulations have long been used as an effective tool in engineering, economics, psychology, and a number of other social sciences. Engineers typically use simulations to predict performance of a system that has known dynamic characteristics. These characteristics are typically obtained from theory and are then articulated in the simulation as difference or differential equations. The goal of engineering simulation is then to assess the dynamic performance of a system based on a priori knowledge of the dynamic relationships among the various elements of the system. Forrester (1961, 1973) was one of the earliest and most influential advocates of simulation modeling of dynamic social systems. Forrester advocated this approach as a way to model and assess the dynamics of industrial and world phenomena. Sterman (2000) provides a recent review of research on dynamics simulation from this tradition. While this approach has produced a considerable number of studies, it too is based on the assumption that the researcher has a priori knowledge of the dynamic relationships among elements of the system. Indeed, many of the results of these models have been criticized for specifying relationships that were at best untested and at worst flawed. In response to these criticisms, more recent interest has focused on redefining the utility of simulations in the social sciences. Rather than using simulations to test the long-term dynamics of systems with known interrelationships, theorists (Carley & Prietula, 1994; Contractor, 1994; Hanneman, 1988) have suggested that social scientists should use simulations to help construct theory, to identify the heretofore-unknown interrelationships. This section describes the traditional use of computer simulations as well as the adaptation of this approach toward theory construction and testing in the social sciences. Later sections will apply these general approaches to the computational modeling of networks in particular. Carley and Prietula (1994) suggest that the emergence of the new field of computational organizational theory (COT) signals the growing interest in the construction of computational models to augment and assist theory building. Most social science theories are richly evocative but highly abbreviated (Poole, 1997), that is, they offer explanations that suggest complex interrelationships but do not provide precise, falsifiable mathematical formalizations of the theory.

Networks and Flows in Organizational Communication

Communication networks are the patterns of contact that are created by the flow of messages among communicators through time and space. The concept of message should be understood here in its broadest sense to refer to data, information, knowledge, images, symbols, and any other symbolic forms that can move from one point in a network to another or can be cocreated by network members. These networks take many forms in contemporary organizations, including personal contact networks, flows of information within and between groups, strategic alliances among firms, and global network organizations, to name but a few. This book offers a new multitheoretical, multilevel perspective that integrates the theoretical mechanisms that theorists and researchers have proposed to explain the creation, maintenance, dissolution, and re-creation of these diverse and complex intra- and interorganizational networks (Monge & Contractor, 2001). This focus provides an important new alternative to earlier reviews of empirical literature, organized on the basis of antecedents and outcomes (Monge & Eisenberg, 1987) or research themes within organizational behavior (Krackhardt & Brass, 1994). Although examining the emergence of communication networks is in itself an intellectually intriguing enterprise, the inexorable dynamics of globalization provide an even more compelling impetus for communication researchers and practitioners (Held, McGrew, Goldblatt, & Perraton, 1999). This chapter begins by underscoring the rationale for studying the emergence of communication networks and flows in a global world. The chapter also situates the contributions of this book in previous communication perspectives on formal and emergent communication networks in organizations as well as current philosophical perspectives on the study of emergence in structures. Communication networks and the organizational forms of the twenty-first century are undergoing rapid and dramatic changes (Fulk & DeSanctis, 1999). What is unfolding before our collective gaze is being driven by spectacular advances and convergences in computer and communication technology and by the collective economic, political, societal, cultural, and communicative processes collectively known as globalization (Grossberg, Wartella, & Whitney, 1998; Monge, 1998; Robertson, 1992; Stohl, 2001; Waters, 1995). While many of the changes brought about by globalization are beneficial to humankind, others are clearly detrimental (Scholte, 2000).