Emergence of Communication Networks in Organizations
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STRUCTURATION THEORY AND SELF-ORGANIZING NETWORKS
Noshir Contractor Departments of Speech Communication & Psychology University of Illinois at Urbana-Champaign Robert Whitbred Department of Speech Communication Texas Tech University Fabio Fonti College of Commerce, University of Illinois at Urbana-Champaign Andrew Hyatt NASA Barbara O’Keefe School of Information University of Michigan Patricia Jones Department of Mechanical & Industrial Engineering University of Illinois at Urbana-Champaign
Running Head: Structuration and Self-organizing Networks
This research was supported by National Science Foundation Grant No. ECS-94-27730. The opinions expressed here are those of the authors and not the National Science Foundation. Please send all correspondence to: Noshir Contractor, 244 Lincoln Hall, 702 South Wright Street, Urbana IL 61801, 217-352-4750 (phone), 217-244-1598 (fax), [email protected] (email). Structuration Theory & Self-organizing Networks Page 2
Abstract
Much of the work on structuration theory (Giddens, 1976, 1984; Poole & DeSanctis,
1990) examines how actors and social systems structure each other. A central tenet of structuration theory is the recursive relationship (a “duality”) between structures (i.e., the rules and resources afforded to the actors) and systems (i.e., the interaction among the actors). This paper proposes that the intellectual apparatus offered by self-organizing systems theory and computational modeling provide a useful approach to articulate and empirically validate the duality of structures and systems. Seven exogenous and three endogenous mechanisms were selected to account for the structuring -- creation, maintenance, and dissolution -- of a communication network over 13 points in time. The results obtained from computational models were empirically validated and compared using longitudinal network data collected at a U.S. public works department. Structuration Theory & Self-organizing Networks Page 3
Introduction
Much of the work on structuration theory (Giddens, 1976, 1984, Poole & DeSanctis,
1990) examines how actors and social systems structure each other. They attempt to account for both the creative and constraining aspects of social structure. A central tenet of structuration theory is the recursive relationship (a “duality”) between structures (i.e., the rules and resources afforded to the actors) and systems (i.e., the interaction among the actors). Empirical studies grounded in this approach are characterized by an explicit concern for the continual production and reproduction of meaning through communication, examining simultaneously how meanings emerge from interaction and how they act to constrain subsequent interaction.
Falsifying Structuration Theory
While the utility of a structurational perspective to the study within and between organizations is well demonstrated, there continues to be a debate about the extent to which empirical studies offer a “test” as opposed to an illustration of structuration theory’s ability to capture complex processes (DeSanctis & Poole, 1994). Indeed if one were to review empirical studies from a structurational perspective, one is hard pressed to identify a single study that concluded that it failed to find support for structuration theory.
Such overwhelming endorsement of a theory belies an underlying concern about the potential falsifiability of the theory. An appropriate challenge therefore is the theory’s ability to specify predictions which, if they were not empirically validated, would Structuration Theory & Self-organizing Networks Page 4
plausibly represent a refutation of the premises of structuration theory. Complexity theory, in conjunction with the methods of computational modeling, offers a new approach to translate the verbal explications of structuration theory into precise, falsifiable hypotheses that can be empirically validated.
Structuration Theory & Self-organizing Networks
In the past decade there has been a plethora of scholarship calling for the extension of complexity theory – arguably a mainstay of many disciplines in the physical and life sciences – to social sciences in general, and to organization science in particular
(Andersen, Meyer, Eisenhardt, Carley, & Pettigrew, 1999; Brown & Eisenhardt, 1997;
Carley & Prietula, 1994; Contractor, 1994; Contractor & Seibold, 1993; Gersick, 1991;
Hanneman, 1988; Morecroft & Sterman, 1994; Senge, 1990; Thietart & Forgues, 1995).
The motivation for this call stems from a widely shared frustration with social scientific theories and methods, which have proven to be inadequate at untangling with precision the complexity in organizational phenomena. The phenomena described in verbal expositions of, say, structuration theory invoke a multitude of factors that are highly interconnected, often via complex, non-linear, dynamic relationships.
Consistent with the metaphors of complexity theory, intellectual progress in this realm has been “strange,” sporadic, isolated, and sensitive to the initial conditions (and conceptions) of key early intellectual leaders in this area. While these distinct strands of work continue to weave a quilted fabric we loosely refer to as “complexity theory,” there is widespread agreement that the maturation of “complexity theory” as a viable intellectual tradition must be accompanied by a move from advocating one or more Structuration Theory & Self-organizing Networks Page 5
perspectives on complexity theory to executing studies that adopt these perspectives.
Lamenting the failed promise of earlier forays into systems theory, Poole (1997, p. 50) notes that “most often, systems theory became a metaphor, rather than an instrument of analysis.” This study is offered as an attempt to go beyond the use of complexity theory as a metaphor by applying the instruments of analysis offered by complexity theory to study the self-organizing processes, as proposed by structuration theory, to the emergence of communication networks in organizations. More specifically, this study advocates networks as an appropriate vehicle to examine the process of structuration.
Structuration Theory and Networks
Scholars (Barley, 1990; Haines, 1988) have long argued for the use of network analytic techniques to articulate and extend structuration theory. In an early attempt,
Goodell, Brown, and Poole (1989) use a structurational argument (Poole & McPhee,
1983) to examine the relationship between communication network links and shared perceptions of organizational climate. Using four waves of observation over a 10-week period from an organizational simulation, they found that members’ communication networks were significantly associated with shared perceptions of the organizational climate only at the early stages of organizing (weeks two and four).
Barley (1990) used network analytic tools to describe the situated ways in which relatively small role differences in initial conditions reverberated through seemingly similar social systems resulting over time in widely different social structures. Barley
(1990) rejected contingency theories because it offers static predictions of a match between technologies and social structures. Instead, he argued for using networks as a Structuration Theory & Self-organizing Networks Page 6
way of making explicit the theory of negotiated order (Fine & Kleinman, 1983).
According to this theory, structures are byproducts of a history of interactions and are subsequently perceived as fact by organizational members. However, he notes that theories such as structuration or negotiated order provide few analytic tools for explicating the links between the introduction of a technology, the interaction order, and the organization's structure. He offers network-analytic tools as one way of explicating these links. Barley (1990) chronicled how the material attributes of a CT scanner recently adopted in two radiology departments affected the non-relational elements of employees’ work roles, including their skills and tasks; this, in turn, impacted their immediate communication relationships and precipitated more widespread changes in the department’s social network. Significantly, his analysis explains why the technology was appropriated differently in the two radiology departments. Barley's empirical work exemplifies several symbolic interactionists who argue for the importance of understanding the emergence of social order as a process of social construction (Giddens,
1976, 1984).
From Barley’s (1990) standpoint, network techniques offer an opportunity to illustrate the ideographic and idiosyncratic nature of organizational phenomenon. The ideographic assumption reflects an ontological viewpoint that rejects the nomothetic goal of seeking generalizable regularities in explaining organizational phenomenon. Instead, the goal of the researcher with an ideographic viewpoint is to understand the processes that unfold in the particular organization being studied. Zack and McKenney (1995) offer a more recent example of work in this tradition. They examined the appropriation Structuration Theory & Self-organizing Networks Page 7
of the same group-authoring and messaging computer system by the managing editorial groups of two morning newspapers owned by the same parent corporation. Drawing upon Poole and DeSanctis’ (1990) theory of adaptive structuration, they discovered that the two groups’ appropriation of the technology, as indexed by their communication networks, differed in accordance with the different contexts at the two locations. Further, they found evidence that the group’s performance outcomes for similar tasks were mediated by these interaction patterns.
The study of organizational networks have, in the past two decades, emerged as an influential and intriguing tradition within organizational science. It has been an influential domain judging by its widely acknowledged use by organizational scholars.
Network researchers have sought to explain organizational behavior in terms of formal organizational structures as well as informal organizational structures such as communication networks, influence networks, advice networks and task networks
(Monge & Eisenberg, 1987). More recently, reviewers have identified a number of theories that have been used in network research within (Krackhardt & Brass 1994) and between (Mizruchi & Galaskiewicz, 1994) organizations.
The study of organizational networks is an intriguing tradition because of the instinctive sense among several network and organizational scholars that its true potential as an explanatory framework is yet to be harnessed. In pursuit of that goal, over the past decade, many scholars have called for greater attention to the emergence – creation, maintenance, and dissolution – of organizational networks. For example, in a recent essay, Salancik (1995) considered the limitations of Burt’s (1992) Theory of Structural Structuration Theory & Self-organizing Networks Page 8
Holes. Although Salancik acknowledged the significance of Burt's finding that a person occupying a structural hole will gain political advantage, he argued that “a more telling analysis might explain why the hole exists or why it was not filled before. A network theory that accounts for the appearance and disappearance of structural holes – rather than how they can be used to disadvantage – can provide us with a better understanding of how collective action is organized" (Salancik, 1995, p. 349). Salancik challenged network researchers to invest efforts in creating a more specific and precise network theory. Such a theory would not take a network as given. Instead, it would seek to uncover the evolution of the network. In describing the new post-bureaucratic
“interactive” forms of organizations, Krackhardt (1994), echoes similar sentiments: “We must first agree on the fundamental process by which these networks emerge before we can agree on what effect they might have” (Krackhardt, 1994, p. 218).
In addition, two of the more comprehensive reviews of network studies have called for greater attention to the emergence of networks (Brass, 1995; Monge &
Eisenberg, 1987). While both reviews were organized around antecedents and outcomes of organizational networks, the authors acknowledged that such distinctions are often both nonexistent and potentially misleading. Monge and Eisenberg (1987, p. 310) offered a hypothetical scenario to illustrate the ongoing evolution of a network, a concept they term as “reorganizing.” Acknowledging Monge and Eisenberg (1987), Brass (1995) underscored the substantively compelling argument to articulate the dynamic nature of the inter-relationships among networks, their antecedents and outcomes. Structuration Theory & Self-organizing Networks Page 9
Echoing these concerns, in a special issue of the Journal of Mathematical
Sociology on “The Evolution of Networks,” Stokman and Doreian (in press) underscore the distinction between the terms “network dynamics” and “network evolution.” The study of “network dynamics” has as its goal the quantitative or qualitative temporal characterization of change, stability, simultaneity, sequentiality, synchronicity, cyclicality, or randomness in the phenomena being observed (Monge & Kalman, 1996).
The focus here is on providing sophisticated descriptions of the manifest change in networks (e.g., Burkhardt, 1994; Burkhardt & Brass, 1990). In contrast, the study of network evolution should contain an important additional goal: an explicit, theoretically- derived understanding of the mechanisms that determine the temporal changes in the phenomenon being observed (Stokman and Doreian, in press). Most of the impressive number of longitudinal network studies conducted to date could be plausibly characterized as studies of “network dynamics” rather than “network evolution.”
The growing interest in the emergence of networks serves as the nexus for scholars of organizational networks and complexity theory. As a first step, attention has been paid to identifying and examining the underlying logics (Kontopoulos, 1993), or generative mechanisms, that explain how networks enable and constrain organizational and inter-organizational behavior. Krackhardt (1994) proposes three relational dimensions for a model of network formation: (i) dependence, the extent to which individuals rely on one another to accomplish their tasks, (ii) intensity, the extent to which they interact with one another, and (iii) affect, the feelings (love, hate, reverence) individuals have towards one another. Structuration Theory & Self-organizing Networks Page 10
Monge and Contractor (in press) identify ten generative mechanisms. These are:
(a) theories of self-interest (social capital theory and transaction cost economics), (b) theories of mutual self-interest and collective action, (c) exchange and dependency theories (social exchange, resource dependency, and network organizational forms), (d) contagion theories, (social information processing, social cognitive theory, institutional theory, structural theory of action), (e) cognitive theories (semantic networks, knowledge structures, cognitive social structures, cognitive consistency), (f) theories of homophily
(social comparison theory, social identity theory), (g) theories of proximity (physical and electronic propinquity), (h) uncertainty reduction and contingency theories, (i) social support theories, and (j) evolutionary theories.
Monge and Contractor (in press) note that there are at least two implications of reviewing the extant literature on organizational networks in terms of the underlying generative mechanisms. First, most network studies in organizations typically hypothesize and examine organizational behavior only in terms of one of these generative mechanisms. For instance, network explanations for employee job satisfaction have been based on a contagion mechanism (Hartman & Johnson, 1989) or a balance mechanism
(Kilduff & Krackhardt, 1994). Often the predictions based on these two mechanisms are contradictory and are thus not easy to parse out empirically. Second, Monge and
Contractor (in press) note that the preponderance of research on organizational networks has been inspired by four of the eleven theories reviewed: exchange theories, contagion theories, cognitive theories, and theories of homophily. The few studies based on one of Structuration Theory & Self-organizing Networks Page 11
the other seven theories provide ample evidence of their potential explanatory power, and should be actively considered by network researchers.
Clearly, the appreciation of multiple, often contradictory, theoretical network mechanisms operating in a non-linear and time-dependent fashion situates any study of the emergence of organizational networks substantively within the metatheoretical umbrella of structuration theory and logically within the realm of complexity theory. Like other disciplines in the physical and life sciences, in the social sciences interest in complexity theory has often been accompanied with a reliance on computational tools to assist in the simulation of non-linear relationships that do not lend themselves to analytic closed-form solutions. Following in the footsteps of fields such as computational physics, computational chemistry, and so on, Carley and Prietula (1994) have advocated launching the field of Computational Organization Theory.
The study reported here employs a computational organizational modeling approach. It begins by identifying the theoretically derived structurational processes entailed in the emergence of an organization’s communication network. From this perspective, a complex system must seek to explain the creation, maintenance, and dissolution of a communication network based on exogenous as well as recursive endogenous factors. Each of the theoretical mechanisms is presented in the form of a mathematical equation. Taken together, the theoretical mechanisms constitute a complex self-organizing system consistent with the tenets of structuration theory. The Methods section begins by describing details of an organizational context used to validate the theoretically specified complex self-organizing system. Following that description, the Structuration Theory & Self-organizing Networks Page 12
Methods section describes how the complex systems of theoretical mechanisms are computationally modeled using an object-oriented simulation tool called Blanche. The study concludes with the description and discussion of results from a comparison of the dynamic communication networks obtained from simulation with those observed in the field.
THEORY
The emergence of communication networks in organizations is influenced by exogenous and endogenous mechanisms. Exogenous mechanisms are conceptually distinct from the communication network, while endogenous mechanisms are characteristics of the communication network itself. For each mechanism, we explain its effect on the communication network. The seven exogenous mechanisms include the following: two dimensions of hierarchy (supervisor-subordinate relationships and peer- relationships), spatial proximity, adoption of email, workflow, friendship, and common activities foci. The three endogenous mechanisms are transitivity, group cohesion, and structural holes. Figure 1 schematically illustrates the theoretical mechanisms leading to emergence of the communication network in an organization. Structuration Theory & Self-organizing Networks Page 13
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Insert Figure 1 about here
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Hierarchy influences emergent communication patterns in organizations in two distinct ways: through supervisor-subordinate (vertical) relationships and through peer
(horizontal) relationships.
Supervisor-Subordinate Relationships
Supervisor-subordinate relationships are interactions between organizational members that entail formal authority over task-related activities (Jablin, 1979). This relationship is often present in organizational charts. Previous research (for an extensive review, see Jablin, 1979, 1987) has demonstrated that a substantial proportion of supervisors’ communication is with subordinates (Berkowitz & Bennis, 1961; Brenner &
Sigband, 1973; Dubin & Spray, 1964; Lawler, Porter & Tenenbaum, 1968). In addition, most of this communication is task-based (Baird, 1974; Richetto, 1969; Zima, 1969). The reasons for these communication patterns are related to the nature of the relationship: the supervisor needs to communicate directions, procedures, and feedback, while the subordinate usually requests task-related clarification, and provides personal information
(Jablin, 1979). Structuration Theory & Self-organizing Networks Page 14
The supervisor-subordinate relation affects the communication between
individuals i and j by adding a value CSij which represents the change (increase or decrease) in communication, calculated by following equation:
C S Sij ij (1)
where S is the supervisor/subordinate matrix, and the cell entry Sij has a value of one if i is j’s supervisor.
Peer interaction.
Peer interaction is defined as the communication between individuals who are are the same level (or similar levels) in the hierarchy. More specifically - ceteris paribus
-those higher in the hierarchy will communicate more with peers than with individuals at lower hierarchical levels. This tendency occurs because managers need to coordinate with one another to make sense of and enact the environment in which the organization is embedded (Daft & Weick, 1984). Coordination and control are functions that pertain extensively to managers, and by their nature require these tasks require efficient, direct, ongoing communication among them (Lincoln & Miller, 1979). Empirical findings support this argument (Marting, 1969).
Even though a greater amount of communication is expected among managers, there will be differences in interactions among them, that will depend on their relative level within the hierarchy. Strategic communication is more likely to happen between actors that have higher status. That is, two high level manager will communicate more with one another, than two middle level managers. This is because the higher the Structuration Theory & Self-organizing Networks Page 15
managerial position the greater the strategic responsibility - and therefore the greater the amount of coordination and control - associated with it.
The influence of the peer interaction mechanism on the emergence of the
organization’s communication network is represented by CHLij, the change (increase or decrease in communication) between individuals i and j calculated by the following equation:
C HL HLij ij (2) where HL is a matrix where the cell entry HLij of i and j will be zero if i and j are not in the upper hierarchy. If they are in the upper hierarchy the entry for i to j will be the difference weighted by the hierarchy levels.
Spatial Proximity
Employees who are spatially proximate are physically located close to each other . In an early study of 96 university faculty, Hagstrom (1965, p. 122) notes that
“spatial propinquity . . . is likely to lead to informal communication.” Since then, research has consistently shown that close spatial proximity is positively correlated with communication. Kraut, Egido, and Galegher (1990, p. 158) offer two general explanations for the effect of physical proximity on communication: “colocation of similar others and the availability of frequent, high-quality, low-cost communication.”
Conrath (1973) researched the relationship between spatial proximity of employees and the mode of communication (face to face, telephone, written message). A study of 384 employees of a Canadian manufacturing and sales company (all the Structuration Theory & Self-organizing Networks Page 16
management, senior staff members, and a sample of 25% of the first line supervisors) showed that at distances up to 100 feet, employees were 22 times more likely to choose the face to face mode of communication over the telephone, and seven times more likely to communicate face to face than via written message. Additionally, more communication occurred between employees located at shorter distances from each other.
Bochner, Duncan, Kennedy, and Orr (1976), in a study of the social interaction patterns in an apartment complex, found spatial proximity increased the likelihood of two people knowing and interacting with one another.
Allen (1978) reported the results of a study of seven R & D laboratories, of which two were in the aerospace industry, two were in universities, and one each was in the computer industry, the chemical industry, and a government agricultural research laboratory. Spatial proximity was measured by the walking distance between two lab member’s desks. Results showed that the probability of two people communicating about technical and scientific matters decreased asymptotically with physical distance, with the greatest decrease being in the first 10 meters and leveling off at about 30 meters.
In a study of approximately 500 researchers in an R&D organization, Kraut et al.
(1990) found that researchers who had offices next door to each other had approximately twice as much communication as those whose offices were simply on the same floor. In a study of a sub-unit of a manufacturing organization, Zahn (1991) analyzed the relationship between spatial proximity, exposure, and communication. Spatial proximity was measured as the walking distance between any two employees’ regular work location, while exposure was measured by having employees report the number of hours Structuration Theory & Self-organizing Networks Page 17
per week they spent in each location in the building. Analyses showed that employees who were more proximate were more likely to communicate with one another.
Theoretically, spatial proximity in and of itself does not cause increased communication. Rather, those who are proximate are exposed more to one another, which increases the likelihood of communication among them (Festinger, Schachter, &
Back, 1950; Monge, Rothman, Eisenberg, Miller, & Kirste, 1985; Rice, 1993; Zahn,
1991). As the amount of exposure increases, so does the likelihood of communication.
The influence of the spatial proximity mechanism on the emergence of
communication networks is represented by CPij, the change (increase or decrease) in communication from i to j due to proximity calculated by the following equation:
C P Pij ij (3)
where P is a matrix where the Pij entry represents the proximity of i to j.
Adoption of Email
Email is a technology which enables communication between employees across geographic boundaries. In the case of organizations, these boundaries consist of employees physically located in different buildings, on different floors of the same building, on the same floor in different offices, or in the same office but separated by physical boundaries (e.g. cubicles). By allowing these boundaries to be overcome, email creates electronic proximity (Rice, 1994; Zack & McKenney, 1995). Similar to spatial proximity, electronic proximity increases the opportunity for two employees to interact.
This increased interaction occurs in two ways. First, employees no longer need to be spatially co-located to communicate. Second, email allows asynchronous communication; Structuration Theory & Self-organizing Networks Page 18
two employees no longer need to be available at the same time to interact with one another. Constant, Sproull, and Kiesler (1996) report that email was particularly useful in forging weak ties for technical advice among physically dispersed employees. Hinds and
Kiesler (1995) report that electronic mail was particularly influential in creating and maintaining communication links across boundaries.
The use of email as a mechanism to influence the emergence of communication networks
is represented by CEij, the change (increase or decrease) in communication between individuals i and j calculated by the following equation:
C E Eij ij (4)
where E is the matrix of email use and Eij indicates that individual i and individual j both use email.
Workflow
Van de Ven and Ferry (1980) define workflow as “the materials, objects, or clients and customers that are transacted between units, hierarchical levels, and organizations” (p. 242). Brass (1981) further conceptualizes workflow in terms of a
“network that locates task positions in relation to each other. The basis for the relationships or interdependencies among different positions is the patterned interactions that occur between related positions as the work flows through the organization.” (p.
332). Workflow transactions are the inputs to and outputs from task positions. Since
“each link in this structural network represents the acquisition of inputs by one worker Structuration Theory & Self-organizing Networks Page 19
and, at the same time, the distribution of outputs by another worker, the link is viewed as a mutual interdependency” (Brass, 1981, p. 332).
Malone and Crowston (1994) offer coordination theory as a framework for understanding how organizational members manage dependencies between goals, activities, and actors. Typically, the accomplishment of these interdependent activities will require and create resources. Crowston (1997, pp.159-160) notes that “according to coordination theory, the activities in a process can be separated into those that are necessary to achieve the goal of the process (e.g., that directly contribute to the output of the process) and those that serve primarily to manage various dependencies between activities and resources.” Dependencies are managed via coordination mechanisms, which as Crowston (1997) points out, are primarily information processing activities.
The workflow network in an organization serves as a trail of the information processing activities associated with managing these dependencies. Individuals i and j reciprocally depend on each other for resources such as information about what tasks to do next, information about progress on previous tasks, and work skills and knowledge needed to complete tasks. A mutual dependency in the workflow between i and j, this will increase the likelihood of communication between i and j.
The influence of the workflow mechanism on the emergence of the
communication network is represented by the value CWij, the change (increase or decrease) in communication resulting from interdependencies in the workflow. This value is calculated by the following equation:
C W Wij ij (5) Structuration Theory & Self-organizing Networks Page 20
where W is a workflow matrix and the cell entry Wij indexes the level of interdependence between individuals i and j.
Friendship
In work settings, Fischer (1982) found employees considered their friends to be either those with whom they had social interaction or those with whom they would discuss personal matters. Brass (1984) defined friendship as social liking. As a consequence, a friendship network is conceptually independent of a task communication network; two employees may have a task communication tie with one another and not be friends, or vice versa.
Albrecht and associates (Albrecht & Adelman, 1984; Albrecht & Hall, 1991;
Albrecht & Ropp, 1984) argue that uncertainty reduction theory (Berger, 1987; Berger &
Calabrese, 1975) explains the relationship between friendship and task communication networks. Uncertainty reduction theory suggests that employees will communicate with others to reduce uncertainty in their task environment and their relationships. As a relationship between two individuals develops, the levels of uncertainty entailed in the relationship, as well as how each person will react in different situations diminishes. In situations of crisis, or very high uncertainty - for instance, when people feel more threatened - one will likely seek social support and information from those with whom there is less uncertainty in the relationship (Albrecht & Adelman, 1984), that is their friends. To the extent this increased communication is about task related issues, this will increase the likelihood of two friends’ task communication. Structuration Theory & Self-organizing Networks Page 21
The effect of friendship as a mechanism influencing the emergence of the
communication network is represented by the value CFij, the change (increase or decrease) in communication between individuals i and j based on their friendship. This value is calculated by the following equation:
C F Fij ij (6)
where F is the friendship matrix, and where cell entry where Fij is one if individual i reports j as a friend.
Common Activity Foci
Activity focus theory (Corman & Scott, 1994; Feld, 1981, 1984; McPhee &
Corman, 1995) posits that in addition to personal and formal structural characteristics, interpersonal interactions are organized around activity foci. An activity focus is defined as “a social, psychological, legal, or physical entity around which joint activities are organized” (Feld, 1981, p. 1016). People who engage in a common activity are more likely to develop interpersonal relationships, as they are exposed to one another and meet those with common interests. McPhee and Corman (1995) report that the likelihood of communication links between members of a church congregation increased when the members engaged in common activities, such as social events or committees.
A church is a unique type of organization in that the majority of activities are voluntary in nature (McPhee & Corman, 1995). In a more traditional organization, activity focus theory provides a means for explaining task communication ties between employees independent of their spatial proximity; these employees may be working on Structuration Theory & Self-organizing Networks Page 22
common activity foci. Employees who work on common activity foci are more likely to communicate with one another.
The influence of the common activity foci mechanism on the emergence of the
communication network is represented by CAij, the change (increase or decrease) in communication between individuals i and j, resulting from sharing common activity foci.
This change is described in the following equation:
C A Aij ij (7)
where A is a shared activity foci matrix, and the cell entry Aij is the number of common activity foci between individuals i and j.
Endogenous mechanisms
The seven mechanisms described above represent exogenous factors that influence the emergence of the communication network. However, the extant configuration of a communication network will also enable and constrain the subsequent emergence of the network. This section describes three such endogenous mechanisms: transitivity, group cohesiveness, and structural holes.
Transitivity
Transitivity is defined in terms of a triad. A triad is a set of three actors and the relationship between them (Wasserman & Faust, 1994). A triad that includes actors i, j, and k is transitive if, when there is a relationship from i to j, and from j to k, then there is also a relationship from i to k. Basically, this is premised on the principle that ‘the friends of my friends are my friends’: therefore, if Bob is friends with Jim, and Jim is friends Structuration Theory & Self-organizing Networks Page 23
with Mary, then Bob will likely be friends with Mary. In communication networks, the mechanism underlying the emergence of transitive triads could be one of information proximity: that is, if Bob communicates with Jim, and Jim communicates with Mary, then it is likely that Bob will get information about Mary through his interaction with Jim, and therefore initiate communication with her. Empirically, it has been repeatedly shown that transitivity is an important characteristic of social networks (Fararo & Sunshine, 1964;
Holland & Leinhardt, 1972; Rapoport, 1953, 1963; Wasserman & Faust, 1994). To the extent that communication between individuals is motivated and/or accompanied by positive affect, the drive towards transitivity can also be explained in terms of balance theory (Heider, 1958). That is, individuals are more likely to create communication links with friends of friends and dissolve links with friends of enemies and enemies of friends.
The equation explaining the Ctrijt, the change in communication network between individuals i and j at time t due to the transitivity mechanism is given by:
N C C C (8) trij ikt 1 kjt 1 t k 1
where, Cikt-1 and Ckjt-1 are the communication links at time t-1 from individuals i to k and individuals k to j respectively This equation explains transitivity’s effects on the communication from i to j by examining each triad that contains i and j. If i communicates with k, and k communicates with j, the transitivity mechanism suggests that the individual i will increase communication with individual j.
Group Cohesion Structuration Theory & Self-organizing Networks Page 24
Group cohesion is defined as the result of the forces which hold group members together (Seashore, 1954, McCauley, 1989). For individuals, cohesion is the attraction to the group of which they are members (Back, 1951; Seashore, 1954; Festinger,
Schachter, & Back, 1950). Thus, a group’s level of cohesion is often measured as the average of each individual member’s attraction to the group. Members of groups with high levels of cohesion are more likely to follow group norms (Festinger et al, 1950;
Back, 1951; O’Keefe, Kernaghan, & Rubenstein, 1975), and are more likely to attempt to influence and interact with other group members (Back, 1951; Gerard, 1954).
Several authors have addressed the relationship between cohesive groups and communication. Specifically, Seashore (1954) and Homans (1950) hypothesized that cohesive groups will be characterized by increased interaction. This relationship has been empirically demonstrated by Back (1951) and Bovard (1951) and more recently, in the context of computer-mediated communication, by Fulk and her colleagues (Fulk,
1993; Fulk, Schmitz, & Steinfield, 1990; Schmitz & Fulk, 1991). Festinger et al (1950) defined the cohesiveness of a group as the number of sociometric linkages within a group. Taken together, these results indicate that the density of a group, or the average strength of linkages within a group, reflects the level of cohesion. Specifically, groups with higher levels of cohesion have higher network densities. The level of communication among individuals within groups with high network densities will tend to increase or remain constant, while communication between members of groups with low densities will tend to decrease (Back, 1951; Festinger, et al, 1950). The equation below Structuration Theory & Self-organizing Networks Page 25
describes Ccoijt, the change (increase or decrease) in communication between individuals i and j who belong to the same group at time t:
Cco gd gd (9) ijt t 1 meant 1
where gdt-1 represents the density of the group to which i and j belong and gdmeant-1 represents the mean density of all groups within the communication network. For this study, the groups are identified using hierarchical clustering of the communication network. The density of the group is subtracted from the mean density of all the groups, so that members of groups with lower density will have a lower propensity to sustain their intra-group communication network links as compared to members in groups with higher density. This is because group cohesion theory postulates that individuals are more attracted to dense cohesive networks than less dense networks.
Structural Holes
A structural hole is a position in the network that connects two non-redundant - that is, disconnected - actors (Burt, 1992). The individual that fills that hole is called tertium gaudens. These individual will draw a competitive advantage from their positioning, in terms of collecting higher volume and better quality of information from their contacts, and exercising greater control over them. They tend to be entrepreneurial and actively seek to position themselves in the structural hole, both for cultural (for instance, because of a Calvinist profit seeking ethic), and psychological (for instance, a need to achieve; McLelland, 1961) reasons. Structuration Theory & Self-organizing Networks Page 26
In terms of a task communication network, the structural holes mechanism implies that the more entrepreneurial among the individuals will influence the emergence of the communication network by strategically building structural holes in the network.
CHOijt, the change (increase or decrease) in the communication between individuals i and j at time t due to structural holes is accomplished by both initiating relationships with unconnected others, and acting in order to keep them from interacting. The change is described in the equation below
N C C N C jkt 1 kit 1 kit 1 C (10) HOij 2 t k 1 k 1 CSE CSE 1 C jkt 1 mint 1 maxt 1
This equation represents the first step in calculating structural holes. There are two parts to this equation. The first term in Equation 10 examines each triad that involves i and j. Each triad consists of i, j, and k, where k represents in turn every other individual in the network. The communication of j to k at t-1 is multiplied by the communication of k to i at t-1. Therefore, the greater the flow of information from j, through k, to i, the lower the change in communication from i to j. This is because i is able to get the information of j through k, and thus does not need direct communication with j. This change is then weighted by the maximum value squared in the communication matrix at time t-1, thereby ensuring that the value is less than 1.
The second term in Equation 10, again examines triads. The communication from k to i is this time divided by the structural equivalence of j and k minus the minimum structural equivalence. j and k are structurally equivalent to the extent that they have Structuration Theory & Self-organizing Networks Page 27
similar communication patterns to others in the network and are thereby exposed to similar information. (A value of one is added to make sure no division by zero occurs).
This specification reflects the fact that if k communicates with i, and if j and k are structurally equivalent, the more likely it is that i will get the same information from j and k. Hence, from a structural holes mechanism, i is likely to reduce (or dissolve) a communication link to j.
METHODS
Sample
The organization used in this study is the Public Works Division (hereafter PWD) of a military base of approximately 35,000 located in the southeast United States. The data reported here is part of a larger ongoing study examining the communication and organizational infrastructure at the PWD (Jones, Contractor, O’Keefe, and Lu, 1994;
Jones et al., 1995). The PWD is organized into five departments based on function. PWD
Administration (N=2) acts as an interface between PWD and the rest of the base, while coordinating the activities of the other departments. Engineering Plans and Services
(N=16) is charged with both the maintenance of existing civil infrastructure and buildings, and with developing plans for future development. Facilities Management
(N=18) is charged with overseeing construction projects which are underway, and ensuring funds are available for future projects. Housing (N=8) is charged with meeting the housing needs of military personnel and their families. Environment (N=11) is Structuration Theory & Self-organizing Networks Page 28
charged with assuring activities at the base are in compliance with environmental regulations.
Data was collected every two months from March of 1995 through March of
1997, for a total of 13 time periods. During this time, a total of nine employees left the organization, and 11 joined. Response rate for each time period was 100%. The data used in this analyses were limited to the 55 employees who were employed over the duration of the entire study. The average age of these employees was 45, ranging from 28 to 60 years; 40 were male and 46 were white. These employees had worked at the base for an average of 11 years, ranging from 2.8 to 28 years.
Procedures
Employees participated in a total of 13 structured interviews, which occurred every other month for two years. Employees were provided with a cover letter which explained the purpose of the study and guaranteed confidentiality of responses.
Employees were then provided with a copy of the survey, and responded orally while the surveyor recorded responses. Both employees and surveyors requested clarifications when needed.
Instrumentation
Task Communication Networks
For each time period, employees were provided with the current organizational roster of the PWD. Employees were asked to read each name, and determine if they had Structuration Theory & Self-organizing Networks Page 29
any task related communication with him/her during the past two months.
Communication was defined as “conversations in person, in meetings, by phone, via electronic mail, or by memoranda.” Employees estimated the amount of communication per week. This data was entered into a 55 by 55 asymmetric matrix, where cell ij equaled the number of minutes per week i reported communicating with j.
Hierarchy
Employees were coded for their appropriate hierarchical level, where: 1 = support staff/technician; 2 = specialist/engineer; 3 = team leader; 4 = area chief; 5 = division chief. These codes were verified by the head of the PWD. The theoretical mechanism proposed that employees who are similar in hierarchical level will be more likely to communicate with one another, but only for high levels in the hierarchy. To operationalize this concept, hierarchical levels were re-coded. First, employees at the two lowest hierarchical levels were assigned codes of zero, since their communication was not altered by this mechanism. Second, the remaining hierarchical levels were re- coded as follows: level “3”s as .333, level “4”s as .667, level “5”s as one. According to the proposed mechanism, two employees at the highest hierarchical level will be most likely to increase their communication; employees at middle level will be next most likely to increase their communication, followed by those at the lowest remaining level; as the difference between the hierarchical levels of two employees increased, the likelihood of these two employees communicating decreased. Structuration Theory & Self-organizing Networks Page 30
Post-multiplying the vector of codes of hierarchical levels by its transpose gives a
55 by 55 symmetric matrix where ij is equal to: 0 if either i or j are not in the top three hierarchical levels; 1 if both i and j are at the highest hierarchical level; .67 if i is at the upper level and j is at the middle level (1 * .67); .33 if i is at the upper level and j is at the lower level (1 * .33); .44 if i and j are both at the middle level (.67 * .67); .22 if i is at the middle level and j is at the lower level (.67 * .33); and .11 if both i and j are at the lower level (.33*.33).
Supervisor/Subordinate Structure
Each dyad in the organization was coded to indicate if they were in a supervisor/subordinate relationship. Data was entered in to a 55 by 55 asymmetric matrix, where cell ij equaled 1 if i was j’s supervisor, 0 otherwise.
Spatial Proximity Network.
A spatial proximity matrix was developed as follows: cell ij equals 3 if i and j share the same office; ij equals 2 if i and j are in adjacent offices; ij equals 1 if i and j are in the same building; ij equals 0 otherwise.
Adoption of Email
Employees were asked to report the number of minutes per week of electronic task communication they had with all other employees during a typical work week. This data was entered into a 55 by 55 matrix, where ij equaled the number of minutes of Structuration Theory & Self-organizing Networks Page 31
electronic task communication i reported with j. Summing the rows of this matrix gave a vector of the total number of minutes each employee reported communicating over email.
This vector was dichotomized so any number of minutes of email communication greater than zero became a one. An employee was considered to have adopted email if he/she reported at least one minute of task communication via email with at least one other employee. Post multiplying this matrix by its transpose gave a 55 by 55 matrix, where cell ij equaled one if both i and j adopted email, zero otherwise.
Workflow Network.
As discussed previously, workflow is best represented as a network of inputs and outputs in the work process. Workflow was measured in the PWD based on the employees’ use of a specific government form, which indicates and tracks the principal activities performed in the organization. Specifically, employees were asked to report the number of these forms they gave to and received from other employees in the PWD during a typical work week. This data was entered into two 55 by 55 matrices. In the first matrix, cell ij equaled the number of forms i reported giving to j; in the second matrix, ij equaled the number of forms i reported receiving from j. Summing the cells of these two matrices provided an index of the strength of the workflow link between i and j.
Friendship Network. Structuration Theory & Self-organizing Networks Page 32
Employees were provided with a roster of organizational members; they were asked to identify those employees they considered to be their friends. This data was entered in to a 55 by 55 asymmetric matrix, where cell ij equals one if i reported j as a friend.
Common Activity Foci
To facilitate the identification of which employees worked on common activity foci, researchers began by examining the formal job descriptions of all employees in the
PWD. Each job in the PWD had a job description developed by the Human Resources
Department at the base. These descriptions were used by the researchers to develop a list of 130 tasks, which represented the specific activities carried out by PWD employees.
Researchers utilized the Theme Machine (Lambert, 1996) to facilitate the thematic identification of activity clusters. Specifically, the job descriptions were entered into text files, where each sentence of text was treated as a separate document. Theme Machine is a computer program which assigns term weights based on the frequency with which words appear in the total set of documents. It then computes similarities between documents based on both the number of common words and the term weights. Finally,
Theme Machine clusters documents based on their similarities. This procedure resulted in 161 clusters. Inspection of the clusters showed that some were based on non-activity related language, and these were dropped. In addition, clusters which referred to the same activity in different terms were collapsed. This resulted in the identification of 130 activity types (O’Keefe, 1996). Structuration Theory & Self-organizing Networks Page 33
Each of the 130 tasks was printed on an index card. Employees identified which tasks they performed, and grouped these tasks into activity piles so that tasks which contributed to a common activity were together. These activity piles were taken to be activity foci in PWD. Employees were then asked to name others in the organization with whom they worked while performing this activity. This data was entered into a 55 by 55 matrix, where cell ij equaled the number of common activities i reported doing with j.
Design and implementation of computational network models
Previous sections described the theoretical mechanisms which influence the emergence of communication networks in organizations, specified equations for each of the exogenous and endogenous mechanisms, and described procedures for collecting the empirical data for validating the dynamic implications of these mechanisms. This section describes the computational strategy used to derive the dynamic implications of the theoretical mechanisms.
This study identified 10 different mechanisms - seven exogenous and three endogenous - which influence the emergence of communication networks in organizations. As with most complex systems, the human mind is limited in its ability to deduce the long term dynamic implications of any one of these non-linear theoretical mechanisms (Carley & Prietula, 1994; Hanneman, 1988). The task becomes even more daunting, when one tries to predict the combined influences of multiple mechanisms operating simultaneously. Given the limitations to mentally construe the dynamics of the Structuration Theory & Self-organizing Networks Page 34
theoretically specified complex system, computer simulations were used to reveal the implications of the simultaneous interaction of these mechanisms on the emergence of the communication network.
The following sections describe four models that were simulated. In each of these models, the goal was to examine the extent to which the specified mechanisms explained the observed average variation M in communication across the 2970 (55 times 54) dyads over the 13 time periods. First, the standard deviation of each dyad’s communication for the 13 points in time was computed. This provided a 55 by 55 matrix where cell ij equaled the standard deviation in the time i spent communicating with j communication over the 13 time periods. Next, the mean of all the cell values in this 55 by 55 matrix were computed. This mean value, M (= 43.41 minutes), represented the average standard deviation in time spent communicating across all dyads and over the 13 points in time. In essence this represented an index of the variation in dyadic communication unexplained by simply predicting the value of the communication dyad at each point in time to be identical to the mean over the 13 time periods. Structuration Theory & Self-organizing Networks Page 35
Model 1: Baseline Model
The baseline model was specified by the following equation:
C C f (C ) ijt ijt 1 Rij (11)
where Cijt is the communication from i to j at time t, Cijt-1 is the communication from i to j at time t-1, f is a function that makes sure that the mean of the change in communication (represented by the term on the right hand side of equation 11) from i to j is centered at 0, with a standard deviation of M. The change in communication for the baseline model is given by the equation:
C N(0, M) Rij (12) where N is a function which gives a normal distribution with mean 0, and standard deviation M. This model was run 50 times resulting in 50 realizations of the communication network predicted by the baseline model at the end of the thirteenth time period.
Substantively, this model predicts the evolution of the communication network based on the prior communication network and a series of random shocks with a mean of zero and a standard deviation equal to the mean variation in communication observed over the 13 time periods (that is, 43.41). Structuration Theory & Self-organizing Networks Page 36
Model 2: Exogenous Model
The second model includes the seven exogenous factors which were posited to influence the emergence of communication networks: supervisor/subordinate relations, peer relations, spatial proximity, e-mail, workflow, friendship, and common activities.
The equation below describes how the communication from person i to j changes over time, based on the seven exogenous factors:
C C f (C ,C ,C ,C ,C ,C ,C ) ijt ijt 1 Sij HLij Pij Eij Wij Fij Aij (13)
where Cijt is the communication from i to j at time t, Cijt-1 is the communication from i to j at time t-1; here again, f is a function that makes sure that the mean of the change in communication (represented by the collection of the seven terms on the right hand side of equation 13) from i to j is centered at 0, with a standard deviation of M. The change
() terms themselves are described by Equations 1 through 7 above.
Substantively, this model predicts the evolution of the communication network based on the prior communication network and a series of structural changes (increases and/or decreases) that are determined by the extent to which individuals i and j (i) are involved in a supervisor-subordinate relationship, (ii) are peers at a higher level in the hierarchy, (iii) are spatially proximate, (iv) are email users, (v) are mutually dependent within the organization’s workflow, (vi) are friends, and (vii) are engaged in common activities.
Model 3: Endogenous Model Structuration Theory & Self-organizing Networks Page 37
The endogenous model includes the three endogenous communication network mechanisms – transitivity, group cohesion, and structural holes – that influenced the emergence of the communication network. They are represented by the equation:
C C f (C ,C ,C ) ijt ijt1 TRij COij HOij (14)
where Cijt is the communication from i to j at time t, Cijt-1 is the communication from i to j at time t-1; here again, f is a function that makes sure that the mean of the change in communication (represented by the collection of the three terms on the right hand side of equation 14) from i to j is centered at 0, with a standard deviation of M. The change
() terms themselves are described by Equations 8 through 10 above.
Substantively, this model predicts the evolution of the communication network based on the prior communication network and a series of structural changes (increases and/or decreases) that are determined by the extent to which individuals i and j (i) are involved in a structural hole in the communication network, (ii) are involved in transitive triads within the communication network, and (iii) are both members of a cohesive group.
Model 4: Combined Model
Finally, Model 4 is a combination of all the seven exogenous and the three endogenous mechanisms. They are represented by the equation:
Cij Cij f (CS ,CHL ,CP ,CE ,CW ,CF ,CA ,CHO ,CTR ,CCO ) t t 1 ij ij ij ij ij ij ij ijt ijt ijt (15)
where Cijt is the communication from i to j at time t, Cijt-1 is the communication from i to j at time t-1; here again, f is a function that makes sure that the mean of the change in Structuration Theory & Self-organizing Networks Page 38
communication (represented by the collection of the ten terms on the right hand side of equation 15) from i to j is centered at 0, with a standard deviation of M. The change () terms themselves are described by Equations 1 through 10 above.
Executing Simulations Using Computational Network Model
The simulation models were created and run in Blanche, an object-oriented tool specifically designed for executing network simulations (Hyatt, Contractor & Jones,
1997). All simulation models were run for 13 iterations, representing the 13 time period for which empirical communication network data were collected. The initial data for each of the simulation runs were the observed communication matrix at the first point in time
(March 1995). Simulations were run for each of the four models described above: baseline, exogenous, endogenous, and combined models. exogenous. In addition, simulations were also run for each of the seven exogenous factors and the three endogenous factors.
Analysis for validation of simulation data
The procedures described above resulted in 13 simulated communication matrices. To assess which theoretical mechanisms best predicted the observed communication networks, the simulated communication network matrix at the thirteenth time period for each model was compared with the empirical communication data collected at the thirteenth time period (in March of 1997). Structuration Theory & Self-organizing Networks Page 39
Quadratic Assignment Procedure (QAP) was used to assess the strength of association between the simulated communication networks and the observed communication networks (Hubert & Schultz, 1976; Krackhardt, 1987). The standard
Pearson product moment correlation is not appropriate for significance testing of association between networks, due to the lack of independence in the data (Krackhardt,
1987; Wasserman & Faust, 1994).
The strength of associations between the observed communication network and those predicted by the baseline model as well as the alternative theoretical models indicate the adequacy of the various theoretical exogenous and endogenous mechanisms.
In order to be adequate, one would expect a stronger association between the observed communication network and those predicted by theoretical models than those predicted by the baseline model (which was based on random variation).
RESULTS
The results comparing the networks predicted by the various simulation models and the empirically observed communication networks are reported in Table 1. Table 1 reports the strength of association between the simulated and observed communication networks at the thirteenth (the last) point in time. It is interesting to note that all the correlations reported here – including those for the baseline model -- are statistically significant at the p < 0.05 level.
______
Insert Table 1 about here Structuration Theory & Self-organizing Networks Page 40
______
Comparing the Baseline Model to the Observed Communication Network
The mean correlation of the communication networks generated by 50 runs of the baseline model and the observed communication network was 0.320 (s.d. = 0.009).
Comparing the Exogenous Models to the Observed Communication Network
Table 1 also reports the correlations between the simulated network based on each of the exogenous variables and the observed communication network. Two of the six exogenous theoretical mechanisms, supervisor-subordinate relations and spatial proximity, generate communication networks that are most highly associated (r = 0.455 and r = 0.493) with the observed communication network than the baseline model.
Further, the simulated communication matrix generated on the basis of the six exogenous mechanisms taken together is significantly more associated with the observed communication network (r = 0.547) than the baseline model.
Comparing the Endogenous Models to the Observed Communication Network
The next set of results in Table 1 report the association between simulated communication networks modeled from each of the three endogenous theoretical mechanisms and the observed communication network. Two of the three endogenous mechanisms -- transitivity and group cohesion – perform significantly better at predicting
(r = 0.364 and 0.396 respectively) the observed communication network than the baseline Structuration Theory & Self-organizing Networks Page 41
model. However, the communication network simulations based on the structural holes theoretical mechanism was not more associated (r = 0.118) with the observed communication network than the baseline model. Further, the simulated communication network based on the three combined endogenous mechanisms - weighted down by the ineffective structural holes mechanism - was only modestly associated ( r = 0.267) with the observed communication network data.
Comparing the Combined Exogenous and Endogenous Models to the Observed
Communication Network
Finally, Table 1 indicates that the simulated communication network based on the seven exogenous and three endogenous theoretical mechanisms is strongly associated ( r
= 0.524) with the observed communication network. Structuration Theory & Self-organizing Networks Page 42
Comparing the adequacy of the four models over time
Figure 2 plots the association between the simulated network models and the observed communication network at each of the thirteen points in time. The plots indicate that the communication networks resulting from the simulation models consistently track the observed communication through all the intervening time periods. At each of the time periods, the model based on all the exogenous mechanisms model and the model based on the combined exogenous and endogenous mechanisms model have the highest association with the observed communication network. In general, and not surprisingly, the strength of association between the simulate and observed communication networks decline over time.
______
Insert Figure 2 about here
______
DISCUSSION
This study began by deriving, from the tenets of structuration theory, a complex self-organizing models for the emergence of a communication network in an organization. Four general computational organization models were specified: a random model, a model based on the seven exogenous mechanisms, a model based on the three endogenous mechanisms, and a combined model including all ten exogenous and endogenous mechanisms. Using the observed communication network at the first point in time as initial conditions, simulations were used to generate communication networks Structuration Theory & Self-organizing Networks Page 43
over thirteen points in time. These simulated networks were compared with the observed communication networks over the same time periods.
First, the correlation between the network simulated by the random model and the observed communication network was 0.32 and is statistically significant. This, though it may seem counter-intuitive at first, simply reflects the fact that the communication network at the initial point in time (March 1995), which was used as the initial condition for the execution of the simulation, was itself significantly correlated with the communication network at the thirteenth point in time (March 1997).
Second, the results indicate a significant association for two of the exogenous mechanisms. Specifically, involvement in supervisor-subordinate relationships and being physically proximate contribute significantly to the emergence of a communication network, although peer communication at higher levels in the hierarchy, adoption of email, workflow interdependence, friendship, and common activity foci do not significantly contribute to the emergence of a communication network link.
Third, the results indicate that two of the three endogenous mechanisms were also empirically validated. Specifically, the results suggest that the drive towards transitivity in the communication network and the cohesiveness of groups in the networks play an important role in the emergence of the communication network. However, there was little evidence that the emergence of the communication network was being influenced by members’ attempts at creating or sustaining structural holes. There are two plausible explanations for the lack of support for structural holes as a theoretical mechanism. First,
Burt (1992, p. 163) notes that “managers with networks rich in structural holes tend to be Structuration Theory & Self-organizing Networks Page 44
promoted faster, and they tend to reach their current rank earlier.” The lack of support for this mechanism in the present study may be due to the fact that this mechanism is invoked only by competent, upwardly mobile managers, rather than uniformly by all organizational members. Further, Walker, Kogut, and Shan (1997, p. 109) studying the formation of inter-firm networks in the biotechnology industry, found that “structural hole theory may apply more to networks of market transactions than to networks of cooperative relationships.” Given that the individuals within the PWD were engaged in cooperative -- rather than a competitive -- task relationships, it is perhaps not so surprising to see that the structural holes network mechanism was not a powerful explanation for the emergence of the communication network in the PWD.
Further, the results of the combined model implies that the set of ten theoretical mechanisms posited in this study explain a substantial portion of the variation in the emergence of the observed communication network. The plot (Figure 2) tracking the association between the simulated communication networks and the observed communication network over the 13 time periods also offers some intriguing insights. Of particular interest is the substantial decline in the fit of all the models at two time periods
- September 1995 and September 1996. This suggests an annual episodic variation in the emergence of the communication network between mid-July and mid-September (which was reported in the survey conducted in mid-September). Post hoc speculation points to a transient reorganization of the communication network at the start of the annual budget cycle within this organization. Structuration Theory & Self-organizing Networks Page 45
This study reported here can best be described as an early and tentative first step in response to the call by some structuration theorists to offer a more precise, testable, and falsifiable set of predictions based on the duality of structures and systems. It also responds to the call by complexity theorists to move from the era of hand-waving about the virtues of complexity theory to actually attempting a field study that upholds many of the unique features that characterize complexity theory: multiple theoretical mechanisms, non-linear dynamic relationships, and sensitivity to initial conditions. While there have been several attempts at specifying and executing simulations of complex systems, (e.g.,
Contractor & Grant, 1996; Contractor & Whitbred, 1997; Corman, 1996; Levitt, et al.,
1994; Lin, 1994), including specifically in the area of structuration theory (Contractor &
Seibold, 1993), the present study is one of a handful that has attempted to validate the results of the simulation data with longitudinal field data. The fact that the results were substantively encouraging should serve as further motivation for the viability of the computational organization approach to the study of complex systems from a structuration perspective.
The substantive findings of the study as well as the methodology deployed ushers in a host of opportunities for further research on organizational networks as complex systems. Even though the study employed ten theoretical mechanisms, the fact that only four were found to be substantively significant suggests the need for specifying additional theoretical mechanisms – including some suggested by Monge and Contractor (in press) and enumerated earlier in this study. One particularly interesting avenue would be to incorporate theoretical mechanisms to examine the effect of entry and exit by Structuration Theory & Self-organizing Networks Page 46
organizational members. Note that in this study, the model was specified only for the 55 members who remained with the organization for the entire duration of the study. Future modeling could convert this “bug” (incomplete networks) of the present model into a
“feature” by explicitly modeling the entry and exit of members.
Finally, another major limitation of this study is that the exogenous mechanisms in the self-organizing model were held time-invariant. For instance, the friendship network and email use were not allowed to mutually co-evolve with the communication network. This assumption was deemed plausible for the present study on the grounds that friendship and email use, unlike communication, are not particularly volatile networks.
However, research on the evolution of friendship networks by Zeggelink and her colleagues (Stokman & Zeggelink, in press; Zeggelink, 1993; Zeggelink, Stokman, &
Vandebunt, 1996), and theories on the structurational relationship (Giddens, 1984; Poole
& DeSanctis, 1990) between the use of communication technology and communication networks (Contractor & Eisenberg, 1990), undermine the validity of this argument.
Future efforts should dynamically explicate the theoretical mechanisms underlying the relationship between these exogenous factors and emergent communication.
In summary, the methodology and results of the emergence of communication networks as a complex system suggest that complexity theory is most useful when it is not possible to deduce the complex interrelationships, hence making it difficult to meaningfully estimate statistical relationships even using sophisticated field research methods such as those described in a special issue of Organization Science (Huber and
Van de Ven, 1990) ten years ago. Structuration Theory & Self-organizing Networks Page 47
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Table 1 Correlations between Simulated and Actual Communication Networks
Type of Model Variables Explanatory Correlation with Mechanism Observed Data Baseline Random model Random Variation Mean = .320* Model (50 runs) Model s.d.=0.009
Individual Supervisor/Subordin Reporting relationship .455* Exogenous ate Relationship Mechanisms Hierarchical Need for .291* Similarity Coordination/Control Spatial Proximity Exposure .493* Adoption of Email Electronic Proximity .162* Workflow Coordination Theory .298* Friendship Uncertainty Reduction .254* Common Activities Activity Focus .285* Combined All of the Above Complexity Theory .547* Exogenous Exogenous Variables Mechanisms Individual Transitivity Increase Balance .364* Endogenous Mechanisms Group Cohesion Attraction to Group .396* Structural Holes Increase Autonomy .118* Combined All of the above Complexity Theory .267* Endogenous Endogenous Mechanisms Variables Combined All of the Above Complexity Theory .524* Exogenous and Endogenous and Endogenous Exogenous Variables Mechanisms
* All correlations are significant at p < 0.05 Figure 1. Exogenous and endogenous factors influencing the emergence of communication network in an organization
- Hierarchy s • Supervisor/Subordinate m Random Factor s • Peer Interaction i n a
h - Spatial Proximity c e
M - Adoption of Email s u o - Common Activities n e g
o - Friendship x
E Communication - Workflow Network s m s i -Dyadic n a Prior Communication h c e - Transitivity M s u
o -Group Cohesion n e g -Structural Holes o d n E Figure 2. Comparison of observed communication networks with those predicted by dynamic simulation models
1.000
0.900
0.800
0.700 Baseline Random n
o 0.600 i t Full Exogenous Model a l e r Full Endogenous Model r 0.500 o C
Full Exogenous and Endogenous P
A 0.400 Q
0.300
0.200
0.100
0.000 6 5 5 6 7 5 5 5 6 6 6 6 7 9 9 9 9 9 9 9 9 9 9 9 9 9 ------l l r r r y n p v n y p v u u a a a a a e o a a e o J J J J M M M S N S N M M Tim e
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