Download Emergence of communication networks in organizations:

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).
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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.
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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
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ability to specify predictions which, if they were not empirically validated, would
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
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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
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
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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
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
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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 of the same groupauthoring 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.
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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
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
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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.
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
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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.
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
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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
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
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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
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-
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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.
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________________________
Insert Figure 1 about here
________________________
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).
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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:
CSij  Sij
(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
Structuration Theory & Self-organizing Networks
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with one another, than two middle level managers. This is because the higher the
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 HLij  HLij
(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.”
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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
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
Structuration Theory & Self-organizing Networks
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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
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 Pij  Pij
(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
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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;
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 Eij  Eij
(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
Structuration Theory & Self-organizing Networks
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“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
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
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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:
CWij  Wij
(5)
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
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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.
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 Fij  Fij
(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
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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
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 Aij  Aij
(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
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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
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.
Structuration Theory & Self-organizing Networks
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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
Ctrij   Cikt 1 Ckjt 1
t
(8)
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
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
Structuration Theory & Self-organizing Networks
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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 describes Ccoijt,
the change (increase or decrease) in communication between individuals i and j who
belong to the same group at time t:

Ccoij  gdt 1  gdmean
t
t 1

(9)
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.
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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.
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
C HOij
t
 N

N
Ckit 1
 C jkt 1 Ckit 1

  
2 
 CSE min  1
 k 1 C
k 1 C SE jk
t 1
t 1
max t 1




(10)
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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
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
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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
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
Structuration Theory & Self-organizing Networks
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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
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.
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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.
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
Structuration Theory & Self-organizing Networks
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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
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
Structuration Theory & Self-organizing Networks
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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.
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.
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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).
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
Structuration Theory & Self-organizing Networks
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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
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.
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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.
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Model 1: Baseline Model
The baseline model was specified by the following equation:
Cijt  Cijt 1  f ( C 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 Rij  N (0, M )
(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).
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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:
Cijt  Cijt 1  f ( CSij , C HLij , C Pij , C Eij , CWij , C Fij , C 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
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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:
Cijt  Cijt 1  f ( CTRij , CCOij , CHOij )
(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:
Cijt  Cijt 1  f ( CSij , C HLij , C Pij , C Eij , CWij , C Fij , C Aij , C HOij , CTRij , CCOij ) (15)
t
t
t
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
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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
Structuration Theory & Self-organizing Networks
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time period for each model was compared with the empirical communication data
collected at the thirteenth time period (in March of 1997).
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
Structuration Theory & Self-organizing Networks
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correlations reported here – including those for the baseline model -- are statistically
significant at the p < 0.05 level.
________________________
Insert Table 1 about here
________________________
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
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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
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.
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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
Structuration Theory & Self-organizing Networks
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time as initial conditions, simulations were used to generate communication networks
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
Structuration Theory & Self-organizing Networks
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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
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
Structuration Theory & Self-organizing Networks
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transient reorganization of the communication network at the start of the annual budget
cycle within this organization.
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
Structuration Theory & Self-organizing Networks
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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
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
Structuration Theory & Self-organizing Networks
Page 49
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 50
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Table 1
Correlations between Simulated and Actual Communication Networks
Type of Model
Variables
Explanatory
Mechanism
Baseline
Model
Random model
(50 runs)
Random Variation
Model
Correlation
with
Observed Data
Mean = .320*
s.d.=0.009
Individual
Exogenous
Mechanisms
Supervisor/Subordin
ate Relationship
Reporting relationship
.455*
Hierarchical
Similarity
Spatial Proximity
Adoption of Email
Workflow
Friendship
Common Activities
All of the Above
Exogenous Variables
Need for
Coordination/Control
Exposure
Electronic Proximity
Coordination Theory
Uncertainty Reduction
Activity Focus
Complexity Theory
.291*
Transitivity
Increase Balance
.364*
Group Cohesion
Structural Holes
All of the above
Endogenous
Variables
All of the Above
Endogenous and
Exogenous Variables
Attraction to Group
Increase Autonomy
Complexity Theory
.396*
.118*
.267*
Complexity Theory
.524*
Combined
Exogenous
Mechanisms
Individual
Endogenous
Mechanisms
Combined
Endogenous
Mechanisms
Combined
Exogenous and
Endogenous
Mechanisms
* All correlations are significant at p < 0.05
.493*
.162*
.298*
.254*
.285*
.547*
Exogenous Mechanisms
Figure 1. Exogenous and endogenous factors influencing the emergence of communication network in
an organization
- Hierarchy
• Supervisor/Subordinate
• Peer Interaction
- Spatial Proximity
- Adoption of Email
- Common Activities
- Friendship
- Workflow
Endogenous Mechanisms
Random Factor
-Dyadic
Prior Communication
- Transitivity
-Group Cohesion
-Structural Holes
Communication
Network
Figure 2. Comparison of observed communication networks with those predicted by dynamic
simulation models
1.000
0.900
0.800
Baseline Random
0.600
Full Exogenous Model
Full Endogenous Model
0.500
Full Exogenous and Endogenous
0.400
0.300
0.200
Time
Mar-97
Jan-97
Nov-96
Sep-96
Jul-96
May-96
Mar-96
Jan-96
Nov-95
Sep-95
Jul-95
0.000
May-95
0.100
Mar-95
QAP Correlation
0.700
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