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The online version of this article can be found at:
DOI: 10.1177/0021886303258338
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Journal of Applied Behavioral Science
Ramkrishnan V. Tenkasi and Marshal C. Chesmore
Change Implementation and Use
Social Networks and Planned Organizational Change : The Impact of Strong Network Ties on Effective
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10.1177/0021886303258338
ARTICLE
THE JOURNAL OF APPLIED BEHAVIORAL SCIENCESeptember 2003
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
Social Networks and Planned
Organizational Change
The Impact of Strong Network Ties on
Effective Change Implementation and Use
Ramkrishnan V. Tenkasi
Benedictine University
Marshal C. Chesmore
Deere and Company
This study of 40 units in a large multinational corporation considers the influence of the
density of networks of strong ties on the implementation of planned organizational
change between organizational units and unit leaders in both a change implementation
and a change recipient network. Also examined are the effects of the density of networks
of strong ties within the change recipient network of units and unit leaders on the use of
change. The results suggest the need for implementers of change to create strong ties with
the change recipient units for successful change implementation. Both unit level and unit
leader density of strong ties within the change recipient unit network were significant
predictors of change use, indicating that both unit members and unit leaders may play
central but independent roles in influencing the change use process. Implications for
using social network analysis in planned organizational change are discussed.
Keywords:
social networks; planned change; knowledge transfer; information tech-
nology implementation
T
he implementation of large-scale planned organizational change has become
increasingly important in recent years as organizations continually attempt to recon-
figure themselves to meet the challenges of an ever-shifting competitive landscape
(Tenkasi, Mohrman, & Mohrman, 1998). Planned approaches toward the implementa-
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DOI: 10.1177/0021886303258338
© 2003 NTL Institute
tion of large-scale organizational changes have been an enduring interest of the organi-
zation development field. Considerable theory and research has examined rational,
technical, cultural, and political approaches to change, as well as top-down, bottom-
up, and whole-system participative strategies in affecting change implementation suc-
cess (French & Bell, 2000). Likewise, organizational adaptation to change has long
occupied a predominant place in various important research traditions within organi-
zational theory (Pfeffer & Salancik, 1978; Thompson, 1967). Despite this long history
of research, significant gaps in our understanding of this phenomenon persist. One
particularly prominent void is in the area of networks within organizations and the role
that interunit ties may play in effective large-scale change implementation and use.
A growing number of organizational theorists taking a network perspective have
recently emphasized how ongoing strong and weak social ties between organizations
can significantly influence organizational actions and outcomes (Davis, 1991; Kraatz,
1998; Uzzi, 1996). With a few exceptions (Hansen, 1999; Tsai, 2001; Tsai & Ghoshal,
1998), the bulk of these discourses have focused on interorganizational network ties.
Limited attention has been devoted to the study of social networks in relation to change
adaptation within organizations, particularly the role of intraorganizational networks
and their influence on change implementation effectiveness and use. A recent sympo-
sium at the Academy of Management meetings (Bartunek, 2001) brought together
researchers in the planned change and social network arenas to examine this very issue
and further the links between these domains. As pointed out by participants, the lack of
attention to social networks in the change process is even more surprising because
overcoming resistance to change has long been a central focus of organization devel-
opment and change practitioners, and networks often are the locus of change accep-
tance or resistance. Cross theorizing and researching change from a network perspec-
tive can significantly augment existing models of planned change implementation and
organizational adaptation (Bartunek, 2001).
In this study, we seek to explore some of these questions. We consider how
intraorganizational network ties, particularly the density of strong ties between units
from two intraorganizational networks, and the density of strong ties of units within an
intraorganizational network influence the likelihood of successful implementation of
large-scale organizational change and use. We also examine the bridging role of unit
leaders and the role of the density of their individual between and within strong ties in
successful change implementation and use. We propose that planned organizational
change, particularly when it is fundamental and large scale, brings about two separate
but interrelated problems, the knowledge transfer problem that has implications for
effective change implementation and the learning problem that has implications for
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September 2003
Both authors contributed equally to this paper. Aprevious version of this paper was presented at the annual
Academy of Management meetings, Denver, Colorado, 2002.
Ramkrishnan V. Tenkasi is a professor in the Ph.D. Program in Organizational Change and Development in
the Department of Management and Organizational Behavior in the College of Business, Technology, and
Professional Programs at Benedictine University.
Marshal C. Chesmore is worldwide manager of performance development at Deere and Company in
Moline, Illinois.
effective change use, and that these can be better solved through a denser interconnec-
tion of strong intraorganizational ties.
THEORY AND HYPOTHESES DEVELOPMENT
Interorganizational networks have been implicated in the diffusion of a wide vari-
ety of organizational practices including matrix and multidivisional structures, strate-
gic alliances, corporate acquisitions, takeover defenses, and college curriculum
changes (Burns & Wholey, 1993; Davis, 1991; Kraatz, 1998; Palmer, Jennings, &
Zhou, 1993). Studies also have examined how interorganizational links can affect
directly organizational outcomes including organizational survival, competition, net-
work effectiveness, and adaptive change (Baum & Oliver, 1991; Kraatz, 1998; Uzzi,
1996).
There also has been some, albeit limited, research from an intraorganizational net-
work perspective examining the structures, types, and outcomes of network ties. Tsai’s
(2001) study of 60 business units indicated that the interaction between the centrality
of an organizational unit’s network position and its absorptive capacity had significant
effects on business unit innovation and performance. Hansen (1999), in a network
study of new-product development projects undertaken by 41 divisions, examined the
task of developing new products in the least amount of time with a focus on the role of
weak ties in searching for knowledge and strong ties in transferring complex knowl-
edge across organizational subunits. Although these studies were mainly concerned
with the role of intraorganizational network ties on innovation and knowledge sharing,
emerging theory and evidence suggest the benefits of considering the nature of an
organization’s interunit network ties as one factor influencing organizational capacity
for large-scale change implementation and adaptation.
Network Conditions for Successful Organizational Change:
The Role of Strong Ties
The extant thinking on networks implies two basic views of how network ties may
affect organizational outcomes, views that also have implications for organizational
change. The first of these is the “strong ties” perspective (Granovetter, 1982;
Krackhardt, 1992; Marsden & Campbell, 1984; Uzzi, 1996), whereas the second con-
cerns the “weak ties” perspective (Burt, 1992; Granovetter, 1973; Hansen, 1999).
Strong network ties, on one hand, show some key characteristics between the parties to
the relationship, such as frequent interaction, an extended history, intimacy and shar-
ing, and reciprocity in exchanges that allow for mutually confiding, trust-based inter-
actions (Granovetter, 1982; Krackhardt, 1992). Weak ties, on the other hand, are char-
acterized by distant and infrequent relationships that may be casual, less intimate and
sharing, and nonreciprocal in nature (Granovetter, 1973; Hansen, 1999;
Haythornthwaite, 2001). Both strong and weak ties are critical for organizational func-
tioning because they provide access to different kinds of resources (Haythornthwaite,
2001). Strong ties facilitate the flow of richer, detailed, and redundant information and
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
283
knowledge resources between individuals and groups. Weak ties, by contrast, are of
greater importance in encouraging the exchange of a wider variety and potentially new
information between groups by drawing in more peripheral communicators and
extending access to a wider set of contacts and knowledge resources (Granovetter,
1973; Granovetter, 1982; Hansen, 1999; Haythornthwaite, 2000). For example, Hansen’s
(1999) network study of 120 new-product development projects undertaken by 41
divisions in a large company showed that weak interunit ties helped a project team’s
search for useful knowledge that may lie in other subunits, but transfer of complex
knowledge tended to require a strong tie between the two parties to a transfer.
However, with respect to large-scale organizational change, extant theory suggests
that strong ties, rather than weak ties, may be better suited for the change implementa-
tion process. Although there is some argument for the role of weak ties in the change
process, such as in searching for ideas from the external environment for new change
models (MacDonald, 1997) or affecting the dynamics of emergent organizational
change, especially when the change may pertain to the gradual and incremental diffu-
sion of innovations across organizational units (Krackhardt, 1997), strong ties appear
to be particularly relevant for planned large-scale change implementation. The case
for the role of weak ties in the gradual and incremental diffusion of innovations draws
on the notion that innovations, particularly when they are controversial, can be threat-
ening to some actors because they may involve institutional and/or cultural changes
that are a departure from familiar routines of behavior and thought. In such cases, there
can be a backlash to broad implementation efforts for the innovation. However, if the
willing change recipient units are located on the periphery, with little contact and
exposure to the rest of the organization, they can safely adopt the innovation, demon-
strate its effectiveness, and then spread the word to the neighboring subunits one unit at
a time. Krackhardt (1997) modeled this process on the computer to show how the com-
plex dynamics of gradual and incremental adoption of controversial innovations favor
this periphery/low connectivity strategy.
In contrast to the gradual and incremental adoption of innovations in organizations
unit by unit, planned large-scale change typically is an immediate and fundamental
change that encompasses the whole or significant portions of the organization within a
finite time window. Further, it is a complex and systemic change that involves changes
to multiple subsystems of the organization rather than the adoption of a singular inno-
vation (Tenkasi et al., 1998). In such a situation, weak ties may make it more difficult to
change organizations. On the contrary, there are sufficient reasons to expect that strong
ties will be more valuable in facilitating organizational units’ attempts to adapt their
core features in conformance with the planned change (Krackhardt, 2001).
Strong ties are essential for achieving a shared understanding around the purposes
and content of the changes. People, in the face of the uncertainty that surrounds crises
or major change efforts, particularly in the absence of common information and com-
munication of change purposes, tend to rally around their own local interests and local
decisions that impede organization-wide cooperative behaviors necessary for the radi-
cal, strategic change to succeed (Krackhardt, 2001). However, a stronger density of
interconnectedness resulting from strong interunit ties may enable the different actors
to see the systemic nature of the changes, understand the purposes and content of the
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September 2003
changes, and recognize their mutual interdependence in ensuring that the change suc-
ceeds (Tenkasi et al., 1998). As lucidly stated by Cook (2001), “Perhaps the most
straightforward application of social networks is within the paradigm of symbolic
interactionism, which stresses that shared understandings and meanings are generated
through [strong] social interaction” (p. 3). Strongly interconnected social networks
have a higher number of interconnections between nodes in a network, and these con-
necting lines are the conduits along which the tugs of conformity will be felt as individ-
uals or units inform and influence one another to create shared meaning and a sense of
common purpose. Paucity of ties characterized by a division between nodes in the net-
work indicates cultural barriers that make the transmission of ideas, information, and
attitudes difficult, resulting in more stratification and rallying around local interests
(Cook, 2001). Likewise, Carley (1991) argues that bridging strong ties acts as cultural
conduits, and without them different parts of a network will not be in communication;
strong cultural differences are more likely to result rather than shared understanding
and a focus on common interests. In summary, the strong ties model suggests that
change is more likely to be successfully accepted, understood, and implemented when
the social network in the organization is densely interconnected with many redundant
(strong) ties (Krackhardt, 1994). Radical change succeeds best when the social net-
work within the organization includes an abundance of strong ties that cut across for-
mal organizational subunit boundaries such as teams, departments, divisions, and so
on (Krackhardt, 2001), a proposition that can be better explored when considered in
light of the process and activities underlying planned large-scale organizational change.
The Process of Planned Large-Scale Organizational Change
In contrast to serendipitous or emergent organizational change, large-scale planned
organizational change is a fundamental or radical change that is deliberate, purposive,
systemic, complex, and typically encompasses the whole organization within a finite
time window (Tenkasi et al., 1998). It is based on deliberate or willful action, typically
from top management, and is purposive in that the change is initiated based on some
defined ends such as becoming more profitable, cost efficient, or competitive. Often
these changes are systemic, which entails changes to several subsystems of the organi-
zation such as the social architecture (e.g., moving from a functionally based hierar-
chical organization to a team based cross-functional organization form), technical
architecture (e.g., moving from independent and idiosyncratic information platforms
to integrated enterprise-wide information systems such as SAP), or strategic architec-
ture (that may entail partnerships, alliances, joint ventures, and mergers), making it
also an inherently complex process (Tenkasi et al., 1998). These changes also typically
involve the whole organization, such as divisions and business units that may be fur-
ther composed of sites or teams, all requiring to change to a new modus operandi
within a fixed time boundary.
The implementation of such fundamental planned change has two interrelated
components—organization-wide change implementation and local-level learning.
There are organization-wide aspects to the planned change; nevertheless, effective
operation within an organization-wide architecture that is changing also requires each
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
285
unit to learn so that it can enact and/or use the changes in its day-to-day conduct of
work. In pursuing a new strategy, the organization embarks on some overarching
changes, such as reconfiguring its basic building block units (e.g., organizing based on
processes vs. functions) or introducing new systems with the capability to integrate
and distribute information enterprise wide. However, successful planned change also
requires and depends on the ability of the many units of the organization to take up
these new directions and establish local practices that will make these new directions a
reality to continue achieving performance outcomes over time. The measurement of
successful change requires an assessment of both these factors—not only the effective
implementation of the change on time in terms of putting in place a team structure or a
new information system, but also that the actors are able to actualize, enact, and/or use
the change in their day-to-day conduct.
In the typical planned change process, one could conceive of two distinct types of
networks: the change implementation network that includes the various units respon-
sible for implementing the changes, and the change recipient network that includes the
multitude of units who are receiving the change. There typically will be an array of
organization-wide implementation activities coordinated by the change implementa-
tion network and directed toward the various units who are the recipients of the
change. It is a relatively straightforward process for the implementation network to
describe the new attributes of the system at a level of minimal specification. In reality,
however, implementing the planned changed activities is not neat and orderly in the
sense of being cleanly masterminded in advance, described, and then rolled out. Fre-
quently, the specification of a blueprint for change simply begins the process of
planned change. There are two interrelated but separate challenges to contend with for
successfully implementing the planned change on time and ensuring that the change is
enacted/used: the knowledge transfer challenge and the learning challenge, issues that
have implications in terms of strong interunit ties both between and within
intraorganizational networks.
The Knowledge Transfer Challenge for Effective Change Implementation
and the Importance of Between Network Strong Ties
In this study, we looked at an organization reconfiguring itself to incorporate cus-
tomer teams and acquiring a new information system to support a new strategy of cus-
tomer partnership. The change implementation network embarked on activities to
implement these changes in the various units that are part of the change recipient net-
work. Examples of such activities included tailoring the information system to the
local requirements of each unit, helping employees develop the skills for using the new
information system, and putting in place artifacts such as new measurement systems,
roles, and positions to support the new way of operating. Successfully implementing
these changes as planned requires interest, participation, and frequently, input from
the change recipient units.
When crafting changes such as these, designers have certain intentions that are
related to the strategy of the organization and the kind of performance that needs to be
delivered. They generate architectures that embody a logic or chain of reasoning and
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September 2003
are concerned with how particular architectures will yield intended patterns of behav-
ior and performance. Successfully implementing new organizational architectures
requires the establishment of new webs of shared meaning that shape a different inter-
pretation of organizational situations, inform action and behavior, and underpin ongo-
ing work decisions. However, the logics underpinning the choices in the new architec-
tural features are not always immediately self-evident to the people in an organization,
and failure to understand or accept the meanings embedded in organizational changes
often correlates with faulty or delayed implementation (Tenkasi et al., 1998) because
units may not be faithfully appropriating the intentions of the change (Poole &
DeSanctis, 1994). For the change to be successfully implemented on time, the organi-
zational change challenge is to stimulate in the change recipient units a comprehension
of the cognitive and behavioral implications of the changes for the changes to take
form and to become the true modus operandi of the organization.
Hansen’s (1999) network study of knowledge search and transfer behavior among
120 new-product development projects suggests that when the knowledge to be trans-
ferred between units is complex, strong rather than weak interunit ties best facilitate
the process. Complex knowledge is an elementof a setof interdependentcomponents
and is part of a larger knowledge system (Winter, 1987). Large-scale organizational
change of a fundamental nature is both systemic and complex. Less complex knowl-
edge can be a stand-alone component, such as a distinct software module that can be
uprooted from its existing use and transferred easily with the focal team having little or
no knowledge of the larger system, for example, sharing self-explanatory software by
sending it through the mail (Hansen, 1999). In contrast, when the knowledge to be
transferred is complex and dependent, the software module functions in conjunction
with other components, and uprooting such a piece will require that the project receiv-
ing it have some knowledge of the larger system of which it is part. Further, in complex
change projects, each unit of the implementation network frequently conveys a piece
of the complex knowledge (Tenkasi et al., 1998). In such a situation, existing strong
between interunit ties among the various parties to the transfer are likely to be most
beneficial because the source unit is likely to spend more time articulating the complex
knowledge. Strong ties allow for a two-way interaction between the source and the
recipient (Boland & Tenkasi, 1995; Leonard-Barton & Sinha, 1993), and the recipi-
ents have the opportunity to try, err, and seek instruction and feedback from the
strongly tied source, ensuring successful implementation. The two-way interaction
afforded by a strong tie is important for assimilating complex knowledge because the
recipient most likely does not acquire the knowledge completely during the first inter-
action but needs multiple opportunities to assimilate it. Such frequent interactions
reduce the likelihood of resistance and provide the recipient with an opportunity to
understand the big picture and the cognitive, behavioral, and action implications of the
change. In contrast, in weak interunit ties, the necessary interactions for transferring
complex knowledge are absent. The interaction between the source units and the recip-
ient project team is likely to be infrequent. When problems occur and questions arise,
the source is not immediately available, resulting in questioning the change that could
result in displaying resistance to the change and hampering the progress of the imple-
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
287
mentation as planned resulting in long delays. The foregoing suggests the following
hypothesis:
Hypothesis 1: The higher the density of network strong ties between change recipient and change imple-
mentation network units, the higher the likelihood of on-time complex change implementation.
The Learning Challenge for Effective Change Use
and the Importance of Within Network Strong Ties
Irrespective of how well the knowledge or its embodiment in a technology may
have been conceived and communicated, for a certain unit to receive, adopt, and use
this knowledge from a different community the knowledge may have to be reconfig-
ured or adapted locally to fit the local meaning systems as well as the demands of the
local task environment (Tenkasi & Mohrman, 1999). Such reconfiguration of received
knowledge is essential for successful innovation adoption and use, a view advanced by
Lewis and Seibold (1993) who studied the dynamics of innovation modification dur-
ing intraorganizational adoption. These modifications are the adaptation of innova-
tions to suit the local context and variations on a general theme created by the change
implementation network forming an integral part of “appropriating” the knowledge or
fitting it into the situational context of the transfer domain. This might well be a prereq-
uisite for successful adoption of an innovation because social designs are abstractions
that have to be “made” in the realm of action (Perlmutter & Trist, 1986).
For successful on-time change implementation, learning has to occur organization
wide as the whole system assumes a new architecture, and for effective use of the
change there has to be learning within the units of a network as they craft local
approaches. Unit by unit and team by team, learning enables performing units to find
new ways to operate in the changing organizational context in response to local tasks,
the local environment, and local opportunities, which may result in units crafting their
own local approaches to accomplishing their targets (Tenkasi et al., 1998).
Providing a new information system does not ensure that people will use it, let alone
that they will use it to accomplish the performance gains made possible by greater
access to information. This is because enough local-level learning has not taken place
to tailor the changes to the local context (Tenkasi et al., 1998). New shared understand-
ings and meanings that people attach to the new purposes and to the new architectures
that the organization is striving to put in place have to be developed. These new under-
standings are less a result of the activities unleashed by the introduction of change by
the implementation network than they are of local sense-making and learning
processes.
The emergence of new, local shared meaning depends on a collective learning pro-
cess that is achieved through dialogue. Dialogue is conversation that brings in multiple
perspectives of different members of the various units and is able to transcend individ-
ual views (Weick, 1995) while keeping the big picture in context (Senge, 1990). In the
case of a change involving transition into self-managing teams, shared understandings
have to be developed around questions such as “What does it mean to be self-managing?”
or in the case of a new information system “How best to use it to accomplish the perfor-
mance gains made possible by greater access to information?” Rich dialogue between
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September 2003
the various units in the recipient network facilitates the assignment of collective mean-
ing to the events that are taking place in the context both of local realities and of the
broader change mandate (Tenkasi et al., 1998). It is a function of strong ties and the
richer dialogue made possible by having many structural bridges across units that
enable ongoing connectedness and allow cross-unit reviews and sharing of lessons
learned. These bridges enable units to learn from each other’s experience, inform and
influence each other, expand perspectives, provide mutual feedback, and encourage
the dissemination of new practices and shared meanings (Carley, 1991; Cook, 2001;
Tenkasi, et al., 1998), suggesting the following hypothesis:
Hypothesis 2: The higher the density of network strong ties within the network of change recipient units,
the higher the likelihood of the successful use of the change.
The Central Role of Team Leaders in Creating Network Connections
Hansen’s (1999) study suggested as an area of future research the central role of
unit leaders in establishing strong interunit ties. Although several studies (cf., Clark &
Fujimoto, 1991) have shown the importance of having a strong team leader, especially
in acquiring external knowledge and information for the team, Hansen suggests that
one of the advantages of having heavyweight team leaders may have to do with the use
of the interunit network. Such effective team leaders may have the capability to initial-
ize and use interunit relations to the team’s advantage. A critical competency of effec-
tive team leaders therefore may include interunit networking skills in addition to other
managerial competencies. Granovetter (1973, 1982) also has commented on the
importance of bridging roles in networks. A bridge in a social network is the connec-
tion that provides the only path between two points, potentially another network or
unit. In small networks, a key actor typically performs the role of bridge. In large net-
works, it rarely happens that a specific tie provides the only path between two points.
In the planned change process, however, unit leaders of the change recipient units
often are assigned coordinating responsibilities for successful change implementation
and become the central conduit for change information to the rest of the unit or team.
Their ability to create and maintain strong ties can be a crucial determinant of change
implementation and use success, suggesting the following hypotheses:
Hypothesis 3: The higher the density of network strong ties between the change recipient network unit
leaders and the change implementation units, the higher the likelihood of on-time complex change
implementation.
Hypothesis 4: The higher the density of unit leader strong ties within the network of other change recipi-
ent units, the higher the likelihood of successful use of the change.
Interaction Effects
The above theoretical perspectives on knowledge transfer, learning, and the bridg-
ing role of the team leader suggest interaction effects between these factors. One could
logically surmise that units and unit leaders in the change recipient network who have
a higher density of between network strong ties with units in the change implementa-
tion network also will have a higher likelihood of successful change implementation.
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
289
Likewise, units and unit leaders who enjoy a higher density of within network strong
ties with units in the change recipient network will have more success with change use,
suggesting the following hypotheses:
Hypothesis 5: Units in the change recipient network that display a higher density of both unit and unit
leader between network strong ties with units in the change implementation network will have a
higher likelihood of on-time implementation of change than will other units.
Hypothesis 6: Units in the change recipient network that display a higher density of both unit and unit
leader within network strong ties with other units in the change implementation network will have a
higher likelihood of successful use of change than will other units.
METHOD
Research Context
We tested these predictions in a large, multinational, multidivisional farm and con-
struction equipment company headquartered in the United States with annual sales of
U.S.$13.3 billion and employing more than 43,000 people in 8 divisions and 73 sites
across its worldwide operations. The context of the large-scale planned change was the
implementation of an enterprise-wide integration system (SAP/R3 software) across
the organization that allowed the independent information systems to integrate data
and interact with each other. The implementation of the SAP/R3 followed a recent
reorganization of the divisions and sites from organizing around functions such as
marketing or manufacturing to being organized around processes such as order fulfill-
ment, customer acquisition, and product delivery (Hammer & Champy, 1993). This
initiative represented a fundamental and radical change for the organization because it
presented the opportunity and challenge of dealing with and taking into account inte-
grated data from multiple sources to make integrative decisions to optimize process
efficiencies.
The change implementation network. Located in the corporate offices of the com-
pany, the change implementation network was complex and large because it had to
implement the SAP system across 8 divisions and 73 sites covering approximately
40,000 employees. The network was composed of five subnetworks and several units
within each. The executive leadership subnetwork, the overall design team, consisted
of several units of the members of top management. The program leadership team
included middle management and had several units tasked with the responsibility of
overseeing the different aspects of the change. The project team lead subnetwork had
units that were responsible for the subprocesses of each of the major processes that the
program leadership unit was tasked with. The project member subnetwork was the
bread and butter of the SAP change implementation and consisted of several teams of
skilled professionals and experts with hands-on responsibilities for the design and
configuration of the change being implemented. Also included in the change imple-
mentation network were division-level steering committees, one for each division,
responsible for coordinating the implementation of SAP within their divisions. Each
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September 2003
such committee had 4 subunits. In total there were 48 units across the five subnetworks
of the change implementation network.
The change recipient network. The focus for this study was the planned change
implementation of the SAP system in 40 change recipient units spread across two sites
(A and B) in two different divisions of the organization. Site A was an engine manufac-
turing facility, and Site B was an international marketing facility. Site A had 22 units
and Site B had 18 units. Given the focus on “organizing by processes,” the units in each
site were organized around core and enabling processes (Hammer & Champy, 1993)
such as order fulfillmentand plan/make, which were core processes, and quality man-
agement, maintenance, and human resources, which were enabling processes. These
40 units (22 in Site A and 18 in Site B) were the first receivers of the new SAP system,
and these units and their unit leaders were the focal points in relation to which we stud-
ied the density of between network strong ties with the units in the change implementa-
tion network described above and the density of within network strong ties with the
other change recipient units within each site. These groupings fit well with the mem-
bership criterion used in network research (Wasserman & Faust, 1999).
Data Collection and Measures
Organizational entry was unproblematic because one of the authors also was a
member of the organization and part of the implementation network. Using socio-
metric techniques, a survey was developed to collect relational data that described how
the change recipient units interacted with one another within each site as well as with
units in the change implementation network. The unit leaders responded to the same
questionnaire, and they identified themselves as unit leaders. The survey was pretested
and refined with 12 members randomly selected from units of the change implementa-
tion and recipient networks. Drawing on Krackhardt’s (1992) notion of “philos” that
indicates the attributes of strong ties as being interaction, sharing, trust, and time, four
questions were developed: (a) “How often do you go to each of the following teams/
members for advice/information?” (b) “How often have you worked with members of
each of these teams to solve an issue/problem?” (c) “How often have you shared time,
resources, and space with each of these teams/members?” and (d) “How often do you
provide advice/information to the following teams/members?” The last question was
included as a check for cross-validating responses at the unit level described later in
this section.
A list of all units of the change implementation network and all other units at each
site (change recipient network) was provided in the survey. The respondents were
asked to go through and indicate the amount of interaction they had with each of the
units for the above questions. Legitimate responses were scored 0 for less than two
times per week, indicating a weak tie, and were scored 1 for two or more times per
week, indicating a strong tie. Interactions could entail any medium through which a
message was conveyed and there was an opportunity to respond, such as face-to-face
meetings, e-mails, conference calls, voice mails, net meetings, videoconferences, and
faxes. Complete absence of ties was not an option in this scenario because all units and
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
291
individuals within the units of the change implementation and change recipient net-
works were informed aboutchanges by e-mail and opinions were soughtby e-mail
about issues pertaining to the overall change efforts and local issues for each site on a
monthly basis.
After appropriate assurances of confidentiality that no individual respondent would
be identified and that the purpose of the study was university-based research, the sur-
vey was administered to 329 members distributed across the different units of the two
types of networks. The survey was administered in February of 2001, which was 3
months subsequent to the start of the implementation activities and thus a good time
period to study the interaction patterns concerning the change. Except for the unit lead-
ers, all other respondents identified themselves only as a member at the unit level. We
obtained about 241 usable responses (73% return rate). We secured at least 2 responses
from each unit of the change recipient network and the change implementation net-
work and responses from the unit leaders of all 40 units of the change recipient
network.
Independent Variables
Our independent variables of interest were the density of between network strong
ties and the density of within network strong ties of units and unit leaders. Density of
strong ties for a unit or unit leader is a proportion computed as the actual number of
strong ties that a unit engages in divided by the actual number of strong ties that a unit
could engage in. To arrive at strong and weak between and within interunit ties of the
40 units of the change recipient network, we employed cross-validation techniques
suggested by several network researchers (Hansen, 1999; Krackhardt, 1990; Tsai,
2001). To arrive at a score of 1 (strong tie) or 0 (weak tie) for Unit i, with respect to its
interaction with Unit j (a unit either from the change implementation or recipient net-
work), for Question 1—“How often do you go to Unit j for advice/information?”—we
matched the response of Unit j to Unit i with respect to Question 4—“How often do
you provide advice/information to Unit i?” If both responses matched, we considered
the interaction score as valid and assigned a score of 1 or 0. We followed similar proce-
dures for Questions 2 and 3. Because we had multiple respondents (at least two) per
unit, following previous network research conventions (Tsai, 2001), we considered an
interaction of more than twice a week valid (for the assignment of a score of 1) for any
of the four questions if any respondent from Unit i indicated that he or she had a rela-
tionship to Unit j and if this was confirmed by any respondent of Unit j in relation to
Unit i. Further, to check the extent of consistency in the multiple responses from each
group, we computed convergence indexes. This index is defined as C
ki
= A
ki
/B
ki
, where
C
ki
is the index of consistency for Measure k for Unit i, A
ki
is the number of units
selected by at least two of the multiple respondents of Unit i for Measure k, whereas B
ki
is the number of units selected by at least one of the two respondents for Unit i. The
value of C
ki
can range from 0.0 (perfect inconsistency) to 1.0 (perfect consistency), and
in this study the value of C
ki
was 0.87 for Site A and 0.82 for Site B. However, because
our focus in this survey was on the unit level of analysis, we could not extend our cross-
validation procedures to the unit leader and had to rely on unit leaders’ unidirectional
292
THE JOURNAL OF APPLIED BEHAVIORAL SCIENCE
September 2003
responses (vs. confirmation from a Unit j respondent) to determine the density of their
strong ties.
Once the number of actual between and within network strong ties were computed
at both the unit and unit leader levels, the density of within network interaction of Unit i
with respect to the rest of the units in the change recipient network and the density of
between networks interaction of Unit i with respect to units in the change implementa-
tion network were calculated as a proportion of all strong ties existing between Unit i
and the rest of the units between networks or within the network, divided by all possi-
ble strong ties that could exist between Unit i and the rest of the units within the net-
work or between networks. We used UCINET IV software (Borgatti, Everett, & Free-
man, 1992) for the calculations. Density scores range between 0 and 1.00, representing
the extremes of a totally disconnected (empty) and a totally connected (complete)
matrix of relations.
Once density scores were computed for each item, relying on network research con-
ventions, we averaged the four density score items (Hansen, 1999) to arrive at one
between network strong ties density score and one within strong ties network density
score for each unit and unit leader. As a check, we also ran intercorrelations among the
four density scores. As we suspected, they were highly intercorrelated, ranging from
.90 to .97, confirming the viability of the averaging process (Hansen, 1999).
Because we were dealing with two research sites, we performed a Chow test (Tsai,
2001) to determine if there were significant differences with respect to the independent
variables (density of within and between strong ties at the unit level and unit leader
level) between the two sites prior to running pooled regressions (Chow, 1960). The
results indicated that the levels of significance for the independent variables were not
statistically significant across the two sites. Given the results of the Chow test, we were
in a position to pool the data from the two sites for subsequent analyses.
Dependent Variables
The effectiveness of change implementation was measured using an implementa-
tion cycle time variable (Hansen, 1999), and change use was assessed through an infor-
mation system usage variable. Together, we felt that these two measures could ade-
quately capture if the change was installed on time and if the members were using it to
perform their work. The cycle time variable was whether the SAP system was imple-
mented as scheduled and available to the users in a fully functional form on the planned
“go-live” date. Cycle time can be measured as the extent to which the project is fin-
ished on schedule (Hansen, 1999). Archival data for each unit to help compare devia-
tions, if any, between the planned and the actual go-live date were available. All units
had the same “kick-off” (implementation start) and go-live (implementation end)
dates. However, due to a confidentiality agreement we signed with the company, we
were not allowed to use the exact deviation figures between the actual implementation
dates to the planned implementation date. We instead could rate each project on a 5-
pointscale (5 = implementation within 12 days of the planned date, 4 = implementation
within 13 to 25 days of the planned date, 3 = implementation within 26 to 38 days of the
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
293
planned date, 2 = implementation within 39 to 51 days of the planned date, and 1 =
implementation within 52 days of the planned date).
The second index measured the extent to which the units as end users were using the
new SAP system for conducting their day-to-day work, which was the intention of the
architects of the change. These data were collected through interviews with at least one
key member from each of the units from both sites approximately 1 month after the go-
live date of the new system implementation, allowing for learning curves to stabilize.
As part of the interview, respondents were asked to rate the extent to which they were
using the SAP system on a scale of 1 to 9, with 1 representing not at all and 9 represent-
ing completely, all the time. If there were more than two respondents for a unit, we
averaged their scores. Once we had complete data on the independent and dependent
variables for all the units of the change recipient network, we transferred the data to
SPSS for further analyses.
RESULTS
Based on the results of the Chow test, we pooled the site information for subsequent
analyses. Table 1 shows the mean values, standard deviations, and correlations for the
measured variables.
After adding a dummy variable to control for unit type, based on whether the unit
was responsible for a core process (e.g., order fulfillment, plan/make, order acquisi-
tion) or enabling process (e.g., quality management, human resources, logistics), we
tested our hypotheses with hierarchical regression analysis, entering the control vari-
able in the first step, each of the independent variables in subsequent steps, and the
interaction terms in the final step and tracing change in the multiple squared correla-
tion coefficient (R
2
) from step to step.
Tables 2 and 3 indicate the results of the hierarchical linear regression analysis esti-
mating the effects of the independent variables on change implementation cycle time
and change use.
Hypotheses 1, 3, and 5 state that the higher the density of between network strong
ties among the change recipient network units and change implementation network
units at the unit level, the unit leader level, and their mutual interaction, the higher the
likelihood of on-time change implementation. As shown in Table 2, Model 1, unit type
was nota significantpredictor (
β
= –.11, p < .37). As predicted, and indicated in Model
2, the beta coefficient for the unit density of between network strong ties was positive
and significant(
β
= .65, p < .000,
∆
R
2
= .43). However, both unit leader density of
between network strong ties (Model 3) and the interaction term of unit level and unit
leader density of between network strong ties (Model 4) with regard to the change
implementation network units were not significant (
β
= .02, p < .92,
∆
R
2
= .00;
β
= .02,
p < .91,
∆
R
2
= .00). The results may suggest that on-time change implementation
requires interaction with the unit as a whole and the need to respond to unique individ-
ual needs. Relying on the unit leader alone to act as a conduit, and not communicating
with the whole team to take into consideration their unique individual needs and
294
THE JOURNAL OF APPLIED BEHAVIORAL SCIENCE
September 2003
requirements, may not be a very effective change strategy, especially when the transfer
of knowledge entails complex information.
Hypotheses 2, 4, and 6 (see Table 3) predict that the density of within network
strong ties with other units in the change recipient network at the unit and unit leader
levels and their interaction would have a significant effect on change use. We found a
high intercorrelation between the unit level density of within network strong ties and
the unit leader density of within network strong ties (r = .82). Multicollinearity did not
appear to be a problem. When both variables were entered together in the regression
equation, the variance inflation factor (VIF) value was 3.07. Although the VIF has a
range of from 1 to infinity, with 1 indicating no multicollinearity and a preferred value
and higher values indicating its presence, a rule of thumb is that as long as the VIF
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
295
TABLE 1
Means, Standard Deviations, and Correlations (N = 40)
Sites A & B
M
SD
1
2
3
4
5
6
Unit level density of within
network strong ties
.254
2.27
Unit level density of between
network strong ties
.127
2.68
.20
Unit leader density of within
network strong ties
.245
8.37
.82**
–.25
Unit leader density of be-
tween network strong ties
.119
1.78
.14
.12
.11
Change implementation
cycle time
2.10
1.36
.02
.65**
.14
.54**
Change use
4.75
2.85
.87**
.12
.61**
.09
.63**
Unit type
.60
.65
–.03
.19
.06
.02
–.11
.05
**p < .01.
TABLE 2
Hierarchical Regression Analysis: Density of Between Network
Strong Ties on Change Implementation Cycle Time (N = 40)
Variable
1
2
3
4
Unit type
–.11
–.11
–.11
–.11
Unit density of between network strong ties
.65***
.63**
.63**
Unit leader density of between network strong ties
–.02
–.02
Unit density of between network strong ties
×
Unit leader density of between network strong ties
.02
R
2
.013
.43
.43
.43
Adjusted R
2
.013
.40
.38
.38
∆
R
2
.43
.00
.00
F
.484
13.95***
9.05***
9.06***
df
1, 38
2, 37
3, 36
4, 35
**p < .01. ***p < .001.
value does not exceed 10, it still is within acceptable limits (Belsley, Kuh, & Welsch,
1980). As indicated in Table 3, Model 1, unit type was not significant (
β
= .05, p < .87,
∆
R
2
= .003). We found significant results (Model 2) with respect to the density of
within network strong ties of the unit (
β
= 1.15, p < .001,
∆
R
2
= .75), and as shown in
Model 3, also with respect to the density of within network strong ties of the unit leader
(
β
= .35, p < .01,
∆
R
2
= .04). However, the interaction term between unit and unit leader
density of within network strong ties on change use (Model 4) was not significant,
indicating that both unit members and the unit leader may play important but nonethe-
less independent roles in influencing the change use process. Another explanation may
be that the sample size of 40 did not have enough power to pick up interaction effects
(Aiken & West, 1991).
DISCUSSION AND CONCLUSIONS
The general objective of this research was to examine whether network ties in
intraorganizational networks influence the implementation and use of planned organi-
zational change. We proposed that a higher density of between and within network
strong ties of organizational units, unit leaders, and their interaction will influence on-
time change implementation and change use. The logic behind our reasoning was that
planned organizational change, particularly of a fundamental or radical nature, brings
about two interrelated and separate issues, the knowledge transfer problem and the
learning problem, both of which must be contended with for successful change imple-
mentation and use. We indicated that a high density of strong ties could solve the
knowledge transfer and learning problems, because strong ties are likely to promote
in-depth two-way communication and facilitate the exchange of detailed information
among organizational units between networks and within a network. Further, the trust
and mutual identification that are likely to exist when ties are strong make it more
296
THE JOURNAL OF APPLIED BEHAVIORAL SCIENCE
September 2003
TABLE 3
Hierarchical Regression Analysis:
Density of Within Network Strong Ties on Change Use (N = 40)
Variable
1
2
3
4
Unit type
.05
.05
.04
.04
Unit density of within network strong ties
.865***
1.16***
1.15***
Unit leader density of within network strong ties
.36**
.35**
Unit density of within network strong ties
×
Unit leader density of within network strong ties
.04
R
2
.003
.75
.79
.79
Adjusted R
2
–.02
.74
.77
.77
∆
R
2
.75
.04
.00
F
.11
55.85***
45.34***
45.35***
df
1, 38
2, 37
3, 36
4, 35
**p < .01. ***p < .001.
likely that when units share valuable information, the information provided will be
taken into account and acted on by the interacting units.
Findings revealed positive evidence and support for the role of both unit level
between and within strong ties for on-time change implementation and change use,
respectively. However, the significance of unit leader strong ties as a predictor varied
based on whether the outcome variable was change implementation or change use.
With change implementation, no significant relationship was found for the density of
unit leader between strong ties, suggesting that the unit leader may play a marginal role
when it comes to change implementation. This may be due partly to the fact that unit
members in this organization are responsible for different subprocesses and may
require individual interaction with the various units of the change implementation net-
work, each of whom convey a piece of the complex knowledge. It appears that for suc-
cessful on-time implementation of this complex and large-scale change, a unit cannot
rely solely on the intervention or information/knowledge channeling of the unit leader.
However, for change use, both unit level density of within network strong ties and unit
leader density of within network strong ties with other units in the change recipient
network were significant. It appears that both the unit members and unit leader may
play a central role in the emergence of shared meanings around the change (Tenkasi
et al., 1998). None of our interaction effects were significant. The interaction term of
unit and unit leader density of between strong ties showed no significant association
with change implementation, and the interaction term of within strong ties was not sig-
nificantly associated with change use. It appears that both unit members and unit lead-
ers may play central but potentially independent roles in influencing the change use
process. A more straightforward explanation may be that our sample size did not have
enough power to detect interaction effects.
A broader goal of this research also was to pay heed to the call for better integration
of social network analysis methods with planned organizational change (Bartunek,
2001). Organizations are networks of actors, and these networks often are the locus for
exploring change acceptance and/or resistance because change is a social influence
process that is inherently mediated and moderated by network relations (Carley, 1991;
Cook, 2001; Krackhardt, 2001). Understanding the dynamics of network relations can
complement extant approaches to planned change, whether one adopts a political,
technical, or cultural strategy. To that effect, social network analysis can play a diag-
nostic role in identifying and assessing the patterns of strong and weak ties within and
between organizational networks to determine the most appropriate change strategy
and identify pockets of potential acceptance and resistance. Loosely coupled units
may offer more resistance in comparison to strongly coupled units and could be the
focus of prechange interventions to create and reinforce strong ties with one another.
The results of the study also indicate the need for implementers of change to create
strong ties with change recipient units for successful change implementation.
In using a network perspective, this study also brought into the fore issues of knowl-
edge transfer and learning, which we believe to be ubiquitous across all kinds of
change efforts and a process facilitated by strong network ties. It may be that the
reported success of organization development interventions, such as whole system
design (Weisbord, 1987) and search conferences (Emery & Purser, 1996), can be
Tenkasi, Chesmore / SOCIAL NETWORKS, PLANNED CHANGE
297
explained at least partially by social network theory, in that such forums enable the cre-
ation of networks and strong ties between networks of actors in the organization. An
interesting area of future research would be to examine whether and what kinds of net-
works emerge as a result of whole system design interventions and whether they rein-
force the strength of extant ties and/or create new strong ties. In addition, other contex-
tual antecedents of strong network ties may be worthy of exploration. For example, do
factors such as organizational culture, organizational identity and image, and leader-
ship style have a role in the establishment of strong versus weak ties?
The study also has limitations. It looked at two sites within a single organization,
which limits its generalizability. Our sample size was small, unfortunately a problem
when data are aggregated at the unit level, although other recent network studies have
used comparable sample sizes (Hansen, 1999). The lack of significance in our interac-
tion effects may be associated with the small sample size and hence, the absence of the
required statistical power to discern interaction effects (Aiken & West, 1991). An
important omission, and one we hope to rectify in the future, concerns the patterns of
intraunit ties. Our focus in this study was to examine interunit strong ties between and
within networks. However, a critical aspect of learning in the context of change in
order to enact and use the change is the collective dialogue and sense-making pro-
cesses that occur within each unit to establish a shared and collective meaning about
the changes. Intraunit strong ties can be a good index of these dynamics. Likewise, we
did not consider the density of within network strong ties among units of the change
implementation network, which also can influence change implementation success
given their mutual coordination requirements. These and other limitations notwith-
standing, we believe that this study has contributed in furthering the dialogue between
planned organizational change and social network practitioners and theorists.
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