9 A Preliminary Theory of Interorganizational Network Effectivenes.docx

9 A Preliminary Theory of Interorganizational Network
Effectiveness: A Comparative Study of Four Community Mental
Health Systems Keith G. Provan H. Brinton Milward This
chapter presents the results of a comparative study of
interorganizational networks, or systems, of mental health
delivery in four U.S. cities, leading to a preliminary theory of
network effectiveness. Extensive data were collected from
surveys, interviews, documents, and observations. Network
effectiveness was assessed by collecting and aggregating data
on outcomes from samples of clients, their families, and their
case managers at each site. Results of analyses of both
quantitative and qualitative data collected at the individual,
organizational, and network levels of analysis showed that
network effectiveness could be explained by various structural
and contextual factors, specifically, network integration,
external control, system stability, and environmental resource
munificence. Based on the findings, we develop testable
propositions to guide theory development and future research on
network effectiveness. The study of relations between
organizations has been a major concern of organization theorists
for at least the past 25 years. While most of the work in this
area has focused on the determinants or predictors of
interorganizational relations (see Oliver, 1990, for a review), as
an understanding of the phenomenon has grown, the unit of
analysis has gradually shifted from the dyad to the organization
set, to the network. Especially in recent years, the study of
organizational networks has proliferated. Much of this interest
has been generated by an emerging recognition by academics
that businesses, as well as organizations in the not-for-profit
and public sectors, are increasingly turning to various forms of
cooperative alliances as a way of enhancing competitiveness
and effectiveness that would not be possible through the
traditional governance mechanisms of market or hierarchy
(Powell, 1990). While a good deal of what has been written

about networks has been atheoretical, discussing the advantages
of networks or examining issues of measurement and analysis,
considerable theory-based research has also emerged (e.g.,
Cook, 1977; Burt, 1980; Granovetter, 1985; Jarillo, 1988;
Williamson, 1991; Cook and Whitmeyer, 1992; Larson, 1992;
Provan, 1993). In the organization theory literature, work on
networks has been guided primarily by two theoretical
perspectives: resource dependence, and related exchange
perspectives, and transaction cost economics, with most recent
work focusing on the latter approach. Each of these perspectives
offers both complementary and contrasting views about the
network form. For the most part, however, each perspective
focuses essentially on the organizational antecedents and
outcomes of network involvement, with little attention paid to
the network as a whole, except for its governance and structure.
This organizational view is understandable, since it is
organizations that make up a network and organizations that
stand either to lose or benefit by network involvement. In both
the transaction cost and resource dependence literatures, for
instance, the motivation and rationale for cooperative,
interorganizational integration of activities and services is at
the organizational level, either for reasons of efficiency related
to reduced transaction costs (Williamson, 1985) or to gain
resources and power (Pfeffer and Salancik, 1978). Individual
organizations make strategic choices to form or become part of
a cooperative network of other organizations when it appears
that the advantages to such an arrangement, especially enhanced
survival capacity (Uzzi, 1994), outweigh the costs of
maintaining the relationship, including any potential loss of
operating and decision autonomy. Thus, much of what is known
about the rationale for network involvement is based on an
extension of the literature on interorganizational relations.
Absent from these views, however, is a focus on nonstructural
outcomes of the network as a whole. Even in the general
network literature, which places a heavy emphasis on network-
level properties and structures (Aldrich and Whetten, 1981;

Knoke and Kuklinski, 1982; Marsden, 1990; Scott, 1991), issues
of network outcomes and effectiveness are mostly ignored.
While focus on organizational effectiveness is clearly
appropriate when outcomes can readily be attributed to the
activities of individual organizations, not all problems can be
solved by the actions of individual organizations. Particularly in
the area of community-based health care and social services for
such groups as the homeless, people with severe mental illness,
drug and alcohol abusers, and the elderly, a focus on
organizational outcomes is insufficient, because such outcomes
reflect only how well individual providers are performing their
particular component of the many services needed by their
clients. If the overall well-being of clients is a goal, then
effectiveness must be assessed at the network level, since client
well-being depends on the integrated and coordinated actions of
many different agencies separately providing shelter,
transportation, food and health, mental health, legal, vocational,
recreational, family, and income support services. Although
individual agencies obviously have an important role to play in
service delivery, and some agencies will clearly be more
involved and provide higher-quality services than others, if
overall client well-being depends on receiving different services
provided by multiple agencies, client outcomes should be
explainable by focusing on network-level activities and
structure. The critical issue, both for clients and system-level
planners and funders, is the effectiveness of the entire network
of service providers, not whether some agencies that are part of
the network do a better job than others in providing a particular
component of service. Obviously, a network may be well
integrated and still be ineffective if individual provider
agencies do a poor job. At the opposite extreme, even though
the agencies in a system may provide excellent services, overall
outcomes may be quite low if clients can only gain access to
some of these services. The prevailing view among many
service professionals, policymakers, and researchers is that by
integrating services through a network of provider agencies

linked through referrals, case management, and joint programs,
clients will gain the benefits of reduced fragmentation and
greater coordination of services, leading to a more effective
system (Warren, Rose, and Bergunder, 1974; Rogers and
Whetten, 1982; Department of Health and Human Services,
1991; Goldman et al., 1992; Alter and Hage, 1993). In the
public and not-for-profit sectors, where a public interest motive
is involved, network outcomes are especially salient, and the
rationale for organizations cooperating to accomplish system
goals rather than organizational ends is often stronger than in
the private sector, even when specific incentives to integrate
and cooperate are weak. For key external groups like
policymakers and funders, as well as for service professionals
and clients themselves, emphasis is often on achieving
outcomes that enhance the overall well-being of clients, without
regard to whether the goals of individual provider organizations
are met. Such outcomes may or may not have any direct impact
on the effectiveness of many of the organizations that make up
the network and may even result in a reduction in the number of
organizations that provide services to a particular clientele if
services are provided more efficiently. Arguments for the
effectiveness of integrated networks of organizations have been
particularly prevalent in discussions of the community-based
care of people with severe mental illness (Windle and Scully,
1976; Turner and TenHoor, 1978; Tessler and Goldman, 1982;
Grusky et al., 1985; Morrissey, Tausig, and Lindsey, 1985;
Goldman et al., 1992; Alter and Hage, 1993). Much of the
rationale for the need for integrated, coordinated services for
these individuals stems from the failure of efforts to serve the
many patients released from state mental hospitals since the
1960s. Deinstitutionalized patients were to draw on the services
offered by community health and human service organizations,
but little effort or money was expended to ensure that needed
services were available or that clients would be able to move
among community agencies to get these services. Since long-
term hospitalization was no longer socially acceptable or

politically feasible, the alternative treatment ideology that
emerged was based on the integration of services into a system
or network of providers (Bassuk and Gerson, 1978; Weiss,
1990). Integrated systems of community agencies can provide
the full range of health and human services needed by severely
mentally ill adults in a way that ensures continuity of care (Dill
and Rochefort, 1989). Such continuity is particularly critical for
this illness, which is estimated to afflict between 1.7 and 4
million Americans (National Institute of Mental Health, 1991),
not only because of clients' multiple needs but because of the
disorienting nature of the illness itself. This latter problem
makes it extremely difficult for clients to be responsible for
ensuring that their own treatment is effectively coordinated
across a variety of autonomous agencies that are often scattered
geographically. In addition, through coordination, an integrated
system supposedly minimizes duplication of services by
multiple provider agencies while increasing the probability that
all essential services are provided somewhere in the system and
that clients will have access to these needed services. Thus,
system structure, particularly integration among service
providers, is presumed to have a strong impact on outcomes for
the mentally ill. Many of the original ideas behind services
integration for the mentally ill were incorporated under the
National Institute of Mental Health's (NIMH) Community
Support Program, begun in 1977, although the specific form that
integration was to take was purposely left vague (Grusky et al.,
1985). Because funding for implementing this program was
limited and no mandates were imposed, mental health
professional groups and NIMH frequently relied on normative
pressures (supported by some demonstration grants) to influence
local integration behavior through establishment of a so-called
community service ethic (Weiss, 1990). NIMH continues to call
for research on the topic (Steinwachs et al., 1992), firmly
believing that integration of the broad range of services needed
by the severely mentally ill is critical for favorable client
outcomes. Unfortunately, although the rationale for network

involvement may be strong, there is almost no empirical
evidence to support the presumed relationship between
integration and network effectiveness or to indicate what
network characteristics are associated with effective outcomes.
Consistent with the ideas discussed above, our study was
organized around a single fundamental research question: What,
if any, is the relationship between the structure and context of
mental health networks and their effectiveness? We
operationalized effectiveness using a multitrait,
multiperspective methodology designed to assess the overall
well-being of severely mentally ill clients collectively served by
the agencies that make up the health and human service delivery
system in each of four mid-sized U.S. cities. From the study, we
draw conclusions for theory and research on the effectiveness of
organizational networks and for issues of health care policy that
apply to service delivery through integrated and coordinated
networks of organizations. Research Methods This study uses
what Yin (1984) has described as a case survey approach, in
which multiple levels of analysis (individual, agency, and
network levels) are used to develop an in-depth picture of a
single case. We used this approach in each of four systems, so
that our study has a comparative case research design. We draw
on extensive qualitative and questionnaire data collected from
hundreds of individuals and organizations that we first
aggregate by system to reflect the properties of that system and
then compare. Research Sites and Criteria for Selection The
community mental health systems in four U.S. cities were
studied: Tucson, Arizona; Albuquerque, New Mexico;
Providence, Rhode Island; and Akron, Ohio. Our choice of city
was guided by three selection criteria. First, we wanted to
choose cities of roughly comparable size that would be large
enough to contain the full range of organizations, clients, and
services existing in nearly all medium to large cities while not
being so large that data collection and analysis would be
unmanageable. Our cities ranged in size from 369,000
(Providence) to 667,000 (Tucson), using 1990 census data from

the 1991 Statistical Abstract of the United States. Second, we
wanted to choose cities having at least one core mental health
agency whose role it was to provide services and to coordinate
the services of other community agencies, if only through case
management. These dual roles are typical of the core agency in
most medium- to large-sized communities, although significant
variance exists in funding, structure, actual services provided,
and coordination emphasis. Finally, we used a most
similar/most different research design (Przeworski and Teune,
1970), selecting two cities from states with relatively high
levels of per capita mental health spending and two from states
with low mental health spending. This would allow comparisons
based on the relative resource munificence of the broader state
system in which our community systems were embedded. We
chose this approach because the majority of funding for
community mental health flows to localities through the state
(Mechanic and Surles, 1992). Using data reported by Torrey et
al. (1990), resource munificence was operationalized as per
capita mental health spending by each state's mental health
authority. Spending was high in Rhode Island ($52.34) and Ohio
($45.33) and low in New Mexico ($23.79) and Arizona
($19.76). Author's Note: This chapter is a reprint from
Administrative Science Quarterly, 40(1), 1–33, 1995. This
research was funded by a grant from the National Institute of
Mental Health (R01-MH43783). The authors would like to thank
Mike Berren, Dick Bootzin, A. J. Figueredo, Julie Sebastian,
Lee Sechrest, and Ken Sublett for their contributions to this
project and Terry Anfiburgey, Rob Burns, Christine Oliver,
Chuck Windle, and three anonymous ASO reviewers for their
many valuable comments on various drafts of the paper.
Network Data Collection Procedures In each of the four cites,
we used identical data and data collection procedures, based on
a modified version of methods used in a pilot study (Provan and
Milward, 1991). Data were collected in 1991 and early 1992.
After each site was identified and we had confirmed initial
cooperation by the core mental health agency, we traveled to

each site and met with system leaders, including heads of key
mental health providers, funders, and support groups. At this
meeting, we explained procedures, sought further cooperation,
and presented, discussed, and refined a list of organizations in
the system. It was our intent to develop a comprehensive list of
all agencies in the community that provided services to adults
with severe mental illness. Our primary method for determining
inclusion was what Laumann, Marsden, and Prensky (1983)
referred to as positional, where an agency had to provide actual
services or funding to our target population. Besides agencies
having mental health as a prime goal, the mental health system
at each site included agencies providing permanent and
temporary housing and shelter, counseling, legal, food,
transportation, income assistance, employment, rehabilitation,
medical, drug and alcohol abuse, and recreation services. For
many of these agencies, severely mentally ill (SMI) adults made
up only a fraction of the total clientele served, but each agency
and its services were an important piece of the entire
community mental health system in that city. Consistent with
the positional selection criterion, agencies were selected
because of their involvement with SMI clients and not because
they were already linked to other service providers for the SMI.
Thus, we made every possible attempt to avoid including only
those agencies that were already well integrated into the mental
health network. From the list of mental health provider agencies
that we generated and refined at each site, we developed a
questionnaire for use at all four sites, with identical questions
related to agency characteristics and network involvement. The
main network question listed all agencies initially identified as
being part of the system, at each site (43–55 agencies,
depending on the site). The questionnaire was then mailed to
each agency, addressed to the agency head or a key staff
member, depending on who would be most knowledgeable about
SMI adult services. The survey was accompanied by a cover
letter from us as well as a letter of support signed by the head
of the city's core agency, primary mental health funder, or both.

About 10 days after mailing, we telephoned each agency to
resolve any initial problems and set up an interview. At this
point, a number of agencies at each site were eliminated when it
became apparent that their level of involvement with the target
population was so minimal that inclusion in the study made
little sense. We then visited each site, jointly interviewing
members of every agency in the system. One purpose of these
in-depth meetings was to review questionnaire items and
responses to ensure that respondents were interpreting them as
we intended. The interviews also allowed us to eliminate
several more agencies that should not have been included in the
first place and to add additional ones, if mentioned by three or
more respondents. This modified snowball procedure (Knoke
and Kuklinski, 1982: 23) ensured capturing of any network
agencies we might otherwise have missed. Finally, and most
important, the interviews enabled us to collect much qualitative
information on respondents' views and impressions of the city's
mental health system, its history and functioning, and the
agency's role in that system. We took extensive notes at each
interview, which we discussed and compared and compiled into
a final set of field notes. We made no attempt to structure these
interviews, except that we made every effort to pursue issues
that respondents frequently mentioned as important at each site.
Questionnaires were either collected after the interview or
returned by mail. We followed up with telephoning and
additional interviewing to collect missing data and check data
that appeared to be inaccurate when we compared questionnaire
responses with field notes. In addition, we collected extensive
mental health funding data from documents provided by state
agencies and through interviews with state agency officials
responsible for community mental health funding. Overall, the
comprehensive data collection procedure took one year to
complete but resulted in final response rates of 92 percent in
Albuquerque (N = 35) and Providence (N = 35), 97 percent in
Tucson (N = 32), and 100 percent in Akron (N = 36), with few
missing data. Nearly two years after initial data collection

began, as a final step in the process, we visited each of the sites
once again, after all data were collected and initially analyzed,
to present and discuss findings with system members. We did
this as a reality check to ensure that our major conclusions were
not inconsistent with their understanding of system operation.
Client Data Collection Procedures: Network Effectiveness
Network effectiveness was assessed using data collected from a
5 percent random sample of adult SMI clients in each
community: 64 in Tucson, 59 in Albuquerque, 62 in Providence,
and 80 in Akron. SMI clients were defined as adults (over 18)
whose emotional or behavioral functioning is severely and
persistently impaired in a way that interferes with their capacity
to remain in the community without supportive treatment.
Decisions about who qualified for this designation were made
by mental health professionals at each site using standard
evaluation criteria published in the Diagnostic and Statistical
Manual of the American Psychiatric Association (3rd ed.),
commonly referred to as the DSM-III-R. By using a random
sample and by focusing only on individuals whose illness was
severe and chronic, yet whose current problems were not
sufficiently acute to warrant institutionalization, we were
confident that our client populations would be comparable,
although not identical in illness severity across sites. The
limited data on severity we were able to collect in Tucson and
Akron showed no significant differences in clients at these two
sites. Clients were selected directly from the coded rosters of
the core agency in each city and were only identified by name
once they were actually selected for the sample. Clients were
then contacted, through their case manager, and asked to
participate in the study. Those who agreed met individually
with our trained personnel who, through structured interviews,
recorded clients' responses to questions concerning quality of
life, adjustment, psychopathology, and satisfaction with
services. The same information was obtained about the clients
from a family member, if one was available, and from the
clients' case manager or therapist. A clinical psychologist who

was part of the research team, and had conducted such work
previously, trained the interviewers and coordinated the client
data collection effort. Measurement Network effectiveness.
Comparative research on the effectiveness of organizational
networks is virtually nonexistent, except for recent work by
Morrissey et al. (1994) that relied on global perceptions of
network performance and work by Lehman et al. (1994) that was
inconclusive. As noted earlier, part of the problem is that most
researchers are more concerned with organizational outcomes,
even when the organizations studied are part of a network. In
addition, actual assessment of network effectiveness, like
organizational effectiveness, is extremely problematic (see
Goodman and Pennings, 1977, and Cameron and Whetten, 1983,
for reviews of organization-level problems). The early
effectiveness literature often focused on the accomplishment of
predetermined organizational goals that were ill-defined and
often in conflict or on the receipt of scarce and valued resources
(Yuchtman and Seashore, 1967) with minimal concern for
process, outcomes, or a diversity of views. A more recent
alternative has been to consider the views of an organization's
multiple constituencies, or key stakeholders (Zammuto, 1984).
This approach has been a considerable advance in thinking
about effectiveness, although actual measurement has still
proved troublesome, owing primarily to problems in
determining how to evaluate the needs of key constituent groups
and how they should be weighted, relative to those of other
groups. Despite problems, it is this approach that we have
adopted here. In community mental health networks, as in any
publicly funded system, there are many constituencies whose
views must be considered in policy decisions. These include
clients, families, service professionals, state-level
policymakers, funders, agency staff and administrators, and
taxpayers, just to name the most prominent ones. For this
research, we felt it was critical to tie effectiveness measures to
enhanced client well-being. This outcome reflects what is most
certainly a top priority of these constituent groups. While it is

reasonable to argue that these groups will have additional goals
that are perhaps more self-serving (e.g., maximizing career
opportunities, enhanced influence, increased resources), and
each may have different beliefs as to the means of treatment and
service, disagreement over the fundamental goal of improved
client outcomes is unlikely. In addition, and of substantial
importance for this study, we had to choose a measure of
effectiveness that reflected the outcomes of network-level
activities and structures. To assess client outcomes, we selected
a series of items from several standard assessment instruments,
most notably, the Colorado Client Assessment Record and the
New York Functioning Scale, and modified them to fit our
study. Pretesting of methods and measures was done as part of
an earlier project (Bootzin et al., 1989). Items focused on
quality of life, client satisfaction, psychopathology, and level of
functioning. Consistent with a multiple-constituency approach,
we collected outcome data from three different groups: the
clients themselves, their families (if available), and the clients'
case managers or therapists. We believed these three groups
would have the most complete understanding of client
outcomes, although each might have a somewhat different
perspective (Bootzin and Sadish, 1986). Hence, we used a
multimeasure, multiperspective approach. Once data collection
was complete, we pooled results from all subjects from all four
sites and did factor analyses to test for the existence of
similarities in perspectives both within and across the three
groups. Initial analyses showed that each group maintained its
own perspective, having only modest overlap with the other two
groups. When each group was analyzed separately, four distinct
perspectives ultimately emerged. The clients themselves
assessed their outcomes in two different ways: One factor
comprised items related to their quality of life, including
various economic, social, and daily living services, while a
second factor was made up of items related to their
psychopathology and physiological status. These factors,
labeled QofL/satisfaction and psychiatric/medical status,

respectively, explained 48 percent of the variance in all the
client-reported items. In contrast to client self-assessment, only
one factor emerged for the family perspective (42 percent of
variance) and one factor for that of the case manager (53
percent of variance). These were labeled overall quality of life.
As a final step in the analysis of outcome data, we computed
factor scores for each of the four factors. We then regrouped
respondents by city and computed mean factor scores for each
site so that each system could be compared according to its
factor score for each of the two clients, one family, and one
case manager perspectives. These final factor scores, which we
used as our measures of network effectiveness, are reported in
Table 9.1. In general, both families and the clients themselves
considered the network to be most effective in meeting client
needs in Providence and least effective in Tucson and Akron.
Scores for Albuquerque were more equivocal but generally fell
in the mid-range. In contrast, case managers/therapists felt best
about client outcomes in Albuquerque and Tucson and worst in
Akron, with Providence being slightly negative. As shown in
Table 9.1, most of these differences were statistically
significant. It is important to keep in mind, however, that these
scores only reflect relative differences in effectiveness across
the sites, not absolute evaluations of client well-being. Outcome
scores were statistically unrelated to differences in client age,
gender, race, and ethnicity. Demographic data for clients across
all four sites were as follows: mean age = 42.3 years (range =
19 to 78); gender = 50.9 percent female; race = 64.9 percent
white, 18.5 percent Afro-American, 14 percent Hispanic, and
2.6 percent other. We originally hoped to develop a single
measure of network effectiveness, combining the perspectives
of clients, families, and case managers, but the multiple-
constituency approach resulted in multiple views of
effectiveness. Nonetheless, the pattern of factor scores across
sites made it possible to group findings so as to make
comparisons and draw conclusions. The patterns of results for
the two client factors and the single family factor were

sufficiently similar to warrant combining them into a single
dimension of network effectiveness. We averaged the three
factor scores for clients and families for each site to enable us
to place that site on a continuum of relative effectiveness. This
method showed Tucson to be the least effective system (mean =
−.19), followed by Akron (−.12), then Albuquerque (−.02), and
then Providence (+.42), which was rated as significantly more
effective than the other sites, especially Tucson and Akron.
While all predictors of network effectiveness are presented and
discussed below in terms of their relationship to both the
client/family and case manager views of effectiveness, our
primary discussion and interpretation of results focuses on the
combined client and family perspective. Client and family views
were generally consistent across sites, offering convergent
validity for the measures used. In addition, it is the experiences
of families and, particularly, the clients themselves that
ultimately matter the most, since they are the ones who actually
receive the system's services, are most knowledgeable about the
client's overall well-being, and must live with the results. It is
this combined client and family view that agency and program
heads were most concerned with in the follow-up site visits we
made to report our findings. Table 9.1 Network Effectiveness
Comparisons: Factor Scores of Client Outcome Scales (N =
265)* *Sample size indicates the total number of clients from
and about whom outcome data were collected at each site.
†When a mean factor score from a particular constituent group
is significantly different (p < .05) from the score from that
group at another site, a letter (or letters) follows the score,
indicating the significantly different comparison site (i.e., P =
Providence). A letter in parentheses indicates p < .10. Network
integration. As discussed earlier, the basic question underlying
our research is whether network structure, particularly
integration and related issues of coordination, is related to
network effectiveness. Unfortunately, the concept of integration
is ill-defined, making operationalization difficult and
interpretation of outcomes confusing (Aldrich, 1978; Bolland

and Wilson, 1994). Because the research was exploratory, we
chose to consider integration rather broadly, in an attempt to
determine which aspects, if any, were related to network
effectiveness. Integration was generally considered to focus on
issues of both interconnectedness among provider agencies and
the extent to which provider agencies are integrated and
coordinated through a central authority (Morrissey et al., 1994).
Our operationalization is consistent with the general network
structure concepts of density and overall centralization.
According to Scott (1991: 92), these concepts “refer to differing
aspects of the overall ‘compactness’ of a graph (i.e., a network).
Density describes the general level of cohesion in a graph;
centralization describes the extent to which this cohesion is
organized around particular focal points. Centralization and
density, therefore, are important complementary measures.” In
more general terms, density is simply a measure of the extent to
which all network organizations are interconnected, or linked to
one another, and reflects network cohesiveness. Centralization
refers to the power and control structure of the network, or
whether network links and activities are organized around any
particular or a small group of organizations. Overall
centralization of the network is an extension of Freeman's
(1979) concept of point centrality, which refers to the centrality
of individual network members. Our density and centralization
measures of network integration and coordination are reported
below, as are our findings on the relationship between these
measures and network effectiveness. The basic building block
of any network study is the linkages among the organizations
that make up the network. To develop measures of agency
integration through both density and centralization, every
agency in each system was first listed alphabetically on a
questionnaire insert that was unique to that system. Key
informants from each agency surveyed were asked to indicate
whether their agency was involved over the previous year with
every other agency listed, through each of five different types
of service links: referrals sent, referrals received, case

coordination, joint programs, and service contracts.
Respondents were not asked about frequency of involvement
except for referrals sent and received, where they were asked to
consider only referral activity occurring “with some regularity.”
While other types of links, such as friendship ties or
information links, were possible, our concern was solely with
links specifically related to service delivery. In addition, the
questionnaire and the face-to-face interviews continually
emphasized reporting only those links involving SMI adults.
Thus, our results exclude non-SMI-service linkages among
agencies that may provide SMI services. For instance, a hospital
and a community center may refer clients to one another and
provide important services to the mentally ill, but their referral
activity might not involve the mentally ill clients they serve.
The distinction we make between construction of a general
organizational linkage network and a more focused service
implementation network represents what we believe to be an
important advance in network methodology, which in the past
has often focused on organizational links without fully
considering whether or not the type of linkage measured is
relevant for the particular question being researched (see also
Hjern and Porter, 1981). We validated the reported linkages in
two ways. First, whenever possible, at least two agency
respondents were asked to attend the data collection interview.
Discrepancies in opinions about the link were discussed and
resolved, usually in favor of the service professional at the
meeting rather than the head administrator, who might have
only limited knowledge concerning operating-level links such as
referrals. Second, after data were collected, we confirmed each
link (Marsden, 1990). Thus, for instance, a joint program for
SMI adults listed by Agency A with Agency B would only be
counted as a viable link if Agency B also indicated that the link
existed. Results of Network Analysis Network Density The
overall density of each network was computed in two ways.
First, we computed a mean agency service link score by adding
confirmed scores for each of the five types of linkages for all

agencies in each system and dividing by the total number of
agencies in that system. While this service link density score
reflects the depth, strength, or durability of network integration
(what network analysts refer to as multiplicity), moderately
high scores may be achieved by relatively few agencies
integrating all or most of their SMI services. Thus, we
computed a second score, organizational link density, that
indicates the mean number of agency links of any of the five
types that occurred in each system. This second score is a
measure of organizational integration through SMI service links
rather than a measure of the strength of this integration across
the network. Dividing the total number of linkages in each
network by the number of agencies in that network, the
maximum service link score for any network is 5(n − 1), while
the maximum organizational link score is n − 1. Service and
organizational link density scores for the entire network at each
site and their relationship to each of the two perspectives for
network effectiveness are reported in Table 9.2. Table 9.2
Relationships Between Network Effectiveness and Services
Integration Measured by Overall Network Density* *Numbers
in parentheses after each city's name refer to the client-
outcome-based effectiveness score for that site and for the
particular perspective listed. The mean density scores for each
network were first statistically compared with the scores at each
of the other sites, using t-tests. Despite apparent differences
between Providence and the other three sites, the only
significant finding was that organizational link scores for
Albuquerque were marginally higher than for Providence (p <
.10 for a two-tailed test). Most revealing, however, was that
there was little relationship between density-based integration
and network effectiveness, regardless of the measures of density
or effectiveness used. Network density scores for Tucson,
Albuquerque, and Akron were all extremely close, despite
differences in effectiveness at each site. The one strong
relationship found was that the most effective network based on
client and family views, Providence, was also by far the least

integrated system, a finding that is completely contrary to the
prevailing assumptions and beliefs of mental health
professionals about the value of integrating services. While
these findings were unexpected in view of prevailing
assumptions about integration, we resisted attempts to draw
conclusions without first considering findings from our
centrality-based measures of integration described below.
Network Centralization We next shifted our attention to issues
of integration through centralized control of the network. A real
problem with integration through decentralized linkages among
providers is that system coordination can be exceptionally
complex, unless the network is quite small (Provan, 1983).
Because there are limits to how many links an organization can
handle effectively, the number of service linkages per
organization in moderate to large networks may well be no
greater than the number per organization in small networks.
Large networks with many decentralized links will have
difficulty organizing and effectively operating as a complete
system, depriving clients of the potential benefits of integration
across the full range of agencies and services offered in a large
community. As an alternative, centralized control of integration
within larger networks allows the coordination of integration
across many agencies as well as closer monitoring of services.
This is the logic behind the formation of a federation structure
(Provan, 1983; D'Aunno and Zuckerman, 1987). With this in
mind, we examined two aspects of overall network
centralization: core agency centrality and concentration of
influence. Core agency centrality. As the primary organizer and
integrator of services in most communities, a core mental health
agency, often a community mental health center, is typically at
the center of all activities relating to services for individuals
with severe mental illness. When the core agency is central in
the flow of services, the network can operate more efficiently,
as the other providers need not devote much time and effort to
the task of coordinating the services they provide with the many
other agencies in the network. Whether such a centralized

system of integration is particularly effective, however, and
whether highly centralized networks are more effective than
relatively decentralized ones is not known. To measure core
agency centrality, we computed service and organizational link
density scores for only the core agency (see Table 9.3).
Network density scores, excluding the core agency, were also
recomputed for all agencies in each system. These calculations
revealed some important differences across sites, as shown in
Table 9.3. Statistical comparisons of means using two-tailed t-
tests revealed significant differences between the core agency
centralization scores (via service links) for Tucson and
Providence (p < .001), Akron (p < .001), and Albuquerque (p <
.05) and between Albuquerque and both Providence (p < .05)
and Akron (p < .10). In contrast to the findings reported in
Table 9.2, when the core agency was excluded from this
analysis, we also found significant differences in network
density when we compared across sites. Significant differences
were found for service link density scores between Providence
and Tucson (p < .10), Albuquerque (p < .10), and Akron (p <
.10) and for organizational link density scores between
Providence and Tucson (p < .10), Albuquerque (p < .05), and
Akron (p < .05). Table 9.3 Comparisons of the Structure of
Integration: Network Centralization Through the Core Agency
and Core-Excluded Density Scores* *Sample size indicates the
number of agencies from which data were collected at each site.
Figures in parentheses indicate links of each type expressed as a
percentage of the maximum possible number of links of that
type within each system. Most important, the pattern of scores
across sites for network centralization through the core agency
were substantially different than density scores. Providence had
the highest core agency linkage scores (97 percent of all
agencies in the system were linked to the core through an
average of 2.41 types of service links) but the lowest level of
linkages, or density, among all agencies other than the core,
indicating a highly centralized control system. Akron's core
agency was also highly central in its network, though somewhat

less so than Providence (94 percent of all agencies were linked
to the core through an average of 2.31 links). Akron's network
density scores were far closer to those of Tucson and
Albuquerque than to Providence, however, reflecting a system
integrated in two ways; Albuquerque's core agency centrality
score was lower than the two eastern sites (88 percent of
agencies linked to the core through an average of only 1.82
links), while density-based integration among the other agencies
was quite high. Finally, Tucson exhibited characteristics of a
highly decentralized but integrated system, with the lowest core
agency linkage scores of any site (only 65 percent of all
agencies linked to the core through an average of 1.12 links),
coupled with the highest core-excluded network density score
through service links. Concentration of influence. We also
assessed integration through centralization by examining the
network influence structure. We speculated that integration and
coordination of services across the network would be enhanced
when influence over decisions related to SMI clients was
concentrated in a single organization. Under this condition,
whether agencies were actually linked to the highly influential
organization, agency actions would likely reflect the influential
organization's preferences and policies about SMI services.
When agencies in a system act in ways consistent with the
views and expectations of a single organization, centrally
controlled, coordinated actions are attainable. Although past
studies of networks have relied on linkage-based measures of
centrality to assess power within the network (Cook, 1977;
Cook and Yamagishi, 1992), this approach assumes that
structural position is equivalent to actual influence, which may
not be true. To determine influence in each system, respondents
at each agency were first asked to list up to six other
organizations in the system “whose needs, goals, decisions,
and/or expectations… are generally taken into consideration by
your agency” when major decisions are made concerning
services to SMI adults. Responses from all agencies in each
system were then totaled, and each agency received an influence

score indicating the number of times it was mentioned by
others. Scores were converted to reflect the number of mentions
as a percentage of total possible mentions in each system,
thereby allowing comparisons across systems. To assess
concentration of influence, we examined the percentage scores
of the five most influential agencies in each system. We then
considered two separate aspects of influence, consistent with
the approach used by Eisenhardt and Bourgeois (1988: 743) in
their case study of firms in high-velocity environments. First,
we determined the most influential agency in each system. As
shown in Table 9.4, in Tucson, Akron, and Providence this
agency received mentions by between 71 and 76 percent of
respondents. In contrast, the most influential agency in
Albuquerque received only 56 percent of possible mentions.
Second, we examined the distribution of influence among the
top players, focusing on the distance between the scores of the
most influential agency and the one (ones) in second place. If
the second-place agency did not have more than half the
influence score of the top agency, then we considered that
system to have concentrated influence, as was the case in
Providence and Albuquerque. Because Albuquerque's top score
was so much lower than the most influential agency at the other
three sites, however, we labeled that system concentrated/weak
and Providence as concentrated/strong. Akron was labeled as
moderately dispersed, since its two second-place (tied) agencies
received relatively high influence scores (mentioned by 54
percent of respondents). Tucson was labeled as dispersed since
four other agencies in the system were two thirds as influential
as the most influential player. Table 9.4 Concentration of
Influence Over Mental Health Decisions (Top Five Most
Influential Agencies at Each Site)* Notes: MH, mental health;
HS, human service; AMI, Alliance for the Mentally Ill.
*Influence scores reflect the number of times each agency was
reported as being influential in mental health decisions as a
percentage of the total number of agencies in that network.
Combining findings on the two centrality-based measures of

network integration with the effectiveness results allows us to
make some conclusions that go beyond what could be said from
a consideration of density-based measures only, at least for
client and family assessments of effectiveness. As discussed
earlier, density measures were unrelated to effectiveness, except
that Providence, the least cohesive system, had the highest
effectiveness score. In contrast, when focusing on integration
through network centralization, a distinct pattern emerged.
When influence over mental health decisions was highly
concentrated in a single core agency, as in Providence, client
outcomes were highest. At the other extreme, when influence
was widely dispersed among a number of agencies, as in
Tucson, effectiveness was lowest. Akron's influence structure,
which was more dispersed than any system other than Tucson's,
had outcomes that were also lower than all but Tucson's.
Finally, influence in Albuquerque, while far weaker than in
Providence, was more concentrated than either Tucson or
Akron, and consistent with this finding, its network
effectiveness score on client and family assessment of outcomes
was higher than Tucson and Akron but lower than Providence.
Conclusions from the linkage-based measure of centralization
were consistent with this pattern, with the exception of Akron:
We found the highest effectiveness in Providence, which was
highly centralized through the core agency, and the lowest
effectiveness in Tucson, which was decentralized, while
Albuquerque, a moderately decentralized system, had a mid-
range effectiveness score. Akron, the anomaly, was a
centralized system with low effectiveness. Discussion Network
Effectiveness: Client and Family Perspective The clear linear
relationship between our influence-based measure of
centralization and client and family assessments of network
effectiveness, coupled with the findings for the linkage-based
measure of centralization, which seemed to explain
effectiveness for all systems except Akron, might lead to the
conclusion that a positive tie between network integration and
effectiveness is most likely when integration and coordination

occur from the top down but not when agencies take it upon
themselves to integrate their services. This conclusion is
consistent with those of Goldman, Morrissey, and colleagues
from their research on mental health systems funded by the
Robert Wood Johnson Foundation (Goldman, Morrissey, and
Ridgely, 1994; Morrissey et al., 1994). While such a conclusion
would not be inaccurate, it is based only on partial information
about the systems under study. Using our interviews and
observations, we were able to supplement the quantitative
network findings with qualitative insights, allowing us a deeper
understanding of why some mental health networks are more
effective than others. Our qualitative data from Providence were
entirely consistent with the network structure results. The city's
core mental health agency was easily the most powerful player
in the system. It was the focus of discussion in nearly all our
interviews with other agencies in the system, although many
viewed its powerful role in an uncomplimentary way, variously
describing the center as “bureaucratic,” “insensitive,” and
“arrogant.” As described by its director, the primary mission of
the core agency was to provide psychiatric and case
management services to all individuals with severe mental
illness. In fact, unlike any of the other systems, the core agency
in Providence provided its own psychiatrists to staff the
psychiatric beds of several area hospitals, thereby controlling
patient treatment even during institutionalization. Through
control of both case management and outpatient psychiatric
services, the core agency could also control which services
offered by the other community agencies the SMI clients
received and when they received them. Perhaps of greatest
importance, Providence's core agency had had for many years a
unique and direct relationship with the state's mental health
authority, in which all funding for the needs of the severely
mentally ill would be paid directly to the core agency. In
addition, if services were needed that the core agency did not
provide, such as housing or vocational rehabilitation, the state
could contract with other agencies for such services only after

core agency approval. Agencies not having such approved
contracts might provide certain services to the SMI, but these
agencies would not receive state funding expressly for that
purpose. This “monopoly model,” as we referred to the
Providence system, made integration among the various
providers largely unnecessary. Integration and coordination of
the system was still critical for attaining effective client
outcomes, since the core agency did not provide many of the
services needed by clients. Where integration occurred, it was
through the core agency, which controlled access to mental
health funding and clients and was the obvious focal point for
mental health services throughout the community. An
explanation of why network centralization through the core
agency had a positive impact on client effectiveness was
perhaps best stated by the head of the state's department of
vocational rehabilitation, who said that because “[the core
agency's] clients already have their housing, medication, and
most other needs met when they come to us, it is easier to
effectively rehabilitate them.” Providence also had the
advantage of relatively high state funding, enabling the system
to provide a level of services that might not be possible in
Tucson or Albuquerque. This high funding was allocated to a
local system that, because of its strong central control, could
function relatively efficiently with little duplication and little
questioning of decisions. In addition, direct control over
funding to a single core provider allowed the state to monitor
closely the outcomes of the core agency, while the core could
closely monitor the activities of the other agencies in the
network through its role as network gatekeeper. Finally, the
Providence system was highly stable. The core agency had been
in place as a mental health center for over 20 years, and its
director had been in place since its founding. Most of the
directors of the other key agencies had known each other for
years and had longstanding working relationships with the core
agency. As a result, the system and its operation were well-
known to agency professionals, resulting in little uncertainty for

them or their clients. The situation in Tucson, the least effective
system, was substantially different. Unlike the stable system in
Providence, the system in Tucson was undergoing rapid and
substantial change at the time we collected data. The changes
were brought about in large part by what was referred to by one
respondent as “a revolution from below,” in which a key system
leader joined forces with the local chapter of the Alliance for
the Mentally Ill (AMI), a client advocacy group formed by
families of the SMI, to put pressure on the state to change
Tucson into a case-managed, capitated system headed by a
single agency responsible for both funding and service delivery.
The revolution was only partially successful, resulting in
formation of a new but scaled-back core agency, primarily
responsible for case management. Although it contracted with
local doctors and the psychiatric unit of a local hospital to
provide psychiatric treatment for its clients, the core agency
provided few substantive mental health services on its own.
Thus, while it served as a key coordinator of SMI services in
the community, it had no monopoly on services. In fact, even
the core agency's role in Tucson was spread among several
other agencies, most notably a traditional community mental
health center and another multiservice mental health provider
that was set up primarily to serve the city's large Hispanic
population. The most powerful local agency in Tucson was a
private not-for-profit agency that was the local funding entity.
Unlike Providence, where funding was direct from the state to
the core provider agency, all treatment dollars flowed from the
state's department of health services to Tucson's funding entity,
which made all local funding decisions. Instead of working
closely with local providers, however, especially the new core
and the two other mental health agencies, the funding entity
used its control over resources to build its own power, both in
Tucson and at the state level. Treatment dollars were provided,
but the system was poorly funded, and little if any monitoring
of services took place. One respondent described the fund
allocation and monitoring system as being akin to “leaving

money on a tree stump and seeing who claimed it.” That the
funding entity and its president were not strongly committed to
goals of client service was amply demonstrated by a scandal
that erupted shortly after our data collection. It was discovered
that the entity's president was diverting precious treatment
dollars to a fund to purchase a new building, something that
outraged the mental health community but was perfectly legal
under the state's poorly specified system of contracting.1
Because of these problems, the mental health system in Tucson
operated largely on the goodwill and professionalism of those
who operated the key provider agencies. The director of one of
these providers pointed this out to us, noting that the heads of
the three main providers described above were all good friends
and were willing to work together. The other main integrating
force in Tucson was the local chapter of the Alliance for the
Mentally Ill. Because of the interpersonal skills and visibility of
its president and the presence of several powerful people on its
board, AMI was able to organize local provider agencies and
client families into an effective force for lobbying the state.
AMI also published a regular newsletter that further helped to
knit together both providers and families and to point out
problems with the new system. All this decentralized integration
of the network was critical to maintain minimal levels of
acceptable services in a system that was underfunded and had
no strong central authority to pull things together. Our
observations and interviews in Tucson led us to the conclusion
that decentralized integration was critical for helping a weak
system limp along but was not sufficient to result in the high
levels of client outcomes that were observed in Providence.
Based on the quantitative findings, Albuquerque fell between
the highly centralized and effective Providence system and the
decentralized and ineffective Tucson system. Albuquerque's
network effectiveness was particularly surprising in light of the
state's weak funding of and commitment to care for the severely
mentally ill. The simplistic, structural explanation for this is
that effectiveness is enhanced with greater levels of network

integration through centralization but not through decentralized
links among providers. Since Albuquerque's centralization was
between Tucson's and Providence's, its effectiveness should also
be intermediate, as we found. Yet this explanation says nothing
about why Albuquerque's system was structured as it was, how
effectiveness might have been causally related to structure, and
what other factors may have contributed to the relative
effectiveness of the Albuquerque system. Unlike any of the
other three systems we studied, case management, a key
service-level integration mechanism, was formally provided to
severely mentally ill individuals by four different agencies.
Although the core agency was by far the largest of these
agencies and served an estimated “90 percent of the city's adult
SMI population at one time or another,” centralized integration
and coordination of the system through the core agency was not
really possible, since the core agency did not manage the
treatment plans of many of these clients. Thus, while the other
three case management agencies and many other agencies both
sent and received clients to and from the core, each also
developed its own subnetwork of linkage partners. The agencies
in Albuquerque were dependent on and thus linked to the core
for many of the services it offered, including most emergency
and inpatient care, but the core agency did little to actively
integrate the system. It is telling that only one agency
maintained four or more of the five types of service links we
measured with the core agency, as opposed to six agencies in
Akron and eight in Providence (see Table 9.3). As one agency
head told us, “The [core agency] isn't the glue that holds the
system together. They are unresponsive to the community, aren't
protective, and do only what will be funded.” System
decentralization was enhanced by the state's mechanism for
funding. Unlike any of the other three systems, the four main
case management agencies in Albuquerque received state mental
health funds from three primary sources, and any agency in the
city could apply for these funds. Because funding was direct,
with no local intermediary as in Tucson, the state had somewhat

greater control over how resources were spent than in Tucson.
Because funding was fragmented, however, with each fund
source having its own priorities and funding preferences, the
state was unable to centralize mental health activities and
services around a single agency by concentrating resources on
that agency, as in Providence. The largest share of state funding
did go to the core agency, giving it resources and subsequent
services that other agencies could not provide in a poorly
funded system. The core agency thus became a central player in
the system, even though its management did not seek out a
strong integrative role. The integrative shortcomings of the core
agency appeared to be made up for by other agencies in the
system, several of which viewed the idea of an integrated and
coordinated system of services far more seriously than the core
agency. Thus, Albuquerque displayed some of the decentralized
elements of Tucson while still having a single, large,
multiservice core agency. Albuquerque also had a system that,
despite its poor funding and integrative shortcomings, had not
undergone any significant changes in many years. Clients,
families, and agency professionals thus understood the system
and could work around its shortcomings. A good example of
this was offered by one respondent at the city's jail, who told
us, Because evaluation and intake at [the core agency] can take
five hours of a police officer's time on weekend nights, the
officer will slap a mentally ill person with a petty offense like
disorderly conduct to get them into the jail's psychiatric unit.
Because it takes so much less time to “book” the person at the
jail, it is often the mental health facility of first resort, rather
than the last. After the person's emergency needs are taken care
of, they are sent on to [the core agency] or another agency.
Agency service professionals in Albuquerque knew how the
system operated and were able to work with the core agency
when its services were needed or around the core agency when
it could be avoided. The findings in Akron are somewhat more
difficult to explain. Structurally, the system was closest to
Providence, having service linkages that were centralized

through a core agency. In addition, both systems were in states
that provided relatively munificent resource environments. Yet
in terms of client and family assessments of effectiveness, the
system was closer to Tucson. Only concentration of influence,
which was moderately dispersed in Akron, seems to fit the
linear relationship between aspects of network structure and
effectiveness found with the other three systems. Using our
qualitative data, however, we can offer several explanations,
primarily centered around the issue of stability, to help make
sense of the discrepancy in findings for Akron. Like Tucson, the
community mental health system in Akron was new. Passage of
Ohio's Mental Health Act of 1988 significantly altered system
funding and structures in Ohio's cities. In Akron, three mental
health centers had served the community in each of three
separate catchment areas, each competing for clients and state
funding. After passage of the Mental Health Act, an Alcohol,
Drug Abuse, and Mental Health Board was established as the
local contracting, monitoring, and integrative entity for all
state- and local-government-funded services for mental health,
as well as for drug and alcohol abuse. Thus, like Tucson, but
unlike the other two systems, fiscal control by the state was
indirect, in this case, through a public rather than a private not-
for-profit entity. The ADM board, as it was known, hired as its
president an outsider activist who was a self-described
“tinkerer.” Within a year, he stopped all government funding to
two of the three mental health centers, effectively shutting them
down, while revising the role of the third center to focus on
clients with acute, rather than severe, mental illness and to
perform intake evaluations for all clients. He then created and
funded a new core agency as the sole provider of and case
manager for those with severe mental illness. While the new
core agency was very much at the center of service delivery to
the SMI population in Akron, it was apparent that effective
integration and coordination of services was still very much
evolving and that there was considerable confusion about the
new system. As one agency director told us, “The major changes

in the mental health system in the past few years means we
don't really have a handle on how the system works.” Another
said, “The system here is confusing to understand, and I don't
see how people with mental illness can comprehend it.” One
problem was that the system for handling emergency services
was not only confusing, involving different agencies at different
times of the day, but was creating animosity among the agencies
involved, particularly between the acute-care agency and its
supporters and the new core agency. Another problem was that
there was still considerable underlying resentment stemming
from the recent system restructuring. Not only was there a
change in the organizations that provided key services and
funding, creating uncertainty and loss of resources for some
agencies, but there was also a shift in treatment philosophy.
This shift was a conscious effort by the ADM president first to
emphasize the community-based care and treatment of all but
the most severely mentally ill, as opposed to emphasizing acute-
care patients, and, second, to place less emphasis on a
traditional psychiatric/medical orientation and more emphasis
on a psychosocial, community support philosophy exemplified
by the case management approach of the core agency. Agencies
not supporting the new approach still established service links
to the core agency, not just because of its services but also
because the core (through the ADM board) formally controlled
and managed the treatment plans of and funding for all
officially designated SMI clients. Thus, while the system in
Akron was structurally nearly as centralized through the core
agency as Providence, uncertainty about the system was still
high, and many agencies were not yet committed to a system
dominated by the new core agency. This interview-based
conclusion is consistent with the quantitative finding that while
Akron was centralized through the core agency, it al

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