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FORMATION AND IMPLICATIONS OF INTERORGANIZATIONAL
NETWORKS AMONG FISHERIES STAKEHOLDER ORGANIZATIONS
IN MICHIGAN’S PERE MARQUETTE RIVER WATERSHED
By

Kristine Dawson Lynch

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Fisheries and Wildlife

2001

ABSTRACT
FORMATION AND IMPLICATIONS OF INTERORGANIZATIONAL
NETWORKS AMONG FISHERIES STAKEHOLDER ORGANIZATIONS
IN MICHIGAN’S PERE MARQUETTE RIVER WATERSHED
By

Kristine Dawson Lynch

Fisheries and aquatic resource managers increasingly recognize the importance of
stakeholder collaboration and watershed-based management approaches, but managers
may not always know the most effective and efﬁcient way to integrate watershed
stakeholders. These managers need tools that will enable them to better understand the
potential for communication and collaboration among stakeholder groups in a watershed.
Organized groups represent the collective interests of many ﬁsheries stakeholders, and
mangers need to know if, how, and why these stakeholder groups engage in management
issues. To better understand these dynamics, we analyzed the communication and
collaboration networks of 112 ﬁsheries stakeholder organizations in and around
Michigan’s Pere Marquette (PM) River watershed. Using a mail survey, we collected
data from each stakeholder group on their organizational attributes, organizational ties (or
interactions) with other organizations, and attributes of these interorganizational ties.
Using multi-level social network analysis, we analyzed if and how an organization’s
attributes inﬂuence the types of ties it has with other organizations as well as their
position in the overall communication and collaboration network. We found that ties are
likely between two organizations in the same county, from the same organizational

domain (e.g., education, economic development, etc.), and more than 51 years old.

Interorganizational ties were most likely to be reported by organizations that are
generalist in scope, satisﬁed with PM ﬁsheries, unsatisﬁed with PM ﬁsheries, located
inside the watershed, and have a natural resource or environmental orientation. Within
the greater ﬁsheries network, sub-networks formed around counties and organizational
domain areas, and organizations at the center of each sub-network tended to be concerned
about habitat, have paid staff, more than 51 years old, generalists, satisﬁed with PM
ﬁsheries, and have a planning function in their cluster (either natural resources or local
government). These ﬁndings have implications for enhancing ﬁsheries managers’
communication strategies, and we recommend a network-based framework for utilizing
and enhancing stakeholder interactions. With a better understanding of how and why
stakeholder organizations interact, ﬁsheries managers can more effectively create
opportunities for stakeholder interactions and develop the potential for meaningful
watershed-scale communication and collaboration, thereby facilitating better ﬁsheries

management plans.

Dedicated to the memories of
Chris Vanderpool
and

Francis and Marguerite Baumgardner

iv

ACKNOWLEDGMENTS

This publication is the result of research funded by the Michigan State University
Department of Fisheries and Wildlife, Michigan Agricultural Experiment Station,
Michigan State University Extension, and the Ofﬁce of the Provost. Other organizations
that supported and facilitated my graduate program include the Michigan Department of
Natural Resources, the American Fisheries Society, the Great Lakes Fishery Commission,
and the Canadian Consulate General.

The successful completion of a dissertation can only be achieved with the
cooperation and support of many dedicated individuals. First, I need to acknowledge the
substantial contributions of my major professor, William W. Taylor, at all stages of this
research. He provided (and continues to provide) intellectual guidance and
encouragement, and he supplied the many types of resources needed to undertake and
present this study. Other committee members included Tracy Dobson, Ken Frank,
Angela Mertig, and Chris Vanderpool. All were instrumental in helping me develop
research ideas, reﬁne the data collection tools, and explore the theories and implications
of this research. Dr. Frank provided signiﬁcant guidance throughout the data analysis
process, and the success of this study is largely due to his patience, generosity, and
dedication to learning.

In addition to my committee members, other individuals also contributed to the
success of this research. Early on, Mike Moore and John Robertson shared their thoughts
and ideas, which helped lay the theoretical foundation of this research. Jim Dearing, Bob

Holsman, and Daniel Rutledge also helped reﬁne my survey instruments (in addition to

my committee), serving as a test audience for multiple iterations of the survey.
Information about the study site and survey population was provided by Chuck Nelson,
Dick Schwikert, Tom Rozich, Dianne Walker, Fred Kirchner, Julie Traver, Kate
Thompson, Ed McCoy, Jessica Mistak, Dave Thomas, and Kim Thomas. Collectively,
their knowledge and input helped make this study more accurate and relevant. Dan
Hayes, Meegan Dom, and Kate Wendt helped me with other assorted research tasks.
From an administrative view, Jane Thompson made sure I met all program requirements,
which enabled me to focus on research (and not on bureaucratic tasks).

Perhaps the greatest range of contributions came ﬁom my husband, Joel. Having
recently completed his own dissertation in a similar ﬁeld, he was a constant source of
advice and encouragement. He was helpful in brainstorming research ideas, developing
data collection tools, and presenting research ideas, yet his contributions extend even
further. In countless ways, he provided a supportive, stress-reducing personal
environment that facilitated my academic success, and for that I am ever grateful. Other
family members and close friends also helped in'this regard, and their constant support
was both empowering and rewarding—more than they probably realize. All those listed
here helped make this dissertation possible, and I hope they all take satisfaction in its

successful completion.

vi

TABLE OF CONTENTS

List Of Tables ............................................................ x
List of Figures .......................................................... xiii
Prologue ................................................................ 1
Introduction ....................................................... 1
Fisheries Ecosystem Management ...................................... 2

Key Concepts in an Ecosystem Approach .......................... 2

Implications for Fisheries Management Agencies .................... 4

Collaborative Fisheries Management .................................... 5
Collaboration and Resource Management .......................... 5
Watershed-based Collaboration .................................. 7
Interorganizational Stakeholder Networks ......................... 10

Research Approach ................................................ 12
Research Setting ..................... ' ........................ 12

Goals and Objectives ......................................... 15

Research Overview ........................................... 16

Chapter 1: USING ECOSYSTEM CONCEPTS TO DEFINE A POPULATION
OF FISHERIES STAKEHOLDER ORGANIZATIONS FOR A MICHIGAN

WATERSHED ................................................................... 1 8
Introduction ...................................................... 18
Stakeholder Theory. ................................................ 20
Stakeholder Identiﬁcation ........................................... 26

Stakeholder Criteria .......................................... 26
Identiﬁcation Process ......................................... 27
Results: Fisheries Stakeholder Organizations ............................ 29
Discussion ....................................................... 42

Chapter 2: FORMATION OF INTERORGANIZATIONAL RELATION TIES
AND NETWORKS: UNDERSTANDING THE INTERACTIONS OF FISHERIES

STAKEHOLDER ORGANIZATIONS ............................................. 46
Introduction ...................................................... 46
Organizations in the Pere Marquette River Watershed ..................... 48
Interorganizational Relations Theory ................................... 52

Resource Dependence Theory .................................. 53
Political Theory ............................................. 56
Institutional Theory .......................................... 58
Summary of Theories ......................................... 6O

vii

Organizational Behavior as Indicators of Interorganizational Relations ........ 61

Communication Between Organizational Leaders ................... 62
Contribution of Resources Between Organizations .................. 64
Interorganizational Collaboration ............................... 66
Hypotheses on Interorganizational Tie Formation ................... 69
Clustering and Centralin in Interorganizational Networks .................. 71
Properties and Hypotheses of Network Clusters .................... 72
Properties and Hypotheses of Organizational Centrality .............. 74
Analytical Approach ............................................... 77
Data Collection and Variable Deﬁnition .......................... 77
Modeling Framework ......................................... 82
Network Analysis Process ..................................... 85
Results .......................................................... 91
Description of Responses ..................................... 91
Tie Formation Patterns ........................................ 92
Other Observations on Tie and Network Properties ................ 103
Summary and Conclusions .......................................... 106

Chapter 3: INTEGRATING FISHERIES STAKEHOLDER ORGANIZATIONS INTO A
COMPREHENSIVE WATERSHED PLANNING PROCESS: IMPLICATIONS OF

INTERORGANIZATIONAL NETWORK DEVELOPMENT AND STRUCTURE ................. l 1 1
Introduction ..................................................... 1 l 1
Interorganizational Networks and Fisheries Stakeholder Integration ......... 114

Importance of a Network Approach ............................. 114
Network Characteristics and Organizational Function .............. 117
Management Implications of the Pere Marquette Stakeholder Network ....... 120
Interactions Between Two Organizations ........................ 121
Organizations that Report Having Interactions .................... 123
Types of Interorganizational Relations .......................... 125
Emergent Network Properties ................................. 126
Overview of Management Process Implications ................... 129
Framework for Issue-based Stakeholder Involvement ..................... 131
Conclusions ..................................................... 138

Epilogue .............................................................. 141
Introduction ..................................................... 141
Sources of Error .................................................. 141
Areas for Future Study ............................................. 144

Appendix A: Complete List of Fisheries Stakeholder Organizations ............... 149

Appendix B: Replication of the Survey Instrument ............................ 153

viii

Appendix C: Upcoming Survey Notice Letter ................................ 171

Appendix D: Cover Letter for First Survey Mailing ............................ 173
Appendix E: Reminder Postcard for First Survey Mailing ....................... 175
Appendix F: Cover Letter for Second Survey Mailing .......................... 177
Appendix G: Descriptive Statistics for Organizations and Ties ................... 179
References ............................................................ 187

ix

Table 1.1.

Table 1.2.

Table 1.3.

Table 1.4.

Table 1.5.

Table 1.6.

Table 1.7.

Table 1.8.

Table 1.9.

Table 1.10.

Table 1.11.

Table 2.1.

Table 2.2.

Table 2.3.

LIST OF TABLES

Federal government agencies and ofﬁces concerned about or having
impacts on PM watershed and/or ﬁsheries ......................... 30

State government agencies and Ofﬁces concerned about or having impacts
on PM watershed and/or ﬁsheries ................................ 32

County government ofﬁces concerned about or having impacts on PM
watershed and/or ﬁsheries ...................................... 33

Township governments concerned about or having impacts on PM
watershed and/or ﬁsheries ...................................... 34

City governments concerned about or having impacts on PM watershed
and/or ﬁsheries. ............................................. 34

Local and regional economic development organizations concerned about
or having impacts on PM watershed and/or ﬁsheries ................. 35

Protection-oriented conservation organizations concerned about or having
impacts on PM watershed and/or ﬁsheries ......................... 37

Recreation-oriented conservation organizations concerned about or having
impacts on PM watershed and/or ﬁsheries ......................... 39

Education and information organizations concerned about or having
impacts on PM watershed and/or ﬁsheries ......................... 4O

Partnership organizations concerned about or having impacts on PM
watershed and/or ﬁsheries ...................................... 41

Other special interest organizations concerned about or having impacts on
PM watershed and/or ﬁsheries. . . ................................ 42

Organizational attributes serving as independent variables, with
dichotomous coding. ......................................... 79

Interaction data serving as dependent variables, with dichotomous
coding. .................................................... 82

Effects of chooser, chosen, and dyadic attributes on tie formation ....... 94

Table 2.4.

Table 2.5.

Table 2.6.

Table 2.7.

Table 2.8.

Table 2.9.

Table A.1.

Table A2.

Table A3.

Table A4.

Table A5.

Table A6.

Table A7.

Table A.8.

Table A.9.

Table A. 10.

Variance components for the chooser and chosen organizations ........ 95

Effects of chooser, chosen, and dyadic attributes on tie formation, with
domain similarity replaced by each domain type. ................... 96

Effects of dyadic attributes on strong tie formation. ................. 99

Effects of dyadic attributes on strong tie formation, with domain similarity
replaced by each domain type .................................. 100

Effects of chooser and dyadic attributes on strong tie formation, with
domain similarity replaced by each domain type. .................. 101

Important predictors of interorganizational relations at the dyadic and
chooser levels. ............................................. 106

Categories of Organizations (the broad organizational classiﬁcation

type). .................................................... 179

Proximity of Organizations (the organization’s location relative to
watershed boundaries) ........................................ 179

County of Organizations (the county in which the organization is
located) ................................................... 180

Age of Organizations (how many years the organization has existed). . .180

Size of Organizations (the number of employees, members, or others
associated with the organization). .............................. 180

Membership Type of Organizations (whether membership is mostly
voluntary or non-voluntary). .................................. 181

Involvement of Organizations (the degree to which an organization is
engaged in natural resource issues in the PM watershed) ............. 181

Philosophy of Organizations (how the organization views the relationship
between people and natural resources) ........................... 181

Purpose of Organizations (the organization’s reason for existing and
bringing people together). .................................... 182

Satisfaction of Organizations (whether or not the organization is pleased
with ﬁsheries management in the PM watershed). ................. 182

xi

Table All.

Table A.12.

Table A.13.

Table A. 14.

Table A.15.

Table A. 16.

Table A.17.

Concerns of Organizations (the occurrence of issues or topics about which
the organization is most interested). Organizations could indicate more
than one concern. Concern types (type 1 = ecological concerns; type 2 =
recreational concerns; type 3 = user behavior concerns) are used to
determine the scope of concerns ................................ 183

Scope of Organizations’ Interests (the number of types Of issues about
which the organization is concerned) ............................ 183

Frequency of Ties (how often a given interaction occurred with another
organization. This was the primary identiﬁer of a tie; no response
indicates no interaction). ..................................... 184

Type(s) of Ties (the type(s) of ties indicated by choosers) ............ 184
Absolute Tie Type (the occurrence of each type of tie indicated in the 991
cases (1088 minus 97 with no information) in which a chooser indicated a

relationship. Some choosers reported more than one type of tie). ..... 185

Absolute Tone (the perceived mood or attitude surrounding the
interactions. Some choosers reported more than one type Of tone for an
interaction). ............................................... 185

Importance of Ties (the perceived weight or signiﬁcance that the chooser
organization assigns to maintaining a tie with the chosen). .......... 185

xii

Figure 1.

Figure 2.1.

Figure 2.2.

Figure 2.3.

LIST OF FIGURES

Approximate locations of the Pere Marquette River Watershed and select
counties and cities in Michigan .................................. 13

Theoretical relationships between organizational attributes and
interorganizational network structure, and the mechanisms by which they
inﬂuence each other. ......................................... 83

Selected sociogram representing the approximate PM stakeholder network
structure. Diamonds represent organizations (excluding the outliers), lines
represent strong ties, and circles represent clusters. ................. 87

Clusters identiﬁed in the PM ﬁsheries stakeholder network and central

organizations (represented by ® ) within each cluster (outlier
organizations and lines representing ties are omitted for clarity) ....... 104

xiii

PROLOGUE
Introduction

Aquatic ecosystems function as holistic integrators of complex ecological and
social processes, interrelationships, and dynamics throughout the landscape. Therefore,
ecosystem management programs and policies must be based on holistic, integrative,
multi-disciplinary, and collaborative strategies. For many natural resource management
agencies, these ecosystem requirements pose signiﬁcant challenges to conventional
structures and processes for planning and implementing management activities.
Management of ﬁsheries ecosystems requires not only management of ﬁsh, but it also
necessitates management of habitats and watershed processes (Alexander et a1. 1995).

i’These habitats and processes constitute the unique biological, physical, and chemical
environments that support ﬁsh production (Hayes et a1. 1993), and these systems are
directly and indirectly affected by human activity. Fisheries ecosystem management
therefore demands opportunities for collaboration and communication among those in the
watershed whose activities affect water quality, water quantity, ﬁsh populations, and their
habitats (Alexander et al. 1995; Selman and Wragg 1999).!»I Organized groups represent
the collective interests of many ﬁsheries stakeholders in a watershed, and the
characteristics of these organizations and the situations surrounding ﬁsheries issues
inﬂuence if and how they collaborate in ﬁsheries management/,1:l By reviewing ecosystem

management concepts, stakeholder theory, collaboration principles, and

interorganizational network theory, we can better understand the potential for integrating

stakeholders in a collaborative ﬁsheries management framework. Through learning about
interorganizational stakeholder collaboration networks in a Michigan watershed, ﬁsheries
management agencies can more effectively develop and implement integrative and

collaborative ﬁsheries management strategies.

Fisheries Ecosystem Management

Key Concepts in an Ecosystem Approach
Ecosystem scientists have long demonstrated that aquatic ecosystems function as
integrators of regional watershed and airshed dynamics (Hynes 1975). For example, the
quality and quantity of water in aquatic ecosystems, the basis for ﬁsh production, is
directly affected by physical, chemical, and biological inputs which are a result of
watershed-speciﬁc environmental and social processes (Alexander et a1. 1995; Lundqvist
2000; Taylor et al. 2001). These include inputs from processes such as erosion and
sedimentation, landscape runoff, point and non-point source pollution, and atmospheric
deposition. Diverse human activities, such as urbanization, deforestation, industrial
manufacturing, energy production and consumption, and intensive agriculture, drive
many of these processes and therefore affect ecosystem productivity and sustainability.
Such land use and development activities are products of diverse and fragmented
planning and policy-making bodies, which allow development to proceed with little or no
coordination or consideration of the structure and function of ecological systems
(Caldwell 1994; F erreri et a1. 1999; Selman and Wragg 1999).

A ﬁshery is a system in which living aquatic resources are produced and harvested

 

(Clay and McGoodwin 1995).; Fisheries management is an integrative process that seeks
l
\

to ensure long term ecosystem productivity and the development of conservation and

allocation systems that allow people to sustainably use the resource (Ferreri et a1. 1999).

/
For aquatic ecosystems and their ﬁsheries resources to be effectively managed, they must

be understood in context of their function as holistic integrators of regional social and
ecological processes (Knight and Meffe 1997). J This integration is a guiding principle of
ecosystem management, a management philosophy that emphasizes: (1) the integration
of the physical, chemical, biological, social, economic, and political components Of
resource systems; (2) the interactions of these components; and (3) how these
components relate to ecosystem productivity and resource sustainability (Ferreri et a1.
1999). Management based on these principles will help maintain and improve ecosystem
sustainability.

While the concept of ecosystem management is commonly accepted by managers,
scientists, and stakeholders as an appropriate management paradigm, it has undergone
intense scrutiny and reﬁnement throughout its evolution (Y affee et al. 1996). A review of
ecosystem management literature reveals a wide array of ecosystem management
concepts and theories (Czech and Krausman 1997). Nevertheless, the majority of
deﬁnitions emphasize certain common elements, such as integration of dynamic systems
and processes in a region, collaboration among ecosystem scientists and stakeholders,
and adaptability in management approaches (emphasis added; Christensen et a1. 1996).
Therefore, while the term ‘ecosystem management’ does not have a single Operational
deﬁnition, these commonly emphasized principles indicate that natural resource

management is evolving away from single-species authoritarian management regimes

based on segregated management efforts developed in isolation from other ecosystem

systems, processes, and stakeholders.

Implications for Fisheries Management Agencies

Due to the diverse and dynamic nature of regional social and environmental
processes, hOlistic management of aquatic ecosystems requires high levels of
coordination and collaboration among those in the watershed whose activities affect water
quality and quantity (Alexander et a1. 1995; Selman and Wragg 1999).\jThose who
manage ﬁsheries are very often administratively, procedurally, and disciplinarily
separated from those who manage other related ecosystem elements, such as forests,
wildlife, water quality, and land use. These organizational and philosophical divisions
within and between management agencies can render ecosystem management programs
incomplete, inefﬁcient, and ineffective (Knight and Meffe 1997; Cortner et a1. 1998).
Agency management activities are also usually separated from other watershed activities
that inﬂuence aquatic ecosystems and ﬁshery production; for example, activities by
private landowners, non-governmental organizations, organized citizen groups, local
planning boards, and commercial industries (Cortner and Moote 1999). These groups
engage in a wide range of water- and land-based activities that directly and indirectly
affect various components of aquatic ecosystems, such as nutrient cycling, temperature
regulation, and ﬂow rates (Alexander et a1. 1995; Taylor et a1. 2001). ‘The management

L-

community clearly becomes larger and more diverse in ecosystem management
approaches?)

To achieve ecosystem management, agency-based management of ﬁsheries and

aquatic ecosystems must often be transformed from a closed system of short-term ﬁsh
production and habitat manipulation to a more Open, dynamic, and integrative process
based on long-term communication and collaboration with other ecosystem scientists,
managers, and watershed stakeholders (Krueger and Decker 1993; Knight and Meffe
1997; Cortner et a1. 1998; Danter et al. 1999; Imperial 1999).:Imp1ementation of
ecosystem management will require natural resource agencies to re-evaluate their
management institutions and philosophies, which will reveal ways in which their
management structure and processes can be adapted to ecosystem concepts. Since
management structures and processes inﬂuence how managers conceptualize and respond
to resource issues, it is essential that agencies strive to create management systems that

are congruent with watershed processes and ecosystem dynamics.
Collaborative Approaches to Fisheries Management

Collaboration and Resource Management

The availability, access, and use of ﬁsheries and other resources are inﬂuenced by
both ecological and social systems (Krueger and Decker 1993). The complexity of these
interrelated systems, however, makes effective resource management an increasingly
elusive goalﬁs natural resource managers are saddled with the difﬁcult and challenging

L, ,
responsibilities of sustaining the productive bases of resources while managing for

appropriate methods of resource use and extraction}! Despite the importance of these

responsibilities, however, most resource managers are constrained by a lack of time,

ﬁrnding, personnel, and authority—all of which are necessary to effectively manage

resources (Krueger and Decker 1993; Mersmann et al. 1994). They are becoming aware
that they cannot meet their responsibilities without the input, cooperation, and support of

'ﬁpv

resource stakeholders;

Managers of natural resources are increasingly recognizing the importance of
collaboration among resource stakeholders, as they have indicated through surveys
(Y affee et al. 1996). The term ‘stakeholder’ refers to any person or group that has a role
or interest in the status Of a resource (Decker and Krueger 1993). In the case of ﬁsheries,
Obvious stakeholders include ﬁshers, processors, traders, managers, researchers,
conservationists, and policy-makers. Yet in a less obvious way, ﬁsheries stakeholders
include those industries, landowners, and municipalities in the watershed that affect ﬁsh
habitat (Clay and McGoodwin 1995). As long as these groups affect the quality and
quantity of ﬁsh habitat, they may be subject to current and future habitat-conserving
regulations and environmental liabilities. Whether they realize it or not, many of these
groups in the watershed have a stake in the status of ﬁsh habitat and therefore, in a
resource management context, they can be considered stakeholders.

Generally speaking, collaboration and cooperation are processes by which
different persons or groups work together on a common interest. Individuals or groups
may decide to collaborate or cooperate with others if they recognize that greater beneﬁts
are possible through collective (rather than individual) action: ‘In the context Of resource
management, collaboration between stakeholder groups can facilitate the spread of
information and support that is necessary to achieve resource management goals (Cortner

and Moote 1999). Through their knowledge, Opinions, beliefs, and behaviors, watershed

stakeholder groups can provide resource managers with valuable information about how

they use and relate to natural resources (Krueger and Decker 1993; Selman and Wragg
1999). Their input can thus enhance the accuracy and relevance of management
approaches. At the same time, resource managers can provide other stakeholders with
information on resource status and access as well as appropriate methods of use and
conservation; this should help promote appropriate forms of resource use. With the
increased opportunities for mutual communication, education, and support, collaboration
between stakeholders can increase the likelihood of achieving sustainable and productive
resource management (Mersmann et al. 1994; Clay and McGoodwin 1995; Yaffee 1996;
Cortner and Moote 1999; Selman and Wragg 1999). It is highly unlikely that effective

resource management can occur without stakeholder collaboration.

Watershed—based Collaboration

A watershed is a geographic region in which all water (such as precipitation,
overland ﬂow, and river channels) ﬂows to and eventually combines in a common outlet
(Dzurik 1990). Thus, using a watershed approach, all the water, land, and air that is
upstream to a point on a river potentially affects aquatic habitat and should therefore be
incorporated into management efforts (Alexander et al. 1995; Selman and Wragg 1999).
A watershed perspective in ﬁsheries management allows managers to deﬁne natural
boundaries for aquatic ecosystems (Taylor et al. 1995). Without a holistic view of how
watershed processes affect ﬁsh and ﬁsh habitat, management actions narrowly focused on
one aspect of an aquatic ecosystem can counteract other management initiatives in some
other aspect Of the ecosystem (Lundqvist 2000). For example, within a watershed, timber

management can affect the quality and quantity of overland ﬂow and thus alter aquatic

habitat (Alexander et al. 1995). In addition, narrow management actions can overlook
many other important social and ecological processes in the watershed (Krueger and
Decker 1993). To overcome such isolation and fragmentation in management and
improve resource conditions, managers need to pursue Opportunities for stakeholder
collaboration and integrate watershed-wide input (Schweer 1987; Selman and Wragg
1999).

:13 a watershed context, jurisdictional boundaries and multiple types of property
ownership pose signiﬁcant challenges to resource managers (Schweer 1987; Ferreri et al.
1999; Selman and Wragg 1999). Watershed boundaries do not match political
boundaries, such as those that deﬁne cities, townships, counties, states, and nations. As a
result, any given watershed can contain dozens (even hundreds) of policy and planning
bodies that independently direct various forms of regional land use and development.
Any lack of coordination in land development planning can lead to excessive rates of
urbanization, agricultural conversion, and habitat fragmentation—all of which directly
and indirectly affect water quality and quantity (Alexander et al. 1995; Lundqvist 2000;
Taylor et al. 2001). A watershed approach requires that these jurisdictional boundaries
become secondary to the ecosystem boundaries, allowing more holistic and integrative
management practices to be developed. llln addition, watersheds usually contain a
patchwork of public, private, and tribad—owned lands, thereby increasing the complexity
of land use patterns and resource conservation incentives, which can lead to conﬂict
among competing sectors and interests (Heissenbuttel 1996). {F or example, ecosystem

management efforts on pubic lands can be largely negated by destructive uses of adjacent

private lands, thereby requiring different conservation incentives for the private

landowner. In the interest of watershed management, managers must overcome
fragmentation based on jurisdictions or land ownership patterns and move toward
forming collaborative relationships with watershed stakeholders who impact aquatic and
ﬁsheries resources (Schweer 1987; Lundqvist 2000),

Managers also face challenges in understanding and identifying the stakeholders
in each watershed. Every watershed is unique in terms of its ecological conditions, social
(and political and economic) systems, and stakeholder composition. Stakeholder groups
in any given watershed have distinct ideologies, attitudes, and behaviors, although
individuals within any stakeholder group are not necessarily homogeneous (Clay and
McGoodwin 1995). Nevertheless, afﬁliation with a stakeholder group is assumed to be
associated with an individual’s attitudes and behaviors. Managers, therefore, can assume
that organized stakeholder groups adequately (although not completely) represent most of
the diverse interests and desires of individuals in a watershed (Clay and McGoodwin
1995). Since each watershed is different, however, managers must still invest time and
effort in becoming familiar with each watershed’s stakeholder population.

Managers of ﬁsheries and aquatic resources need a framework which will allow
them to understand the potential for communication and collaboration among stakeholder
groups in a watershed (Cortner and Moote 1999; Selman and Wragg 1999). While this
framework needs to be comprehensive enough to provide a proﬁle of stakeholder
interactions, it should also be ﬂexible enough to be applied to the unique stakeholder
compositions in different watersheds. First, managers need to know what kinds of
stakeholders are present, how many there are, and how they relate to resource

management. It is very likely that many of these stakeholder groups maintain certain

kinds of interactions and relationships; therefore, managers need to understand how
groups relate to each other. This will help managers identify existing areas of
communication and collaboration, and it will also identify which groups do not interact
with other groups. Having this information, managers can then more efﬁciently and
effectively create opportunities for increasing stakeholder interaction and develop the

potential for watershed-wide collaboration.

Interorganizational Stakeholder Networks

Beginning in the early 19705, sociologists and anthropologists devoted great
attention to the development of theories and tools to study social networks (Lincoln 1982;
Cook and Whitrneyer 1992; Mizruchi and Galaskiewicz 1993). The term ‘networks’
refers to the patterns of ties or relationships that link members of some group or
population (Lincoln 1982; Wellman 1983). By studying social networks, sociologists are
recognizing that social actors affect—and are affected by—their structural relationships
with other actors. Instead of analyzing only the motivations of individual social actors,
network analysts also study how patterns of interaction, or networks, relate to social
behavior and social change (Wellman 1983). The unit Of analysis in social network
analysis is the relationship or interaction (referred to as a ‘tie’) between social actors (or
‘nOdes’; KnOke and Rogers 1979; Lincoln 1982). A tie can be any kind Of relationship
between actors (such as ﬁiendship or the exchange of resources); very often network
analysts study multiple kinds of ties to understand the diverse ways that actors may be
networked (Lincoln 1982; Wellman 1983). The social actors may be persons, groups, or

organizations; in the case of inter-organizational network analysis the population under

10

study includes all organizations which have some common functional interest (Knoke and
Rogers 1979).

In studying the potential ﬁsheries collaboration networks in a watershed, the
population includes all stakeholder organizations that have an interest or role in ﬁsheries
management. We are interested in understanding the patterns Of communication and
collaboration between watershed stakeholders; therefore, our ties will focus on network
patterns such as the presence of information exchanges, resource exchanges, and joint
programs. By studying the direction and symmetry of these network ties, we can
understand how an organization’s relative position in the watershed network determines
their potential role in an integrative management framework. More recent developments
in network analysis allow us to also collect data on organizational attributes for these
watershed stakeholder groups, so that we can determine if and how these attributes are
related to their position in the watershed networks. Managers of ﬁsheries and aquatic
resources in the watershed will then have a comprehensive assessment of what
stakeholder groups are present, whether they are (and are not) interacting with each other,
how they are interacting, and how organizational attributes are associated with their level
of interaction. This assessment will allow managers to most efﬁciently and effectively
develop an integrative management framework that facilitates stakeholder collaboration
and, therefore, better resource management. It‘is provides a general ﬁ'amework for later
analyzing other aspects Of the stakeholder community, such as (but not limited to) the
formation of expectations and attitudes, the distribution of inﬂuence and power, decision-

making processes and outcomes, and conﬂict resolution strategies.

11

Research Approach

Research Setting

The Pere Marquette (PM) River has long been the site of one Of Michigan’s most
important recreatiOnal ﬁsheries (Krueger 1985; Cassuto 1994; Supinski 1994). Located
in the center of Michigan’s western lower peninsula, the river and its tributaries (Big
South Branch, Little South Branch, Middle Branch, and Baldwin River) span 153 miles
and feed into Lake Michigan (Figure 1; USDA Forest Service 1976). Having unique
ecological and aesthetic features, it has the distinction of being a ‘wild and scenic’ or
natural river under both state and federal agencies. As one of the last remaining ﬁee-
ﬂowing river systems in Michigan with limited shoreline development, it is an ideal site
to study lotic ecosystems (Cassuto 1994). This coldwater river system is well-known for
its resident and anadromous salmonid populations that attract numerous anglers to the
region (Krueger 1985; Cassuto 1994; Supinski 1994). The outstanding natural features of
this river system also attract many canoeists, hunters, hikers, campers, and other
recreation and nature enthusiasts (U SDAF S 1976; Nelson and Smith 1998).

The PM watershed covers 740 mi2 and contains numerous jurisdictions and
property types (U SDAF S 1976). As a relatively small Michigan watershed, it spans
sections of four counties (Mason, Lake, Oceana, and Newaygo Counties). Ludington
(population 8816), at the mouth of the river, is the largest city in the watershed and serves
as the port for the Badger, the car-passenger ferry that crosses Lake Michigan. Baldwin
(population 1337) and Scottville (population 842) are other small towns. The watershed

covers sections Of the Pere Marquette State Forest and the Manistee National Forest; the

12

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remaining land is mostly privately-owned (U SDAF S 1976).

Many watershed residents (both seasonal and permanent) are keenly aware of the
important natural features of this river system, and many have strong—and sometimes
conﬂicting—Opinions about using, conserving, and protecting this great regional resource
(Cassuto 1994; Supinski 1994). Watershed-based conservation activities are relatively
well developed as compared to other Michigan watersheds, largely due to the efforts of
the Pere Marquette Watershed Council and other recreation-oriented conservation
organizations (i.e., Trout Unlimited; Supinski 1994; Nelson and Smith 1998).

Several features of this watershed make it an ideal case study for analyzing
stakeholder collaboration networks. First, through the records and documentation of
watershed organizations, historians, ﬁsheries managers, and wild and scenic river
designators, a great deal is known about the ecological, social, economic, and political
features of this watershed. This information will make our research more accurate and
relevant. Second, the limited size and scope of activities within this watershed will help
focus our analysis on relevant variables; a larger watershed with extensive land use
impacts would introduce numerous confounding variables which would weaken our
abilities to draw accurate conclusions. This PM study can serve as a prototype which can
help identify ways to develop a more robust analysis of a larger system. Third, the
ecological signiﬁcance and increasing social demands surrounding the PM ﬁshery compel
managers to seek inclusive approaches to watershed management. If we can demonstrate
more effective and efﬁcient ways to integrate stakeholder collaboration in management of

this ﬁshery, managers would be able improve this and other ﬁsheries management

programs.

14

Goals and Objectives

The goals of this research are to understand the watershed—based stakeholder

collaboration network and incorporate this information in an integrative ﬁsheries

management framework. To achieve these goals, we have developed four objectives,

each intended to answer several questions:

1.

To identify aquatic- and ﬁsheries-related organizations in the PM watershed and
describe their communication and collaboration interactions:

who’s interacting with whom?

what kinds of organizations are interacting with others?

how often are they interacting?

what kind of interactions are they having?

what is the tone of the interactions (i.e., positive or negative?)

how important do they think it is to interact with other organizations?

To determine attributes of stakeholder organizations in the PM watershed and
assess if these are related to their degree of communication and collaboration with
other groups:

when were they founded?

how many members are in the organization?

what is the organizational mission?

what is their philosophy about natural resources management?

to what extent are they concerned about natural resources in the watershed?
are they satisﬁed with ﬁsheries management in the watershed?

what kind of ﬁsheries issues (if any) are they concerned about?

to what degree are these attributes related to their level of interactions?

To compare the network patterns, positions, and organizational attributes
observed in the PM watershed to theory and literature on interorganizational
communications and collaboration:

is there an ‘ideal’ network pattern to compare the observed to?

what organizational attributes facilitate communication and collaboration?
what types of organizations serve as information sources/gatekeepers/sinks?

are there opportunities to create relationships and bridges among organizations?
what organizations should be included in the management network? how?
what network properties should we incorporate into a recommended network?

To recommend a collaborative ﬁsheries management framework that integrates

15

watershed-based stakeholder organizations:

what is our recommended collaboration network for the watershed?

what network features are transferrable and can be applied to other watersheds?
what lessons can be learned from experiences in this watershed?

what problems should managers expect/overcome?

is network analysis useful for promoting collaborative ﬁsheries management?

Overall, we are interested in understanding the relationships among social actors
in the PM watershed that serve as potential pathways of collaboration, what
organizational attributes facilitate these relationships, and how these relationships can be
utilized or re-structured in the development and implementation of an integrated ﬁsheries
ecosystem management approachl We will not attempt to determine how stakeholder
collaboration and communication are actually affecting or contributing to current
management decisions; research on decision-making processes would be a second phase

of the research that depends on the outcome of this phase of the research?»

Research Overview

In the following three chapters, we will explore these objectives and questions in
detail. Chapter 1 presents an overview of stakeholder theory and describes our criteria for
identifying ﬁsheries stakeholder organizations in the PM watershed. The results Of this
identiﬁcation process establish our stakeholder population, which is the subject of
analysis in Chapter 2. In the second chapter, we review literature on interorganizational
relations and apply network analysis to our study of the interactions between stakeholder
organizations. For this study, we undertook a quantitative survey of organizations’
attributes and interactions with each other, which enables us to describe their

commrmication and collaboration patterns and assess how organizations’ attributes are

16

 

related to their degree of interorganizational relations. In Chapter 3, we discuss how this
information can be understood and utilized watershed-based collaborative ﬁsheries
management. We discuss how organizational attributes, interaction dynamics, and
network properties have implications for integrative forms of ﬁsheries management. We
describe various approaches for stakeholder involvement in resource management and
explore how network information can enhance communication and collaboration, thereby

promoting integrative, comprehensive, and more effective management approaches.

17

CHAPTER 1

USING ECOSYSTEM CONCEPTS TO DEFINE A POPULATION OF
FISHERIES STAKEHOLDER ORGANIZATIONS FOR A MICHIGAN WATERSHED

Introduction

To understand the role of humans in natural resource management, one needs to
ﬁrst deﬁne some basic management concepts. Ecosystem features become ‘resources’
when they are used and managed to meet people’s speciﬁc wants and needs. In cases
where organisms are the resource to be managed (e.g., ﬁsh or wildlife), the term
‘management’ refers to the attempted control of species populations, their abundance,
their habitats, and the people that somehow affect these populations and habitats (Taylor
et al. 1997). Managers attempt to manipulate these ecosystem features in order to meet
their goals for appropriate forms of resource use, allocation, and conservation. Although
speciﬁc goals will vary among management issues and settings, management goals are
rooted in the interests of resource stakeholders. In the context of natural resources
management, ‘stakeholders’ can include any person or group that has a role or interest in
the status of a resource to be managed (Decker and Krueger 1993).

Managers Of ﬁsheries and other natural resources have long recognized the need
to consider how stakeholders relate to resource management (Grimble and Wellard 1997).
Studies Of the human dimensions Of natural resources management have explored various

facets of stakeholders’ knowledge, attitudes, opinions, perceptions, and behaviors

18

regarding resource use and management (see Johna 1994 and Duda et al. 1998 for
numerous examples and reviews of such studies). Information obtained through these
studies can help managers determine appropriate management goals, strategies, and
techniques; assess public communication, education, and outreach needs; understand
humans’ roles in ecosystem processes; and otherwise gauge stakeholders’ general
awareness of and involvement in natural resource issues (Grimble and Wellard 1997;
Duda et al. 1998). Given the inherent links between resource stakeholders and resource
management, the importance of understanding natural resource stakeholders and their
potential roles in management cannot be overestimated.

Natural resource managers have generally considered stakeholders to be those
who directly use the resource (e. g., ﬁshers and hunters), members Of resource-related
organizations (e.g., conservation or recreation clubs), and others that have some direct
impact on or interest in the status of the resource (e.g., landowners who control access to
a use site). Considering the great variability that surrounds different management issues
and settings, however, the speciﬁc population of relevant stakeholders will likewise vary
among management situations. Managers may experience difﬁculty in determining, for
each management scenario, who the stakeholders are and which ones should become
involved with the management process (Grimble and Wellard 1997).

The purpose of this chapter is to explore the concept of natural resource
management stakeholders, considering how the composition of a stakeholder population
may vary with different management issues, goals, settings, and circumstances. Our

study will focus on the Pere Marquette (PM) River watershed (see Prologue Figure 1), a

geographic area (740 mil) in western lower Michigan with a popular recreational

l9

salmonid ﬁshery. This watershed contains a mix of property types (public and private);
land use activities (agriculture, forestry, urban development, etc.); socioeconomic issues
(tourism, poverty, etc.); and environmental issues (conservation, protection, and
management of ﬁsh, water, forests, and wildlife; USDAF S 1976; Kruger 1985; Cassuto
1994; Supinski 1994; Nelson and Smith 1998). By evaluating the potential stakeholder
population in the PM River watershed, we can explore how the ‘stakeholder’ concept can
be more effectively applied to a speciﬁc ﬁsheries management setting. We develop
criteria for deﬁning a ﬁsheries stakeholder population, apply these criteria to ﬁsheries
management in the PM watershed, and explore the management implications of this

stakeholder identiﬁcation approach.

Stakeholder Theory

The Decker and Krueger (1993) stakeholder deﬁnition includes any person or
group that has a role or interest in natural resource management. Considering the myriad
ways in which humans interact with and depend upon their environment, however, this
means that virtually anyone can be considered a stakeholder in natural resources
management. Furthermore, natural resources like ﬁsh, wildlife, air, water, and public
lands belong to the public, and, under the public trust doctrine, managers are responsible
for managing resources in the interest of the greatest public good (Plater et al. 1992; Sax
1993; Lundqvist 2000). In this sense, managers can consider all current and future

members of the general public to be resource management stakeholders.

This broad concept of stakeholders is generally reﬂected in the managers’ mission

20

statements and is Often embraced as a management tenet. For most management
activities and operations, however, managers need to reﬁne the stakeholder concept to
include a more practical set of stakeholders (Mitchell et al. 1997). Natural resource
managers have ﬁnite stafﬁng, funding, time, and other resources, and it is not practical or
possible to involve or consult with all stakeholders in all management decisions or
activities. Conversely, managers must not use overly restrictive or narrow criteria to
identify management stakeholders (e. g., limited to anglers, hunters, or other primary user
groups); this will cause managers to overlook the resource-related interests of other
sectors of the public. iManagers need a framework for identifying a practical, relevant,
and representative population of stakeholders that can then be meaningfully incorporated
into natural resource management and communication processes (e.g., outreach; Grimble
and Wellard 1997).

Mitchell, Agle, and Wood (1997) provide a comprehensive review of stakeholder
identiﬁcation literature, including broad, narrow, and intermediate deﬁnitions of
stakeholders. In developing their stakeholder concept, they begin with a broad deﬁnition,
“...so that no stakeholders, potential or actual, are excluded from analysis arbitrarily or a
priori” (Mitchell et al. 1997, p. 854). They start with Freeman’s now-classic deﬁnition: a
stakeholder is “any group or individual who can affect or is affected by the achievement
of the organization’s objectives” (1984, p. 46). They acknowledge that such a broad
deﬁnition potentially includes everyone, but it is a necessary starting point from which to
identify a meaningful set of stakeholders:

“The idea of comprehensively identifying stakeholder types, then, is to

equip managers with the ability to recognize and respond to a disparate,
yet systematically comprehensible, set of entities who may or may not

21

have legitimate claims, but who may be able to affect or are affected by the

ﬁrm nonetheless, and thus affect the interests Of those who do have

legitimate claims” (Mitchell et al. 1997, p. 857).

Furthermore, they believe that stakeholders do not need to maintain an active
relationship with a managing organization; they believe that “the potential relationship
can be as relevant as the actual one” (Mitchell et al. 1997, p. 859). Identiﬁcation of
latent, or potential, stakeholders can help organizations anticipate issues, concerns, or
problems and possibly improve their effectiveness (Grimble and Wellard 1997). Building
on this idea of potential stakeholders, they establish that one does not necessarily need to
currently be aware or conscious of their potential stake in management to be considered a
stakeholder (Gray 1985; Mitchell et al. 1997). Managers should, therefore, not limit their
stakeholder population only to those that are actively aware of their potential stake.

While Mitchell et al. (1997) start with a broad deﬁnition of actual and potential
stakeholders, they recognize the need to develop acceptable and justiﬁable criteria for
differentiating stakeholders from non-stakeholders. They propose that a ‘stakeholder’
(whether a person, group, organization, society, etc.) has at least one of the following
attributes: power, legitimacy, and/or urgency (Mitchell et al. 1997); these attributes more
explicitly describe how a stakeholder affects (or is affected by) management. They
establish that power enables social actors to impose their will on others (see also Gray
and Hay 1986; Jamal and Getz 1994). A social actor has legitimacy, to the extent that
their actions “...are considered desirable, proper, or appropriate Within some socially
constructed system of norms, values, beliefs, and deﬁnitions” (Suchman 1995, p. 574)
and they are perceived to have the right and capacity to participate (Gray 1985; Gray and

Hay 1986; Jamal and Getz 1994; Lawrence et al. 1999). Urgency exists to the extent that

22

a social actor has a concern, relationship, or stake that is important, critical, or time-
sensitive. The degree of each of these attributes is based on the perceptions Of managers
and other stakeholders; therefore, these attributes exist in a subjective reality and may
vary as time, issues, and relationships change (Gray 1985; Lawrence et al. 1999).
Whether alone or in combination, these attributes allow managers to determine who is a
stakeholder. As we will see later, the salience or relative importance of a stakeholder
varies according to how many of these attributes they possess (i.e., a stakeholder with all
three attributes is more salient that one with a single attribute; Mitchell et al. 1997).

While some stakeholder theorists argue that the natural environment can also be
considered a stakeholder (Stone 1974; Starik 1995; Worrell and Appleby 2000), we are
limiting this study to humans and their social units due to unresolved philosophical
questions surrounding non-human attainment of stakeholder status (see Phillips and
Reichart 2000). For example, although the environment is affected by management
actions, does it have interests and concerns? Can it determine how to impose its power,
enhance its legitimacy, or assess its urgency? It may not matter. The intrinsic and
instrumental values of environmental resources are widely recognized, and humans have
moral and ethical reasons to maintain environmental quality and sustainability regardless
of stakeholders’ environmental interests (or lack thereof; Phillips and Reichart 2000).
While we do not attempt to resolve these debates, we assume that interests regarding the
use, conservation, and protection of the natural environment will be represented by the
environmental interests and concerns of some stakeholders.

Although most of Mitchell et al.’s (1997) literature review is rooted in business

management theory, their proposed criteria for identifying stakeholders are applicable to

23

any management setting, including natural resources. These criteria, which maintain a
potentially large and diverse set of stakeholders, are based on the growing recognition
that managers have some degree of social responsibility and responsiveness for the claims
and interests of multiple social actors, including those outside their ‘frrrn’ (Mitchell et al.
1997; Argandona 1998). Natural resource management, operating under the public trust
doctrine, is a ﬁeld with strong roots in public service and social responsibility; therefore,
the above criteria are highly applicable to identifying natural resource stakeholders.

In the case of natural resources management, the word ‘organization’ in
Freeman’s (1984) deﬁnition would refer to the resource management agency (i.e., the
state’s Department of Natural Resources), and ‘managers’ are those who are responsible
for planning, implementing, and evaluating natural resource use, allocation, and
conservation. Many sectors Of the general public have power, legitimacy, and/or urgency
regarding resource management issues, as resources like water, land, minerals, wildlife,
soil, air, and vegetation are widely utilized and enjoyed by multiple, of’ten competing,
stakeholders (Grimble and Wellard 1997). Whether resources are local or distant,
humans and their social systems are highly and inextricably dependent on the continued
access to and use of natural resources and their ecosystems; therefore, the stakeholder
population is potentially extensive, diverse, and intensely committed to their resource
management interest. For complex social problems, like natural resources management, a
well-deﬁned stakeholder population will “match the complexity inherent in the issue
itself” (Gray 1985, p. 919).

In this study, we are speciﬁcally interested in identifying the stakeholder

population for a speciﬁc kind of natural resource: recreational ﬁsheries. In Michigan,

24

where the study site is located, ﬁsh and their water bodies are public property. Applying
the above stakeholder identiﬁcation principles to the topic Of recreational ﬁshing, anyone
who wants to ﬁsh or is concerned about ﬁsh or ﬁshing can be considered a stakeholder.
Additionally, when ﬁsheries are considered in context of their role in the landscape,
anyone who engages in land- or water-based activities that affect ﬁsh populations or
habitats can be considered a stakeholder (Selman and Wragg 1999). These groups are
included because “[t]he collection of stakeholders must include those whose expertise is
essential to building a solution” (Gray 1985, p. 918). Anyone in these groups can affect
managers’ abilities to do their jobs, and they can, in turn, be affected by managers’
activities. In this sense, such people have some degree of power, legitimacy, and urgency
regarding the management of the recreational ﬁshery; these are our stakeholders.

Instead of trying to identify each individual person or social unit with ﬁsheries
stakeholder attributes, however, we are limiting our study to ﬁsheries stakeholders at the
organizational level. We believe this will facilitate an efﬁcient, yet still effective,
stakeholder identiﬁcation process. Organizations serve and represent the collective
interests, concerns, and values of its members (Scott and Lane 2000); therefore, most
ﬁsheries stakeholders’ interests and concerns will be reﬂected at the organizational level.
Furthermore, the principles of power, legitimacy, and urgency apply to stakeholders
organizations, as they apply to any social unit (Mitchell et a1. 1997). We do not propose
that managers only consider stakeholder organizations; nevertheless, organizational-level
studies enable resource managers to understand the breadth and depth of stakeholder

interests, concerns, and impacts.

25

Stakeholder Identification

Stakeholder Criteria

Using the principles of stakeholder theory and ecosystem-based management, we
developed four criteria to be consistently applied in the evaluation of organizational
characteristics; these criteria will guide the ﬁsheries stakeholder organization (F SO)
identiﬁcation process for the PM watershed. First, FSOs must meet the following
criteria: (1) they are organized as a group, serving or representing the interests of
individuals or businesses, and (2) they are located or interested in the PM watershed. The
ﬁrst criterion includes clubs with voluntary membership as well as government agencies,
universities, and other groups of people brought together for some common purpose. The
second criterion enables us to capture those organizations that are physically located in
the watershed, those that have a jurisdictional or geographic scope that encompasses the
watershed, and other groups that are interested in PM Watershed-speciﬁc issues.
Watershed boundaries in this study are based on those identiﬁed in the USDA Forest
Service’s (1976) wild and scenic river study report.

In addition to the ﬁrst two criteria, either or both of the following criteria must
apply: (3) they have an interest in ﬁsheries and/or (4) they have an impact on aquatic
habitat and ﬁsheries. While the ﬁrst two criteria capture all organizations in or interested
in the PM watershed, these criteria narrow the F80 set to those that have interests in or
impacts on the ﬁshery (i.e., they have power, legitimacy, and/or urgency regarding this
ﬁshery). While some stakeholder organizations would almost certainly be aware of their

interest in ﬁsheries issues, some organizations may or may not be aware of their impacts

26

on the ﬁshery or ﬁsh habitat. As long as an organization may affect the quality and
quantity of ﬁsh habitat (e.g., a county road commission), they may be subject to habitat-
conserving regulations and environmental liabilities (e. g., related to road maintenance or
bridge construction). Whether realizing it or not, many groups in the watershed have
such a stake in the status of ﬁsh habitat and therefore, in an ecosystem-based resource
management context, they can be considered stakeholders.

These four criteria exclude individual stakeholders that are unafﬁliated with an
organization and those that have only transient or temporary concerns about the
watershed (e.g., tourists). The criteria also exclude individual for-proﬁt private
businesses that are not already represented by a chamber Of commerce or other economic
development organization. Including these individual stakeholders would have produced
an unmanageably large population and would have introduced numerous additional
variables that would complicate any further analysis. Nevertheless, these criteria likely
capture and represent most of the stakeholders concerned about or having impacts on
ﬁsheries issues in the PM watershed. We assume that these FSOs are capable of
representing most of the ﬁshery-related interests and concerns of individuals and

businesses in the PM area.

Identiﬁcation Process

Using these four criteria, we developed a list of FSOs in or concerned about
ﬁsheries in the PM watershed. First, based on previous studies related tO natural resource
stakeholder organizations, we generated a list of the types of organizations that would

meet the criteria. For example, we expected to ﬁnd special interest organizations

27

(focused on conservation or outdoor recreation), various levels of government agencies
(from local units to national agencies), university-based extension and information
groups, and economic development groups interested in ﬁshing-based tourism. Second,
we used the criteria to look for the names of organizations that would ﬁt in each category.
In doing this, we consulted environmental directories, government directories, and
management documents (e. g., meeting records) about the ﬁshery and the PM river area.
We also searched on the Internet and by word-of-mouth. As we performed this search,
we examined the mission statement or purpose Of each organization to verify that it meets
our criteria. This search enabled us to prepare a preliminary list of FSOs.

In the next phase of identifying stakeholders, we used a theoretical, or purposive
(as opposed to random), sampling design to identify a set of reviewers who could help
further reﬁne this preliminary list. This qualitative approach, similar to the Delphi
Technique (Patton 1997, p. 151), involves: 1) selecting participants to review and expand
upon information already obtained; 2) reﬁning the information based on participants’
input; and 3) repeating the review stage of the process until no additional changes are
recommended, indicating the participants’ consensus (Lawrence et al. 1999).
Accordingly, we had our preliminary list reviewed by people familiar with the PM
watershed and its ﬁsheries issues. For example, the list was reviewed by scientists who
conduct research in the area, professionals who work in the area, and by private citizens
who live in the area. Many reviewers also recommended knowledgeable others who
could review the list. In their examination of the list, we asked reviewers to add
organizations that they felt would meet the four criteria, and we asked them to delete

those organizations that they felt did not meet the criteria. We collected information

28

(e.g., about their purpose, mission statement, etc.) on the added organizations to verify
that they met our criteria. This review process was repeated until the reviewers

recommended that no further changes be made to the list.

Results: Fisheries Stakeholder Organizations

Using this stakeholder identiﬁcation and review process, we generated a list of
101 FSOs. These organizations fell under the categories of federal, state, county,
township, and city government; local and regional economic development organizations;
conservation- and recreation-oriented organizations; education and information
organizations; partnership organizations; and other special interest organizations. A
complete list of these FSOs can be found in Appendix A.

Several types of government agencies are represented by the nine F $08 in the
federal government category (Table 1.1). The US. Fish and Wildlife Service,under the
Department of the Interior, operates the Ludington Biological Station. This ofﬁce,
located at the mouth of the PM River, primarily conducts research and activities to
control populations of sea lamprey, a non-native parasitic species that reproduces in Great
Lakes tributaries (including the PM River). The US. Department of Agriculture houses
agencies such as the US. Forest Service, the Natural Resource Conservation Service, and
the Farm Service Agency, all of which have Ofﬁces in the PM watershed. The Forest
Service manages the Huron-Manistee National Forest, which is largely located in the PM
watershed. The Natural Resource Conservation Service provides technical expertise and

support for conservation of soil, water, wetlands, and other resources, both public and

29

private. Their network of local Conservation Districts includes Ofﬁces in Mason—Lake,
Oceana, and Newaygo Counties, all of which include portions of the PM watershed. The
Farm Service Agency also has a network of county-based ofﬁces that encompass parts of
the PM watershed, via Mason-Oceana, Osceola-Lake, and Newaygo Counties. These
ofﬁces provide a range of agriculture support programs, including conservation programs
that give farmers incentive to engage in land stewardship behaviors such as planting
vegetation for wildlife habitat, reducing erosion, maintaining buffer strips, and reducing
waste production. The Tribal Council of the Little River Band of Ottawa Indians
oversees a wide range of tribal interests, including their stake in ﬁsheries management in
their “home waters” in western lower Michigan. The Little River Band of Ottawa Indians
is included in the federal government category of F SOs because of their status as a
sovereign nation and their association with other agencies in the US. federal government

(e.g., the Bureau Of Indian Affairs).

 

Table 1.1. Federal government agencies and ofﬁces concerned about or having

   

USDA Forest Service, Baldwin Ranger Station

USDA Natural Resource Conservation Service, Mason-Lake Conservation District
USDA Natural Resource Conservation Service, Oceana Conservation District
USDA Natural Resource Conservation Service, Newaygo Conservation District
USDA Farm Service Agency, Mason—Oceana County Ofﬁce

USDA Farm Service Agency, Osceola-Lake County Ofﬁce

USDA Farm Service Agency, Newaygo County Ofﬁce

Tribal Council, Little River Band of Ottawa Indians

 

 

 

The State of Michigan has twelve agencies and Ofﬁces that are responsible for
various aspects of the PM watershed and ﬁsheries (Table 1.2). Seven of these are

associated with the Michigan Department of Natural Resources (MDNR), which manages

30

the state’s natural resources of behalf of its citizens. The MDNR is organized into several
different Divisions or Bureaus that are responsible for different resource types, and each
Division maintains management units and ﬁeld ofﬁces through which they carry out
management activities. Several MDNR Divisions and management units encompass
portions Of the PM watershed, including the Fisheries Division, Forest Management
Division, and Parks & Recreation Bureau, which work out of a ﬁeld ofﬁce in Cadillac,
MI. The Wildlife Division maintains a ﬁeld ofﬁce in Baldwin, MI. The Law
Enforcement Division has two Districts, 6 and 9, that Operate from ofﬁces in Cadillac and
Grand Rapids, MI, respectively (multiple ofﬁces of the same division are considered
separate FSOs, due to differences in local issues, perspectives, and stafﬁng). The Natural
Rivers Program, administered from MDNR headquarters in Lansing, MI, runs the state’s
program for designating ‘Natural River’ systems, which includes the PM River and its
tributaries. Through this program, the state works with local governments and riparian
landowners to protect and enhance the rivers’ wild and scenic qualities through zoning
rules and other land use regulations. Collectively, these MDNR units manage ﬁsh, forest,
wildlife, and river resources; provide outdoor recreation opportunities; maintain public
safety and resource protection; and minimize conﬂicts between resource users. In
addition to the MDNR, three other state agencies are responsible for or somehow affect
areas of the PM watershed: the Department of Environmental Quality (DEQ), the
Department of Agriculture (MDA), and the Department of Transportation (DOT). The
DEQ is responsible for many aspects of water, air, and land quality throughout the state,
yet most of their resource protection activities applicable to the PM watershed are

managed by the Land and Water Management Division, with ofﬁces in Grand Rapids and

31

Cadillac. The MDA is also organized into multiple divisions and regions, and the PM
watershed is entirely located in Region 3. This region’s ofﬁce is in Grand Rapids, and it
houses parts of the Food & Dairy Division, Pesticide & Plant Pest Management Division,
and TB Unit (related to bovine tuberculosis disease). The DOT has two Regions that
encompass parts of the PM watershed, the North and the Grand; these regions have
Transportation Service Centers in Cadillac and Muskegon, MI, respectively. Through
these service centers, the DOT engages in maintenance and construction activities for
state roads and bridges; these activities can affect river quality through bank erosion and

pavement runoff and by altering land use patterns in the watershed.

 

  
 

Table 1.2. State government agencies and Ofﬁces concerned about or having impacts
PPM Wm ﬁshema

  

MDNR Fisheries Division, Cadillac Field Ofﬁce
MDNR Forest Management Division, Cadillac Field Ofﬁce

MDNR Wildlife Division, Baldwin Field Ofﬁce

MDNR Parks & Recreation Bureau, Cadillac Field Ofﬁce

MDNR Law Enforcement Division, District 6, Cadillac Field Ofﬁce
MDNR Law Enforcement Division, District 9, Grand Rapids Field Ofﬁce
MDNR Natural Rivers Program, Lansing

MDEQ Land & Water Management Division, Cadillac

MDEQ Land & Water Management Division, Grand Rapids

MDA, Region 3 Ofﬁce, Grand Rapids

MDOT, North Region, Cadillac Transportation Service Center

MDOT, Grand Region, Muskegon Transportation Service Center

 

 

 

Four counties encompass parts of the PM watershed: Mason, Lake, Oceana, and
Newaygo. County governments engage in a wide range of local planning and governance
issues, although only certain county ofﬁces engage more directly in land use and other
activities that affect watersheds and ﬁsheries. Therefore, instead of listing each county

government as a single FSO, we focused on those county ofﬁces that more clearly met

32

our criteria. We identiﬁed seven resource-related ofﬁces within the four county
governments as FSOs (Table 1.3). These county Ofﬁces include Drain Commissioners
and Road Commissioners (these are combined in one ofﬁce for Lake County). Drain
Commissioners oversee patterns and types of water consumption, discharge, and
drainage. Road Commissioners oversee the maintenance and construction Of county
roads and bridges, thereby affecting water quality through erosion and runoff processes.
Both types of Ofﬁces affect development and land use patterns in the landscape, which in

turn produce other impacts on land and water resource quality.

 

Table 1.3. County government ofﬁces concerned about or having impacts on PM
warshteed anrd/o ﬁshheries __

   

 

Mason County Drain Commissioner

Mason County Road Commissioner

Lake County Road and Drain Commissioner
Oceana County Drain Commissioner
Oceana County Road Commissioner
Newaygo County Drain Commissioner
Newaygo County Road Commissioner

 

 

 

Twenty-eight townships are located entirely or partially in the PM watershed
(Table 1.4). Although local activities may vary from township to township, they
generally provide for local governance and services that are not met by county or city
ofﬁces. Since townships also perform some land use planning and development
functions, they can affect the quality of local land and water resources. Township
governments generally have a small staff (some may be volunteers), and they do not tend
to have separate ofﬁces or departments for resource-related functions (as are seen at the
county level). We are therefore able to meet our criteria by counting each township

government as a single FSO.

33

 

 

 

 

 

Table 1.4. Township governments concerned about or having impacts on PM
watershed and/or ﬁsheries. l _

Pere Marquette Township Pleasant Plains Township
Amber Township Yates Township

Custer Township Chase Township

Branch Township Crystal Township
Sweetwater Township Colfax Township
Peacock Township Troy Township

Webber Township Lilley Township

Cherry Valley Township Home Township

Pinora Township Barton Township
Summit Township Leavitt Township
Riverton Township Beaver Township

Eden Township Merrill Township

Logan Township Monroe Township

Lake Township Norwich Township

 

 

The PM watershed is generally a rural environment, and there are only three cities
or villages in the PM watershed that have a population more than 500 people: Ludington,
Scottville, and Baldwin (Table 1.5). Cities and villages are other layers of local
government, providing schools, libraries, parks, police and emergency response, waste
management, and other public services. Like townships, city councils and committees
also engage in local planning issues such as zoning, land development, and infrastructure
development; therefore, they also affect the quality of land and water resources through
some of their activities. Also like townships, these city governments are fairly small, and
they tend to have most planning functions concentrated in a central ofﬁce. Our criteria

were satisﬁed by considering each city government as a single F SO.

 

Table 1.5. City governments concerned about or having impacts on PM watershed

 

and/or ﬁshes.

City of Ludington
City of Scottville
Village of Baldwin

 

34

Within the region, counties, and cities that overlap with the PM watershed, there
are 10 organizations that promote tourism, development, and other forms of economic
activity for the PM area (Table 1.6). These organizations, generally govenrment/non-
proﬁt hybrids, support and represent the interests of private enterprises; promote the
beneﬁts of engaging in business locally; and attract customers, tourists, and investors to
the region. Such organizations include chambers of commerce, tourism councils, and
economic development corporations for local cities and counties. Two organizations, the
West Michigan Shoreline Regional Development Commission and the West Michigan
Tourist Association, perform these functions at a broader regional scale. We consider
these organizations to be F SOs, because by attracting businesses and investment they
inﬂuence patterns of land, water, and infrastructure use and development, thereby
affecting resource quality in the landscape. In addition, they attract tourists to the PM
river who seek to ﬁsh, canoe, hunt, camp, hike, snowmobile, and engage in other forms of
outdoor recreation. Through their access to and interactions with land and water
resources, these recreationists affect the status of resources and may become involved in

resource-related issues and conﬂicts.

 

Table 1.6. Local and regional economic development organizations concerned about
‘ or having impacts on PM watershed and/or ﬁsheries.

Ludington Chamber Of Commerce and Convention & Visitors Bureau
Scottville Chamber of Commerce

Mason County Economic Development Corporation

Lake County Enterprise Community Board

Lake County Chamber of Commerce and Tourist Center

Oceana County Economic Development Corporation

Oceana County Tourism Bureau

Newaygo County Tourist Council

West Michigan Shoreline Regional Development Commission

West Michigan Tourist Association

 

 

 

35

 

We identiﬁed six organizations that are interested in promoting resource
conservation and protection for the PM watershed and its ﬁsheries (Table 1.7). These
non-govemment, non-proﬁt organizations consist of private citizens with similar interests
and concerns. Organization members voluntarily engage in activities that support their
common interests; in this case, their interests focus on resource protection and
conservation. Speciﬁcally, the Pere Marquette Watershed Council is a grassroots
organization whose members are dedicated to preserving the natural resources of the PM
watershed. They engage in a wide range of activities and projects, many in cooperation
with other agencies and organizations, to protect and enhance ﬁsh habitat and other
natural features in the area. One of their partners is the Ruby Creek Conservation and
Recreation Club, named after a small tributary of the PM River, which also promotes
education, stewardship, and protection of the PM watershed. Other local groups are
chapters or sub-units of national organizations. The Mackinac (or Michigan) Chapter of
the Sierra Club promotes environmental stewardship through a wide range of activities,
many of which occur through members at the local level. For example, the West
Michigan Group of the Mackinac Chapter is active in counties that encompass the PM
watershed. Similarly, members of the Oceana Sub-chapter of the National Audubon
Society, based in Ludington, are dedicated to conserving and restoring natural ecosystems
for birds and other wildlife. In addition, the West Michigan Environmental Action
Council, based in Grand Rapids, focuses on environmental education, preservation, and
protection in western Michigan counties. Finally, the Michigan Chapter of The Nature
Conservancy (TNC), based in Lansing, relies on members’ volunteer efforts, ﬁnancial

contributions, and other donations to identify, purchase, and protect rare, threatened, and

36

other biologically-diverse habitats, both terrestrial and aquatic. They have identiﬁed a
number of these sensitive habitats throughout Michigan, some of which are located in the

PM watershed.

 

Table 1.7. Protection-oriented conservation organizations concerned about or having

 

Pere Marquette Watershed Council

Ruby Creek Conservation and Recreation Club

Sierra Club, Mackinaw Chapter, West Michigan Group
Michigan Audubon Society, Oceana Sub-chapter

West Michigan Environmental Action Council, Grand Rapids
The Nature Conservancy, Lansing

 

 

 

Many other organizations are also interested in resource conservation, although
their interests tend to be expressed in context of their interests in outdoor sporting
activities. Members of these organizations are generally interested in outdoor activities,
such as ﬁshing and hunting, and issues that affect these recreation opportunities. We
created a separate category for these organizations, since members of groups in each
category may express their conservation interests through different types of value systems
(Holsman 1999; Bright et al. 2000). For example, recreation-oriented conservationists
may be more likely to think that natural resources should be utilized or controlled by
humans (i.e., utilitarian or dominionistic attitudes), while the protection-oriented
conservationists may tend to think that natural resources should be protected from human
impacts (i.e., moralistic or aesthetic attitudes; see Kellert (1980) for more information on
attitudes). These value systems have implications for members’ attitudes, motivations,
behaviors (Holsman 1999) as well as their organization’s priorities, goals, and

involvement with management (Bright et al. 2000). Nevertheless, we recognize that

37

many general conservation concerns (e.g., habitat restoration, pollution reduction, etc.)
and attitudes (e. g., naturalistic and ecologistic attitudes) are applicable to members in
both types of conservation groups.

This blending Of interests and attitudes is represented by the Lansing-based
Michigan United Conservation Clubs, one of the nine organizations we are classifying as
recreation—oriented conservation organizations (Table 1.8). This umbrella organization,
whose membership consists of individuals as well as other conservation organizations,
helps to unify and represent a diverse set of conservation interests throughout the state.
Many Of these interests focus on preserving Michigan’s outdoor hunting and ﬁshing
heritage, and they support educational and scientiﬁc management programs that help
meet these interests. The PM watershed is one of their many interest areas, and many of
their organizational and individual members are active in this area. Some of their
member organizations active in the PM watershed include the Fin & Feather Club of
Mason County, Oceana Sportsmen's Club, Newaygo County Sportsmen's Club, and
Citizen's Sportsmen's Club. These groups’ interests generally focus on hunting, ﬁshing,
and the conservation issues that affect their access to and enjoyment of these activities.
Three of the groups we identiﬁed are local chapters of larger international organizations,
such as the West Michigan Chapter of Trout Unlimited, the West Michigan Hacklers of
the Federation of Fly Fishers, and the Westside Steelheaders (see note in Table 1.8) of the
Michigan Steelhead and Salmon Fisherrnen’s Association. Members of these
organizations are interested in enhancing, protecting, promoting, and enjoying trout and
salmon angling, much of which occurs in the PM River. The last organization in this

category is the Pere Marquette Rod and Gun Club, an association of landowners who are

38

interested in hunting and ﬁshing.

 

Table 1.8. Recreation-oriented conservation organizations concerned about or having
impacts on PM watershed and/or ﬁsheries.

 

Michigan United Conservation Clubs, Lansing

Fin & Feather Club of Mason County

Oceana Sportsmen's Club

Newaygo County Sportsmen's Club

Citizen's Sportsmen's Club

Trout Unlimited, West Michigan Chapter

Federation of Fly Fishers, West Michigan Hacklers

Michigan Steelhead and Salmon F ishermen’s Association, Westside Steelheaders“
Pere Marquette Rod & Gun Club

* NO longer a sub-group; chapters in western lower Michigan were re-organized following
this study.

 

 

 

The next category of FSOs includes those organizations with an educational or
information-oriented mission, part of which focuses on environmental or conservation
information in the PM watershed (Table 1.9). Five of these are afﬁliated with Michigan
State University Extension (MSUE). Extension organizations facilitate the application of
knowledge and information to help people improve their quality Of living (Edgerton
1987), and much of their work in Michigan focuses on agriculture and natural resource
issues in rural communities. They are administratively organized into regional ofﬁces
(the West Central Region includes the entire PM watershed) and county-based extension
ofﬁces (the Mason, Lake, Oceana, and Newaygo County Ofﬁces encompass parts of the
watershed). Like other organizations with multiple administrative units, we consider
each MSUE unit to be a separate F SOs, due to differences in local issues and stafﬁng.
Finally, we include West Shore Community College, located in Scottville. This college

Offers Associate Of Science degrees in some social and natural sciences that are relevant

39

to local ﬁsheries and environmental issues. They also maintain a Natural Resource

Institute, through which faculty and students may engage in watershed-oriented projects.

 

Table 1.9. Education and information organizations concerned about or having

 

MSU Extension, West Central Region ofﬁce

MSU Extension, Mason County ofﬁce

MSU Extension, Lake County ofﬁce

MSU Extension, Oceana County ofﬁce

MSU Extension, Newaygo County Extension

West Shore Community College, Natural Resources Institute

 

 

 

We classiﬁed ﬁve F $05 as partnership organizations, or organizations whose
membership consists of multiple agencies and organizations (Table 1.10). The Great
Lakes Fishery Commission (based in Ann Arbor, MI) is an international government
organization through which the US. and Canadian federal governments coordinate and
support state, provincial, and tribal ﬁsheries management and conservation. The Great
Lakes Fishery Trust is an organization created to manage the settlement of a federal court
case brought against the Consumers Power and Detroit Edison Ludington Pumped
Storage Plant, located at the mouth of the PM River. Members of the trust, representing
state, federal, tribal, and special interest organizations, provide funding and support for
enhancing, protecting, and restoring ﬁshery resources that were lost due to pump storage
plant operation. The Conservation Resource Alliance (based in Traverse City, MI,
covering Mason and Lake Counties) and the Timberland Resource Conservation &
Development (based near Grand Rapids, covering Oceana and Newaygo Counties) are
inter-governmental organizations, through which USDA Conservation Districts, county

governments, and other local government partners work together to conserve soil, water,

40

forests, and ﬁsh and wildlife habitat and promote local community resource-based
development. Both of these organizations have expressed interest in the status of
ﬁsheries and other resources in the PM watershed. The Michigan Natural Features
Inventory (MNF I), a partnership between MSUE, TNC, and the MDNR, maintains a
database on Michigan’s plant and animal species and communities that are endangered,
threatened, or of special concern. They list 95 species and communities for the four

counties encompassing the PM watershed (MNFI 2001).

 

Table 1.10. Partnership organizations concerned about or having impacts on PM

. waersthed mm

 

Great Lakes Fishery Commission

Great Lakes Fishery Trust

Timberland, Resource Conservation & Development

Conservation Resource Alliance, Resource Conservation & Development
Michigan Natural Features Inventory

 

 

 

In our last category, we identiﬁed six other special interest organizations through
which members can voluntarily explore their interests and concerns related to natural
resource issues (Table 1.11); these other organizations, however, do not easily ﬁt into the
above FSO categories. The Lake County Riverside PrOperty Owner's Association is an
organization that represents the interests of those who own property along the PM River.
Their interests may include issues like river access, riparian habitat, recreation, user
conﬂicts, and the legal aspects of riparian land ownership (e. g., how they develop and use
their private property adjacent to the PM; Tremp 1992). The Ludington Area Charterboat
Association represents many ﬁshing charter operations, most of which focus on Lake
Michigan trout and salmon while a few operations include river ﬁshing. The Michigan

River Guides Association also represents the interests of ﬁshing guides, angling experts

41

who are hired to help lead river ﬁshing excursions. Finally, we also include the Michigan
Farm Bureau in this category. The Michigan Farm Bureau (a sub-unit of the American
Farm Bureau) is an agriculture-oriented organization, providing members (usually
farmers) with information and support about the economic, political, environmental, and
operational aspects of farming and food production. They also have a county-based

network of local ofﬁces, including Mason, Oceana, and Newaygo Counties.

 

Table 1.11. Other special interest organizations concerned about or having impacts on

 

Ludington Area Charterboat Association
Michigan River Guides Association
Michigan Farm Bureau, Mason County
Michigan Farm Bureau, Oceana County
Michigan Farm Bureau, Newaygo County

 

 

 

Discussion

This study demonstrates that the F SO population in and around the PM watershed
is large and diverse, and this universe of potential ﬁsheries stakeholders presents
communication challenges to resource managers. We acknowledge that this stakeholder
identiﬁcation process may have missed some potential FSOs, and this may have
implications for managers’ communication efforts. Nevertheless, the range of
organization types revealed in this study illustrate that there is a wide range of interests
surrounding local ﬁsheries and watershed management efforts. We expect that the

application of our four criteria to other watersheds or geographic areas would yield

42

similar types of stakeholder diversity and complexity; in fact, an increasingly large and
complex stakeholder population would likely emerge for larger geographic areas that may
encompass more natural features and resource production activities.

Once ﬁsheries managers recognize the magnitude and diversity of their potential
stakeholder population, how can they incorporate this information in their management
approaches? First, they need to become familiar with these organizations to appreciate
the variability between stakeholders. Organizations likely have different interests, goals,
activities, and resources, and thus have different types of impacts on the ﬁshery. Not all
stakeholder organizations are equal in their importance or relevance for any given issue
(Grimble and Wellard 1997; Mitchell et al. 1997). This leads to a second consideration:
managers need to understand how and why stakeholders may relate to any speciﬁc
management issue. Each issue will attract the interest (or be affected by the impacts) of a
different set of stakeholders. Some issues will generate little involvement, while some
issues will generate great controversies among stakeholders (Grimble and Wellard 1997 ).
Left to their own devices, stakeholder organizations will engage (and disengage) in
management issues as they see ﬁt, and managers may assume a passive, or reactive, role
in these stakeholder interactions.

The value of a stakeholder identiﬁcation process lies in its ability to help
managers assume a more active role in stakeholder interactions (Grimble and Wellard
1997; Mitchell et al. 1997). Once a population of potential stakeholders is identiﬁed for a
given area, managers have an important database from which they can quickly and
accurately distinguish those organizations that may have an interest or stake in any

speciﬁc management objective, issue, and setting. For example, ﬁshery managers

43

interested in a recreation opportunity marketing campaign can easily identify economic-
or tourism-related organizations for possible collaboration. Having identiﬁed
stakeholders and their interests, managers can look for ways to target or package their
information to these groups more effectively, and they can encourage certain groups to
work together on issues of common concern. The resulting patterns of communication
can help managers develop approaches that are more appropriate to local conditions and
interests, facilitate education about appropriate forms of resource use, and minimize the
potential for conﬂicts that can be costly in both time and money (Grimble and Wellard
1997)

Managers must note, however, that some organizations identiﬁed by our criteria
may not necessarily consider themselves to be ﬁsheries stakeholders. For example, some
organizations may express indifference for ﬁsheries issues, and some may not even be
aware that they are located in the PM watershed. We do not believe that this should
preclude their potential involvement in ﬁsheries management discussions, since they may
still affect—and be affected by—ﬁshing, ﬁsh habitat, or landscape hydrology. Managers,
using this stakeholder identiﬁcation process, can develop a more comprehensive
understanding of how these groups may directly and indirectly relate to their management
efforts, and they can seek appropriate forms of communication, education, and ongoing
dialogue with these groups as necessary.

While developing a list of potential stakeholder organizations can be a valuable
management exercise that facilitates stakeholder communication, this process provides
only limited information on potential communication patterns and strategies. A list or

database does not convey any information about the interactions or relationships between

organizations; in other words, it does not capture the social structure of the stakeholder
community. This type of sociological information could help explain the distribution of
power, inﬂuence, investment, and effectiveness among stakeholder organizations. We
believe that managers would also beneﬁt from this type of information as they determine
if, why, and how to more effectively and efﬁciently engage stakeholders in ﬁshery
management decisions. In our next chapters, we explore the development and

implications of the F80 interorganizational network for the PM watershed.

45

CHAPTER 2

FORMATION OF INTERORGANIZATIONAL TIES AND NETWORKS:
UNDERSTANDING THE INTERACTIONS OF FISHERIES STAKEHOLDER ORGANIZATIONS

Introduction

Natural resource issues affect—and are affected by—numerous stakeholders with
diverse goals. The natural resource policy arena is a dynamic forum in which
stakeholders may engage (or disengage) in different types of resource issues, depending
on their interests, goals, and abilities. Various patterns of stakeholder relations thereby
emerge, as stakeholders ﬁnd ways to learn about and inﬂuence the formation and/or
application of natural resource policies. Stakeholder engagement often occurs at the
organizational level, and the resulting patterns of interorganizational relations provide
insight into the nature of a given resource issue or policy.

In this study, we examined the interorganizational stakeholder relations
surrounding Pere Marquette (PM) River ﬁsheries and watershed issues. The existing
pattern of stakeholder relationships establishes the framework for understanding how and
why organizations interact and how future interactions may be facilitated. We explored
these interorganizational relations by asking: what kind of organizations enter these
relations, and what kind of interactions occur? In addition, once we identiﬁed the
structure of the interorganizational relations network, we explored properties of this

network by asking: how and why do clusters (i.e., distinct sub-networks within the

46

overall network) form, and what kind of organizations occupy central positions in these
clusters? Ultimately, answers to these questions will help ﬁsheries and watershed
managers develop more efﬁcient and effective forms of communication with stakeholders
in the interorganizational network. ,
(As a ﬁrst step, we established our level of analysis in studying interorganizational ‘1“
relations. Instead of focusing on the relations of any one particular organization, we
focused on how and why relationships form within the interorganizational ﬁeld or system.
This system, or problem domain, includes the network of stakeholder organizations that
are joined by a shared issue or problem, and it is the appropriate level of analysis for
interorganizational relations (Benson 1975; Van de Ven et al. 1979; Gamm 1981; Gray
1985; Gray and Hay 1986; Wood and Gray 1991; Selin 1993; Jamal and Getz 1994).
This domain, by deﬁnition, is an emergent social phenomenon that represents a
“collectivity in which member organizations are identiﬁed as being interdependent with
respect to some function, issue, or concern” (Gamm 1981, p. 29).

How can an interorganizational problem domain be distinguished, and therefore
studied, from its greater social system? When studying organizations associated with a
social problem or concept (a nominalist approach; see Laumann et al. 1989), identifying
stakeholder organizations within a problem domain has the effect of setting domain
boundaries (Gamm 1981; Gray and Hay 1986; Wasserrnan and Faust 1994). The network
boundary-setting process, therefore, reﬂects current understanding of the problem and its
stakeholders, but it also affects how the problem is subsequently ﬂamed and understood
and, ultimately, how effectively and efﬁciently it may be resolved. For example, studying

an incomplete or incorrect set of stakeholders will likely fail to facilitate resolution of the

47

 

problem or issue. Domain-level problems cannot be solved by any single organization;
their resolution inherently requires collective action by many—if not all—domain
stakeholders (Gamm 1981; Gray 1985; Jamal and Getz 1994). In this study, the domain
encompassed a unique network of ﬁsheries and watershed stakeholder organizations, a
network which may not be applicable or relevant for any other type of issue domain (e. g.,
health care or youth services). We were interested in learning if and how this particular
set of stakeholder organizations forms relationships to collectively address issues in their
domain.

This analysis begins with a description of organizations and issues in the PM
watershed and ﬁsheries domain. We then review the literature about theories of
interorganizational relations, and this is followed by a more targeted exploration of how
these theories can be tested by looking at organizations’ communication, resource
contribution, and collaboration behaviors. With this information, we form numerous
hypotheses about how organizational attributes may act as predictors of
interorganizational tie formation, the properties of these interorganizational ties, and the
structural properties of the interorganizational network that emerges as the patterns of ties
are constructed. We use social network analysis methods to analyze these hypotheses.
The results of this study provide insight into the formation and structure of the PM

stakeholder network.

Organizations in the Pere Marquette River Watershed

The PM River is a prominent feature of the west central region of Michigan’s

48

lower peninsula (see Prologue Figure 1). The river and its recreational salmonid ﬁsheries
are the focal point of many ecological, economic, cultural, historical, and recreational
issues; therefore, a diverse and vocal stakeholder community frequently becomes
involved in PM watershed and ﬁsheries issues (USDAF S 1976; Cassuto 1994; Supinski
1994; Nelson and Smith 1998).

Many stakeholder interests are represented by organizations, which have various
reasons or speciﬁc interests underlying their involvement with watershed or ﬁsheries
issues. Many such organizations are governmental agencies and units. As a public
resource, the PM River is govemment-owned and -regulated on behalf of the public and
accessible to anyone (i.e., regulated open access; Tremp 1992); therefore, state and
federal government agencies are charged with conserving and managing this river and
other regional public resources like forests, wildlife, trails, lakes, and resource access
points (USDAF S 1976).

Governmental organizations are also active at more local scales. As in other areas
of Michigan, local and regional planning is done by local units of government (i.e.,
counties, townships, municipalities; Public Sector Consultants 1998). These units of
government have boards or ofﬁces that determine local development patterns, such as
how land is zoned and developed, how and where roads are constructed, and how water is
consumed and discharged (Dean and Wyckoff 1991; Decker and Steiner 1997). If these
local land use planning boards do not coordinate their planning activities, the resulting
patterns of land use can result in habitat loss and fragmentation, alterations to regional
hydrology, and excessive rates of urbanization and pollution.

In addition to various levels of government agencies, there are many non-proﬁt

49

and non-govemmental organizations that represent other types of local interests that relate
to ﬁsheries and watershed management. For example, certain organizations represent the
economic interests of local developers and those engaged in the tourism and hospitality
industries. Many member businesses of these organizations seek to develop vacation
homes, access sites, and other resource-related attractions. Education-oriented
organizations may focus on spreading information to the public about land use,
watershed, or other resource-related issues. Other special interest organizations represent
the interests or concerns of private citizens on such ﬁsheries-related topics as
environmental preservation, resource-based recreation, private property interests, or
agricultural land-use practices (see Chapter 1 for a list and more complete description of
these organizations).

These organizations have diverse interests and stakes in issues related to ﬁsheries
and watershed management in and around the PM River (see also Jamal and Getz 1994).
For example, the PM watershed is a mix of both public and private lands; therefore,
public interests may focus on different goals for PM watershed and ﬁsheries issues than
private interests. Such public/private interest clashes are evident in the issue of
trespassing, as many riparian landowners actively try to keep anglers and canoeists off
their private portions of the riverbank (McCoy 2001, pers. comm). In addition, the
tourism-oriented development goals of economic organizations may clash with those of
habitat preservation organizations. Some groups are concerned that increased resource
use or the creation of additional access points to the river may lead to greater levels of
human impact on now pristine sections of the river (Cassuto 1994).

Despite having goals that sometimes conﬂict, these organizations have some

50

common features. .They are generally public sector or non—proﬁt, so they are not
competing with each other for proﬁts. They may, however, have limited ability to
generate revenue to meet their resource needs independently, however, and they may I
compete with each other for a ﬁnite set of resources like grant funding or members.
Many organizations, however, especially those with a conservation focus, may recognize
the advantages of forming relationships with each other to secure these scarce resources
(Molnar 1978; Selin and Beason 1991). For example, grant-making foundations often
reward partnerships among organizations (Provan et al. 1980), and sharing organizational
member lists may provide additional information outlets.

In the PM watershed, organizations are generally small (under 20 people), so their
leaders (e.g., club presidents, ofﬁce managers, etc.) may have a good sense of how and
with whom their organizations form relations. For the most part, these organizations
have authority and discretion to voluntarily interact with other organizations as they deem
appropriate, although this degree of discretion may vary for those with mandates (e. g., the
Michigan Department of Natural Resources) or those associated with pre-existing
interorganizational federations (e. g., Michigan United Conservation Clubs afﬁliates; see
Fleisher 1991). These organizations share other common features; for example, given the
relatively low population density of the area, it is possible that there will be some overlap
of members between organizations. It is also likely that many organization members
know each other through various contexts, from social to professional, and they may have
a personal history of working with members of other organizations. Since most
organizations have existed for many decades, there will be a long institutional history

(cooperative and contentious) between many of these groups, with much of this history

51

focusing speciﬁcally on ﬁsheries and watershed issues (Cassuto 1994).

Interorganizational Relations Theory

Many theories of interorganizational relations attempt to explain why
organizations engage in interactions such as communicating, sharing resources, and
collaborating with other organizations. Since our population included diverse types of
organizations (e.g., government agencies, non-proﬁt organizations, university-based
groups, etc.), we sought information on general theories that would apply to multiple
types of organizations. Furthermore, given the rarity of natural resource applications of
interorganizational theory (for exceptions, see Molnar 1978; Hoban 1987; Schweer 1987 ;
Selin and Beason 1991), most of our literature review was based on interorganizational
relations in other types of public service-related problem domains.

We found any single theory of interorganizational relations to be insufﬁcient for
explaining the relationships formed between stakeholder organizations in and around the
PM watershed. We focused on three theories about the formation of interorganizational
relations: resource dependence, political, and institutional (see Gray and Wood 1991 for
more on these and other theories). These theories complement and sometimes overlap
each other, and each contributes to an understanding of why such relations develop in the
PM stakeholder network. This kind of theory-blending is frequently employed by
researchers (Cook 1977; Provan et a1. 1980; Galaskiewicz 1985; Selin and Beason 1991;
Wood and Gray 1991; Jamal and Getz 1994), as there are often many different

interorganizational dynamics at many different levels that may be applicable for a given

52

study. These theories allowed us to develop a comprehensive theoretical foundation,
which enabled us to explore how and why these interorganizational relations are
manifested in the behaviors of interorganizational communication, resource contribution,

and collaboration.

Resource Dependence T heory

Many organizations, especially those in the public service and non-proﬁt arena,
focus on speciﬁc services or functions and have limited ability to independently ﬁllﬁll all
their resource needs (Cook 1977; Molnar 1978; Galaskiewicz 1985; Schweer 1987;
Beatrice 1990; Logsdon 1991; Merritt and Neugeboren 1990; Jamal and Getz 1994).

Resources are anything of value that can be exchanged between organizations, such as

W--H.
_.

funding, equipment, information, expertise/technical assistance, political support and
legitimacy, access to policy processes, service delivery capacity, or any other tangible or
intangible asset that helps organizations function (Cook 1977, Van de Ven et al. 1979;
Gamm 1981; Saidel 1991). Resource dependence theory assumes that organizations
engage in relationships with other organizations to directly procure resources important to
organizational survival (Benson 1975; Offerman 1976; Provan et al. 1980; Gamm 1981;
Van de Ven and Walker 1984; Schweer 1987; Galaskiewicz 1985; Merritt and
Neugeboren 1990; Selin and Beason 1991). For interorganizational relations between
state agencies and nonproﬁt organizations, this theory assumes that resource dependence
is mutual, reciprocal, substantial, and symmetrically distributed (i.e., equally important
for each group; Saidel 1991).

Given the complexity and potential turbulence within any problem domain,

53

resource interdependencies compel organizations to form relationships that enable them

to gain more control over uncertainty in resource procurement (Benson 1975; Molnar
1978; Gamm 1981; Van de Ven and Walker 1984; Turk 1985; Galaskiewicz 1985;
Harrigan and Newman 1990; Gray and Wood 1991; Logsdon 1991; Selin and Beason
1991; Selsky 1991; Wood and Gray 1991; Mizruchi and Galaskiewicz 1993; Jamal and
Getz 1994; Gulati and Gargiulo 1999). Cook (1977) explains resource exchange between i
organizations as a result of resource scarcity and specialization in organizational ﬁinction. A
In other words, “limitations on the availability of resources necessitate organizational
interdependence...and foster specialization” (Cook 1977, p. 64); this specialization, in

turn, further compels organizations to seek predictable sources of other needed scarce
resources from outside organizations (Van de Ven and Walker 1984; Logsdon 1991;

Selin and Beason 1991; Mizruchi and Galaskiewicz 1993). This has the effect of
minimizing uncertainty in securing scarce resources and, thereby, maintaining
organizational function.

This theory predicts some type of collective response or behavior among those in
resource-dependent positions (Galaskiewicz 1985), and such a collective response is
facilitated by other commonalities in organizational attributes. Under conditions of
uncertainty in resource procurement, organizations will more likely seek out other \\
organizations with similar backgrounds, philosophies, and values—such organizations are.“
perceived as being more trustworthy (Molnar 1978; Galaskiewicz 1985; Selin and Beason
1991; Mulroy 1997). An organization’s domain (i.e., their place in the

interorganizational problem domain) includes their set of interests, goals, activities,

values, and philosophies, and domain similarity between organizations is frequently

54

associated with the formation of interorganizational relations (Molnar 1978; Gamm 1981;

Van de Ven and Walker 1984; Gray 1985; Hoban 1987; Beatrice 1990; Saidel 1991;

\ I‘
‘ ‘ ,

5"

Selin and Beason 1991; Mulroy 1997; Banaszak-Holl et al. 1998). Interorganizational l3 ,
relations are further facilitated if organization members have had a previous, reciprocatedj‘.‘
cooperative professional or personal experience with members of the other organization .
(Hoban 1987; Wimpﬂreimer et al. 1990; Mizruchi and Galaskiewicz 1993; Gulati and
Gargiulo 1999; see also Axelrod’s (1984) theory of cooperation).

This theory explains “how and why organizations act to protect a commons, or to
enhance collective interests, when their immediate self-interests and autonomy may
appear to align more closely with non-collective behavior” (Gray and Wood 1991, p. 7).
Resource dependence theory often assumes organizations want to preserve their perceived
autonomy, independence, or identity and minimize overall dependencies on other
organizations (Offerman 1976; Gamm 1981; Van de Ven and Walker 1984; Hoban 1987;
Gray and Wood 1991). This desire often keeps organizations ﬁ‘om actually merging to
form a new, larger organization, and it may also affect their willingness to form any
organizational relationships. Oliver (1991) investigated this assumption, however, and
found that “[t]he propensity for organizations to enter into relations with one another was
shown to be unrelated to the degree of commitment or loss of autonomy required by these
relationships” (p. 959). This outcome may indicate that the beneﬁts of
interorganizational relations (i.e., resource mobilization, reduction of uncertainty, etc.)
outweigh the mutually-bourne costs of autonomy reduction (Oliver 1991). It may also
reﬂect the idea that organizations are inevitably dependent on each other and that the

concept of autonomy is a perception that may not actually apply to interdependent

55

organizations (Astley and Fombrun 1987). Nevertheless, it should be noted that some
perceived autonomy is necessarily lost by interdependent organizations that form
relationships (Saidel 1991), since this may have implications for organizational inﬂuence

and accountability.

Political Theory

According to political theory, organizations enter into relationships with other
organizations to inﬂuence the political system that ultimately affects how they ﬁrnction ‘1
(Gray 1985; Roberts and Bradley 1991). For example, “stakeholders can organize
coalitions to lobby for policy changes important to the domain, they can inﬂuence public
opinion, and they can initiate formal contacts with other organizations or networks” (Gray
1985, p. 931) as a way to enhance their inﬂuence or power in the domain (Beatrice 1990).
As in resource dependence theory, uncertainty and common interests play a role in
political theories (Roberts and Bradley 1991), as organizations will work with each other
to minimize the turbulence—created by governments or other powerful decision
makers—that threatens their legitimacy or ability to meet their goals (Galaskiewicz
1985). In other words, organizations may enter into relationships to preserve, improve, or
otherwise control their role or function within the problem domain (Provan et al. 1980;
Wood and Gray 1991; Jamal and Getz 1994).

Power and authority are two organizational attributes that are important in
political theories of interorganizational relationships. Within any problem domain, power
and authority are unevenly distributed among organizations (Benson 1975; Provan et al.

1980; Gamm 1981; Gray 1985; Gray and Hay 1986; Harrigan and Newman 1990). For

56

example, some organizations may be subservient to other organizations, some may
control access to greater levels of resources upon which others depend, and some may
have more resource altematives—all of this affects their power and ability to form
relationships (Benson 1975; Provan et al. 1980; Gamm 1981; Skinner and Guiltinan
1986; Harrigan and Newman 1990).

As a result of these power inequities, any interactions may be tempered by
organizations’ relationships to the power structure that inﬂuences how the problem
domain operates. For example, some organizations may have the discretion and ability to
enter into voluntary relationships; others may need to seek approval from organizations
that are ‘higher up’ in the chain of command (Benson 1975; Gamm 1981). The
distribution (and possible re-distribution) of power affects the formation of
interorganizational relationships and thereby affects the resulting distribution of inﬂuence
and beneﬁts within a domain’s network of stakeholders (Provan et al. 1980; Boje and
Whetten 1981; Harrigan and Newman 1990; Gray and Hay 1986; Gray and Wood 1991;
Lawrence et al. 1999).

Political theories of interorganizational relationships help explain what is \
happening with the PM stakeholder network, as the ﬁsheries and watershed issues are \
inherently political in nature (i.e., how to manage and allocate limited, common public ‘3
resources for the greatest interest of diverse stakeholders). The PM River and much of its
surrounding lands function as a type of common property, and this presents some political
issues that are addressed by this theory (see also Jamal and Getz 1994). Golich (1991)
explains some features of common resources that present challenges to

interorganizational relations; for example, users cannot be excluded and beneﬁts cannot

57

be divided or appropriated. Nevertheless, regulation of the common resource needs to
occur, and organizations must somehow access the existing power structure to ensure that
they can negotiate or otherwise secure some role in the domain. Their need to protect
their interests compels them to engage in the political system that controls the common

resource (Golich 1991).

Institutional Theory

Institutional theorists recognize that there are institutional and other
environmental constraints—often beyond organizations’ control—that affect options for
the type and ﬁequency of interorganizational relations (Boje and Whetten 1981; Turk
1985; Blau and Rabrenovic 1991; Mizruchi and Galaskiewicz 1993). These theories are
consistent with the assumptions of resource dependence and political theories of
collaboration, yet they go further in recognizing the constraints that either enable or
inhibit the formation of a relationship (Gray and Wood 1991). “[l]nstitutionalization
means that a domain has become a routinized, calculable, well-integrated system, with
rules of legitimate policy participation well understood and accepted by all the players”
(Knoke 1990, p. 168). These institutions, or rules, establish the norms in a social system
and thereby affect perceived options for interaction (Turk 1985; Galaskiewicz and
Wasserman 1989; Sharfman et al. 1991; Wood and Gray 1991).

Formation of interorganizational relationships, therefore, must often be consistent
with the contextual constraints of mandates, laws, traditions, public stands, constituent
expectations, geographical locations, jurisdictional boundaries, and other limits that affect

organizations’ perceived options (Benson 1975; Boje and Whetten 1981; Hoban 1987;

58

Beatrice 1990; Wimpfheimer et al. 1990; Blau and Rabrenovic 1991; Gray and Wood
1991; Selin and Beason 1991; Mizruchi and Galaskiewicz 1993; Selin 1993; Mulroy
1997). In a circular sense, even the structure of the relationships within the
interorganizational network poses institutional and environmental constraints on an
organization’s perceived options for forming relationships (Selsky 1991; Gulati 1995;
Gulati and Gargiulo 1999). Furthermore, organizational interactions are facilitated by
similarity or proclivity of organizations’ institutional constraints—like geographical
locations, institutional mandates, and institutional purposes/products—as well as by
mutual awareness of other organizations’ goals, services, resources, expertise, and
reputations (Van de Ven et al. 1979; Boje and Whetten 1981; Gamm 1981; Galaskiewicz
1985; Gray 1985; Hoban 1987; Selin and Beason 1991; Selsky 1991; Mizruchi and
Galaskiewicz 1993).

Institutional mandates present a type of constraint that is applicable to this study
and warrants additional consideration. Many of our stakeholder organizations are I
government agencies, and many of these are mandated—through law or policy—to
interact with other organizations (Schweer 1987; Merritt and Neugeboren 1990).
Mandates can be a powerful institutional force in creating interorganizational relations,
yet mandated “structure without facilitative conditions is insufﬁcient to promote .3
collaboration” (Gray 1985, p. 929) or other types of relationships. Although much of the
preceding discussion has focused on the voluntary (as opposed to mandated) formation of
interorganizational relations, many of the same concepts apply to the formation of
eﬂective mandated relations (Gray 1985). For example, mandates for interorganizational

relations should recognize and be built upon the interdependeneies, mutual resource

59

needs, shared political interests, and other facilitating conditions that the interacting
organizations have in common.

Institutional theorists further recognize that organizations will structurally adjust
themselves with institutional or environmental forces, such as mandates, as a way to
enhance their legitimacy (Offerman 197 6; Turk 1985; Galaskiewicz and Wasserman
1989; Gray and Wood 1991; Sharfrnan et al. 1991). For example, by conforming to
others’ expectations, mimicking the interactions or behaviors of other similar
organizations, or otherwise aligning with institutional forces, organizations are more
likely to be accepted as valid partners, service providers, or customers within their social
system (Galaskiewicz and Wasserman 1989; Logsdon 1991; Sharﬁnan et al. 1991; Gulati
and Gargiulo 1999). By establishing interorganizational relationships, organizations are
able to obtain endorsements, secure political support, enhance their public image, and
otherwise identify or align with cultural symbols or power ﬁgures—all of which enhance

their perceived legitimacy (Galaskiewicz 1985; Beatrice 1990; Gray and Wood 1991 ).

Summary of Theories

Resource dependence, political, and institutional theories of interorganizational
relations lend insight into the formation of interactions among PM stakeholder
organizations. In the PM watershed stakeholder network, governmental and non-proﬁt
organizations operate under conditions of uncertainty, and they each specialize in some
function important to the operation of the PM ﬁsheries and watershed problem domain.
They need to secure resources, such as funding, expertise, information, facilities, and

political support, from other organizations in order to maintain their function, and they

60

are likely to form relationships with others that have organizational domain similarity.
Much of the resource sharing is done for political reasons—to generate support for
organizations’ positions or efforts regarding common ﬁshery or watershed issues—given
the inevitable uncertainty of the natural and political environment. Resource sharing,
however, must occur under constraining institutional factors such as mandates, missions,
geographical proclivity, and public opinion. The resulting dynamic interactions between
organizations will enable them to operate, adapt to new issues that emerge within the
domain, and inﬂuence the collective social responses that help shape the issues and ﬁrture

of the domain.

Organizational Behaviors as Indicators of Interorganizational Relations

We determined the nature of PM stakeholder interorganizational relations by
studying three speciﬁc behaviors that indicate the presence of an interorganizational
relationship: leadership communication, resource contribution, and joint program
collaboration (Molnar 1978; Gamm 1981; Gray and Wood 1991; Lawrence et al. 1999).
The following sections outline why we looked at each type of tie, how we think each tie
may be exhibited between organizations in the PM river watershed, and speciﬁc
hypotheses linking interorganizational relations theory with observations in the PM
watershed.

Before proceeding, however, we should address why we aggregated the three
types of ties into one network of interorganizational relations, rather than looking at the

separate networks for each of the three tie types. We are primarily interested in

61

understanding the overall patterns and properties of the interorganizational relations ﬁeld,
among all the organizations in the potential stakeholder population. A network based on
only one type of tie would give us only a partial picture of the overall network of
interorganizational relationships (Laumann et al. 1989). Furthermore, theories on
interorganizational relations frequently look at a combination of communication,
contribution, and/or collaboration ties, and the predictors of these types of
interorganizational relations frequently overlap. In other words, the organizational
properties that predict interorganizational communication behavior are similar to those
that predict interorganizational resource contribution and collaboration. Nevertheless,
Knoke (1990) found that different network structures can emerge for different types of
ties; in comparing communication and resource exchange networks, he found that“. . .
these were quite distinct structural relations” (p. 168; see also Cook 1977; Van de Ven et
al. 1979; Wasserman and Faust 1994). In a follow-up study, we can break down our
analysis according to tie types, developing separate networks based on communication,
contribution, and collaboration ties. We could then learn more about how these networks

compare to each other in terms of tie formation and network patterns.

Communication Between Organizational Leaders

Communication between organizational leaders is one indicator of
interorganizational relations, and it is often a precursor for other relations like resource
contributions or joint program collaboration. We examined communication at the
leadership level because organizational leaders generally have ultimate decision-making

authority; ability to commit resources; knowledge of mandates, goals, responsibilities,

62

constraints, and opportunities; and some ability to motivate and direct staff activities.
They may also have a history of working with other organizations, familiarity with other
organizations’ background, and knowledge of their and others’ roles in the problem
domain. Other organizational staff or members may communicate and establish
relationships with those in other organizations without necessarily going through their
leader, but, in general, the leader (especially in a small organization) may at least have
general knowledge about their kind and extent of interorganizational relationships.

Organizational leaders in the PM River Watershed—and elsewhere—may engage
in communication for many reasons related to interorganizational relations theory. For
example, they may talk about ways they can meet common goals or how they can work
with each other for mutual beneﬁt. They may engage in strategic planning activities, as
part of mandates or missions, that require communication. Leaders may serve as
organizational representatives or serve on other organizations’ boards or committees, and
they may commit their support to an issue in efforts to increase their legitimacy or
mobilize power for some common advantage. Communication between interdependent a
organizations is likely to be professional and cordial (Galaskiewicz 1985); however, in I
cases of conﬂict, leaders may represent and negotiate on behalf of their organization.

In the PM watershed, communication patterns may be inﬂuenced by institutional
and environmental constraints, such as county borders, proximity, traditions, and
mandates. Common interests may also underlie communication, and many interests in
this domain may relate to ﬁsheries and watershed issues. Those concerned with
conservation may recognize the strong need to communicate—assuming these leaders are

aware of the interconnected and interdependent nature of ﬁsheries and watershed

63

conservation issues. Furthermore, we expect to see mutual communication ties between
leaders, since leadership communication is inherently a two-way process about which

both sides should be aware.

Contribution of Resources Between Organizations

Given the above explanations of interorganizational relations theory (speciﬁcally
resource dependence theory), it is clear that resource contribution behaviors between
organizations indicate interorganizational relations. In the PM problem domain, public
sector and non-proﬁt organizations specialize in certain ﬁmctions and have limited
resources; therefore, they will need to form relationships to secure other resources to
maintain their existence and achieve their goals. By pooling or sharing resources,
organizations may be able to meet their resource needs, but they may also enhance their
legitimacy, increase support for their positions, leverage political power, and otherwise
inﬂuence the distribution of beneﬁts and power in the domain.

Among PM stakeholder organizations, resource contribution may be a frequent
activity, between all types of organizations. Resource sending may occur more often than
leadership communication or collaboration, since sending resources does not necessarily
require that leaders communicate or organizations collaborate. Many different kinds of
resources may be needed (e.g., money, information, equipment, support, legitimacy, etc.),
and organizations may have many methods and reasons for seeking and providing
resources. For example, many organizations will have mandates, missions, or purposes
that focus on providing resources (e.g., Extension organizations, Chambers of Commerce,

etc.). According to the theory, we expect to see a high degree of reciprocated resource

64

sharing—even for different kinds of resources—since there are many types of resources,
resource needs are mutual, and many resources can be provided easily and inexpensively.

PM stakeholder organizations may be more likely to send resources—especially
high investment ones—when they share interests, concerns, goals, or other features of
their organizational domain. For example, common interest in natural resources may
provide a strong motivator for resource sharing, due to the interdependent and integrative
nature of conservation issues. Organizations with such a common interest may more
willingly share resources, since they may feel this will help them generate support for
their (sometimes controversial or unpopular) environmental goals and positions.
Nevertheless, resource sharing is a common interorganizational activity, and it will more
frequently occur collaboratively or casually (than in a tense or confrontational way).

In this research, we asked organizations to identify other organizations to which
they sent resources. With information on their perceived provision of resources, we may
get a sense of how helpful an organization thinks it may be to another organization or
how much they are trying to invest in a relationship with the other organization. Data on
resource sending may also reﬂect the number of requests that an organization receives for
sending resources. Resource sending behavior, however, may not exactly be mirrored in
resource receiving behaviors (the latter were not included in this study). Since reports of
resource sending and receiving can be inﬂuenced by the perceptions of the research
participant, future research should ask about both types of resource exchanging behaviors
to assess the validity or accuracy of actual resource distribution patterns within the

network.

65

Interorganizational Collaboration

Collaboration is perhaps the most commonly studied form of interorganizational
relations. Given the overwhelming complexity and turbulence of many social issues,
collaboration enables organizations to overcome barriers to opportunities or resources and
achieve goals collectively that could not be achieved individually (Gray 1985; Beatrice
1990; Gray and Wood 1991; Wood and Gray 1991; Lawrence et a1. 1999). In a
comprehensive study of interorganizational collaboration, Wood and Gray offer the
following oft-cited deﬁnition: “[c]ollaboration occurs when a group of autonomous
stakeholders of a problem domain engage in an interactive process, using shared rules,
norms, and structures, to act on issues related to that domain” (1991, p. 146; see also
Gray 1985). In order to establish and maintain this kind of interorganizational
relationship, organization members must think that the beneﬁts of collaboration outweigh
the costs; in other words, members have to perceive that they have gained something or
improved their state of affairs (Gray 1985; Wimpﬂreimer et al. 1990; Gray and Wood
1991; Selin and Beason 1991; Wood and Gray 1991).

Consistent with resource dependence theory, the formation of collaborative
relationships is facilitated by perceptions of high stakes and high interdependencies
regarding a common issue (Gray 1985; Logsdon 1991; Selin and Beason 1991; Wood and
Gray 1991; J arnal and Getz 1994). Collaboration is more likely when they perceive some
shared purposes, goals, or interests, as well as some common current and future risks
(Gray 1985; Galaskiewicz 1985; Beatrice 1990; Wimpﬂreimer et al. 1990; Gray and
Wood 1991; Logsdon 1991; Lawrence et al. 1999). Recognizing their shared views and

dependencies, organizations will be motivated to mobilize their organizational resources

66

toward the common problem (Wimpﬂreimer et al. 1990; Saidel 1991). Their speciﬁc
interests in the issue, however, need not be the same (Gray and Wood 1991; Lawrence et
al. 1999). Any collaborations that develop must serve an organization’s own needs and
interests, yet these are oﬁen consistent with the collective interests of multiple
organizations (Wood and Gray 1991).

Political and institutional theories of collaboration, speciﬁcally regarding
collective action and coalition formation, also recognize the importance of organizational
domain similarity (Gray 1985; Galaskiewicz 1985; Beatrice 1990; Roberts and Bradley
1991; Gray and Wood 1991; Lawrence et al. 1999). Collaboration based on political
theory is motivated by a shared purpose to achieve social or political change (Roberts and
Bradley 1991; Gray and Wood 1991). It can also be motivated by an organization’s need
to protect their interests in common resources (e.g., the PM River) that may be depleted
or conﬁscated by others (Golich 1991; Gray and Wood 1991). Collaboration emerges as
stakeholders come to understand a problem domain (its structures and processes, limits
and possibilities), appreciate their interdependence, and negotiate a collective response
(Gray 1985; Gray and Wood 1991; Lawrence et al. 1999).

The formation of interorganizational collaborations is a function of both
organizational and personal factors. Organizational factors include available resources,
personnel and organizational structures, purpoSes and philosophies, and future goals.
Personal factors include staff skills, abilities, and contacts; interpersonal histories,
relationships, and personalities; and future plans and expectations (Van de Ven et al.
1979; Hoban 1987; Beatrice 1990; Wimpﬂreimer et al. 1990; Selsky 1991). The relative

importance of each set of factors, however, is debatable; Galaskiewicz (1985) believes

67

that the importance of personal factors may sometimes be overestimated, since
collaboration seems to be more consistently dependent upon organizational factors,
regardless of variations in personal factors.

While leadership communication or resource provision are often pre-requisites for
collaboration to occur, they do not necessarily result in collaboration; therefore, this
indicator of interorganizational relations may occur least oﬁen. Formation of
collaborative relationships requires recognition and removal of institutional barriers,
leadership commitment, investment of resources, and possibly some degree of joint
decision-making (i.e., losses in perceived autonomy). Working together on joint
programs may be more cooperative or casual, as opposed to tense or confrontational, if a
working relationship is to be developed and maintained (Galaskiewicz 1985). We expect
to see a high degree of mutuality, since collaborating on a joint program is inherently a
two-way process about which both sides should be aware.

The literature suggests that collaboration formation requires a greater degree of
domain similarity than communication or resource sharing. In the PM stakeholder
network, organizations may think they can achieve more and increase the likelihood of
success by committing to working jointly with others. This may be especially true with
conservation-oriented organizations, since conservation approaches require integrative
ecosystem-level planning and coordination. We may therefore see more cases of
collaboration among conservation-oriented organizations, possibly due to the greater

domain similarity and the integrative nature of conservation issues.

68

Hypotheses on Interorganizational Tie Formation

Based on the above theories on interorganizational relations and descriptions of
potential communication, resource contribution, and collaboration between organizations
in the PM stakeholder population, we have developed the following hypotheses to explain
the formation of interorganizational relationship. Speciﬁcally, we predict that an
organization may be associated with a tie (i.e., either they indicate a tie or another
organization indicates a tie with them) based on the following attributes:

H1: Generalist organizations (i.e., interested in multiple types of issues) will be
more likely to form interorganizational relations, since having more
interests can lead to more and diverse types of relations.

H2: Organizations with a natural resource orientation will be more likely to
form interorganizational relations, due to the integrative nature of natural
resource issues.

H3: State and federal government agencies will be more likely to form
interorganizational relations, likely having mandates and missions to serve
the public this way.

In addition to the above organizational attributes that may explain the presence or
absence of a tie, we believe there are other attributes that—when shared by two
organizations (i.e., a dyad)-—help predict the presence of a strong tie between them. We
refer to these types of dyadic ties as ‘strong ties,’ since organizational respondents must
indicate that they are reciprocated (i.e., two organizations each identify a tie with each
other), positive (i.e., cooperative and/or casual as opposed to tense and/or
confrontational), and important (i.e., the interaction is worthwhile and should be
maintained). We look speciﬁcally at these ties because these may be the types of ties that

are the most effective or productive within the problem domain. These are stricter tie

criteria than those used in the ﬁrst three hypotheses, and they may therefore only be

69

predicted by a stricter, or more restrictive, set of organizational attributes.
H4: Similarity of organizational domain (as measured by purpose, philosophy,
and having at least two common interests) will be positively associated

with strong interorganizational relations.

H5: Similar high levels of involvement will be positively associated with
strong interorganizational relations.

H6: Similarity of geographic attributes (measured by county) will be positively
associated with strong interorganizational relations.

In addition, we can better understand the nature of interorganizational relations by
looking at the relative proportions of each type of tie within the complete set of relations.
We expect there will be more resource sending ties than strong leader communication or
strong collaboration ties, since there are many different ways in which organizations
could exchange resources, many types of resources can easily be sent, and many different
staff members could provide resources. Leadership communication ties will occur more
often than collaboration ties, but it will not occur as often as mutual resource provision.
There will be fewer strong program collaboration ties than communication or resource
sending ties, as collaboration requires the greatest levels of investment and it is not likely
without the existence of the other two types of ties.

H7: Within the complete set of interorganizational relations, there will be more
resource contributions ties than leadership communication ties, and there
will be more leadership communication ties than collaboration ties.

We expect most interorganizational relations to be positive experiences for

organizations (indicated as being cooperative or casual), since professionals and
volunteers that engage in these ties likely behave in a polite, respectful manner.

Nevertheless, since ﬁsheries and watershed issues are political in nature, we can expect

that conﬂicts will arise when parties are not satisﬁed with the existing allocation of

70

 

resources, power, or beneﬁts within the network. In attempting to change such
unsatisfactory allocations, organizational leaders, representing the interests of their
groups, may engage in potentially contentious interactions with other groups’ leaders. On j
the other hand, negative interactions may lease likely between groups that have already
invested in collaborative relationships. By looking at which types of ties are more likely
to be negative experiences (indicated as being tense or conﬁontational) for organizations, -
we can better understand how to anticipate and possibly minimize the extent of
interorganizational conﬂicts.

H8: Negative ties will occur most frequently in leadership communication ties,

less frequently in resource contribution ties, and even less frequently in
collaboration ties.

Clustering and Centrality in Interorganizational Networks

So far we have looked at the preconditions for tie, or relationship, formation
between organizations. By aggregating information on these ties, we can examine how
their patterns collectively create an interorganizational network. Why is this important?
As Knoke (1990, p. 9) explains, “[a] complete explanation for some social phenomena
requires knowledge about the relationships among system actors.” An organization’s
position in the overall network structure reﬂects their role in the problem domain; for
example, an organization’s position relative to other organizations may indicate the kind
and extent of information or other resources they can easily access (Knoke 1990), and it
can reﬂect their ability to mobilize political support or other forms of domain inﬂuence

(Galaskiewicz 1985). We will look at two network properties, clustering and centrality,

71

that emerge among organizations in the PM watershed network to understand how and

why certain patterns and positions emerge in the network.

Properties and Hypotheses of Network Clusters

Clustering is a network property that emerges when network ties form identiﬁable \
sub-networks of ties (Wasserman and Faust 1994; Banaszak-Holl et al. 1998). The sub-
networks are known as clusters, and their formation may be explained by theories of
structural equivalence or cohesion (Frank 1996). According to structural equivalence,
clusters emerge when a set of nodes displays similar patterns of strong or frequent
interactions—as well as similar patterns of weak or infrequent interactions—with other
organizations (Lincoln 1982; Knoke 1990). These clusters of organizations that behave
similarly are likely to emerge among organizations with similar organizational domains,
as predicted by resource dependence and political theories. For example, cluster
formation has been associated with similar functional areas or activities (Gamm 1981;
Mizruchi and Galaskiewicz 1993; Banaszak-Holl et al. 1998).

Clustering based on cohesion, or the tendency to form bonds, occurs when
organizations form micro-systems of dense interactions with each other (Frank 1996).
Higher densities or concentrations of ties among sets of nodes (i.e., a higher ratio of
existing ties:possible ties) indicate the presence of cohesive subgroups. Organizations
within a cohesive subgroup may be from similar or different organizational domains,
thereby enabling organizations to complement or fulﬁll their resource needs through
repeated interaction with other kinds of groups in their cluster. They may display other

common attributes, however, such as ideological afﬁnity (Knoke 1990); historical

72

interactions; proclivity of geographical features such as counties, jurisdictions, and
proximity (Mizruchi and Galaskiewicz 1993); or other institutional similarities (Frank
1996). The distribution of various attributes or behaviors among members of cohesive
subgroups helps describe the formation and function of clusters in the problem domain.

Identiﬁcation of cohesive subgroups may also help us understand how
organizations perceive, affect, and otherwise relate to the problem and each other. While
similarity in some organizational attributes may help explain tie—and therefore cohesive
subgroup—fonnation, the structure of the resulting network clusters in turn affects the
behavior of cluster members (Frank 1996). Cohesive subgroup afﬁliation exerts powerful
inﬂuences on cluster members, as Knoke (1990) explains:

“Members of a cohesive group or clique (i.e., cluster) are linked directly

to one another by many intense mutual ties across which inﬂuence

communications are transmitted. They are structurally oriented toward

their internal reference groups for clues to appropriate thoughts and

deeds” (p. 12).
Cohesive subgroups, therefore, also have the effect of reinforcing and normalizing certain
attitudes and behaviors among cluster members (Frank 1996; Banaszak-Holl et al. 1998).

Since clusters based on cohesive subgroups demonstrate a greater tendency to
form (and reinforce) relationships with each other, cluster identiﬁcation can be an
informative analytical exercise. In the PM ﬁsheries and watershed domain, cohesive
subgroups are expected to form among groups with similar organizational domains or
geographical status. First, we expect to see cohesive subgroups among groups with
organizational domain similarity, since, according to interorganizational relations

theories, interactions or ties are more likely to occur between organizations with similar

interests, goals, philosophies, and other features of their organizational domain. Second,

73

we expect to see institutional constraints affect cohesive subgroup formation through
geographical and jurisdictional features of the problem domain. Since most local-level
planning and work activities are performed at the county level, organizations may be used
to working with intra-county partners, thereby forming county-based cohesive subgroups.

H9: For all types of ties, clusters of cohesive subgroups will emerge among

groups with similar organizational domains and/or counties.

Properties and Hypotheses of Organizational Centrality

Centrality is the degree to which an organization forms ties with other
organizations in their cluster (Lincoln 1982). Those having more ties will have greater
access to other nodes in their cluster or network and will, therefore, tend to have a more
central position; those with fewer ties will tend to have a peripheral or less central
position. Centrality is a structural property of interorganizational networks that has often
been associated with an organization’s power or inﬂuence (real, perceived, or reputed)
relative to other organizations in the cluster or network (Benson 1975; Cook 1977; Boje
and Whetten 1981; Schweer 1987; Mizruchi and Galaskiewicz 1993). For example,
“[c]entra1ity prominence in a communication network is synonymous with inﬂuence” K/
(Knoke 1990, p. 13), as central organizations may have greater access to information and
other resources, serve as mediators in the ﬂow of resources, and be in favorable
bargaining positions by having more alternatives and partners. Factors other than
centrality, however, may inﬂuence an organization’s power or inﬂuence (Cook 1977;
Boje and Whetten 1981), and we should not necessarily assume a simple cause-effect

relationship between network centrality and power. Nevertheless, although we are not

examining inﬂuence or power per se, we may expect that our central organizations may,

74

TE

OI;

in some ways, be more inﬂuential in the stakeholder relationship network.

Centrality of a node, or organization, is generally measured in three ways
(Freeman 1979; Wasserman and Faust 1994). First, centrality can be a function of
degree, or the number of other nodes that are connected to a given node (see also Lincoln
1982). Nodes with highest degrees have the highest potential of directly interacting and
serving as major channels or focal points of interaction (see also Knoke 1990). Other
ways to deﬁne centrality fall into two categories, based on betweenness and closeness
(Freeman 1979). Centrality based on betweenness focuses on a node’s presence on
various chains of ties, and this is appropriate when one is interested in a node’s ability to
control resource ﬂow. Centrality based on closeness focuses on the distance from a node
to all other nodes, and this is appropriate when examining how one can avoid the
potential control of others or how efﬁciently one can interact with all other nodes. In
deciding how to deﬁne centrality based on one or more of these methods, one must
consider the context of the intended application (Freeman 1979; Wasserman and Faust
1994). We were most concerned with direct interorganizational relations (as opposed to
the control of information or communication efﬁciency); therefore, we deﬁned centrality
based on an organization’s degree or number of ties.

Organizations in central positions tend to display certain properties, primarily
related to their range of concerns or interests in their policy domain. For example,
organizations with a wider, more diverse range of interests tend to be more engaged in
domain activities, have more ties, and therefore be located in prominent positions of their
cluster or network (Offerman 1976; Galaskiewicz 1985; Knoke 1990). “[T]he broader an

organization’s issue concerns, the greater the number of events in which it is actively

75

TE

P0

engaged” (Knoke 1990, p. 168). Similarly, some studies (Mizruchi and Galaskiewicz
1993; Banaszak-Holl et al. 1998) report that integrative organizations are more likely to
be central, and that highly specialized organizations are more likely to be positioned at
the periphery of the network. These patterns apply to communication as well as resource
exchange networks (Knoke 1990). In addition to having many interests and an increased
likelihood of being inﬂuential, central organizations are more likely to have a
coordinating role in the network, the ability to mobilize resources (Benson 1975; Provan
et al. 1980; Mizruchi and Galaskiewicz 1993), larger staffs or membership (Gamm 1981;
McPherson 1983; Blau and Rabrenovic 1991; Banaszak-Holl et al. 1998), and an
increased likelihood of becoming engaged in controversies (Knoke 1990).

Within the PM stakeholder network, central organizations are expected to display
many of these properties. Since these are organizations that have, in general, more ties to
other organizations than would otherwise occur, these organizations may have missions
or strong interests that compel greater involvement in watershed/ ﬁsheries issues or local
planning; have a wider, or more generalist, range of interests; and have a more permanent
presence with regular staff to devote to these issues (Knoke 1990). These organizations
may have more resources (e. g., expertise, interests, information, advice, funding,
facilities, etc.) to devote to building and maintaining all types of ties with other
organizations. Alternatively, these organizations may also have resource needs that they
can meet by interacting with other organizations; therefore, consistent with resource
dependence theory, these organizations may hold roles as seekers and/or providers of
resources. Whether senders or receivers of resources, these organizations have attained a

position that may reﬂect a higher level of inﬂuence in the problem domain. Once they

76

have assumed a central role, they may perceive themselves—or have others perceive 5'
them—as having more inﬂuence or importance in PM ﬁsheries and watershed issues. /
They may, therefore, have more effective engagement in the political or policy processes .
that affect regional issues, how the domain operates, and how they may operate as an /
organization within that domain.

H10: Organizations with a central role in their cluster will have a natural

resource or local government purpose; be non-voluntary; have high
involvement; and/or be generalist in scope.

Analytical Approach

Data Collection and Variable Deﬁnition

Our study population consisted of the 101 ﬁsheries stakeholder organizations
(FSOs) in the PM River watershed identiﬁed in Chapter 1. To meet our objectives, we
surveyed each FSO about their interactions with other FSOs in the watershed regarding
ﬁsheries management. Since we were seeking organizational-level data (as opposed to
individual-level data), we identiﬁed a person in each organization to respond to the
survey on behalf of their organization. This person needed to be familiar with their
organization’s leadership communications, resource contributions, and collaborations
with other F 808 in the watershed. The organizational representative who answered the
survey was often someone in a leadership position, a public relations position, or a
program specialist (see Van de Ven et al. 1979 for more on this data collection process).

The survey, reﬁned through a pre-testing process with survey design experts and

novices, was administered as a mail questionnaire in the summer and fall of 2000. In the

77

ﬁrst section of the survey, we collected information on the following organizational
variables: organization size, membership type (voluntary or non-voluntary), duration of
existence, organizational purpose and philosophy, level of involvement with local natural
resource issues, level of satisfaction with current ﬁsheries management, and their concern
about nine ﬁsheries-related issues. These issue concerns were aggregated to determine if
an organization had a specialist scope (i.e., concerned about only one type of issue) or a
generalist scope (i.e., concerned about multiple types of issues). Other variables—
purpose, philosophy, and concems—were aggregated to deﬁne domain, a variable that
captures an organization’s role or niche in the network.

In addition to survey-based data collection, we collected information on other
organizational attributes from other sources. For example, we identiﬁed their category
(e.g., state government, conservation special interest, etc.), county, and location relative
to watershed boundaries by reviewing organizational documents and maps. Data on these
organizational attributes served as independent variables (Table 2.1) that may help
explain the formation of ties and network properties.

In the second section of the survey, we listed all other F 805 identiﬁed for the
watershed (see Chapter 1), and we asked the respondent to identify those with which their
organization has interacted regarding a ﬁsheries-related issue in 1998 and 1999. We
limited these interactions to these years in order to assess recent stakeholder network
structure and increase the accuracy of respondent recall. We also provided spaces for
organizations to identify other organizations not already listed on the survey. As they
identiﬁed organizations they have interacted with, we sought additional information on

variables concerning the nature and extent of their interactions. Speciﬁcally, we collected

78

 

size

Table 2.1. Organizational attributes serving as independent variables, with
Wr_o_omous coding.

 

number of staff and/or members:
- 1 to 20 members (yes or no)
- 21 to 100 members (yes or no)
- 101 or more members (yes or no)

 

memb

membership status:
- staff (yes or no)
- volunteers (yes or no)

 

age

duration of existence:
- O to 10 years (yes or no)
- 11 to 50 years (yes or no)
- 51 or more years (yes or no)

 

purpose

self-described purpose or reason for existing
- natural resource conservation/management (yes or no)
- natural resource recreation/tourism (yes or no)
- natural resource protection/preservation (yes or no)
- education and information (yes or no)
- property owner representation (yes or no)
- private business representation (yes or no)
- local government planning (yes or no)
- local/regional economic development (yes or no)
- agricultural interests (yes or no)
- other (yes or no)

 

philos

philosophy about natural resource management:
- protect from human impacts (yes or no)
- develop for economic activity (yes or no)
- multiple social and environmental beneﬁts (yes or no)
- other (yes or no)

 

involve

involvement in local ﬁsheries/watershed issues:
- not (yes or no)
- somewhat (yes or no)
- very (yes or no)

 

satis

 

 

satisfaction with current ﬁsheries management:
- not (yes or no)
- somewhat (yes or no)
- very (yes or no)

 

79

 

 

Table 2.1 (cont’d).

 

concern

topics or issues of concern:
- ﬁshing access (yes or no)
- ﬁshing regulations (yes or no)
- enforcement of regulations (yes or no)
- status of ﬁsh populations (yes or no)
- resource use conﬂicts (yes or no)
- ﬁsh stocking (yes or no)
- habitat quality (yes or no)
- recreational experiences (yes or no)
- sea lamprey control (yes or no)
- no concerns about the ﬁshery or watershed (yes or no)

 

scope

identiﬁcation of interest scope:
- generalist (yes or no)
- specialist (yes or no)

 

category

category or type of organization:
- federal government (yes or no)
- state government (yes or no)
- county government (yes or no)
- township government (yes or no)
- city government (yes or no)
- local economic development (yes or no)
- environmental/conservation special interest (yes or no)
- recreation/Sportsmen’s special interest (yes or no)
- education/infomation interests (yes or no)
- other (e. g., agriculture, prOperty owner) (yes or no)

 

sdomain

two groups with the same purpose, philosophy, and concerns (yes or no)

 

county

county where organization is located
- Mason (yes or no)
- Lake (yes or no)
- Oceana (yes or no)
- Newaygo (yes or no)
- other (yes or no)

 

 

proxim

 

proximity to the watershed:
- inside (yes or no)
- outside (yes or no)

 

80

 

data on communication, resource contribution, and collaboration ties between
organizations (based on Knoke and Rogers 1979). We deﬁned each type of tie as

follows:

0 communication: “Our leaders talked about goals or plans.”

0 resource contribution: “We sent them information, advice, or other resources.”

0 collaboration: “We worked together on joint programs.”

In addition to determining the existence of these kinds of network ties, we used
the mail survey to seek other information on the nature of these ties. Such additional
information is important because properties of ties can convey other information about
the nature of interorganizational relationships. We explored these properties by asking
about the frequency of interactions, the tone or mood surrounding these interactions, and
the importance they place on maintaining the relations. By combining data on
reciprocated, positive, and important ties, we could deﬁne the strong ties used in H4, H5,
and H6. Along with other data on the presence or absence of a tie, these served as our
dependent variables (Table 2.2). While this contextual information may not capture all
variables that could describe the nature of interactions, we could get a sense of why and
how interactions arise and identify important contextual variables for follow-up research.
A complete replication of the questionnaire (adjusted for page size) can be found in
Appendix B.

Network analysis methods were developed to study an entire population, not a
representative sample of that population; therefore, a higher response rate will enable a
more reliable and accurate analysis of the network (Wasserman and Faust 1994). To

maximize our response rate, we initially mailed a notice of the upcoming survey

(Appendix C) then distributed the survey (cover letter in Appendix D), sent postcard

81

 

Table 2.2. Interaction data serving as dgendent variables, with dichotomous coding.

chooser an organization that indicated a tie with another organization (identiﬁed by
a number)

 

chosen an organization with which a tie was indicated by another organization
(identiﬁed by a number)

 

strongti a reciprocated, positive, and important tie (yes or no)

 

freq frequency of interaction:
- occasional (yes or no)
- frequent (yes or no)

 

type type of interactions:

- leadership communication tie (yes or no)
- resource contribution tie (yes or no)

- joint program collaboration tie (yes or no)

 

tone tone of interactions:

- cooperative interaction (yes or no)

- casual interaction (yes or no)

- tense interaction (yes or no)

- confrontational interaction (yes or no)

 

import importance of maintaining a given tie:
- not (yes or no)

- somewhat (yes or no)

- very (yes or no)

 

 

 

 

reminders to non-respondents (Appendix E), mailed second survey mailings (cover letter
in Appendix F), and followed the mailings with phone interviews to obtain the above

information until we achieved the highest possible response rate.

Modeling Framework

The development of our analytical model is rooted in theories about the
relationship between organizational attributes and interorganizational network structure.

The relationship between social actors (e.g., organizations) and their place in system

82

 

 

Part A: : Part B:
(the selection model) : (the inﬂuence model)
I
System structure
/ or network
I
1
Patterns of i Inﬂuences &
behavior & i constraints on
. . I .
interaction : optrons
I
Organizational
attributes
:
I

 

 

Figure 2.1. Theoretical relationships between organizational attributes and
interorganizational network structure, and the mechanisms by which they inﬂuence each
other.

structure can be characterized by a circular model, in which organizational attributes and
network structure affect—and are affected by—each other (Figure 2.1). Network
structure inﬂuences an organization’s access to resources (e. g., information, ﬁrnding,
technology, etc.) and imposes other constraints on their identity and behavior; these
constraints thereby affect or deﬁne an organization’s attributes (Lazega and van Duijn
1997; Frank and Fahrbach 1999). This does not, however, lock in an organization to a
certain role or position in the network structure. An organization may engage in
behaviors that inﬂuence or change overall network structure. Generally, these behaviors
are facilitated by similarities in organizations’ attributes, which drive interactions among
organizations that may thereby change network structure (Homans 1950; Leenders 1995;
Lazega and van Duijn 1997; Frank and Fahrbach 1999). This structure again inﬂuences

organizational attributes, and the cycle continues. In reality, these processes are thought

83

to occur simultaneously and iteratively.

This conceptual model has led to two types of analytical models for social
network analysis (Leenders 1995; Frank 1998; Frank and Fahrbach 1999). The ﬁrst type
focuses on how organizational attributes affect the patterns of relationships in network
structure (Part A of Figure 2.1), and it is known as the selection model. The second type,
known as the inﬂuence model, focuses on how network structure affects organizational
attributes (Part B of Figure 2.1). Each type of modeling approach has its own
mathematical framework for capturing the relevant relationships (Frank 1998; Frank and
Fahrbach 1999). The choice of either model is based on the theories and case studies of
the intended application.

We chose to use the selection model, since questions of interorganizational tie
formation are ﬁequently the subjects of interest in natural resource management.
Resource managers interested in promoting holistic approaches to watershed and
ecosystem management appreciate the importance of interorganizational relations like
communication, resource contribution, or collaboration. They need to know how and
why such interorganizational ties can be facilitated, which may help managers develop
more efﬁcient and effective forms of stakeholder involvement. This, in turn, can increase
the likelihood of achieving management goals. Our model will help us examine how
attributes affect tie formation and, therefore, stakeholder network structure, but we do not
completely disregard the effects of the network on organizations. Institutional theories of
interorganizational relations include network structure, such as clustering patterns, as a
type of constraint that affects organizational choices for interactions. Therefore, while

our model is based on the mathematical approach of the selection model, some of our

84

hypotheses recognize certain structural factors that inﬂuence organizations’ options for
interaction.

Our modeling framework has implications for our identiﬁcation of dependent and
independent variables. In the selection model, the organizational attributes listed above
are the independent variables that are used to explain stakeholder relationships and
network structure. It is possible, however, to switch these variables if one is interested in
using the inﬂuence model. For example, organizational attributes—especially variables
like involvement and satisfaction with ﬁsheries management—could easily serve as the
dependent variables that are explained by the patterns of ties observed in the network
structure. Future studies of the PM problem domain, perhaps with the objective of
understanding the characteristics of a particular organization, may utilize the inﬂuence

modeling framework.

Network Analysis Process

The ﬁrst step of the interorganizational network analysis was to construct
sociograms, or two-dimensional maps, of the network. These maps enable one to
visualize the spatial relationships between nodes (i.e., organizations) and various types of
ties (i.e., relationships) that exist between them (Wasserman and Faust 1994; Frank 1996,
1998). In doing this, it indicates the presence of clusters, or concentrations of ties
between nodes, as well as those organizations that hold central positions in their clusters.

One can also link data on organizational attributes‘ to the nodes in the resulting

 

‘ Organizational identity can also be linked to each node, but we do not report
identity in order to protect survey respondents’ privacy.

85

sociogram, which allows one to examine the distribution of various attributes in the
network and their possible roles in facilitating ties. This information can help verify
hypotheses for forthcoming statistical analyses, as well as indicate other possible
explanations for tie formation and network structure. We produced these sociograms
with KliqueFinder© software (Frank 1995, 1996), a clustering algorithm that iteratively
scans databases for information on ties to produce a visual map of non-overlapping
cohesive subgroups (see Frank 1996, 1998). Figure 2.2 represents one of the many
sociograms utilized at this stage of the analysis, indicating the relative network position
of organizations and the distribution of strong ties.

The second step involves using statistical tools to analyze the occurrence of
relationships between organizations, given variations in 1) the attributes of organizations
indicating a tie (i.e., the chooser); 2) the attributes of organizations with which they
indicate a tie (i.e., the chosen); and 3) the attributes of dyads, or pairs of organizations.
Data were collected at the individual (organizational) level of analysis, yet combining
these data allow us to analyze relationships at the dyadic (between two organizations)
level of analysis. Therefore, we needed to utilize a multilevel model that combines
individual- and dyadic-level data within a common framework. The pz-like model,
developed by Lazega and van Duijn (1997) and applied by Frank (in prep), provides a
multilevel, cross-nested model for analyzing how organizational characteristics
(independent variables) may explain the presence or absence of a tie (the dependent
variable). As a cross-nested model, it nests pairs of organizations within the chooser and
chosen organizations, thereby capturing the dyadic dependencies (with random effects for

chooser and chosen organizations) that underlie sets of relationships, then models the

86

 

 

 

Figure 2.2. Selected sociogram representing the approximate PM stakeholder network
structure. Diamonds represent organizations (excluding the outliers), lines represent
strong ties, and circles represent clusters.

87

random effects as functions of organizational characteristics (Frank et al. 2000).

This analytical approach begins with a logistic regression model for predicting ties
(Menard 1995), but it is expanded to account for the differential effects of chooser and
chosen organizations as well as reciprocity effects2 (Lazega and van Duijn 1997).
Logistic regression is used to assess how well independent variables can explain or
predict a dichotomous response (e. g., whether or not a tie occurs; Menard 1995). Since
interorganizational relationships may depend on a set of varying characteristics of the
chooser, chosen, and/or dyad, logistic regression is insufficient for accurately predicting
dyadic ties. It is useful, however, for preliminary identiﬁcation of potentially important
independent variables that should be incorporated into the pz-like model (Wasserman and
Pattison 1996). We used SAS© statistical software (SAS Institute 1999) to run logistic
regression models on tie formation, which enabled us to perform preliminary hypotheses
tests and to explore other possible explanations for tie formation. The outcomes of these
models provided the basis for further exploring tie formation patterns in the pz-like
model. For example, preliminary analyses indicated that both organizations having high
levels of involvement (H5) was so strongly associated with strong tie formation (this
practically deﬁnes it) that it would have diminished our ability to model other variables.
We excluded this variable from the pz-like model.

The pz-like model was applied to the analysis of both regular ties (Hl , H2, and

H3) and strong ties (H4, H5, and H6); both are represented by “Tie” in the model below.

 

Reciprocity effects were not modeled in this analysis because they would have
washed out the effects of our variables deﬁned by chooser and chosen attribute
similarity.

88

Mathematically, whether organization i ’ formed a relationship with organization i is a
function of the tendency of i’ to be a chooser (05,.) and i to be chosen ([3,) (Frank et al.

zoooy

level 1 (dyadic):

P[Tie,,/=l] (1)
. = up + Bi
1-P[Tze,,,]

 

The measure of a tie between i and i’ was dichotomized (with 1 indicating the presence of
a tie); therefore, equation (1) was estimated via logistic regression. We then included
various combinations of organizational attributes (covariates for same county, same
domain, and same scope are represented below) in the dyadic level of the model to assess

their relative importance in explaining or predicting a relationship:

level 1 (dyadic):

P[Tie"/= 1]
. = up + [3, +
l-P[Tze“/]

 

2
61(same county)“; + ( )

62(same domain)“, +

63(same scope)“;

In addition, an organization’s tendencies to be the chooser (05,.) and the chosen ([3,), based
on their attributes (being a generalist is represented in the equations below), were

modeled at the individual organizational level:

89

level 2a (chooser):
a], = Yoo + 701 generalisti/ + am;
(3)
level 2b (chosen):
[3, = 710 + y“ generalist, + V0:

where no... and v0, represents random effects of the chooser and chosen, respectively.
Equation 3 enabled us to evaluate the effects of the chooser and the chosen, in addition to
the effects of the pair of organizations constituting a dyad in equation 2. We used
HGLM© software (Byrk et al. 2001) with cross-nested effects to estimate the models
(Frank et al. 2000).

Our observations of interorganizational relations are not independent (i.e., an
organization can be involved in many interactions), which violates the standard
assumption of independence. As a result, the standard errors from ordinary logistic
regression routines—and, therefore, t-ratios and p-values—are incorrect. Thepg-like
models account for these dependencies through um. and v0,, but signiﬁcance testing is still
not well understood for these models (see Lazega and van Duijn 1997; Wasserman and
Pattison 1996). At the very least, these models provide valuable descriptive information
in terms of regression coefﬁcients and odds ratios, which are different ways to interpret
the relative importance of tie formation predictors (i.e., a higher coefﬁcient or odds ratio
indicates a stronger predictor; Menard 1995). We can make these comparisons because
the predictors, or independent variables, were each coded to be dichotomous (i.e., 0 or 1,

indicating an organization does not or does have an attribute, respectively); therefore, a

coefﬁcient can be interpreted as the change in the dependent variable when an attribute is

90

present (Menard 1995). The t-ratios, however, can also serve as rough guides to
statistical signiﬁcance. When the t-ratio is greater than 3, concerns about the model
accommodations for dependencies would likely not alter an inference. On the low end,
when the t-ratio is less than 1.5, a coefﬁcient is not likely to be statistically signiﬁcant
using any modeling framework that minimally accounts for dependencies in the dyads.

As a ﬁnal step in this analysis, we calculated variance components for the chooser
and chosen organizations. We determined unconditional (i.e., unmodeled) chooser and
chosen variances with HGLM©, which enabled us to estimate a baseline for evaluating
the variance explained by the model. By comparing the unconditional with conditional
(i.e., modeled) variances for chooser and chosen, we could determine the percent of

variation attributed to chooser and chosen organizations in the model.

Results

Description of Responses

We received responses from 90% of the organizations in the PM stakeholder
population (i.e., 91 out of 101 organizations). Out of the ten organizations that did not
respond, six were township governments and four were other types of organizations.
Their lack of response was likely due to communication difﬁculties or organizational
indifference to the survey subject matter. For the most part, these ten organizations were
not suspected to have signiﬁcant roles in the PM ﬁsheries and watershed domain; for
example, the other townships that responded did not generally exhibit numerous

interactions. We were not concerned that these non-responses affected our results or

91

conclusions.

Out of the 91 organizations that responded, 60 were chooser organizations that
reported having interorganizational relations with at least one other organization; on
average, 18.1 ties were reported (standard deviation 15.3) by a chooser. Eight of these
organizations each indicated at least one ‘other’ group (not initially identiﬁed in Chapter
1 or listed on the survey) with which they formed interorganizational relationships,
resulting in 11 additional organizations. These new organizations, and any available
attribute information on them, were included as chosen organizations in the analysis.
These additional organizations increased the ﬁnal population size in the analysis to 112,
out of which 109 were chosen by at least one chooser organization; on average, an
organization was chosen 10.0 times (standard deviation 6.7) by choosers.

Between these organizations, chooser organizations reported having 1088
interorganizational relations with chosen organizations. Some of the relations indicated
by a chooser consisted of more than one type of tie with a particular chosen organization.
When each of the three types of ties are counted separately, there were 1594 ties (657
communication ties, 505 resource contribution ties, and 432 collaboration ties) indicated
by chooser organizations. A complete set of descriptive statistics on the properties of ties

and organizations can be found in Appendix G.

Tie Formation Patterns
Preliminary analyses indicated that certain attributes were more likely to be
associated with tie formation. Each independent variable, taken individually for choosers

and chosens and in combination when they were similar at the dyadic level, was

92

examined to assess its relative importance in determining tie formation patterns. Only
those attributes (for chooser, chosen, and dyadic levels) that appeared to explain ties in
the sociograms and logistic regressions were analyzed with the pz-like model.

Based on the preliminary analyses, the tendency to be associated with a regular tie
(i.e., not a strong tie), either as the chooser or chosen organization, was suspected to be
associated with the following attributes at the chooser, chosen, and dyadic similarity
level: location inside the watershed, interest in multiple issues (generalist), satisfaction
with PM ﬁsheries (as opposed to neutral/undecided), dissatisfaction with PM ﬁsheries (as
opposed to neutral/undecided), small size (under 20 members), old (over 100 years), and
new (less than 20 years). Since categories for satisfaction, age, and size were represented
with sets of dichotomous variables, coefﬁcients could be interpreted relative to one
another. The pz-like model calculated the coefﬁcients and odds ratios, which we used to
approximate the relative importance of each attribute in explaining tie formation; we
generally considered odds ratios above 1.50 (i.e., a 50% increase in the odds of having a
tie, given a certain attribute; Menard 1995) and t—ratios near 3 or greater to indicate
noteworthy attribute effects. By comparing coefﬁcients (Table 2.3), we concluded that
the odds of a tie occurring are four times greater when the chooser is located in the
watershed, a generalist, satisﬁed, or unsatisﬁed. The odds of a tie occurring were 83%
greater when the chooser is new, but the t-ratio is indicated this is insigniﬁcant. The odds
of a tie occurring were at least twice as great when pairs of organizations are both in the
same domain, both inside the watershed, and both old. Among chosen organizations,
being satisﬁed displayed the most association with tie formation (odds ratio 1.498 and t-

ratio of 1.713), but this is not as strong as the effects of those chooser or dyadic attributes.

93

 

Table 2.3. Effects of chooser, chosen, and dyadic attributes on tie formation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Approx. Odds
Independent Variable Coeff. Std. Err. T-Ratio Ratio
Chooser Attribute
located inside the watershed“ 1.557 0.451 3.451 4.745
generalist (multiple interests)* 1.735 0.489 3.552 5.669
satisﬁed with PM ﬁsheries“ 1.760 0.492 3.578 5.812
unsatisﬁed with PM ﬁsheries* 1.651 0.642 2.571 5.212
small (less than 20 members) 0.373 0.422 0.882 1.452
old (over 100 years old) -0.267 0.463 -0.577 0.766
new (less than 10 years old) 0.606 0.881 0.688 1.833
Chosen Attribute
located inside the watershed 0.140 0.208 0.674 1.150
generalist (interested in multiple issues) 0.138 0.244 0.565 1.148
satisﬁed with PM ﬁsheries 0.404 0.236 1.713 1.498
unsatisﬁed with PM ﬁsheries 0.089 0.306 0.290 1.093
small (less than 20 members) 0.041 0.196 0.209 1.042
old (over 100 years old) -0.582 0.225 -2.577 0.559
new (less than 10 years old) 0056 0.411 -0137 0.946
Dyadic Similarity Attributes
both in the same domain* 0.778 0.197 3.957 2.177
both inside the watershed“ 0.934 0.158 5.928 2.545
both generalists 0.268 0.163 1.645 1.307
both satisﬁed with PM ﬁsheries -0.088 0.164 -0.535 0.916
both unsatisﬁed with PM ﬁsheries -0.452 0.309 -1.462 0.636
both small 0.230 0.156 1.474 1.259
both old* 0.877 0.163 5.373 2.404
both new -1.229 0.729 -1 .685 0.293

 

"' those attributes most strongly associated with tie formation

94

 

Both being new organizations actually decreased the odds of tie formation by 71%, but
the t-ratio indicates this may not be signiﬁcant.

We used this initial model to calculate the conditional variance components,
which were compared with the unconditional variances for chooser and chosen. The
unconditional variances were 4.806 for the chooser (u) and 0.716 for the chosen (v),
which indicated that choosers vary six times more than chosens in tendencies to engage in
ties (Table 2.4). The conditional variances for this model were 3.138 and 0.628 for
chooser and chosen, respectively. The attributes included in the above model, therefore,
explained approximately 35% of the variation for choosers and 12% of the variation for
chosens. There was much more variation attributed to characteristics of the chooser
organizations; therefore, our models tended to emphasize the effects of choosers. We did
not re-calculate the variance components for each of the following models, since they

were not expected to vary drastically for each set of modeled attributes.

 

   

 

 

Table 2.4. Variance components for _’ Janrzatins; .,_ -_. f
Unconditional Conditional

Unconditional as Percentages (Modeled) % Explained

Chooser 4.806 0.870 3.138 0.347

Chosen 0.716 0.130 0.628 0.123

 

 

 

 

 

 

 

In the above pz-like model results, domain similarity between organizations was
found to be an important dyadic predictor of tie formation, yet we had information from
our preliminary analyses that indicated that some types of domains were more important

predictors than others. We repeated the above analysis, replacing domain similarity with

95

 

 

C.

   
 
 

    

Table 2.5. Effects of chooser, chosen, and dyadic attributes on tie formation, with

domain similariz replaced by each domain t

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Independent Variable Coeff. Std. Err. T-Ratio 13:21:
Chooser Attribute
located inside the watershed“ 1.517 0.520 2.915 4.559
generalist (multiple interests)* 1.959 0.565 3.466 7.092
satisﬁed with PM ﬁsheries“ 2.011 0.568 3.539 7.471
unsatisﬁed with PM ﬁsheries“ 1.814 0.745 2.435 6.135
small (less than 20 members) 0.335 0.488 0.686 1.398
old (over 100 years old) -0.391 0.537 -0.729 0.676
new (less than 10 years old) 0.562 1.013 0.555 1.754
Chosen Attribute
located inside the watershed -0.132 0.252 -0.521 0.876
generalist (interested in multiple issues) 0.170 0.295 0.576 1.185
satisﬁed with PM ﬁsheries 0.485 0.288 1.684 1.624
unsatisﬁed with PM ﬁsheries 0.047 0.376 0.124 1.048
small (less than 20 members) -0.02-1 0.239 -0.089 0.979
old (over 50 years old) 0686 0.273 -2.517 0.504
new (less than 10 years old) -0.166 0.504 -0.330 0.847
Dyadic Similarity Attributes
both natural resource agencies 0.313 0.197 1.589 1.368
both other government agencies“ 0.581 0.210 2.770 1.788
both local government ofﬁces“ 0.603 0.242 2.492 1.828
both education organizations“ 4.413 0.775 5.349 82.517
both economic organizations“ 2.214 0.547 4.049 9.152
both agriculture organizations“ 3.722 0.563 6.609 41.347
both environmental organizations“ 1.621 0.889 1.824 5.058
both outdoor recreation organizations“ 1.459 0.362 4.025 4.302
both from the same county“ 2.856 0.137 20.871 17.392

 

96

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2.5 (cont’d).
both inside the watershed -0.414 0.190 -2.184 0.661
both generalists 0.306 0.179 1.710 1.358
both satisﬁed with PM ﬁsheries -0.064 0.179 -0.356 0.938
both unsatisﬁed with PM ﬁsheries -0.378 0.346 -1.092 0.685
both small 0.276 0.171 1.612 1.318
both old“ 0.810 0.180 4.503 2.248
both new -1.144 0.773 -1.479 0.319

 

 

* those attributes most strongly associated with tie formation

each of the eight domain types at the dyadic level: natural resource management
agencies, other government agencies, local government ofﬁces, education organizations,
economic organizations, agriculture organizations, environmental organizations, and
outdoor recreation organizations (Table 2.5). We also added the dyadic variable
representing similar counties. In this analysis, each of the speciﬁc domain categories,
except for natural resource agencies, produced an odds ratio of at least 1.5, although
signiﬁcance based on t-ratios may be questionable for dyadic relationships in the
following domains: other government, local government, and environmental
organizations. Two dyadic-level domains, education and agriculture organizations, had
very high odds ratios (82.517 and 41.347, respectively), indicating that the organizations
in these domains may be extremely well tied to each other. Being from the same county
made the odds of a tie occurring at least 17 times greater, and both being old made the
odds to form ties twice as great. Being both inside the watershed decreased in importance

from the ﬁrst model, and being both new still decreased the odds of having a tie. The

97

relative importance of other chooser and chosen independent variables from Table 2.3 (p.
94) did not change considerably, although the odds associated with a satisﬁed chosen
organization tie increased to 62% (but signiﬁcance based on the t-ratio of 1.684 is still
inconclusive).

These results generally support our ﬁrst three hypotheses, but this support is not
consistent among attributes of choosers, chosens, and dyads. Generalist organizations
were associated with the formation of interorganizational relations (Hl), but this was
strongest when the chooser was a generalist. Being a generalist was not found to be as
important for both organizations in a dyadic relationship, and it was even less important
for chosen organizations. Organizations with a natural resources-related orientation (H2)
included natural resource management agencies, environmental organizations, and
outdoor recreation organizations; the latter two groups, at the dyadic level, tended to form
interorganizational relations within their group. State and federal government agencies
(H3) included natural resource management agencies and other government agencies, the
latter of which also formed interorganizational relations within their group. These were
not, however, the only important predictors of tie formation. Among choosers, tie
formation was also associated with being inside the watershed, satisﬁed with PM
ﬁsheries, and unsatisﬁed with the ﬁsheries. Among chosens, however, ties were only
very weakly associated with being satisﬁed with PM ﬁsheries. At the dyadic level, being
in the same domain (for most domain types), both old in age, and in the same county were
also found to be important predictors of tie formation.

Strong ties (i.e., reciprocated between chooser and chosen, positive, and

important) were more strictly deﬁned than the regular ties explored above, and they may

98

only be predicted by a more stringent set of independent variables. Based on preliminary
analyses, the strong ties were suspected to be associated with the following attributes at
the dyadic similarity level: both in the same county, both in the same domain, both
generalists, and both satisﬁed with PM ﬁsheries (Table 2.6). Being in the same county
and same domain were associated with more than a seven- and four-fold increase in the
odds, respectively, of forming strong ties. Having lower odds ratios and inconclusive t-
ratios, however, the importance of being both generalists and both satisﬁed with PM

ﬁsheries was questionable.

 

     

Tble 2.. Effects of d adic abutes on stron - tie formation. _

   

 

 

 

 

 

Odds

Independent Variable Ratio
Dyadic Similarity Attributes

both in the same county“ 1.971 0.189 10.444 7.178

both in the same domain“ 1.411 0.264 5.343 4.100

both generalists 0.433 0.242 1.791 1.542

both satisﬁed with PM ﬁsheries 0.474 0.241 1.966 1.606

 

 

 

 

 

 

 

* those attributes most strongly associated with strong tie formation

Again, domain similarity can be broken down into domain types, and we modeled
the relative importance of each domain in predicting the formation of strong ties. We ran
the strong tie model with each of the eight domain types at the dyadic level (Table 2.7).
The importance of being in the same county, both generalists, and both satisﬁed with PM
ﬁsheries was indicated in this analysis, and the odds ratios and t-ratios of the latter two

variables increased. Not each domain type, however, was found to be important. Strong

99

 

Table 2.7. Effects of dyadic attributes on strong tie formation, with domain similarity

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

replaced by each domain type.
Approx. Odds
Independent Variable Coeff. Std. Err. T-Ratio Ratio
Dyadic Similarity Attributes
both natural resource agencies“ 0.818 0.280 2.925 2.266
both other government agencies“ 1.214 0.290 4.179 3.367
both local government ofﬁces 0.046 0.490 0.094 1.047
both education organizations“ 3.374 0.897 3.762 29.195
both economic organizations -7.21 7 47.238 -0.153 0.001
both agriculture organizations“ 4.486 0.678 6.618 88.766
both environmental organizations* 3.160 0.956 3.304 23.571
both outdoor recreation organizations -0.368 0.797 -0.461 0.692
both in the same county“ 1.949 0.191 10.201 7.022
both generalists“ 0.837 0.240 3.481 2.309
both satisﬁed with PM ﬁsheries“ 0.610 0.243 2.514 1.840

 

* those attributes most strongly associated with tie formation

ties could be explained by a pair of organizations each being in the following domains:

natural resources management, other government agencies, education organizations,

agriculture organizations, and environmental organizations; the latter three of which had

extreme increases in the odds of having ties. Strong ties were not predicted if both

organizations were in the local government, economic, or outdoor recreation domains.

The preliminary analyses on strong ties also indicated that some chooser effects

may be important predictors of strong ties, in addition to the dyadic similarity attributes

explored in Tables 2.6. and 2.7 . Speciﬁcally, strong ties were expected if the chooser was

 

 

 

Table 2.8. Effects of chooser and dyadic attributes on strong tie formation, with
domain similarity replaced by each domain type.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Approx. Odds
Independent Variable Coeff. Std. Err. T-Ratio Ratio
Chooser Attribute
natural resource management agency"l 2.065 0.360 5.733 7.885
environmental organization“ 2.718 0.667 4.075 15.150
generalist" 1.529 0.356 4.294 4.614
satisﬁed with PM ﬁsheries 0.660 0.350 1.885 1.935
Dyadic Similarity Attributes
both natural resource agencies 0.298 0.298 1.001 1.347
both other government agencies* 1.181 0.291 4.059 3.258
both local government ofﬁces 0.472 0.504 0.937 1.603
both education organizations" 3.727 0.927 4.020 41.554
both economic organizations -6.814 44.506 -0.153 0.001
both agriculture organizations“ 4.795 0.703 7.080 120.904
both environmental organizations“ 2.771 0.922 3.006 15.975
both outdoor recreation organizations -0.144 0.802 -0.179 0.866
both in the same county“ 2.075 0.197 10.553 7.965
both generalists 0.388 0.264 1.469 1.474
both satisﬁed with PM ﬁsheries 0.337 0.261 1.288 1.401

 

* those attributes most strongly associated with tie formation

a natural resource management agency, an environmental organization, a generalist, or

satisﬁed with PM ﬁsheries, so these attributes were added to the strong tie model (Table

2.8). The ﬁrst three of these chooser attributes added to the model were found to be

important predictors. Among each domain type, however, both being natural resource

 

management agencies was no longer found to be as important in predicting strong ties;
this may indicate that any importance associated with this domain at the dyadic level is
actually a function of these agencies engaging in ties.3 Again, the education, agriculture,
and environmental organization domains were found to be extremely important in
predicting tie formation. Being in the same county remained as a strong predictor, but the
importance of both being generalists or satisﬁed with PM ﬁsheries decreased when these
other chooser independent variables were added—this may also indicate that the
importance of these variables is actually more of a chooser effect.

These results generally support H4 and H6. Being in the same domain (H4) was
important at the dyadic level (Table 2.6), although the importance of each type of domain
similarity varied in subsequent analyses (Tables 2.7 and 2.8). While natural resource
management domain similarity was important in Table 2.7, both being other government
agencies, education organizations, agriculture organizations, and environmental
organizations were consistently important dyadic predictors of strong tie formation.
Being in the same county (H6) consistently increased the odds of a tie between
organizations at least seven times.

We found other independent variables to be important predictors of strong ties.
At the chooser level, these are: being a natural resource management agency, an
environmental organization, and a generalist (Table 2.8). At the dyadic level, however,
the importance of being both generalists and both satisﬁed with PM ﬁsheries was

inconsistent and inconclusive.

 

3 This was not consistently found to be the case, however, in the preliminary
analyses of chooser and chosen effects for regular (i.e., not strong) ties.

102

Other Observations on Tie and Network Properties

We can learn about other tie and network properties by observing certain trends
and patterns ﬁ'om the survey results and the sociograms. Some of these observations
reinforce the results of the pz-like model; others provide supplemental information that
helps explain other network dynamics. For example, in H7, we hypothesized that within
the complete set of interorganizational relations, there will be more resource contribution
ties than leadership communication ties, and there will be more leadership
communication ties than collaboration ties. This was not the case, as leadership
communication (657 ties) occurred more often than resource contribution (515 ties). As
expected, however, collaboration occurred least oﬁen (432 ties).

We also hypothesized about the occurrence of negative ties. As expected, most
interorganizational relations were positive (indicated as being cooperative or casual)
experiences for organizations, with 88.8% of relations being described as cooperative
and/or casual. When negative ties occur (5.8% were described as being tense or
confrontational), we hypothesized that they will occur most frequently in leadership
communication ties, less frequently in resource contribution ties, and even less frequently
in collaboration ties (H8). This was the case, with 40 negative reports of leadership
communication, 33 negative reports of resource contribution, and 28 negative reports of
collaboration.

Appendix G provides other information on tie properties that may help explain
network dynamics. For example, 77.4 % of ties were described as occasional (i.e., once a
month or less) and 22.6% were frequent (i.e., more than once a month) (Table A.13).

Furthermore, 68.9% of ties were perceived to be very important, 25.1% were somewhat

103

 

Newaygo Co.

   

Natural Resource

rientation
0 Mason Co.

 
 
 
  

Economic
Development

   

Agriculture
& Extension

Oceana Co.

Figure 2.3. Clusters identiﬁed in the PM ﬁsheries stakeholder network and central
organizations (represented by ® ) within each cluster (outlier organizations and lines
representing ties are omitted for clarity).

104

important, and only 1.2% of ties were described as not important (Table A. 1 7).

The sociograms helped illustrate the structure of the PM ﬁsheries and watershed
network, and they provided other information on network properties such as clustering
and centrality. In H9, we hypothesized that cluster formation will emerge among groups
with similar organizational domains and/or counties. As illustrated in Figure 2.3, this
was the case for the seven clusters in the network. Four cohesive subgroups were based
on county similarities, and three were primarily based on domain similarities in natural
resource , agriculture/extension, and economic development orientations. These ﬁndings
complement the results from the pz-like model, which found tie formation patterns to be
strongly inﬂuenced by county similarity and domain similarity between organizations.

The sociograms also enabled us to determine which organizations held a central
position in their cluster. Central organizations were—quite literally—the organizations
located near the center of a cluster, yet they also had to serve as the focal point of most
interorganizational relationships in the cluster to be considered central. In H10, we
hypothesized that organizations with a central role in their cluster will have a natural
resource or local government purpose; be non-voluntary; have high involvement; and/or
be generalist in scope. Each of the clusters had at least one central organization (Figure
2.3), and there were 15 such organizations that met our criteria. As expected, 11 were
non-voluntary, 10 had a natural resource or local government planning purpose, and 8
were generalists; however, only 6 indicated having high involvement. Although beyond
the scope of our hypotheses, we noted that 14 believed in the “multiple social and
environmental beneﬁts” philosophy of natural resource management, 11 indicated having

a concern about habitat, 9 were more than 51 years old, and 8 were satisﬁed with PM

105

ﬁsheries. Many of these attributes agree with the ﬁndings of the pz-like model, such as
being a generalist, old, and being satisﬁed with PM ﬁsheries. In addition, purpose,
philosophy, and concern attributes, which were important for centrality, were combined

to create the domain variable, which was important in the pz-like model.

Summary and Conclusions

Collectively, these results point to several organizational attributes that can
predict organizational relations (Table 2.9). At the dyadic level, the most important
predictors were being from the same county and the same organizational domain (with
certain domains being more important predictors). Having a similar age of more than 51
years was also associated with interorganizational relations. Having the same generalist
interest scope was also suggested as a predictor, although much less consistently. These

results are consistent with interorganizational relations theories. All three theories

 

Table 2.9. Important predictors of interorganizational relations at the dyadic and
gooser levels.

 

 

 

Dyadic level Chooser level
same county“ generalist
same organizational domain“ satisﬁed with PM ﬁsheries
(especially certain domains) unsatisﬁed with PM ﬁsheries
both more than 51 years old inside the watershed
natural resource and environmental club domains

 

 

 

 

* those attributes most strongly associated with tie formation

106

explored here emphasized the importance of organizational domain and interest
similarity, and the institutional theory would predict that similarities in county and long
duration of existence would be important.

At the chooser level, important predictive variables include the effects of being a
generalist, satisﬁed with PM ﬁsheries, unsatisﬁed with PM ﬁsheries, being inside the
watershed, and from certain domains (Table 2.9). We would expect organizations that
are generalists and/or satisﬁed with PM ﬁsheries to indicate the presence of
interorganizational relations, as they may recognize the value of such relationships in
maintaining and enhancing organizational function. Interestingly, dissatisfaction with
PM ﬁsheries was also found to be an important predictor, perhaps due to some
organizations’ desire to promote their goals or positions on a speciﬁc issue, improve their
role in the network, or otherwise garner support for making desired changes in the
management framework. The watershed boundary is a conceptual constraint that frames
many PM-related issues, and we would expect that organizations located inside the
watershed may have a greater interest in forming or recognizing relationships with others.
They may need resources ﬁ'om organizations outside the watershed, or they may need to
establish, elevate, or protect their interests with local and distant political decision
makers. The only chooser effect to suggest any importance in predicting tie formation
was satisfaction with PM ﬁsheries, although the signiﬁcance of this effect was highly
questionable. Any importance, however, may indicate that organizations tend to indicate
having ties with other organizations that may be perceived as being positive or
supportive.

The relative importance of various domains was found to vary between analyses,

107

although natural resource orientations (management agencies and environmental clubs),
state and federal government agencies, educational organizations, and agriculture
organizations were the domains most consistently found to be important predictors of
strong ties at the chooser and/or dyadic levels. Given the very nature of the problem
domain—ﬁsheries and watershed management issues—we would expect natural
resource-oriented organizations to be well-linked with each other. The ecosystem- and
landscape-level issues in this domain require holistic, integrative approaches to
management, and this requires sustained interactions between those who manage the
resource. Why, however, would other domains like agriculture exhibit (sometimes much)
greater odds of tie formation? As this variable, domain, at the dyadic level only applies
to ties between organizations in the same domain, we can interpret this as being an
indicator of how well-connected a given domain is. Natural resource agencies and other
government offices had relatively lower odds of forming ties (strong and regular) with
each other, but this may indicate that they also form more ties with organizations in other
domains. If this is the case for natural resource agencies (as indicated by the chooser-
level effects in Table 2.8), then this is also very consistent with holistic, integrative
ecosystem management efforts. Other domains with greater tendencies to interact may be
relatively more insular; for example, education and agriculture domains demonstrated
high tendency to form strong ties, indicating they maintain interactions with each other as
part of their routine operations. Domains such as local government, economic
development, and outdoor recreation were not consistently shown to be signiﬁcant
predictors of strong ties, although they were important in predicting regular ties. This

suggests that organizations in these domains may demonstrate more independence from

108

each other (and possibly more interactions with other types of organizations) in their
routine operations.

Certain conclusions can be drawn from these analyses. First, the importance of
each organizational attribute varies according to whether it is applied to the chooser, the
chosen, or the chooser-chosen dyad. Satisfaction with PM ﬁsheries was the only attribute
that may be important at each level, which may indicate a general trend that these
organizations have stronger incentive to engage and invest in domain-level issues and
relationships. Other attributes were important as chooser level-effects, which may reﬂect
the wider range of variance among attributes that cause one to seek or recognize
interorganizational relationships. Those attributes important at the dyadic similarity level
indicate the strong tendency of organizations to form relationships with other
organizations that hold similar attributes.

Second, the overall interorganizational network patterns that emerged for the PM
ﬁsheries and watershed problem domain are generally consistent with interorganizational
relations theory. Most hypotheses on tie formation patterns were generally supported by
our data, and these ties collectively produced interorganizational networks that also
agreed with our hypotheses. For example, our observations on clustering and centrality
patterns generally agreed with the concepts represented in our hypotheses on tie
formation. Cohesive subgroups formed around counties and domains, and central
organizations displayed characteristics (e. g., generalist, satisﬁed, non-voluntary, long
existence, concerned about habitat and natural resources and/or local planning) that
would support the idea that they are inﬂuential network actors.

These conclusions have important implications for PM ﬁsheries and watershed

109

managers and others who would seek to facilitate interorganizational relations in the
problem domain. In trying to conserve, protect, allocate, and sustainably use these
resources, managers should note what kind of stakeholder organizations enter
relationships, why they enter these, and what kind of relations emerge between
organizations. In addition, they should note that in this network—although most ties
occurred occasionally—interactions were overwhehningly perceived to be positive
experiences and very important to maintain; this gives managers a solid base from which
to facilitate future interorganizational relationships. The breadth and depth of ﬁsheries
and watershed management implications will be further explored in Chapter 3, which
integrates these ﬁndings with various perspectives on stakeholder involvement in natural

resource management.

110

CHAPTER 3

INTEGRATING FISHERIES STAKEHOLDER ORGANIZATIONS
INTO A COMPREHENSIVE WATERSHED PLANNING PROCESS: IMPLICATIONS OF
INTERORGANIZATIONAL NETWORK DEVELOPMENT AND STRUCTURE

Introduction

Throughout North America, aquatic resource managers are increasingly
embracing two important concepts: that watersheds deﬁne appropriate management units
(Alexander et al. 1995; Rhoads et al. 1999; Taylor et al. 1999; Wesche and Isaak 1999)
and that stakeholders can serve as both management constituents and participants (Decker
and Krueger 1993; Grimble et al. 1995; Margerum 1999; Rhoads et al. 1999; Beierle and
Konisky 2000; Gregory 2000; Lundqvist 2000). These concepts are transforming
ﬁsheries and other aquatic resources management from what could formerly be described
as a linear, top-down, piecemeal approach to ﬁsh and habitat manipulation to a more
dynamic, inclusive, holistic approach that views ﬁsh communities in the context of
ecological and social landscape processes (Taylor et al. 1997). Many management
agencies are developing or implementing management systems that incorporate these
concepts, and some form of “watershed-based stakeholder participation” is now
frequently emphasized in aquatic resource management approaches (Alexander et al.
1995; Rhoads et al. 1999).

Integrating these concepts in ﬁsheries management, however, presents managers

with a wide range of challenges. Not only do they need to expand their deﬁnition of what

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management is (e.g., considering land along with water, considering stakeholders as
potential partners)—they also need to ﬁnd new ways to plan, implement, and monitor
day-to-day management activities that integrate watershed and stakeholder concepts.
Adopting watershed-based management boundaries can be a straightforward process, as
watersheds can be identiﬁed with geological and hydrological information (Alexander et
al. 1995; Wesche and Isaak 1999). Watershed boundaries can be superimposed on
existing management units (often based on political boundaries or jurisdictions), thereby
revealing where new management units should be geographically bounded or what
political units must become integrated with watershed processes.

Adopting new management concepts and roles for stakeholders, however, is less
straightforward. Managers may struggle with questions like: who are our watershed
stakeholders? How do they affect management, and how does management affect them?
What is—or could be—their role in management? How do we integrate them effectively
and efﬁciently? There are no obvious answers to these questions, as the stakeholder
population, their relation to management, and management issues are unique for every
watershed or management unit. Furthermore, the existing relationships among some
ﬁsheries stakeholders may be rooted in their interests surrounding other types of issues
(e. g., education, economic development, etc)"; therefore, their involvement in ﬁsheries
may affect their relationships surrounding other types of issues. Fisheries managers,

therefore, need a framework or process that can help guide them through these difﬁcult

 

Theories of social capital, or the potential to access resources through social
relationships, have also been applied to explain tie formation among stakeholders.
See Woolcock (1998) and Gittel & Vidal (1998) for more information on social
capital theory.

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stakeholder integration questions, which will ultimately enable them to increase the
likelihood of achieving holistic watershed-based management.

The need for stakeholder engagement in ﬁsheries management has been
recognized for the Pere Marquette (PM) River watershed in Michigan. As described in
Chapters I and H, this watershed is host to over 101 stakeholder organizations, many of
which hold strong opinions about PM watershed management—opinions steeped in a
long history of commitment to the river (Cassuto 1994). Many groups have worked
together on issues that they perceive as affecting their use of the river, although
“[r]elations between the citizens’ groups and goverrnnent agencies can get fractious, and
alliances among the groups themselves shift with the changing issues” (Cassuto 1994, p.
61) regarding the PM River and its resources. Nevertheless, these stakeholders can be
important participants in PM management, and this points to the need for a stakeholder
integration framework. Dave Washburg, a long-time recreationist on the PM, observed,
“‘[e]verybody’s going to have to work together...and I hope they can mark out a pretty
good blueprint of how to do it by using the PM’” (Cassuto 1994, p. 110).

In this chapter, we consider how the existing network of stakeholder relationships
surrounding the PM watershed can be better utilized—or even transformed—to facilitate
stakeholder integration in management. Networks provide great insight into studies of
stakeholder interactions, since “organizations participate in a network because they have a
critical stake in the issue, can make an important contribution in resolving it, or both”
(Chishohn 1996, p. 221, emphasis added). Stakeholder organizations often engage in PM
management issues, although this may be done in a reactionary, ad hoc, contentious, and

costly manner (Cassuto 1994). Nevertheless, there is a network of relationships among

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ﬁsheries-oriented PM stakeholders (see Chapter 2), which managers could utilize to
enhance stakeholder communication and participation. Although members of this
network may overlap with members of other issue-based networks, the potential PM
ﬁsheries-based stakeholder network is a distinct population that affects (or is affected by)
ﬁsheries management; thus, they are the focus of this study. We review concepts about
interorganizational network form and function, followed by a look at how features of the
PM stakeholder network may have implications for ﬁsheries management processes.
Since every watershed—and its issues and stakeholders—is unique, managers need a
framework for determining what kind of stakeholder involvement is appropriate and how
it can be achieved. We offer recommendations for such a framework and examine how it
may be applied to PM watershed issues and stakeholders. Finally, we consider the
strengths and weaknesses of using such a ﬁamework, so that application of this approach
and future research can continue to improve integration of the stakeholder network in

management.

Interorganizational Networks and Fisheries Stakeholder Integration

Importance of a Network Approach

Scholars of organizational sociology (Gray 1985; Lawless and Moore 1989; Hanf
and O’Toole 1992; Chisholm 1996; Agranoff and McGuire 1999; Hillier 2000) have
increasingly recognized that complex social metaproblems (e. g., crime reduction, health
care delivery, educational reform, environmental quality, etc.) cannot be solved by any

single organization. These social problems are multi-faceted, turbulent, and complex,

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and multiple stakeholder organizations each relate to different components of these
problems. Organizational functions are affected by the context of the metaproblem, yet
they can in turn affect only a fraction or subset of the overall problem, depending on their
set of strengths, specialties, responsibilities, and expertise (Hanf and O’Toole 1992).
Recognizing these limitations, many organizations engage in formal (i.e., deliberately
planned) and informal issue networks with other organizations as a way to share
information and other resources, collectively deﬁne problems and solutions, and
coordinate activities (Agranoff and McGuire 1999; Hillier 2000; see also Chapter 2).
This network approach enables them to tackle the issue from different fronts, reducing the
occurrence of overlapping or counterproductive efforts (Lawless and Moore 1989; Hanf
and O’Toole 1992). Given the prevalence of multi-faceted social metaproblems, an issue
network orientation is starting to dominate theories of public organizational
administration, replacing the hierarchical and bureaucratic orientations of traditional
organizational analyses (Agranoff and McGuire 1999; Hillier 2000).

Natural resource agencies, organizations that address the ﬁsheries management
issues in question, are highly subject to the types of problems that call for network-based
solutions. They are responsible for protecting, conserving, and allocating ﬁsheries and
other natural resources across the landscape, yet they generally recognize that they cannot
do this in isolation from other resource management agencies, resource users (recreational
and commercial), private land managers, and other types of direct and indirect
stakeholders (Edgerton 1987; Hanf and O’Toole 1992; Hanna 1999; Margerum 1999;
Hillier 2000). Managers have often tried to identify, consider, and incorporate the

interests and behaviors of stakeholder groups; at the same time, many of these groups

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have become increasingly active in seeking access to management decisions and
processes (Beierle and Konisky 2000; Gregory 2000). “However, what generally appears
to be missing is an overall structure for organizing the dialogue with diverse
stakeholders...” (Gregory 2000, p. 35); therefore, in addition to discerning the technical
aspects of ecosystem function, management agencies need to have personnel that are
skilled in communicating, coordinating activities, resolving conﬂicts, understanding
expectations, and otherwise facilitating joint efforts among a diverse and interacting set of
stakeholders (Rowley 1997; Agranoff and McGuire 1999; Beierle and Konisky 2000).
By recognizing stakeholders’ diversity and interactions in the context of their network
structure, managers can more efﬁciently and effectively integrate these groups and
enhance their likelihood of achieving management goals.

Many agencies are responsible for ﬁsheries and other ecosystem resources in and
around the PM River (Cassuto 1994), and we are interested in how the PM stakeholder
network affects their ability to meet their management goals. The network implications
and recommendations in this study, therefore, are equally applicable to multiple
management agencies with PM responsibilities: the Michigan Department of Natural
Resources, the US. Forest Service, the US. Fish and Wildlife Service, and the Little
River Band of Odawa Indians.5 Any of these agencies—alone or in combination—can
serve as a facilitator or coordinator of stakeholder network activities. Each needs to
understand their role in the network, others’ roles in the network, and the potential for

joint problem resolution via network coordination and mobilization (Chisholm 1996;

 

5 For the remainder of this study, the term “management agency” will be applicable
to any of these organizations (unless otherwise noted).

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Rowley 1997; Agranoff and McGuire 1999; Hillier 2000).

Network Characteristics and Organizational Function

Social network analysis enables one to examine organizations’ interdependence
and determine 1) how relationships form and 2) how the pattern of these relationships
collectively inﬂuence their behavior (Lawless and Moore 1989; Hanf and O’Toole 1992;
Chisholm 1996; Rowley 1997; Hillier 2000; Scott and Lane 2000). A review of issue-
based network characteristics, and how organizations function in these network-based
management settings, will help facilitate our assessment of management implications of
the PM stakeholder network.

In contrast to top-down authoritative structures, interorganizational networks
display a horizontal structure with virtually no ofﬁcial hierarchy between organizations
(Lawless and Moore 1989; Hanf and O’Toole 1992; Chisholm 1996; Agranoff and
McGuire 1999; Hillier 2000). Although interdependent, the organizations are loosely
linked and retain their purpose and responsibilities, perceived autonomy, and decision-
making independence, thereby allowing them to voluntarily engage (and disengage) in the
network (Chishohn 1996; Agranoff and McGuire 1999). Organizations enter the network
by forming relationships that they perceive will meet their needs for information,
resources, or inﬂuence (see Chapter 2); therefore, networks are dynamic. Network
engagement can be motivated by a desire to maintain vested interests as well as by
dissatisfaction with the status quo (Hillier 2000). As a result, the conﬁguration of the
network at any given time reﬂects organizations’ perceptions of the issue and their role in

addressing it. A central organization can be inﬂuential or powerful in the network, but

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they generally cannot directly control the activities of other network members. The
distribution of tasks and activities among network members is allocated through market-
like transactions (i.e., the interactions determine how tasks are assigned and coordinated)
as opposed to being centrally controlled (Lawless and Moore 1989).

As the number of interactions between organizations increase, communication
among network members becomes more efﬁcient (Rowley 1997). It becomes easier to
share ideas, information, and expectations, and organizations may be more likely to reach
consensus about common values and norms (Galaskiewicz 1985; Scott and Lane 2000).
Well-connected networks members (i.e., those having a ‘bonding’ role in the network;
Gittel and Vidal 1998), often forming sub-networks or clusters, can more easily monitor
each others’ actions, and they are therefore harder to manipulate or coerce. In addition, it
is easier for well-connected organizations or clusters to form coalitions or alliances in
response to new issues, and they may collectively exert more inﬂuence than other less-
connected sectors (Rowley 1997; Hillier 2000; Scott and Lane 2000). For well-connected
organizations, networks provide a forum in which “existing interorganizational networks
provided a latent structure that could be used for coalition building” (Galaskiewicz 1985,
p. 294; Chisholm 1996). In contrast, where fewer ties exist between organizations or
clusters, organizations become isolated, segregated, or marginalized, thereby decreasing
their ﬂow of communication, ability to reach consensus, and collective inﬂuence in social
and political decisions (Rowley 1997; Hillier 2000; Scott and Lane 2000). Some
organizations may have relationships that span between clusters or reach these isolated,
marginal groups, and these organizations (i.e., those having a ‘bridging’ role in the

network; Gittel and Vidal 1998) can be instrumental in accessing diverse resources as

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well as expanding and mobilizing network-wide activity (Hillier 2000). Woolcock
(1998) emphasized the need for both bonding and bridging organizations in sustaining
and strengthening network-based activities.

Once organizations are engaged in the issue network, coherent goals, visions, and
decisions may emerge through processes of formal and informal dialogue, debate, and
bargaining among network members (Lawless and Moore 1989; Hanf and O’Toole 1992;
Chisholm 1996; Hillier 2000). Broad access to information and disclosure of costs and
beneﬁts help develop the perceptions, expectations, trust, and visions that underlie
organizations’ goals and activities (Lawless and Moore 1989). This collective goal-
setting process requires some level of agreement in interests, policies, and obligations
among network members, and this multi-organizational perspective is important for
network-level goal attainment (Lawless and Moore 1989). Such agreement promotes
collective action on a higher-level problem (i.e., the metaproblem; Chisholm
1996)—action that is also compatible with organizational-level goal attainment. This
agreement, however, is harder to achieve in interorganizational conﬂict situations;
therefore, conﬂict identiﬁcation and mediation will help organizations ﬁnd ways to
complement, not compete with, each other (Hillier 2000).

Since organizational engagement in the network may or may not occur
spontaneously, a central organization—or closely allied set of central organizations—may
be strategically situated to initiate, encourage, and maintain (although not necessarily
force) interactions and ﬂows of resources between organizations (Lawless and Moore
1989; Chisholm 1996). Central organizations are more likely to have more alternatives

and sources of information and resources, which often translates into greater perceived

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prominence, power, and status (Rowley 1997; Scott and Lane 2000; see also Chapter 2).
When this ability to inﬂuence is combined with trust between organizations, a central
organization can facilitate or coordinate interactions between groups that would otherwise
remain separate from each other or isolated from the network (Rowley 1997; Agranoff
and McGuire 1999). Network coordinators can identify, assemble, and mobilize
stakeholder organizations (Hanf and O’Toole 1992; Agranoff and McGuire 1999);
facilitate the exchange of information, ideas, and resources; be a source of long-term
continuity, compromise, and conﬂict mediation; facilitate ongoing dialogue on network
goals and vision (Hanf and O’Toole 1992; Chisholm 1996); and otherwise “promote
integration by acting as the link needed to bring member agencies together, and get them
conunitted to the overall system” (Lawless and Moore 1989, p. 1176). Network
coordinators can also articulate, record, and monitor organizations’ commitments, thereby
facilitating communication and progress towards achieving network goals (Hanf and
O’Toole 1992). In the PM stakeholder network, a management agency could fulﬁl this
role of facilitator or coordinator, given their responsibilities and strong interests in the
problem domain. Although stakeholders’ trust of agency managers may vary ﬁ'om issue
to issue, stakeholders will likely recognize that these agencies are inﬂuential network

leaders, having the expertise, resources, and obligations to address many domain issues.

Management Implications of the Pere Marquette Stakeholder Network

The PM River watershed, having multiple stakeholders and various ﬁsheries

management issues (see Chapters 1 and 2; Cassuto 1994), provides an ideal setting for

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studying how interorganizational stakeholder networks relate to ﬁsheries management
processes. In Chapter 2, we used social network analysis tools to better understand the
formation of interactions among the 101 PM ﬁsheries stakeholder organizations identiﬁed
in Chapter 1. We studied: 1) the formation of interactions between two organizations
based on the properties of those organizations, 2) the tendency of a single organization to
report or recognize their interactions with others, 3) the properties of the overall network
relationships, and 4) the network properties that emerged when data on organizations and
interactions were aggregated. Results of this network analysis enabled us to understand
more about what kind of organizations form relationships as well as characterize the
network of stakeholder interactions, which in turn enable us to compare the PM network
with the general network properties explored above. In the following sections, we list the

ﬁndings in each of these areas and explore their implications for management processes.

Interactions Between Two Organizations

The network analysis results indicated that, surrounding PM watershed issues,
interorganizational relations are likely to form between two organizations that share
certain attributes:

0 Being from the same county. The PM watershed contains parts of four
counties: Mason, Lake, Oceana, and Newaygo. These political boundaries
inﬂuence organizations to interact with others according to political jurisdictions
and geographical proximity. This may indicate that county governments are
inﬂuential coordinators of sub-networks, or it may reﬂect a perceived need to
conform to any formal or informal local government planning processes.

0 Being from the same organizational domain. Organizational domain refers to a
group’s role or orientation in the network, and it is reﬂected in their purpose,
philosophy, and interests. Organizations in the education, agriculture, and
environmental club domains were most likely to form strong ties (i.e.,

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reciprocated, positive, and important) with each other, although organizations in
all eight domains (the above three plus natural resource agencies, other
government agencies, local government ofﬁces, economic development groups,
and sportsmens’ clubs) demonstrated a tendency to simply have ties associated
with each other. The emergence of ties between similar kinds of groups may
reﬂect a higher degree of familiarity, trust, and reliability that facilitates working
toward common goals.

0 Both more than 51 years old. As organizations exist for a longer period of
time, they become better established among network members. They develop a
more permanent presence in the area, and they may be perceived as having more
inﬂuence, credibility, or experience based on a longer history of involvement.
Members of these organizations may be more familiar and comfortable with each
other and with the issues, and they may perceive each other as being reliable,
stable partners.

These observed predictors of interactions between two organizations have certain
implications for management agencies. Knowing that organizations have a tendency to
interact with others in the same county, managers interested in widening communication
beyond political boundaries could design cross-county programs or initiatives. If, on the
other hand, managers wanted to target information within a certain county, they could
assume that groups in any given county are generally well-connected and that information
may spread easily among them. Similar implications result from organizations being in
the same domains; cross-domain communication efforts would need to be deliberately
designed to overcome the divergent orientations that characterize different domains, but
intra-domain communication could be easily achieved by targeting a few key
organizations in the domain. Knowing that older organizations tend to interact with each
other, managers could help broaden their range of interactions by identifying newer
groups and facilitating the transfer of experience-based knowledge, institutional memory,
and traditional goals. Conversely, newer organizations may have innovative ideas and

insights that could be informative to the traditional administrative culture of some older

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organizations.

Organizations That Report Having Interactions
Different properties emerged when describing those organizations (as opposed to
pairs of organizations) that are more likely to report or recognize having an interaction
with another organization. Those organizations consistently displayed certain attributes:
0 Being a generalist. Some organizations were interested in only one type of
issue (i.e., ‘specialists’ interested in either ecological, recreational, or user
behavior issues), while others were interested in more than one type of issue (i.e.,

‘generalists’). When an organization is a generalists, they have a wider range of
concerns or goals that may compel them to seek a wider range of interactions.

 

0 Being satisﬁed with PM ﬁsheries. When an organization is pleased with the
state of PM ﬁsheries management, they tend to be involved in more interactions.
We studied satisfaction as a cause of interactions, but it is also possible that
interactions cause satisfaction with management. Nevertheless, if an organization
is pleased with management, they may have more enthusiasm or perceive more
stability and rewards in forming and maintaining interactions with other
organizations. This was also the only attribute associated (although very weakly)
with organizations that were selected as partners in interorganizational relations.

0 Being unsatisﬁed with PM ﬁsheries. As an interesting contrast to the preceding
attribute, those organizations that are not pleased with ﬁsheries management may
try to engage in a process of changing management goals or processes. They may
form contentious interactions with organizations that support the status quo, or
they may form other relationships to gain support for their positions on how they
think management should be conducted.

0 Being located inside the watershed. Those organizations located inside the
watershed, as opposed to elsewhere in Michigan, have the most direct experience
with PM watershed issues. They have the most immediate access to the river, and
they may perceive a greater sense of urgency to engage in issues that could affect
their lifestyle. They may, therefore, have more incentive to work with other
organizations that could help them achieve their goals.

0 Being from a natural resource agency or environmental club. Organizations in
these two domains demonstrated a greater likelihood of reporting ties with other
organizations—both within and outside their own domain. Given the nature of
the topics (i.e., ﬁsheries and watershed issues) that helped deﬁne our stakeholder

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population, we would expect to see groups concerned with resource conservation,

protection, and management be highly involved in the network. Their high

involvement with numerous types of organizations may reﬂect an understanding
that ecosystem management requires input from diverse stakeholders.

Knowing what kind of organizations tend to report or recognize interactions—in
cases where interactions are not necessarily based on attribute similarity—enables
managers to identify these other active network participants. Since these organizations
tend to be generalists, managers can assume that active organizations are interested in,
receptive to, and communicative about many topics. Managers who wish to broaden
network activity to include specialists can assess their speciﬁc concerns and facilitate
greater levels of issue-based interaction, dialogue, or goal-setting. Active organizations
also tend to be satisﬁed as well as unsatisﬁed with PM ﬁsheries (as opposed to undecided
or neutral), indicating that active organizations are motivated by some care or concern
about the ﬁsheries, whether or not they want to maintain or change current management
systems. Managers, therefore, can focus on enhancing the exchange of ideas and
concerns among active members in attempts to resolve conﬂicts or facilitate debate, or
they can try to expand the network by reaching the undecided or neutral sectors of the
network. In addition, knowing that active groups are located inside the watershed can
lead managers to further utilize this well-connected and potentially inﬂuential sector to
set watershed-level goals, and it may lead managers to look for opportunities to extend
interactions to organizations in other parts of the state. Furthermore, since active
organizations tend to be natural resource agencies or environmental clubs, managers can

utilize their interactions to spread information throughout the network, and they can seek

other ways to target different domains. For example, under the guidance of skilled

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communication professionals, they could customize messages to better mesh with other
groups’ frames of reference (e. g., highlight agriculture or economic issues related to
ﬁsheries) or identify any common interests they may have with other organizations,

thereby facilitating cross-domain interactions.

Types of Interorganizational Relations
Data on the properties of interactions between organizations revealed information
about the kind of relationships that occur. Interactions among stakeholders surrounding
the PM had the following properties (see also Appendix G):
0 Type of interactions: Most interactions occurred in the form of leadership
communication (reported in 41% of the interactions). Resource contribution
interactions (e. g., providing information, expertise, facilities, etc.) occurred less
often (32%), and joint program collaborations occurred least often (27%).
0 Tone of interactions: The majority of interactions were positive (i.e.,
cooperative or casual) in tone (reported in 89% of the interactions); negative
interactions (i.e., tense or confrontational) were only reported in 6% of the
interactions.
0 Occurrence of negative interactions: When negative interactions did occur,
they occurred most often in leadership communication, less often in resource

contributions, and least often in collaborations.

' Frequency of interactions: Most interactions (77%) occurred once a month or
less; 23% of interactions occurred more than once a month.

' Importance of interactions: Most interactions (69%) were perceived as being
very important to maintain, 25% were thought to be somewhat important, and
only 1% were reported as being not important.

Since leadership communication was the most ﬁ'equently reported tie, this may

indicate that leaders are familiar with each other and that communication, generally

positive but occasionally negative in tone, is a common leadership function. Managers

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could target their communication efforts toward organizational leaders as a way to both
inﬂuence organizational behavior and spread information among other leaders. Resource
contribution and collaboration were reported less often, indicating that these may be
perceived as being less necessary or important, or that there may be institutional,
ﬁnancial, or other barriers that limit a group’s perceived Options for providing resources
or working with other groups. We think the ﬁrst explanation is less likely, as most
network members reported that their interactions were positive experiences and very
important to maintain. This indicates that managers and network members have a
foundation of conceptual agreement about the value of interacting with others. Managers,
therefore, may not necessarily have to tr}l to convince stakeholders of this value, yet the
fewer occurrences of collaboration—and the lower frequency of interactions in
general—may indicate that some organizations lack resources or information on how to
operationalize or implement collaborative activities. If this is the case, managers may
need to help identify and lift perceived barriers to interactions by providing supportive
information or resources or by demonstrating how to build collaborative partnerships and
integrate them with regular organizational functions. Managers should try to learn more
about the speciﬁc contributions and collaborations that now occur between organizations,
looking for ways to expand these efforts and to communicate about the processes of

implementing these interactions.

Emergent Network Properties

Data on the interactions between organizations, when aggregated, describe a

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network. The interorganizational stakeholder network surrounding the PM displayed the
following properties:

0 Cluster formation: Maps of interaction patterns revealed seven sub-networks,

or clusters with higher concentrations of interactions, that formed among the PM

stakeholder organizations (see Figure 2.3 in Chapter 2). Four of these were based
on county attributes, and three were based on common organizational domains

(natural resource, agriculture, and economic development orientations).

0 Central organizations: Within each cluster, certain organizations were

involved in more interactions than other organizations. These central

organizations tended to be concerned about habitat, have paid staff, be more than

51 years old, be generalists, be satisﬁed with PM ﬁsheries, and have a planning

function in their cluster (either natural resources or local government). These

characteristics describe the more well-established—and possibly more
inﬂuential—network actors.

The emergence of clusters, or cohesive subgroups, indicates that certain members
of the overall network are well-connected with each other. Managers may expect these
cluster members to communicate more easily, thereby having a greater likelihood of
developing common values and goals, a greater ability to form alliances, and a reduced
susceptibility to pressures from outside their cluster. Whether these clusters emerged out
of ad hoc interactions or more deliberate coordination attempts, cluster members are
potentially very inﬂuential as a more coherent or uniﬁed set of network members.
Managers should look for ways to build relationships, identify areas of common interest
or compromise, and otherwise work with these clusters—they should not try to split or
regulate cluster function. For example, while managers’ attempts at watershed-based
approaches may compel them to remove county-based communication barriers (i.e.,
political constructs that artiﬁcially divide ecosystems), managers should not try to prevent

or override intra-county communication, as this would risk resistance from county-based

afﬁliations and possibly lead to other unintended consequences (see Portes 2000).

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Instead, managers should encourage organizations in different counties to expand their
relationships across county borders while not abandoning their county-based afﬁliations.
Furthermore, by maintaining and utilizing county-based communication, managers could
also beneﬁt from coordinated land use and environmental planning at the more local (i.e.,
city and township) level.

Clusters are not the only inﬂuential network component—network inﬂuence may
also be demonstrated by central organizations. Conﬁguration of the PM network
indicated that a certain number of central organizations exist6 (at least one per cluster),
and these organizations display characteristics (see above) of being well-established.
Managers can look to organizations that, by reputation or demonstrated ability, are
widely-regarded as important, well-established network members. They can work with
these organizations to collectively coordinate and facilitate cluster and network activities,
or they can target important management information to central organizations so they can
help spread information and ideas throughout their respective clusters. Central
organizations also often served as cluster spanners or bridging organizations; therefore,
they can also help spread information to other clusters (and possibly to isolated
organizations, where such ties exist). Given the perceived power attributed to central
organizations, managers should try to minimize the potential for conﬂict with other
central organizations. Conﬂicts could diminish the ability of network members to
commrmicate effectively or reach any level of consensus, thereby reducing the likelihood

of activity coordination or collective goal setting.

 

6 In the interest of protecting the study participants’ privacy, we cannot reveal the
identity of these organizations.

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Overview of Management Process Implications

To summarize these management implications in the briefest sense: stakeholder
network information enables managers to be more active (as opposed to passive) in their
attempts to foster relationships between resource—dependent sectors of society. In turn,
such action could help managers encourage the collective goal-setting and action that will
enable more sustainable forms of resource use, allocation, and conservation. In the PM
watershed-based network, we were able to identify well-connected network stakeholders,
disconnected network stakeholders, inﬂuential centralized organizations, and channels for
distributing information within and between clusters. Such information is instrumental in .
developing effective and efﬁcient stakeholder communication strategies, as managers can
better target and package information to help them achieve stakeholder coordination,
conﬂict mediation, and goal-setting.

The implications of the stakeholder network can be further understood in context
of the overall natural resource management process. Taylor et al.’s (1995) “eight steps of
management” outlines a general—yet adaptive—sequence of stages that can be utilized in
ﬁsheries management: 1) setting the goal; 2) analyzing the resource in the context of the
goal; 3) diagnosing problems and identifying potentials; 4) prescribing a management
plan (includes objectives, techniques, and courses of action); 5) organizing to carry out
the plan (e. g., staff, equipment, budgets, and other resource needs); 6) implementing the
plan; 7) evaluating the plan’s outcomes; and 8) maintaining the successful components of
the plan. Several of these steps could utilize network-based information. For example, in
goal setting, managers need to understand to what extent their short- and long-term

goal(s) reﬂect watershed stakeholders’ interests and desires. It is possible that goals are

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inﬂuenced by the most vocal sectors of the network, whether or not any degree of
stakeholder consensus has been achieved. While complete consensus is unlikely,
managers should be aware if and how their management goals relate to network-wide
interests. Ifnecessary, they can try to expand the goal-setting discussions between
stakeholders and to other sectors of the network. In steps two, three, and four, managers
need to consider the various ways that different sectors of the stakeholder network affect
their ability to achieve short— and long-term management goals. Stakeholders’ roles in
these steps will vary according to the nature of the management issues; therefore,
managers need to treat stakeholders’ roles as one of many resource-related considerations.
In step ﬁve, organizing to carry out the plan, managers can truly capitalize on the
communication and coordination potential of the stakeholder network. Managers can use
network information to build and facilitate interorganizational partnerships that both
expand the scope of management activities and bring in new management resources (e. g.,
funding, equipment, etc.). They can also identify ways in which other organizations can
help communicate about the plan, and they can look for ways in which management
activities can complement and enhance other groups’ activities. Finally, in steps six,
seven, and eight, managers can again use the stakeholder network to communicate about
management successes and failures, as they send and seek information via network
channels about stakeholders’ perceptions, expectations, and evolving management
interests. In sum, the stakeholder network has great potential to inform many stages of
the resource management process.

Many of these management implications can be further understood by looking at

an example: the Fraser River Estuary Management Program (FREMP). This program is

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a cooperative, multi-agency, integrated resource management approach that was
developed in response to complex environmental problems and diverse user demands in
an urban estuary (Hanna 1999). In evaluating the success of this program, estuary
stakeholders were surveyed about their views on environmental quality, program impact,
and the overall structure of the FREMP. Hanna (1999) found that those who were
included in the program—well-connected government and industry respondents who
shared resources and coordinated efforts—held more similar, consistent, and positive
views about the program and the state of the estuary. Those who were most removed
from the program (i.e., isolated or marginalized network members, usually
nongovernmental environmental organizations) were more likely to hold dissimilar,
inconsistent, and critical views and expectations of the program and the state of the
estuary (Hanna 1999). This lack of stakeholder integration was thought to weaken the
program’s potential for collective goal-setting, planning, and successful implementation.
Although this study was not framed in a network context, its results concur with theories
about network structure and the behavior of stakeholder organizations. We suggest that a
network-based approach to FREMP could help identify other important stakeholders as
well as facilitate the communication and coordination that are necessary for effective

interorganizational management approaches.

Framework for Issue-based Stakeholder Involvement

To this point, we have considered how features of the PM stakeholder network

relate to general management goals and processes. Does this mean that managers should

131

involve all network members in all management issues? No. Although managers should
use the wider stakeholder network as a source of social context and communication, they
do not necessarily have to utilize the entire network to address every speciﬁc management
issue. “The manager must constantly bear in mind the lineup of potential collaborators,
although the actual number engaged may be small” (Agranoff and McGuire 1999, p. 22),
depending on who affects this issue and vice versa. Furthermore, even when managers
utilize the entire network in a general management approach, they would still need to deal
with emerging issues and problems that may not be well-addressed by current members
network or existing management objectives and procedures. Managers need to anticipate
and respond to these speciﬁc issues and topics, which almost inevitably involve some set
of stakeholders’ interests, perceptions, and expectations. For each management issue,
large or small, the network (or sub-network) that managers attempt to mobilize must
reﬂect the diversity and complexity of the problem it needs to address and the methods
used to address it (Chisholm 1996; Agranoff and McGuire 1999). Managers can better
deal with these speciﬁc issues if they understand how the stakeholder network—and
various subsets of it—affect how issues may be framed, understood, and resolved.

The dynamic nature of stakeholder networks, explored earlier, implies that
networks are ﬂexible, adaptive, and responsive to emerging issues (Lawless and Moore
1989; Chisholm 1996; Agranoff and McGuire 1999); therefore, managers can engage
different sets of network members on an “as needed” basis. To do this well, however,
they may need some type of method for identifying which network members should be
involved as well as how they should be involved. Fortunately for managers and

stakeholders, social scientists have developed processes for exploring the social impacts

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of environmental issues and actions. As deﬁned by Burdge et a1. (1994, p. 78), “a social
impact assessment is an analysis of past and present impingements upon social conditions
and processes and a projection of likely future consequences of proposed interventions in
social interactions and relationships between and among people.” Social impact
assessments (SIA) were developed to understand how a proposed action on the bio-
physical environment (i.e., the “intervention”) will affect social communities and
relationships (Burdge et al. 1994). While it can be difﬁcult to predict such consequences
with complete accuracy (see Portes (2000) for more on unintended consequences of social
action), the basic SIA concepts and methods can be adapted to help understand how
speciﬁc management issues affect past, present, and future stakeholders. The last few
phrases in the Burdge deﬁnition—about how events may affect the “...social interactions
and relationships between and among people”—highlight the applicability to studying the
network of stakeholders, their relationships to each other, and their relationships to the
issue in question.

The SIA process occurs in multiple stages, which are described in great detail by
the Interorganizational Committee on Guidelines and Principles for Social Impact
Assessment (1994, p. 111-119). Brieﬂy, once an environmental action or alteration is
proposed, project planners and social impact analysts would: 1) identify potentially
affected publics and their concerns, 2) identify proposed actions and alternatives, 3)
describe the human environment in the area of inﬂuence, 4) identify probable impacts, 5)
investigate the effects of impacts (along with the public’s responses and cumulative
impacts), 6) recommend changes to the proposed action (and prepare a mitigation plan),

and 7) develop a monitoring plan. Steps one and four are based on “scoping,” an

133

 

investigative process through which relevant information is determined by in-depth
exploratory studies on various aspects of this issue (e. g., who is affected? what are the
impacts?; Burdge and Robertson 1994).

How can this proj ect-based social impact assessment process be adapted into an
issue-based process for engaging network stakeholders? Initially, managers should verify
that a network-based approach is appropriate. For example, managers need to recognize
that the speciﬁc issue or problem may be so complex or widespread (i.e., a complex
social metaproblem, as opposed to a simple issue with negligible social impacts) that they
cannot resolve it independently. In addition, managers need to conﬁrm their roles or
responsibilities related to taking leadership on the issue, take stock of the resources (e. g.,
time, effort, funding) they can devote to network coordination, and assess the degree to
which stakeholders trust them and each other. Once the appropriateness of a network-
based approach has been conﬁrmed for a given issue, we recommend the following
process, generally based on the above SIA process, for identifying and involving
concerned stakeholders:

Step 1: Define the issue and any related issues. While it may seem that an issue
is obvious, it is important to ensure that the issue has been examined from all sides and
from all perspectives. Short-, medium-, and long-term aspects of the issue should be
considered through a thorough scoping process, as should any tangential or indirect
aspects. Any relevant social and ecological indicators (e. g., measurable components that
reﬂect the status of the issue) and stakeholders’ values (e. g., preservation, utilization, etc.)
should be identiﬁed. For example, an issue such as streambank stabilization (i.e., the

reduction of erosion through the use of vegetation and physical buttresses) may seem like

134

a straightforward problem with a clear solution, but closer inspection may reveal that the
issue—and its proposed solutions—involves more complex issues related to long term
habitat quality (e.g., native plant use, spawning site materials, ﬂow regimes), private
property (e.g., landowner training, incentives, perceived violation of property rights),
logistics (e.g., access to erosion sites, road and bridge construction, project funding),
angler behavior (e. g., walking down steep slopes, disturbing vegetation), and so on
(Alexander et al. 1995). As an issue (and related issues) becomes better identiﬁed
through scoping, managers can increase the likelihood that they will comprehensively
address it.

Step 2: Identify the stakeholders that relate to the issue(s). Based on the
ﬁndings of the issue deﬁnition stage, managers can identify those network members (and
possibly new stakeholders not previously included in the network) that affect, or are
affected by, the issue(s). Through interviews, focus groups, or other information-
gathering processes, managers can utilize network channels to work with stakeholders in
verifying their understanding of the issue from the ﬁrst step. Continuing the above
example, managers interested in stabilizing riverbanks should clearly consult with other
riparian public land managers and property owners, but they should also consult with
anglers, scientists, construction site managers, vegetation suppliers, and so forth.
Speciﬁcally, managers should determine how stakeholders view the issue in question, and
they should ask stakeholders to identify other possible stakeholders that may have been
overlooked. Having a common understanding of the issue and a (possibly) complete set
of stakeholders, managers and stakeholders can proceed with actually addressing the

issue.

135

Step 3: Determine it; how, and to what extent the issue(s) affects and is affected
by stakeholders. Managers may not be aware of the full range of ways in which the issue
affects the social and ecological surroundings of different stakeholders and vice versa.
Alternatively, stakeholders may perceive that the issue will result in certain impacts that
may or may not actually occur, or they may not be aware of how they affect the issue.
Utilizing another scoping process, this step allows managers and stakeholders to jointly
investigate and commmricate about their relationships, perceptions, expectations, and
knowledge concerning an issue. This can be facilitated by utilizing network
communication channels, and it can increase the likelihood of reaching wider consensus
about the nature of the issue and its resolution. Again, this step may reveal other issue
aspects (e. g., indicators or values) and stakeholders that were previously overlooked, thus
enabling managers to fortify their network-based approach to issue resolution. For
example, managers may realize that they have overlooked farmland erosion issues in
lower stretches of the river. They can then proceed with a wider understanding of
agriculture practices and stakeholders that need to be incorporated.

Step 4: Develop and implement solutions or collective problem-solving
approaches. This step again utilizes the coordination potential of the stakeholder
network. Once an issue-based network (or sub-network) of relevant stakeholders has
been mobilized, they can engage in the dialogue and debate through which collective
solutions, compromises, or partnerships may emerge. Any necessary tasks or
responsibilities related to the issue can be negotiated among network members (avoiding
any redundant or counterproductive efforts), and managers can proceed with

coordinating, implementing, or otherwise facilitating action on these joint approaches to

136

the issue. Revisiting our example, managers can help build linkages between riparian
landowners’ associations, farmers, and Extension agents that will help spread information
about private riverbank management techniques; they can also work with other managers
and private foundations to fund bank restoration projects.

Step 5: Conduct follow-up evaluation and monitoring of the issue and the
network. Once issue-related activities have been implemented, managers can oversee any
necessary evaluation and monitoring to determine the extent to which issue-related goals
have been achieved and how network members may have been affected. Managers and
stakeholders can utilize network communication channels to spread and collect
information about their roles and outcomes—including information about how issue-
based activities may have, in turn, affected the conﬁguration of ties within the greater
stakeholder network. By compiling and analyzing this information, managers can inform
the wider network about trends in issue resolution as well as identify emerging issues,
some of which may need to be addressed by revisiting step one and mobilizing other
sectors of the network. In this sense, network-based management and communication
strategies are iterative, evolving processes in which different sectors of the network are
mobilized at different times. In our example, managers could track the progress of
streambank stabilization projects, communicating to funders, anglers, and the wider
stakeholder network about the evolving status of this issue. If the stabilization projects
were successﬁrl, for instance, the ties between managers and funders may be strengthened
or expanded, thereby facilitating the funding of other projects. On the other hand, if bank
stabilization was not achieved, managers may need to consider if and how this outcome

may hurt relationships between network members (which could hinder future network

137

mobilization efforts). Either way, managers need to maintain relationships with the
active sub—network and other members of the network in order to facilitate the

adaptability and success of future network-based activities.

Conclusions

We have attempted to demonstrate the powerful communication and coordination
potential that is embedded within stakeholder networks. While our speciﬁc ﬁndings on
the PM network led us to explore the management process implications tailored to that
watershed, we believe that any stakeholder network surrounding complex resource
management issues can be identiﬁed and incorporated into both general and issue-based
management approaches. Management agencies and other groups that seek to better
understand stakeholders in context of their network structure need to invest in securing
and maintaining this kind of information, but doing so provides a strong foundation for
more effective stakeholder integration. The greatest strength of this network approach
may lie in its ability to adapt to different management scenarios and issues. A list of
potential stakeholders can be easily modiﬁed as the nature of management issues evolves,
and managers’ understanding of the interrelationships between stakeholders (and their
relationship to the issues) can be updated as needed. This ﬂexible approach to
stakeholder communication and coordination enables managers to develop innovative and
relevant management systems in response to society’s changing resource interests.

This stakeholder network approach is not immune to weaknesses, however. First,

despite all efforts at identifying and integrating stakeholders, some—especially

138

individuals not afﬁliated with an organization—will inevitably be left out of management
discussions. For example, in this study, canoeists are ever-present users of the PM river,
yet they are not represented in the network at the organizational level (except indirectly,
through recreation-promoting organizations). Nevertheless, the network could be adapted
to include individuals, if managers feel they can be easily identiﬁed. Second, managers
cannot assume that all identiﬁed stakeholders will express care or concern about how they
affect management or how management affects them. Even if managers think a group
should become involved in management, members of the group may remain indifferent
despite managers’ efforts to inform and integrate them. When this is the case, managers
may need to be creative in ﬁnding ways to compensate for the group’s decision to opt out
of the network. Third, managers cannot assume that those who are engaged in the
network will easily or harmoniously reach any degree of consensus. Network members
may persist in holding opposing views or positions, and managers may not be able to
facilitate resolution or compromise. “Simply forming a network does not ensure a high
degree of cooperation among member agencies” (Lawless and Moore 1989, p. 1177). To
fully realize the potential of the network, network members must have some degree of
commitment to collective problem solving.

Clearly, there are costs and beneﬁts associated with network-based
communication and coordination strategies. Such strategies can be costly in terms of
managers’ time and effort, which are required to develop a foundation of shared values
and trust among diverse network members (Lawless and Moore 1989; Agranoff and
McGuire 1999; Rhoads et al. 1999). Time and personnel are generally in short supply

among management agencies, and managers may be tempted to resort to traditional

139

modes of bureaucratic, top-down systems that appear easier and less costly in the short
run. These are not the only limiting factors, however. Since network coordination can be
largely dependent on the administrative and leadership support of the “lead” network
agencies, the overall utilization of the network may be vulnerable to each agency’s
technology needs, legal constraints, institutional resources, political power, and other real
and perceived constraints on management options (Agranoff and McGuire 1999).
Furthermore, networks-based approaches present risks related to accountability. Network
members can be held accountable for their limited role in network-based strategies and
approaches, but none—even a coordinating management agency—are fully accountable
for everything since responsibility is dispersed throughout the network.

Despite the potential risks and real costs, the beneﬁts of successful network-based
approaches offer tremendous opportunities for redeﬁning the relationships between
managers, stakeholders, and the issues about which they are concerned. Success is
achieved if organizations perceive that they have gained more via the network than they
would have otherwise by working separately (Agranoff and McGuire 1999). We believe
that successful network utilization—even if pursued through long-term adaptation of
network members and approaches—will enable managers to achieve more management
goals than they otherwise would have. Knowledge of the stakeholder network will
improve their ability to understand, appreciate, and integrate the social dimensions of
resource management. Having obligations to uphold the public trust and develop
sustainable resource management systems, managers may reap long-term beneﬁts by

exploring and utilizing the possibilities inherent in stakeholder networks.

140

EPILOGUE

Introduction

Each of the preceding chapters served as a semi-autonomous contribution, looking
at different stages of the stakeholder identiﬁcation and integration process. As a whole,
the chapters provide natural resource managers with guidelines for identifying ﬁsheries
stakeholder organizations, understanding how and why they interact with each other
regarding ﬁsheries management issues, and exploring how they can become better
integrated with watershed-based management efforts. Lessons learned about stakeholders
surrounding the Pere Marquette (PM) River watershed may be directly helpful to that
watershed-speciﬁc set of resource managers, but these lessons can also provide—for any
management setting—an analytical framework for other stakeholder network studies as
well as a foundation for resource managers’ current and future communication strategies.
To develop these future studies and strategies most effectively, however, we need to
examine the sources of error that may limit the applicability of this overall study. Here
we review the uncertainties surrounding this network-based approach and recommend
ﬁrture research that can help minimize these uncertainties and further build on this

research.

Potential Sources of Error

The quality of any analysis is strongly affected by the accuracy and reliability of

141

the data collected. As a representative of an organization answered the survey, they may
have done so in a way that introduced error in the data collection process. For example,
the answers from any one organizational representative may have differed from the
answers that another representative of the same organization would have provided, due to
differences in their personal knowledge, relationships, and perceptions. Therefore, it was
very important that an appropriate (i.e., knowledgeable of organizational relations)
representative be identiﬁed to answer the survey.

Even if the most appropriate person answered the survey on behalf of their
organization, they may still have misinterpreted or misunderstood the survey instructions.
This may have led them to answer the questions with other guidelines or subject matter in
mind, which may, for example, cause them to report their ties with other groups based on
non-ﬁsheries issues. In addition, they may have misperceived or misrepresented their
actual frequency, type, or tone of involvement with other groups. They may have also
failed to answer some questions completely, thereby providing no data about certain
aspects of their group or their ties with other groups.

Still other sources of error were introduced when we compiled the responses from
many different organizations. Each respondent may have held differing perceptions about
what they think is a PM-related issue, what is a ﬁshery issue, and what constitutes an
interaction with another group. These inconsistent perceptions can lead to error, even if
the respondents answered accurately based on their perceptions. For example, one group
may have indicated on the survey that providing a referral to another group about a
canoeing question from the public was an “interaction” about a “ﬁshery issue,” while

another group may not have indicated the same event on the survey. These types of

142

inconsistencies between respondents can lead to inaccurate conclusions about the overall
nature and extent of interactions.

While some of these errors were due to the input of survey respondents, some
errors may also have been rooted in survey design ﬂaws. This survey was very long and
detailed (see Appendix B), compared to many other surveys, and it required a great deal
of time and attention from the respondent. A more concise survey would not have
required such stamina to complete, thereby decreasing the likelihood of errors. Further,
some instructions may have been unclear, and some of the questions may have been
worded in ways that limit their ability to provide helpful information. For example, by
only asking about three types of interorganizational ties, we may have missed other types
of interactions that are important for ﬁsheries management communication strategies.

Other sources of error were also possible. For instance, if we did not deﬁne our
stakeholder population correctly in the ﬁrst phase of the research, our survey may not
have gone to actual ﬁsheries stakeholder organizations. We may have omitted some
groups that were stakeholders, and we may have included some that were not. In
addition, data collection occurred over a four month period; therefore, those who
answered quickly could not have included information about ties in subsequent weeks
that were reported by later-answering organizations. Finally, we may have erred in data
coding, entry, modeling, or interpretation.

These types of errors could be minimized in future studies, but we believe any
survey-based data collection and analysis process is subject to such limitations. Despite
these potential sources of error in this study, we did not ﬁnd strong evidence of major

errors and we are reasonably conﬁdent that such errors did not signiﬁcantly affect our

143

results or conclusions. For example, we only know of 11 additional organizations that
were not produced by our initial stakeholder identiﬁcation process, and each additional
group was not suspected to be highly involved (each was indicated only once). In
addition, the types and nature of survey answers were reasonably consistent across
organizations, as we did not receive outlandish feedback or other evidence of signiﬁcant
aberrations. Survey pre-testing, therefore, may have helped minimize any
misunderstandings or misinterpretations. We received a very high response rate,
diminishing our concerns about non-response bias, and most respondents completed most
questions. Many responses could also be replicated or veriﬁed with other data sources
(which largely agreed with the responses), and we were able to double-check data coding
and entry. Our overall analysis, while not immune to errors, appears to have produced

reasonable results.

Areas for Future Study

First, we believe this study could be strengthened by supplementing this
quantitative analysis with qualitative analysis. This quantitative analysis can provide
statistical results on tendencies to form ties, but it may not necessarily capture all the
underlying dynamics that led to these results. Qualitative analysis can provide rich
descriptive information, but it may not reveal the extent or prevalence of a certain
outcome like tie formation. Therefore, we believe that both quantitative and qualitative
analyses, in combination, provide the most information about system dynamics. For

example, if we were to follow this study with in-depth interviews or focus groups of

144

organizational members, we could obtain much more information on the speciﬁc issues,
concerns, motivations, and goals that led to tie formation between groups. In addition,
we could utilize the wealth of information that has been developed about stakeholder
activities in and around the PM River watershed, such as watershed-based meeting
records, transcripts of in-depth interviews, ﬁsheries management documents, and
personal observations. By comparing this information to the results of this study, we
could achieve a deeper level of understanding about the dynamics of stakeholder
relationships with each other and to the ﬁsheries issues.

Other aspects of this study could be modiﬁed in ways that provide additional
information about the PM stakeholder network. In this study, we applied the selection
model to study how organizational attributes lead to tie formation, but we could also have
applied the inﬂuence model to see how network structure inﬂuences organizational
attributes and behaviors (see Gulati 1995; Gulati and Gargiulo 1999). In addition, instead
of taking a “snapshot” of all relationships occurring in a given time frame, we could have
conducted longitudinal data analysis to study how the PM network changes, look for
cause-and-effect relationships, or look at other network trends over time (see Perrucci and
Lewis 1989; Selsky 1991; Gulati 1995; Gulati and Gargiulo 1999). Even more
ambitiously, we could combine the inﬂuence, selection, and longitudinal models, along
with qualitative analyses, for the most comprehensive assessment of network structure,
dynamics, and evolution.

Still other future research could also expand the scope of this study. For example,
our population of network members could be modiﬁed in ways that provide additional

information on network dynamics. If we were to change the conﬁguration of the network

145

population (e. g., adding for-proﬁt businesses or deleting some set of stakeholders from
this research) and compare it to this study, we could see if and how changes in the
population of stakeholders affects our results. This would allow us to see how different
stakeholder identiﬁcation criteria may affect our understanding of the network. We could
also identify a population of stakeholders based on another watershed entirely, replicating
this study in another setting. Comparing these results would enable us to see if and how
well our results may apply to other settings, and we could see if any generalizable trends
emerge.

As opposed to expanding this study outward, we could also look inward at the
dynamics within organizations. Instead of having one respondent answer on behalf of
their organization, we could select some set of groups with known relations and study the
intraorganizational networks among organizational members. We could examine if their
personal ties extend to other organizations, and examine how the characteristics of
organizational members affect this tendency. This would help us understand if and how
individual people or dynamics within organizations may affect or better explain the
patterns observed for interorganizational networks (see Lincoln 1982; Selsky 1991); for
example, we could study the networks within a natural resource agency—where
collaboration is essential—to better understand how and why ties are formed and
facilitated in that setting. We could also examine other components of this study in more
depth, for example, by comparing the sub-networks formed around different types of ties
(Van de Ven et al. 1979), resources exchanged, communication media, or collaboration
strategies.

Furthermore, this study could also serve as a preliminary stage in further research

146

about other phases of interorganizational relations processes. This study primarily looked
at network structure and formation—not necessarily network function, processes, content,
or outcomes. Therefore, it excludes some factors that could be important for network-
based management. For example, we could examine if and how stakeholder
characteristics and relationships affect tie maintenance and dissolution, as well as
decision-making processes among network members. We also need to remember that a
ﬁsheries-based network is but one of many issue-based networks, and there may be ways
in which this network overlaps or relates to other networks in the region (e. g., economic
development, public health, etc.). The processes through which these networks affect
each other may provide additional insight into their roles and functions of networks in the
greater social system.

For any setting or social issue in which relationships, interactions, or collaboration
are important, network analysis provides a powerful tool for analyzing and explaining
who is interacting, as well as how, why, and when they are interacting. This information
enables managers in network settings to be more deliberate and proactive in developing
interaction strategies, which has great potential to make management more effective and
efficient. The potential applications of network analyses—in natural resources and other
management settings—is substantial, and the statistical approaches for analyzing
networks are becoming more reﬁned and powerful. We hope to encourage and expand
the application of network analysis in natural resource sociology and policy, since
sustaining ecosystem function and productivity demands integration and collaboration

among resource stakeholders.

147

APPENDIX A

148

APPENDIX A

Complete List of Fisheries Stakeholder Organizations

Fisheries Stakeholder Organizations in, concerned about, or having impacts on the Pere
Marquette River watershed and ﬁsheries.

 

Federal Government:

-I‘.
.I

 

US Fish and Wildlife Service, Ludington Biological Station

US Forest Service, Baldwin Ranger Station

USDA Farm Service Agency, Mason-Oceana County Ofﬁce

USDA Farm Service Agency, Osceola-Lake County Ofﬁce

USDA Farm Service Agency, Newaygo County Ofﬁce

Natural Resource Conservation Service, Mason-Lake Conservation District
Natural Resource Conservation Service, Oceana Conservation District
Natural Resource Conservation Service, Newaygo Conservation District
Little River Band of Ottawa Indians

 

State Government:

 

MDNR Fisheries Division, Cadillac Field Ofﬁce

MDNR Forest Management Division, Cadillac Field Ofﬁce
MDNR Wildlife Division, Baldwin Field Ofﬁce

MDNR Parks & Recreation Bureau, Cadillac Field Ofﬁce
MDNR Law Enforcement Division, District 6, Cadillac
MDNR Law Enforcement Division, District 9, Grand Rapids
MDNR Natural Rivers Program, Lansing

MDEQ Land & Water Management Division, Cadillac
MDEQ Land & Water Management Division, Grand Rapids
MDA, Region 3 Ofﬁce, Grand Rapids

MDOT, Cadillac Transportation Service Center

MDOT, Muskegon Transportation Service Center

 

County Government:

 

Mason County Drain Commissioner

Mason County Road Commissioner

Lake County Road and Drain Commissioner
Oceana County Drain Commissioner
Oceana County Road Commissioner
Newaygo County Drain Commissioner
Newaygo County Road Commissioner

 

 

 

149

 

Township Government:

 

Pere Marquette Township Pleasant Plains Township
Amber Township Yates Township
Custer Township Chase Township
Branch Township Crystal Township
Sweetwater Township Colfax Township
Peacock Township Troy Township
Webber Township Lilley Township
Cherry Valley Township Home Township
Pinora Township Barton Township
Summit Township Leavitt Township
Riverton Township Beaver Township
Eden Township Merrill Township
Logan Township Monroe Township
Lake Township Norwich Township

 

 

City Government:

 

City of Ludington City
City of Scottville
Village of Baldwin

 

Inter-governmental Organizations:

 

Great Lakes Fishery Commission

Great Lakes Fishery Trust

Timberland, Resource Conservation & Development

Conservation Resource Alliance, Resource Conservation & Development

 

Local & Regional Economic Development Organizations:

 

 

Ludington Chamber of Commerce and Convention & Visitors Bureau
Scottville Chamber of Commerce

Mason County Economic Development Corporation

Lake County Enterprise Community Board

Lake County Chamber of Commerce and Tourist Center

Oceana County Economic Development Corporation

Oceana County Tourism Bureau

Newaygo County Tourist Council

West Michigan Shoreline Regional Development Commission

West Michigan Tourist Association

 

150

 

 

Protection-oriented Conservation Organizations:

 

Pere Marquette Watershed Council

Ruby Creek Conservation and Recreation Club

Sierra Club, Mackinaw Chapter, West Michigan Group
Michigan Audubon Society, Oceana Sub-chapter

West Michigan Environmental Action Council, Grand Rapids
The Nature Conservancy, Lansing

 

Recreation-oriented Conservation Organizations:

 

Michigan United Conservation Clubs, Lansing staff

Fin & Feather Club of Mason County

Oceana Sportsmen's Club

Newaygo County Sportsmen's Club

Citizen's Sportsmen's Club

Trout Unlimited, West Michigan Chapter

Federation of Fly Fishers, West Michigan Hacklers

Michigan Steelhead and Salmon F isherrnen’s Association, Westside Steelheaders
Pere Marquette Rod & Gun Club

 

Other Special Interest:

 

Lake County Riverside Property Owner's Association
Ludington Area Charterboat Association

Michigan River Guides Association

Michigan Farm Bureau, Mason County

Michigan Farm Bureau, Oceana County

Michigan Farm Bureau, Newaygo County

 

Education and Information:

 

 

MSU Extension, West Central Region ofﬁce
MSU Extension, Mason County ofﬁce

MSU Extension, Lake County ofﬁce

MSU Extension, Oceana County ofﬁce

MSU Extension, Newaygo County Extension
MSUE Natural Features Inventory, Lansing
West Shore Community College

 

151

 

APPENDD( B

152

APPENDIX B

Replication of the Survey Instrument

Organizational Linkages
In and Around the
Pere Marquette River Watershed

 

 

 

 

 

Michigan State University
Department of Fisheries & Wildlife

July 2000

153

Part I

 

 

For this survey, nagg! rgogrcg include fisheries, forests, water, air, land,
wildlife, and other environmental features that humans use and enjoy.

Please answer the following questions on behalf of your local organization. If
your organization has a statewide or national afﬁliation, please answer this

survey only on behalf of your local chapter or ofﬁce.

Many of the questions refer to the Pere Marquette River watershed. Please refer

 

 

 

 

 

to the following watershed map:
MasonCo. LakeCo.
§ Pere Marquette
£9 hidingtar i M , .. River Watershed
s r i -
g 5 Z\ N
l

 

Newaygo Oo.

 

154

. Approximately what year was your local organization, chapter, or office established?

 

. Approximately how many people currently belong to your local organization, chapter,
or office?

(We would like to know how many people work there, attend meetings, or otherwise
identijy with your local group.)

2a. How many are paid staff? 2b. How many are volunteers?

. Which phrase best describes your organization’s primary purpose?
(If you select more than one, please Qirgle one of them as the primary purpose.)

Cl Natural resource conservation and/or management
CI Natural resource recreation and/or tourism

Cl Natural resource protection and/or preservation

Cl Education and information

[:1 Representation of property owners

Cl Representation of private businesses

CI Local government planning

1:] Other (please specify):

 

. Which statement best reﬂects your organization’s primary philosophy regarding
natural resources? (Check one.)

1:] “Natural resources should be protected from any further human impacts.”

[3 “Natural resources should be developed to maximize regional economic activity.”

[:1 “Natural resources should be managed to produce multiple types of social and
environmental beneﬁts.”

Cl Other (please specify):

 

 

. To what degree is your organization involved in natural resource issues in the Pere

Marquette River watershed? (Check one.)
CI Not involved -' (Please go to 6.)

CI Somewhat involved -' (Please go to 5a.)
U Very involved —9 (Please go to 5a.)

155

5a. Approximately how many years has your organization been involved with these
issues in the Pere Marquette River watershed? years

. Fisheries management addresses the production and harvesting of ﬁsh and the
conservation of aquatic resources and habitats. Is your organization satisﬁed with
ﬁsheries management in the Pere Marquette River watershed? (Check one.)

El Yes
D No -’ (Please go to 6a.)
Cl Undecided or neutral -* (Please go to 6a.)

6a. Please explain your local organization’s concerns with ﬁsheries management in
the Pere Marquette River watershed:

 

 

 

 

 

 

. In the Pere Marquette River watershed, what kinds of ﬁsheries issues is your
organization most interested in? (Check all that apply.)

 

CI Fishing access Cl Habitat quality

CI Regulations El Recreational experiences
CI Enforcement [:1 Sea lamprey control

El Status of ﬁsh populations [:1 None

El Conﬂicts with other river users El Others (please specify):
El Stocking

 

156

Part II

 

We would like to learn about your organization’s interactions with other
organizations in the Pere Marquette River watershed. Please note:

0 We’re only interested in those interactions that have occurred
regarding a fishorigrelated issue.

0 We’re only interested in those interactions that have occurred sinoe
man/.132.

DIRECTIONS: For each organization, local chapter, or ofﬁce listed on the
following pages, please answer the set of questions that follows. Please feel free
to consult with other members of your organization in answering these
questions. If you answog “Nevog” to Question A, oloose move on to tho oog
orgonization listed. While this section appears long, it actually goes quickly for
most organizations.

 

 

 

Acronyms and Abbreviations:
Assoc. = Association
Cent. = Central
Cons. = Conservation
Dept. = Department
Dist. = District
Div. = Division
L. = Lake
MDEQ = Michigan Department of Environmental Quality

MDNR = Michigan Department of Natural Resources
Mgt. = Management

MSU = Michigan State University

Nat. = Natural

Serv. = Service

Trans. = Transportation

US = United States

USDA = United States Department of Agriculture

157

 

 

 

 

 

 

 

 

 

 

 

 

 

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QUESTION: When we report the results of this survey, it may be helpful to
identify your organization. It will not be necessary, at any point, to reveal the
name of the person answering the survey. If necessary, will you allow us to
associate your answers with the name of your organization? (Check one.)

D Yes, if necessary, the researchers may associate the name of our
organization with our survey answers.

D No, the researchers may not associate the name of our organization with
our survey answers.

 

 

D ‘- Check here if you would like to receive a summary of survey results.

 

 

 

OPTIONAL: Please use the space below (and to the right) to tell us if you have
any other thoughts about your organization’s recent fisheries-related
interactions with other organizations in the Pere Marquette River watershed:

 

 

 

‘ Thank you for your cooperation! Your time and effort will help promote more
effective and efficient communication about natural resource issues.

Please return the survey in the stamped envelope (enclosed). Please address
your questions, comments, and this survey to:

Kristine Lynch

Michigan State University

Dept. of Fisheries and Wildlife

13 Natural Resources Bldg.

E. Lansing, MI 48824-1222

phone (517) 353-6697; fax (517) 432-1699; dawsonkr@msu.edu

 

169

 

 

 

APPENDD( C

170

APPENDIX C

Upcoming Survey Notice Letter

Date

Address
Dear [name]:

Fisheries and ﬁsh habitat are directly and indirectly affected by the many ways people use
land and water resources. Different organizations make various decisions about how to
use and manage land and water, and ﬁsheries managers need to understand how each
organization’s decisions and activities may relate to ﬁshery management goals.

We are conducting a survey to understand if and how stakeholder organizations interact
in making decisions that could possibly affect ﬁsh and their habitats in the Pere
Marquette River watershed. Your local organization, chapter, or ofﬁce may have some
kind of role or interest in how this watershed’s ﬁsheries and aquatic ecosystems are used,
conserved, or managed. In the next few days, your organization will be receiving a
questionnaire in the mail that will help us explore interactions among various kinds of
stakeholder organizations. Your organization was selected due to its location in the Pere
Marquette River watershed and/or its potential interest in ﬁsheries and aquatic ecosystem
issues in this watershed.

We would greatly appreciate your input in this study, as it will help provide a complete
and accurate assessment of interactions between potential natural resource stakeholders.
With this information, we can better understand how to enhance communications among
all kinds of ﬁsheries stakeholders in and around the Pere Marquette River watershed.

Thank you in advance for your participation in this study! If you have any questions
before receiving the questionnaire, please feel free to contact us.

Sincerely,
William W. Taylor Kristine D. Lynch
Professor and Acting Dean Graduate Research Assistant

(517) 353-6697; dawsonkr@msu.edu

171

APPENDIX D

172

APPENDIX D

Cover Letter for First Survey Mailing

date
address
Dear [name]:

We are interested in learning about your organization and how it communicates or interacts with other
organizations about ﬁsheries issues in the Pere Marquette River watershed. This information will help us
ﬁnd ways to increase the ease and strength of interorganizational communication. Our goal is to promote
better ﬁsheries management by ﬁnding ways to bring more stakeholders into ﬁsheries management
discussions. Please note:

0 While the questionnaire appears to be lengthy, the questions can be answered fairly quickly. For most
organizations, the questions will take about 20-30 minutes to complete.

0 If necessary, please feel free to consult with other members of your organization in answering these
questions.

0 If you believe that this questionnaire does not apply to your organization, please just complete Part I of
the questionnaire and return it in the enclosed envelope with a statement that Part II does not apply to your
organization.

0 When you have completed the survey, please mail it back in the enclosed stamped envelope.

Your help is critical in enabling us to understand how to structure watershed-based communication
networks. Your participation is voluntary, and you indicate your willingness to participate by completing
and returning this questionnaire. Your responses will be kept conﬁdential and your name will not be
associated with the results. In some cases, it may be helpful for us to associate the name of an organization
with their responses, but we will only use this information with your permission. On the survey, you can
choose whether or not we can associate your answers with the name of your organization. If you have any
questions about participating as a research subject, please call David E. Wright, Chair of the Institutional
Review Board, at (517) 355-2180.

Thank you for your cooperation! Your input can help promote better communication among stakeholder
organizations, which can lead to better ﬁsheries management. If you have any questions about this study,
feel free to contact us.

Sincerely,
William W. Taylor Kristine D. Lynch
Professor and Acting Dean Graduate Research Assistant
(517) 353-6697; dawsonlcr@msu.edu
enc.

173

APPENDIX E

174

APPENDIX E

Reminder Postcard for First Survey Mailing

July 14,2000

A week ago I sent you a questionnaire seeking information about your
organization and its interactions with other organizations in the Pere
Marquette River watershed. If you have already completed and returned this
survey, please accept my sincere thanks! If not, please do so today. Feel
free to consult with others in your organization to answer the questions.

Your participation will help provide a complete assessment of how
organizations interact. With your input, we can ﬁnd ways to enable better
and stronger communication among organizations in and around the Pere
Marquette watershed.

If you did not receive this questionnaire, or it got misplaced, please contact
me (517-353-6697) and I will mail you another copy.

Sincerely,

Kristine Lynch, Project Director

175

APPENDIX F

176

APPENDIX F

Cover Letter for Second Survey Mailing

date
address
Dear [name]:

A few weeks ago, we sent you a questionnaire about organizational linkages in and around the Pere
Marquette River watershed. As of today, we have yet to received your completed questionnaire. In case
you misplaced the questionnaire, we are enclosing another copy for you.

Your participation is very important to us, because it will enable us to have the most complete and accurate
assessment of how groups comrmmicate about ﬁsheries issues in the Pere Marquette River watershed. Our
goal is to understand how organizations in this watershed interact, as this can help facilitate more effective
connnunication about ﬁsheries management.

If you believe that this questionnaire does not apply to your organization, please just complete pages 2, 3,
and 16 and return it in the enclosed stamped envelope. This information is still very important for our
overall assessment of how organizations interact. Please note:

0 While the questionnaire appears to be lengthy, the questions can be answered fairly quickly. For most
organizations, the questions take less than 30 minutes to complete.

0 Feel free to consult with other members of your organization in answering these questions.

0 When you have completed the survey, please mail it back in the enclosed stamped envelope.

Please also note that your participation is voluntary, and you indicate your willingness to participate by
completing and returning this questionnaire. Your responses will be kept conﬁdential and your name will
not be associated with your results. In some cases, it may be helpful for us to associate the name of an
organization with their responses, but we will only use this information with your permission. On the
survey, you can choose whether or not we can associate your answers with the name of your organization.
If you have any questions about participating as a research subject, please call David E. Wright, Chair of
the Institutional Review Board, at (517) 355-2180.

If you have any questions about this study, or if you would prefer to discuss or phone in your responses,
please feel free to contact us. We can also arrange to phone you at your convenience.

We sincerely appreciate your cooperation! Your input will help promote better communication among
stakeholder organizations, which can ultimately lead to better ﬁsheries management.

Sincerely,
William W. Taylor Kristine D. Lynch
Professor and Acting Dean Graduate Research Assistant
(517) 353-6697; dawsonkr@msu.edu
enc.

177

APPENDIX G

178

APPENDIX G

Descriptive Statistics for Organizations and Ties

The following tables list organizational attributes as described by survey respondents and
other organizational information. The total population of 112 includes the 101
organizations identiﬁed in Chapter 1 and the 11 organizations that were added as “other”
organizations by respondents. A response of “no information” indicates that the
respondent did not answer the question. N = the number of organizations with a given
attribute. Percent (%) = the proportion of the population having a given attribute (percent
may not equal 100 due to rounding).

 

 

     

 

 

 

 

 

Table A.1. Categories of Or anzi__s __zationa1i J: z
Response N %
federal government 14 12.5
state government 12 10.7
county government 9 8.0
township government 28 25.0
city government 3 2.7
local economic development 10 8.9
preservation-oriented conservation special interest 9 8.0
recreation-oriented conservation special interest 10 8.9
educational or informational interests 9 8.0
other interests (e. g., agriculture, property owners, etc.) 8 7.1
Total 112 99.8

 

 

Table A2. Proximity of Organizations (the organization’s location relative to

 

Response N %

 

 

inside the watershed 55 49.1
outside the watershed 57 50.9
Total 1 12 100.0

 

 

 

179

 

 

 

 

 

Table A3. County of Organizations (the county in which the organization is located).
Response N %
Mason Co. 24 21.4
Lake Co. 21 18.8
Oceana Co. 15 13.4
Newaygo Co. 18 16.1
other county 34 30.4
Total 112 100.1 I

 

 

 

 

anization has existed .

 

 

 

Response N %
0-10 years 6 5.4
11-50 years 24 21.4
51 or more years 69 61.6
no information 13 1 1.6
Total 112 100.0

 

 

 

associated with the or

     

anization .

 

 

Table A5. Size of Organizations (the number of employees, memb

 

   

ers, or others I

 

 

 

Response N %
1-20 members (small) 46 41.1
21-100 members 23 20.5
101 or more members (large) 15 13.4
no information 28 25.0
Total 1 12 100.0

 

 

180

 

Table A6. Membership Type of Organizations (whether membership is mostly
volunt or non-volunt .

 

 

 

 

Response N %
non-voluntary (paid staff) 65 58.0
voluntary 32 28.6
even split 2 1.8
no information 13 1 1.6
Total 1 12 100.0

 

 

   

 

 

Table A.7. Involvement of Organizations (the degree to which an organization is
__ e_ in Walru i_theP___ Wh V_ __ _ , , , _, ,, , V, _
Response N %
not involved 36 32.1
somewhat involved 38 33.9
very involved 1 8 16.1
no information 20 17.9
Total 112 100.0

 

 

 

 

        

 

 

Table A.8. Philosophy of Organizations (how the organization views the relationship

Response N %
natural resources should be protected from human impacts 5 4.5
natural resources should be developed for economic activity 0 0.0
natural resources should provide social & ecological beneﬁts 73 65.2
other 5 4.5
no information 29 25.9
Total 1 12 100.1

 

 

 

181

 

 

Response N %

 

natural resource management 21 18.8
natural resource recreation and/or tourism 11 9.8
natural resource protection and/or preservation
education and information source

6

7
property owner representation 2 1.8
private business representation 3 2.7
local government planning 40 35.7
local or regional economic development 7 6.3
agriculture interests 7 6.3
other 2 1.8
no information 6 5.4

 

Total 1 12 100.3

 

 

 

 

 

 

 

 

Table A.10. Satisfaction of Organizations (whether or not the organization is pleased

Response N %
satisﬁed 31 27.7
unsatisﬁed 13 1 1 .6
undecided or neutral 35 31.3
no information 33 29.5
Total 112 100.1

 

 

182

 

 

 

 

Table A.11. Concerns of Organizations (the occurrence of issues or topics about which
the organization is most interested). Organizations could indicate more than one
concern. Concern types (type 1 = ecological concerns; type 2 = recreational concerns;

‘ 2e 3 = user behavior concerns) are used to determine the scope of concerns.
Response N % of 112
habitat (type 1) 51 45.5
recreational experiences (type 2) 31 27.7
ﬁsh population size (type 1) 29 25.9
access (type 2) 28 25.0
user conﬂicts (type 3) 27 24.1
sea lamprey (type 1) 25 22.3
regulations (type 3) 22 19.6
enforcement (type 3) 21 18.8
stocking (type 1) 18 16.1
none 14 12.5
other 4 3.6
no information 23 20.5
Total 293

 

 

 

 

  
  

Table A. 12. Scope of Organizations’ Interests (the number of types of issues about
the iehlzeteh eemedhee -

   

 

 

 

Response N %
one type of issue (specialist) 31 27.7
multiple types of issues (generalist) 34 30.4
neither; interested in no issues 14 12.5
no information 33 29.5
Total 1 12 100.1

 

 

183

The following tables list attributes of the ties (i.e., relationships) as described by survey
respondents. There were 1088 cases in which a chooser indicated having a relationship
with a chosen organization. A response of “no information” indicates that the respondent
did not answer the question. N = the number of ties with a given attribute. Percent (%) =
the proportion of ties having a given attribute (percent may not equal 100 due to
rounding).

 

Table A. 13. Frequency of Ties (how often a given interaction occurred with another
organization. This was the primary identiﬁer of a tie; no response indicates no

 

Response N %

 

 

occasionally (once a month or less) 842 77.4
frequently (more than once a month) 246 22.6
Total 1088 100.0

 

 

 

 

 

 

 

Response N %
communication, resource contribution, and collaboration 220 20.2
communication and resource contribution only 72 6.6
communication and collaboration only 64 5.9
resource contribution and collaboration only 27 2.5
communication only 301 27.7
resource contribution only 186 17.1
collaboration only 121 11.1
no information 97 8.9
Total 1088 100.0

 

 

 

184

 

Table A. l 5. Absolute Tie Type (the occurrence of each type of tie indicated in the 991
cases (1088 minus 97 with no information) in which a chooser indicated a relationship.
Some choosers reported more than one type of tie).

 

 

 

 

Response N % % of 991
communication 657 41.2 66.3
resource contribution 505 31.7 51.0
collaboration , 432 27.1 43 .6
Total 1594 100.0

 

 

Table A.16. Absolute Tone (the perceived mood or attitude surrounding the
interactions. Some choosers reported more than one type of tone for an interaction).

 

 

 

 

Response N %
cooperative (positive) 794 69.2
casual (positive) 225 19.6
tense (negative) 41 3.6
confrontational (negative) 25 2.2
no information 62 5.4
Total 1 147 100.0

 

 

Table A.17. Importance of Ties (the perceived weight or signiﬁcance that the chooser

 

 

 

 

 

 

Response N %
not important 13 1 .2
somewhat important 273 25.1
very important 750 68.9
no information 52 4.8
Total 1088 100.0

 

 

185

 

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