PUBLIC HIGHER EDUCATION GOVERNING BOARDS:
THE ROLE OF SOCIAL NETWORKS
By
Emily R. Miller

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Higher, Adult, and Lifelong Education
2011

ABSTRACT
PUBLIC HIGHER EDUCATION GOVERNING BOARDS:
THE ROLE OF SOCIAL NETWORKS
By
Emily R. Miller
A defining feature of American higher education is the provision for authority over the
institution by an external governing board consisting of lay members of the public (Thelin,
2004). Studies of higher education governing boards typically focus on structure and
performance (Kezar & Eckel, 2004; Kezar, 2006; McGuinness, 2003; Minor, 2006; Tierney,
2004b) as well as assume governing boards endorse institutional policies created under the
leadership of the executive officer (Mintzberg, 1979). Research has yet to explore in any depth
the impact of higher education governing board members as social actors.
Situated in the current financial crisis and a trend of declining public investment in higher
education (Fairweather, 2009, 2006; NACUBO/NCSE, 2010; Weerts & Ronca, 2006; Zumeta,
2006), the current study examines the social network patterns of governing board members at
public institutions and expands the knowledge of how governing boards work. The study sought
to determine whether identifiable social networks for public higher education governing board
members exist, to develop an overall picture of public higher education governing board
networks, and to understand the internal and external factors impacting how governing boards’
social networks during times of fiscal crisis.
This study draws upon the technique of social network analysis as a means to understand
how public governing boards work. As an analytical tool, social network analysis examines the

relations and patterns of relations among actors by mapping interactions and relationships (Marin
& Wellman, 2010; Wasserman & Faust, 1994).
Data collected from four public post-secondary governing boards from two states
demonstrated that identifiable social networks did in fact exist for each governing board.
Commonalities in the social networks were present across the four participating institutional
governing boards. Additionally, governing board members’ unanimously indicated that the
recent financial crisis has created a unique period for higher education and is impacting
institutional governance. Implications for research, practice, and policy are discussed.

Copyright by
EMILY R. MILLER
2011

DEDICATION
I dedicate this dissertation to John Thomas who has filled my life with laughter and love.

v

ACKNOWLEDGEMENTS
At Michigan State University a community of colleagues welcomed me. My academic
experience was a product of all my interactions with this community. Thank you for infusing it
with new perspectives, challenging ideas, helpful guidance, and good humor.
I wish to express sincere appreciation to Jim Fairweather. Without Jim’s invaluable
counsel, my dissertation would not have been possible. Thank you for sharing your knowledge
and friendship with me.
The encouragement and support of my family and friends will forever be remembered.
To those who began and to those who joined me on this journey thank you for a
meaningful chapter of my life.

vi

TABLE OF CONTENTS
List of Tables ................................................................................................................................. ix
List of Figures................................................................................................................................ xi
Chapter 1..........................................................................................................................................1
Introduction..................................................................................................................................1
Financial Crisis ............................................................................................................................3
Conceptual Framework..............................................................................................................13
Purpose ......................................................................................................................................14
Research Questions....................................................................................................................16
Chapter 2........................................................................................................................................17
Literature Review ......................................................................................................................17
Higher Education Governance Literature..............................................................................17
Overview of higher education governance. .......................................................................17
Shared governance.............................................................................................................24
Studies related to higher education governing boards.......................................................26
Frameworks to study governance. .....................................................................................31
Social Network Analysis .......................................................................................................33
History of social network analysis.....................................................................................33
Social network analysis as a paradigm. .............................................................................37
Key terminology and language......................................................................................38
Types of networks..........................................................................................................42
Levels of analysis. .........................................................................................................43
Network representation..................................................................................................45
Relevant Studies of Social Networks ....................................................................................46
Social capital research. ......................................................................................................47
Organizational research. ....................................................................................................48
Corporate board interlocks. ...............................................................................................50
Summary of Literature Review .............................................................................................52
Chapter 3........................................................................................................................................53
Study Design..............................................................................................................................53
Research Design ....................................................................................................................53
Sampling Framework.............................................................................................................54
Data Collection ......................................................................................................................62
Instrumentation ......................................................................................................................65
Data Analysis.........................................................................................................................67
Limitations.............................................................................................................................71
Chapter 4........................................................................................................................................73
Results........................................................................................................................................73
Institution and Governing Board Characteristics ..................................................................73
Governing Board Members Perceptions................................................................................74
vii

Social Network Analysis of Governing Boards.....................................................................81
Masters Institutions............................................................................................................82
Masters I. .......................................................................................................................82
Masters II .......................................................................................................................91
Research Institutions..........................................................................................................98
Research I. .....................................................................................................................98
Research II. ..................................................................................................................105
Summary of Results.............................................................................................................112
Chapter 5......................................................................................................................................117
Research Questions..................................................................................................................118
Are there identifiable social networks for public higher education governing boards? ......118
If so, how much variation exists in the social networks across governing board
members and governing boards as a collective team?.........................................................119
To what extent do internal and external factors impact governing boards’
social networks?...................................................................................................................120
Discussion of Findings ............................................................................................................121
Governing Board Composition and Characteristics ............................................................122
Governing Board Members Perceptions..............................................................................124
Governing Board Social Network Characteristics...............................................................127
Implications for Research, Practice, and Policy ......................................................................130
Researchers ..........................................................................................................................130
Institutional Governing Boards............................................................................................133
Association of Governing Boards of Universities and Colleges .........................................135
Conclusion ...............................................................................................................................137
Appendix A..................................................................................................................................139
Appendix B..................................................................................................................................141
Appendix C..................................................................................................................................151
References....................................................................................................................................153

viii

LIST OF TABLES
Table 1.1: Fiscal Mid-Year Higher Education Cuts for 2010 and 2011..........................................5!
Table 2.1: Categorization of State Higher Education Governance Structures ..............................20!
Table 3.1: States with Institutional Level Governing Boards for 4-year Institutions....................57!
Table 3.2: Percentage of Mid-Year Higher Education Cuts for States in Sampling Framework..58!
Table 3.3: Number of Public Research and Masters Level Institutions in Sample States.............59!
Table 3.4: Governing Board Response Rates by Institution..........................................................65!
Table 4.1: Governing Board Members' Occupations ....................................................................75!
Table 4.2: Engagement by Governing Board Members during Financial Crisis ..........................76!
Table 4.3: Variance in Governing Board Members' Policy Position ............................................77!
Table 4.4: Governing Board Information Sources: Percentage Indiciating "Yes" ........................78!
Table 4.5: Governing Board Members' Perceptions......................................................................79!
Table 4.6: Freeman Graph Centralization Measures for Governing Boards .................................82!
Table 4.7: Out-degree Statistics for Masters I Governing Board Members ..................................85!
Table 4.8: In-degree Statistics for Masters I Governing Board Members.....................................86!
Table 4.9: Masters I: Freeman Degree Centrality Measures for each Actor and Overall
Descriptive Statistics ...................................................................................................87
Table 4.10: Masters I: Reach Centrality Measures.......................................................................89!
Table 4.11: Masters I: Freeman Betweenness Centrality Measures..............................................90!
Table 4.12: Out-degree Statistics for Masters II Governing Board Members...............................93!
Table 4.13: In-degree Statistics for Masters II Governing Board Members .................................94!
Table 4.14: Masters II: Freeman Degree Centrality Measures for each Actor and Overall
Descriptive Statistics ..................................................................................................95!
ix

Table 4.15: Masters II: Reach Centrality Measures .....................................................................96!
Table 4.16: Masters II: Freeman Betweenness Centrality Measures ............................................97!
Table 4.17: Out-degree Statistics for Research I Governing Board Members ............................100!
Table 4.18: In-degree Statistics for Research I Governing Board Members...............................101!
Table 4.19: Research I: Freeman Degree Centrality Measures for each Actor and Overall
Descriptive Statistics ................................................................................................102
Table 4.20: Research I: Reach Centrality Measures...................................................................103!
Table 4.21: Research I: Freeman Betweenness Centrality Measures..........................................104!
Table 4.22: Out-degree Statistics for Research II Governing Board Members...........................107!
Table 4.23: In-degree Statistics for Research II Governing Board Members .............................108!
Table 4.24: Research II: Freeman Degree Centrality Measures for each Actor and Overall
Descriptive Statistics ................................................................................................109
Table 4.25: Research II: Reach Centrality Measures .................................................................110!
Table 4.26: Research I: Freeman Betweenness Centrality Measures..........................................111!
Table 5.1: Govering Board Networks Geodesic Distance...........................................................118!
Table 5.2: Standardize Reach Scores for Insitutional Chief Financial Officers and Presidents..120!

x

LIST OF FIGURES
Figure 1.1: Appropriations per $1000 Personal Income...............................................................10!
Figure 2.1: Illustration of Directed and Undirected Ties...............................................................39!
Figure 2.2: Illustration of a Whole Network .................................................................................41!
Figure 2.3: Example of a Network Matrix.....................................................................................42!
Figure 2.4: Illustrations of Ego-centric Networks .........................................................................43!
Figure 3.1: Sampling Framework Flow Chart...............................................................................56!
Figure 3.2: Flow of Governing Board Participation in Study .......................................................61!
Figure 4.1: Governing Board Members’ Policy Position ..............................................................77!
Figure 4.2: Masters I Sociogram ...................................................................................................84!
Figure 4.3: Masters II Sociogram ..................................................................................................92!
Figure 4.4: Research I Sociogram .................................................................................................99!
Figure 4.5: Research II Sociogram ..............................................................................................106!

xi

CHAPTER 1
Introduction
A defining feature of American higher education is the provision for authority over the
institution by an external governing board consisting of lay members of the public (Thelin,
2004). Governing boards are accountable for supporting the mission of their institution and have
legal, fiduciary, and ethical responsibilities (AGB Statement on Board Responsibility for
Institutional Governance, 2010). As stewards, they are responsible for advancing the
institution’s mission, integrity, and quality (AGB Statement on Board Accountability, 2007). In
less stressful times governing boards often are seen as mechanisms for approving formally the
initiatives of the administration. In turbulent times when the fate of the institution may be at
stake the role of the governing board may well be much more consequential.
Most research about governing boards is based on the belief that boards typically assume
a passive role and endorse institutional policies and practices created under the leadership of the
executive officer (Mintzberg, 1979). Not surprisingly the majority of scholarship about
governance is situated in a traditional theoretical structural view (Minor, 2006; Mortimer, 1971)
and focuses on understanding as well as rethinking the concept of shared governance (Birnbaum,
1991a,b & 2004; Baldwin & Leslie, 2001; Eckel, 2000; Finsen, 2002; Gayle, Tewarie, & White,
2003; Hamilton, 2004 & 2009; Johnston, 2003; Longin, 2002; Mortimer & McConnell, 1979;
Tierney & Lechuga, 2004; Tierney, 2004a) or improving the integral relationship between the
governing board and the executive officer (AGB Leadership Imperative, 2006). Scholars who
have synthesized governance literature claim that existing research in this field is commonly
based on structural configurations of governance as well as historical, cultural, and constituency
1

based frameworks (Kezar & Eckel, 2004; Tierney, 2004b). Moreover, a majority of the writing
on governance materializes in the form of association reports and is often based on shared
experiences and reflection (Tierney, 2004b).
In periods of environmental distress, such as the current financial crisis, it is not clear that
governing boards play this passive role. Governing boards may potentially be taking more
deliberate actions on matters of consequence. Nor is it clear that traditional structural models are
effective in examining a potentially more active (and direct) role by governing boards in
institutional governance. What is needed is a study of higher education governance from an
alternative perspective. In particular, thinking about governing board members as social actors
enables understanding of the breadth of information and information sources governing boards
draw from to inform decisions about institutional policies. The current study addresses these
shortcomings by examining the social network patterns of governing board members and
exploring the broad range of relationships that can influence governing board members’ actions
in a time of crisis. A better understanding of the attributes of governing board networks will be
useful for researchers and practitioners interested in the impact of social interactions on higher
education governance.
For the purposes of this study, the term governing board refers to a post-secondary
institution’s structure responsible for the policies, principles, and practices for institutional
governance. Institutional governance refers to accountability for 1) institutional mission and
heritage; 2) American higher education values of self-regulation, academic freedom, shared
governance, educational quality, and fiscal integrity; 3) appointment and assessment of
presidential leadership; and 4) ensuring public trust (AGB Statement on Board Responsibility for
Institutional Governance, 2010). Formally defined by Wasserman & Faust (1994), social

2

networks are a set of network members (commonly referenced as actors/nodes/vertices) tied by
one or more types of relations (commonly referenced as edges/ties/relations). In this study social
networks represent the relationships between governing board members with individuals both
within and beyond the institution (referenced in the current study as actors).
To begin, I highlight the harsh financial realities confronting institutional governing
boards. I then delineate the traditional governing board’s role and boundaries as institutional
stewards and offer an alternative perspective of institutional governing boards. After I establish
this context and conceptual framework, I present the purpose of the study and corresponding
research questions.
Financial Crisis
In the Fall of 2008 the world economic markets entered a period of significant
turbulence, a reality that when compounded with a long-term, steady decline in state funding,
tuition increases, and decreasing endowment returns has intensified the challenges confronting
higher education institutions (Fairweather, 2009; Long, 2010; NACUBO/NCSE, 2010; Weerts &
Ronca, 2006). Experts often indicate that significant factors affecting higher education today
make it difficult, if not impossible, for colleges and universities to sustain the business practices
they have followed for many decades (Chaffee, 2010; Wellman, 2002). It has even been
suggested that the American higher education system is migrating from an era of competition
and relative prosperity to an era of survival (Morrison & Merrill, 2009; Wellman, 2009).
Key points of stress for public higher education institutions are the financial struggles
confronting states. Illuminated in the State Budget Update (July 2009) by The National
Conference of State Legislatures a majority of states projected an overall cumulative state budget

3

shortfall of $142.6 billion for fiscal year 2010, a gap projected to grow during the course of the
fiscal year. Moreover, 24 states reported an anticipated budget shortfall of $58.5 billion for
fiscal year 2011 and nine states were already expecting a budget shortfall of $21.2 billion for
fiscal year 2012.
At the National Governors Meeting (Fall 2010) a fiscal survey of states was released. The
report confirmed growing state budget shortfalls and indicated that one of the clearest signs of
continued state fiscal stress is the use of mid-year budget cuts: “In fiscal 2010, 39 states made
mid-year budget cuts totaling $18.3 billion. In 2009, 41 states made mid-year budget cuts, further
exemplifying how difficult fiscal 2009 and fiscal 2010 were for states. For fiscal 2011, 14 states
have already made $4.0 billion in mid-year cuts” (National Governors Meeting, Fiscal Survey of
States, Fall 2010) (see, Table 1.1).
Policymakers in virtually every state are scrambling to balance budgets while confronting
gaping budget holes. State policymakers are taking a variety of measures to close their budget
gaps. The approaches often include decreased budget allocations to public higher education
institutions (Weerts & Ronca, 2006). As reported by the National Governors Meeting of the 39
states making mid-year cuts in fiscal 2010, 32 of them enacted cuts to higher education general
fund expenditures. Moreover, cuts in fiscal 2011 have thus far mirrored those of fiscal 2010 as
nine states have cut higher education spending (National Governors Meeting, Fiscal Survey of
States, Fall 2010).

4

Table 1.1
Fiscal Mid-Year Higher Education Cuts for 2010 and 2011

State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York

Total State
Budget
Cuts Made
After
Fiscal 2010
Budget
Passed
611.0

2010 MidYear
Higher
Education
Cuts
117.2

% of
Higher
Education
Cuts (MidYear 2010)
19.18%

439.0
246.9

0.0
0.0

0.00%
0.00%

489.4
149.5

231.8
0.4

47.36%
0.27%

1598.4
312.1
187.7
382.9
458.0
564.4
330.0
1110.5
777.0
72.0
565.0
228.0

420.0
52.1
31.8
6.8
142.0
59.8
18.0
110.0
108.9
8.0
66.0
0.0

26.28%
16.69%
16.94%
1.78%
31.00%
10.60%
5.45%
9.91%
14.02%
11.11%
11.68%
0.00%

1456.3
411.8
807.7
11.4
61.2
262.9

0.0
77.7
29.2
1.5
21.3
92.4

0.00%
18.87%
3.62%
13.16%
34.80%
35.15%

38.0
1910.0
368.9
1083.0

0.0
64.7
35.3
160.0

0.00%
3.39%
9.57%
14.77%
5

Total State
Budget
Cuts Made 2011 MidAfter Fiscal
Year
2011
Higher
Budget
Education
Passed
Cuts

% of
Higher
Education
Cuts (MidYear 2011)

6.2

0.0

0.00%

547.0

13.7

2.50%

106.7
207.0

12.5
0.0

11.72%
0.00%

233.7
28.4
153.0
22.2

61.4
5.4
20.1
0.0

26.27%
19.01%
13.14%
0.00%

80.0

0.0

0.00%

150.9

24.3

16.10%

Table 1.1 (cont’d)
North
Carolina
510.1
202.5
39.70%
North Dakota
Ohio
Oklahoma
249.0
33.0
13.25%
Oregon
954.6
94.4
9.89%
Pennsylvania
195.5
0.8
0.41%
212.0
0.0
0.00%
Rhode Island
113.6
12.1
10.65%
South
Carolina
566.5
53.0
9.36%
South Dakota
5.5
0.7
12.73%
Tennessee
Texas
436.9
123.3
28.22%
813.2
395.1
48.59%
Utah
57.3
0.3
0.52%
Vermont
28.0
0.0
0.00%
Virginia
1044.0
214.1
20.51%
Washington
81.0
0.0
0.00%
520.0
8.5
1.63%
West Virginia
119.3
12.3
10.31%
Wisconsin
Wyoming
Total
18339.7
2507.0
13.67%
4034.9
635.4
15.75%
Note. Dollar values are in millions. At time of report publication, budget cuts for fiscal 2011
were currently ongoing. Adapted from “Fiscal Survey of States” by National Governors
Association (NGA), Fall 2010, pp. 9-10,13-14. Copyright 2010 by the NGA & National
Association of State Budget Officers.
Unrelated to state funding, but a clear indication of the effect of the downturn of the global
financial markets, many post-secondary institutions experienced declining endowments
(NACUBO/NCSE, 2010). As reported by the Commonfund Study of Endowments (2010), data
gathered from 850 colleges, universities, and affiliated foundations showed an average decline of
18.7% (net of fees) in fiscal year 2009. The study reported longer-term returns remain lower,
reflecting a legacy of market downturn: “the cautionary note is that three-, five- and 10-year
returns remained below the level needed to fund missions for the long term after accounting for
spending, inflation and expenses. We hope that the data contained in the annual NCSE will serve
as a useful tool for trustees and staff as they make important decisions for their institutions”

6

(NCSE Full Press Release, 2011, p. 4)
The 2010 Private Colleges and Universities Financial Conditions Survey surveyed board
chairs from private universities and colleges to gauge responses to the economic downturn
(Long, 2010). The study focused on fiscal year 2009 and reported that forty-two percent of
private institutions experienced endowment losses of 10-20 percent. Moreover, approximately
90 percent of private institutions reported that the portion of the operating budget supported by
the endowment either decreased or stayed the same. Fewer than 10 percent reported increases. A
highly publicized example was the dramatic announcement by Harvard Universities about the
$11-billion loss of endowment wealth (Harvard Magazine, Nov/Dec 2009). As a result the
institution has drastically cut expenditures in key academic units as well as halted significant
expansion construction projects.
The Association for Public Land-Grant Universities (“APLU”) recently published Coping
Strategies of Public Universities During The Economic Recession of 2009: Results of a Survey
on the Impact of the Financial Crisis on University Campuses. According to this report, “the
picture painted by survey respondents is dreary, with 85 percent of institutions reporting a
decrease in state appropriations and nearly one-half of institutions experiencing cuts of 10
percent or greater” (Keller, Nov. 2009, p. 4). Among the other findings, fifty-five percent of
survey respondents reported that declines in state appropriations are harming their ability to hire
and retain faculty and staff, invest in new technology, sustain student support services, and
maintain campus infrastructure. Furthermore, more than one-half of the institutions responding
believed more cuts in state appropriations were likely and another one-half were pessimistic
about their institution’s fiscal future during the next 18 months. Even respondents from
institutions that escaped the most severe cuts expressed concern about the long-term ability of

7

their state to fund higher education institutions.
It appears that the stabilization funds provided to states from the American Recovery and
Reinvestment Act did not ameliorate these fiscal conditions in a meaningful way. Despite these
funds, many states were forced to cut programs and services. Appropriations for public
universities and student financial aid were not spared in most states. Policy analysts often
indicated the stimulus funds were insufficient and poor band-aids: “Recovery Act funds have
increased the federal share of total state budgets to 34.7 percent in fiscal 2010 from 26.3 percent
in 2008. The removal of these funds, when combined with an extremely slow recovery in state
revenue collections, could result in severe cuts to state programs and services” (National
Governors Meeting, Fiscal Survey of States, Fall 2010). Moreover, stimulus funds and shortterm cost-saving generators such as furlough days only postpone the severity of the financial
impact.
It is important to acknowledge that the current financial crisis impacting higher education
institutions is situated in a trend of declining public investment in higher education (Fairweather,
2009; Fairweather, 2006; Weerts & Ronca, 2006). Decreasing state appropriations are not a new
experience for public universities. A 2003 special report from the National Center for Public
Policy and Higher Education, College Affordability in Jeopardy: A Special Supplement to
National Crosstalk, demonstrated that state support for higher education, measured in current
dollars, increased only 1.2 percent, a sharp decline from the year prior (3.5 percent) and the
smallest increase in a decade. Appropriations actually dropped in 14 states. Moreover, tuition
and fees increased in all states. As reported by Zumeta (2006) in all fifty states there was an
erosion of appropriations for public higher education over a 25-year period. As indicated in
Figure 1.1, state appropriations for public higher education per $1000 of personal income

8

declined from $10.54 in fiscal year 1979 to $6.91 in fiscal year 2005, a 34.4% decrease and
demonstrated a shift away from funding the academic sector after 1980 by state governments
(Zumeta, 2006). In Fairweather’s discussion of the effect of the global financial crisis on
academic systems he states, “data demonstrate that the trends toward the relative reduction in
public financing of U.S. higher education, especially at the state government level, and increased
reliance on student tuition were in place well before the global financial crisis in 2007-2009”
(2009, p. 12).
Although the impact of the collapse of the global economy presented immediate, acute
conditions for both public and private universities, the broader picture suggests a chronic
condition particularly for public institutions. Both situations may require higher education
governing boards to engage more actively in decisions because of the implications for the future
of these institutions.
Respondents of the APLU survey indicated the use of both short-term and long-term
budget cutting and revenue enhancing strategies (Keller, 2009). Nearly 80 percent of survey
respondents reported reductions in both permanent and temporary staff positions with one-half
moving beyond position reductions to staff layoffs. Tuition and fee increases were the norm
across all institutions as universities struggled to find new sources of revenue in face of
increasing or stable student enrollments. The survey results indicate APLU universities are
striving to protect the core educational mission of their institutions, with administrative units,
professional and support staff, facilities and support operations experiencing the most intense
scrutiny and deepest reductions (Keller, 2009). In a similar fashion one of the most common
immediate cost-reduction strategies at private institutions was the implementation of hiring

9

Figure 1.1
Appropriations per $1000 Personal Income
-'&"**%
-'+"**%
-'*"**%

$"*(%
)"$%

-,"**%

("&&%

-("**%
-&"**%

$"#%

'*"+#%'*"&)%'*"#&%'*"+#%

!"#$%

&"&#%

$"&!% $")&% $"+&% $"!'%

,")&%

)"$+% )")#% )"('% )"()% )",'%

)"'#% ("$'%

#"'&%

-+"**%
-*"**%
'$('% '$(!% '$(#% '$()% '$($% '$)'% '$)!% '$)#% '$))% '$)$% '$,'% '$,!% '$,#% '$,)% '$,$% '$$'% '$$!% '$$#% '$$)% '$$$% +**'% +**!% +**#%

Note. Adapted from Zumeta, W. (2006). The new finance of public higher education. In H. Wechsler (Ed.) The NEA 2006 almanac
of higher education. Washington, DC: National Education Association. P. 39

10

freezes or restrictions (51.9 percent) as well as salary reductions or freezes (50.9 percent) as
reported by Association of Governing Boards of Universities and Colleges (“AGB”) (Long,
2010). In addition, eighty-seven percent of private institutions reported that they would increase
tuition in the 2010-11 academic year. The average institution will raise tuition 4.5 percent. These
two reports illustrate the very real financial hurdles higher education institutions must overcome.
However, as noted by the AGB, “compared to their public counterparts, private institutions face
fewer obstacles (e.g. state executive agencies, legislative funding and mandates, etc.) in
preparing for an uncertain future” (Long, 2010, p. 11).
In summary, declining public investment in higher education preceded the current global
financial crisis. However, the current financial situation is especially precarious because of the
magnitude of the recent cuts and the cumulative effects of decades of declining financial support.
As one of many examples, in California, both the University and State systems have ordered
furlough days to generate savings. In the University of California (UC) system, more than
108,000 full-time equivalent positions (faculty and staff), will be required to take from 11 to 26
furlough days amounting to a salary reduction of 4% to 10% over the 12 months beginning in
September 2009. The furlough plan is expected to save the UC system $184.1 million in payroll
savings. The California State University (Cal State) trustees have ordered all full-time faculty
members to take 24 unpaid furlough days that results in about a 10% pay cut. The measure is
expected to save the Cal State system $275 million in payroll savings over a 12-month period.
(NCSL State Budget Update, July 2009). To generate even more savings and address the
continued budget shortfalls, UC recently passed a 32% increase to student tuition.
This reality has resulted in higher education becoming a focus on states’ agendas: “higher
education ranks as the third most important issue in the 50 state capitals for 2010,… In the two

11

decades that NSCL has conducted its annual survey of legislative leaders asking them to rank
their top issues, higher education has never ranked this high” (Novak, 2010, p.3 as reported by
the National Conference of State Legislatures). Another major state-based organization,
National Governors Association, will spend a full year examining post secondary education.
Beginning in 2010, the initiative is intended to investigate accountability, productivity,
graduation rates and degree attainment, and college and university governance (Novak, 2010).
In several states proposals and legislation are percolating. At least five states have created
special commissions looking at various aspects of state-university relationships. The Texas
Higher Education Coordinating Board, under the executive order of Governor Rick Perry, is
undertaking a major study on cost efficiencies including faculty workloads, course redesign,
articulation and student transfer agreements, distance learning, and funding based on course
completion. Other states are pursuing new efforts at deregulation, tuition autonomy, and
performance agreements. A select number of states are advancing ambitious sets of proposals to
recast higher education institutions and systems. For example, Oregon may consider the ideas
contained in a report by former University of Oregon President, David Frohnmayer. He has
proposed making the state’s three largest institutions (Oregon State, Portland State, and the
University of Oregon) quasi-independent public corporations with their own governing boards.
Ties to the university system would continue through formal compacts that would set
institutional performance standards. The compacts would be tied to the system’s strategic goals
contained in its 2007 strategic plan, An Investment in Oregonians for Our Future. The state
board would continue to fully oversee the regional universities. Among other freedoms, creating
the corporations would allow the three universities to set their own tuition levels, be free from
some state reporting requirements, and set their own employment rules (Novak, 2010).

12

The harsh fiscal realities may make it easier to highlight the internal and external factors
impacting governing boards’ social networks that might not be evident in more “business as
usual” times. During less stressful times governing boards often adopt the institutional policies
and practices based on the recommendations of the administration. In contrast, during the
current acute financial crisis governing board members are likely to make decisions that have
dramatic implications for the future of colleges and universities. As such, governing boards
potentially may rely on a broader array of social networks to inform their decisions.
Conceptual Framework
This study draws upon the technique of social network analysis as a means to understand
how public governing boards work. Social network analysis is the formal study of systems of
people with an emphasis on their relations. As an analytical tool social network analysis
examines the relations and patterns of relations among actors by mapping interactions and
relationships (Marin & Wellman, 2010; Wasserman & Faust, 1994). By measuring the
interactivity of individuals through mapping relationships, social network scholarship can
uncover the dynamics between and within groups and illustrate how individuals utilize their
connections to achieve desired outcomes (Coleman, 1988).
Although governing board configurations vary greatly from institution to institution (Kerr
and Gade, 1989), governing boards frequently are studied under the assumption that they
function within their defined structures (Kezar & Eckel, 2004). These structures have remained
relatively static over time (Longin, 2002). Only recently have they been studied in relationship to
institutional effectiveness (Minor, 2006). The application of social network analysis as a
framework to study higher education governing boards is one way to answers calls to further

13

examine governance in consideration of human dynamics (Kezar & Eckel, 2004). Established in
the literature is the scholarship of Baldridge (1971), Cohen & March (1986), and Birnbaum
(1988, 1989), who all linked the importance of human dynamics research to higher education
research. However, this older as well as more recent scholarship (Del Favero, 2003) primarily
focuses on the relationships among faculty and administrators within structural, political, or
symbolic contexts. The current study adds to the literature by examining governing boards from
a new perspective (social actors) as well as by using an analytical perspective not applied to the
study of higher education governing boards previously. Ultimately this current study will bring a
greater understanding to the complexity of public higher education governing boards’ social
networks and expand the knowledge of how governing boards work.
Purpose
Regardless of the framing of the current higher education landscape, it is clear that
colleges and universities will need new models for delivering affordable, accessible, high-quality
education and for ensuring the health and effectiveness of the economic and administrative
structures by which they fulfill their missions. Governing boards will need to encourage
institutional leadership to develop the capacity to make hard decisions much more quickly than
typical structures and practices afford. Ultimately, new strategies that are dynamic and respond
proactively will be a necessity (AGB Strategic Imperatives: New Priorities for Higher
Education, 2009). In the near term, institutional survival is at issue.
During this period of fiscal instability active governing board intervention may be more
necessary than during less turbulent times. Furthermore, it is likely that governing boards’
actions matter in important ways during times of complexity and times when the future trajectory

14

of the institution is determined. Gaining an insight about the attributes of governing board
networks and understanding the influences on governing board networks is one means to study
how governing boards work.
Additionally, considering governing boards as social networks challenges the concept
that governing boards behave in bureaucratic, hierarchical ways. The actions and decisions of
individual board members and of the board as a whole cannot be separated from the social
context under which they operate (Wesserman & Faust, 1994). Interdependency between actors
is a key assumption when viewing governing boards from a social network perspective
(Wesserman & Faust, 1994). An implication of studying governing boards’ social networks is
that it will aid scholars in understanding the influence and impact of social relationships within
governing boards.
Situated in the financial crisis and based on this understanding of social networks, the
purpose of this current study is to identify the social network patterns of governing board
members at public institutions and explore the broad range of interactions that can influence
governing boards’ networks. By expanding the network of relationships of governing boards to
include both external and internal campus stakeholders the study can begin to assess the role
these broader interactions play in influencing an individual board member’s network and
potentially the collective board network during times of financial crisis.
Although considerations for improving board effectiveness and performance are
important, the goal of this study is to determine if identifiable social networks for public higher
education governing board members exist, to capture an overall picture of public higher
education governing board networks, and to understand the internal and external factors
impacting governing boards’ social networks during times of fiscal crisis. The design of this

15

study does not include the influence of governing board social networks on specific institutional
policy decisions.
Research Questions
The study will address the following research questions:
1. Are there identifiable social networks for public higher education governing boards?
2. If so, how much variation exists in the social networks across governing board members
and governing boards as a collective team?
3. To what extent do internal and external factors impact governing boards’ social
networks?
This introductory chapter established a context and conceptual framework for the current
study as well as presented the purpose of the study and corresponding research questions. In
chapter two, I provide a review of pertinent literature related to higher education governance and
social network analysis. In chapter three, I explain the study design, detailing the methods by
which I carried out the study. In chapter four, I present the findings of the study. Finally, in
chapter five, I discuss the results of the study with emphasis on implications for research,
practice, and policy.

16

CHAPTER 2
Literature Review
This chapter will provide a review of literature on higher education governance and
social network analysis. The purpose is to locate the current study within the context of existing
and relevant scholarship. The review is divided into two major categories that inform and situate
the examination of public higher education governing boards’ social networks. The first
category explores literature related to higher education governance, while the second category
reviews literature related to social network analysis.
Higher Education Governance Literature
The discussion of higher education governance literature is presented in four sections.
Section one provides an overview of higher education governance with an emphasis on public
institutions. Section two examines the concept of shared governance prevalent throughout
higher education institutions. Section three reviews empirical research on higher education
governance. Section four assesses the theoretical frameworks used to study governance.
Overview of higher education governance.
The evolution of American higher education has resulted in plethora of post-secondary
institutions with considerable variation among institutions and governance structures
(McGuinness, 2005; Minor, 2008; Thelin, 2004): “American higher education remains perhaps
the most diverse, decentralized, private, market-driven system (if it even can be called a system)
in the world” (McGuinness, 2005, p. 201). The role of the Federal government in higher
education is based primarily on funding research and development and on providing student

17

financial aid (Gladieox, et al., 2005). The role of state governments in U.S. higher education is
more central. It varies considerably from state to state (McGuinness, 2005).
Common to public post-secondary institutions is gubernatorial appointment of governing
board members by governors, state education commissioners, or confirmed political officials
(Minor, 2008). Forty-seven states have systems wherein the governor appoints all or a portion of
board members within their respective states. Fifteen of the 50 states have dual systems in which
a portion of board members are elected and a portion are appointed. Only in two states (Nevada
and South Carolina) are all governing board members elected by general election (Center for
Higher Education & Policy Analysis, 2004). As reported in 2010 Policies, Practices, and
Composition of Governing Boards of Public Colleges, Universities, and Systems, gubernatorial
appointment is the selection method for the vast majority of public post-secondary governing
boards: “the majority of members of most public governing boards (77 percent) were appointed
by the governor, 60 percent with confirmation by the state legislature and 17 percent without; 5
percent of boards were elected, 3 percent were appointed by legislatures, and 15 percent were
selected in a combination of ways or in some other manner” (AGB, 2010).
Beyond the diversity of selection processes of public governing board members, the
structures and authority of governance vary considerably across and within states. A number of
researchers have identified, described, and developed categorization schemas of state higher
education governance structures (Dressel, 1980; Hearn & Griswold, 1997; McGuinness 2003;
Millard, 1980; Millett, 1975; Minor, 2008; Mortimer & McConnell, 1979). As described by
Minor, “15 states have segmented governance systems whereby a portion of public institutions
are governed by a system board and a portion are governed by individual boards. At least 8 states
employ government entities that coordinate institutional activity, some of which still use

18

individual boards in addition to coordinating boards” (2008, p. 832). This statement underscores
the extensive diversity in governance structures that exists from state to state as well as supports
the conclusion by some scholars that attempting to categorize governing structures is virtually
impossible (Kerr & Gade, 1989).
Moreover, leaders in post-secondary education often reference states as having strong or
weak coordination regardless of established coordinating structures with rational-legal authority
(McGuinness, 2003, Novak, 2010). For example, a state with a coordinating governance
structure may espouse a goal of harmonization across institutions yet functionally have little
coordination in the state system. Furthermore, Login (2002) argues that these structures have
remained relatively static over time. Minor (2006) concludes governance structures are often
inconsistent with institutions’ current mission and activity. Regardless of these systemic
differences, governing boards according to Pusser (2003) consistently serve as links to powerful
political and economic interest groups beyond the institution.
McGuinness is often credited for developing one of the better governance structure
categorizations. His scheme was influenced by close work with state boards conducted by the
Education Commission of the States and is more recent than many of the other categorizations
(Hearn & Griswold, 1997). McGuinness (1988, 1997, 2003) delineates three major categories of
state higher education governance structures based on the role a central higher education board
plays in relation to setting state higher education policy.
The three major types of state higher education boards are 1) a governing board, which has
authority to establish and enforce state higher education policy for institutions; 2) a coordinating
board, which has authority to coordinate financial and human resources and curriculum
distribution across institutions; 3) a planning/regulatory/service agency board, which establishes

19

plans and goals for state higher education, but has less authority or power to enforce their
recommendations or plans at the institutional level (McGuinness, 2003). According to this
typology, there are 21 governing board states, 25 coordinating board states, and 4 planning board
states. Within each classification additional layers of governance structures exist for individual
states. The variation primarily consists of the existence and number of state governing and
coordinating boards for university or state colleges as well as community/technical colleges,
autonomous single institutional governing boards, and multi-campus or consolidated system
governing boards for the diversity of higher education institutions in the state.
Table 2.1
Categorization of State Higher Education Governance Structures
Higher Education Governance Structure Category
Governing Board
Combined / Mixed
! Single statewide governing board for universities, coordinating for twoyear campus
! Separate governing boards for universities, coordinating board for locally
governed community colleges
Separate
! One statewide governing board for universities, one statewide governing
board for state colleges and community colleges
! One statewide governing board for research universities and two-year
colleges, one statewide governing board for technical colleges
! One governing board for universities and state colleges, one governing
board for community/technical colleges
Consolidated
! Single statewide consolidated governing board for all public institutions

Coordinating Board
State Coordinating / Separate Governing Board
! State governing board for locally governed universities, single statewide
board with coordinating responsibility for locally governed community
colleges

20

State

KS
AZ, IA, MS,
OR, SD,
WY
VT
GA, WI
ME, NH,
NC
AK, DC,
HI, ID, MT,
NV, ND,
PR, RI, UT
FL

Table 2.1 (cont’d)
! Single statewide coordinating board and two separate state-level
governing boards, one for universities and one for universities,
community colleges, and technical institutions.
! Single statewide coordinating board. Public institutions are organized
under three state-level boards: a governing board for research
universities, a governing board for other state universities, and a
coordinating or governing board for locally governed community
colleges.
State Coordinating / Institutional Board
! Single statewide coordinating board. Institutional level governing boards
for several colleges and universities. Individual governing boards for
community/technical colleges. Multi-campus governing board for
universities and 2-year campuses.
! Single statewide coordinating board. Institutional level governing boards
for several colleges and universities. Individual governing boards for
community/technical colleges. Multi-campus governing board for
universities.
! Single statewide coordinating board. State level governing boards for
multi-campus systems, state level governing or coordinating boards for
community/technical colleges, and institutional level governing boards
for some universities.
State Coordinating / Mixed Institutional Boards
! Single statewide coordinating board. Each public university is locally
governed by a governing board. State governing boards for community
colleges either govern the colleges or coordinate locally governed
community colleges.
Combined / Mixed
! One statewide governing board for research universities and other
university campuses. One statewide governing board for state colleges
and community colleges. The second board also has responsibility for
planning and coordinating all public postsecondary education.
Agency Coordinating Board / Separate Governing Boards
! Single statewide coordinating board for all postsecondary education. Two
separate boards have responsibility for public institutions. One board
governs state-operated universities and coordinates locally governed
community colleges. The other board governs city universities and
community colleges.
Planning/Regulatory/Service Board
! Each public institution has a governing board. The planning/regulatory
agency has no formal coordinating authority.
! Each public university has a governing board. Each community college
has a local governing board. A state-level planning/regulatory agency for
community colleges either governs the colleges or coordinates locally
governed community colleges.

21

TN
CA, CT,
LA, NE

AR, NM,
OH, OK,
WV
MD, MO,
NJ
AL, CO, IL,
IN, SC, TX

KY, VA,
WA

MA

NY

DE
MI

Table 2.1 (cont’d)
! Two separate state-level governing boards are responsible for all public
institutions. The planning/service agency has no coordinating authority
related to governing boards.
! One or more multi-campus or statewide consolidated governing boards for
universities. Institution-level governing boards for some universities.
Locally governed community colleges. The state-level
planning/regulatory board has limited planning and regulatory authority
related primarily to community colleges.

MN
PA

The Association for Governing Boards of Universities and Colleges (“AGB”) recently
conducted a survey to collect and analyze the policies, practices, and composition of governing
boards of public colleges, universities, and systems (AGB, 2010). Of interest is the profile of a
typical governing board presented in the report. Using averages and the most common survey
responses revealed prototypical characteristics and traits for public governing boards.
The typical public governing board has 12 voting members: nine are men and three are
women; nine are White, two are African American, and one is another race. Half of the board,
six members, are alumni of the institution. The President is not included as a member of the
board and neither is the governor; one member of the board is a student, but there are no faculty
or staff members serving on the board. One board member is under 30 years old, two are
between the ages of 30 and 49, eight are 50-69 years old, and one is 70 or older. Ten board
members are currently working and two are retired. Their primary occupations are or were:
business (six), professional services (three), education (two), and other professions (one). Many
serve on other governing boards: five on a nonprofit board, three on a corporate board, two on
the board of a university-related foundation, and one on the board of another college or
university.
Board members are appointed by the governor with confirmation by the state legislature,

22

serve six-year terms, and are not subject to term limits. The board includes no emeritus members.
There is a policy in the state’s statutes for removal of unsatisfactory board members and this
power is vested in the governor. The board chair, a white male, was selected by the full board
and is serving a one-year term. There is no limit on the number of consecutive terms the chair
may serve.
The board meets seven times a year, uses a consent agenda, and the business portion of
each meeting lasts about four-and-a-half hours. Remote participation (e.g. by telephone, video
conference, or Internet) counts as attendance, and electronic voting is permitted. When the board
meets in executive session, it limits the agenda to topics such as personnel, real estate, and legal
issues, and the chief executive is included in at least a portion of the executive session. Nearly all
board members attend each board meeting. The board has five standing committees: finance,
audit, academic affairs, executive, and buildings and grounds. Committee meetings are typically
held in conjunction with board meetings. The executive committee met four times in the past
year. In addition, boards typically held a retreat in the past five years to plan and assess
institutional goals, but it has not recently held a retreat for the purpose of looking at its own
performance. The board budgets for educational activities and engaged an outside consultant
within the past three years to address board education or governance issues.
Another source of variation among states is the funding mechanisms to public higher
education institutions. For example, in the state of New York tuition dollars from SUNY
institutions are captured by the state government and are discretionally reallocated by the
legislature. The Public Higher Education Empowerment and Innovation Act currently before the
legislature would require that all tuition, fees, and other self-generated revenues in the SUNY
system be invested back into the institutions. Louisiana’s Postsecondary Education Review

23

Commission has recommended that the state’s governing boards be granted authority to set
tuition levels. Currently, two-thirds of the legislature must approve all increases approved by the
governing board. These two examples illustrate the control states have in allocating public
resources to higher education (Zimpher, 2010). In addition, state policy trends such as meritbased student aid and performance based funding vary considerably across states (McGuinness,
2005). Despite the variation in funding mechanism overall state appropriations to higher
education have declined and the demands are outpacing the state’s fiscal capacities (Fairweather,
2006; Zumeta, 2006).
In summary, public higher education varies extensively in governance structures at both
the state and institutional levels (Longin, 2002, Minor, 2006). Only recently has public debate
and discussion explored reconfiguring state higher education systems (Novak, 2010).
Moreover, resource allocation by the state varies, but has consistently declined (Zumeta, 2006).
These varied state and federal contexts – declining state resources, widely varied governance
structures – potentially affect individual governing boards and their responsibility to establish a
public agenda for higher education, attain educational quality, and achieve some form of public
accountability. Despite this great variation in state governance structures, governing boards
retain a commitment to the concept of shared governance. The following section explains shared
governance and references the extensive body of literature dedicated to the concept.
Shared governance.
The 1967 Statement on Government of Colleges and Universities by the American
Association of University Professors (“AAUP”), AGB, and American Council of Education
(“ACE”) affirms the shared responsibility and cooperative action for academic governance. The

24

authors of the document recognized the interdependence of various academic leadership roles,
including governing boards, administrators, and faculty members, in the following statement:
“important areas of action involve at one time or another the initiating capacity and decisionmaking participation of all the institutional components” (AAUP Statement on Government of
Colleges and Universities, 1967, p.10). In other words, higher education governance can be
characterized as a system that supports the collaboration of multiple constituents to make
decisions affecting colleges and universities.
Individual colleges and universities define participation in governance consistent with
their history and culture (Johnston, 2003; Morphew, 1999). As stated by Minor, “[t]he diversity
of college and universities and the structures within them make it nearly impossible to prescribe
a ‘one size fits all’ approach to governance” (2006, p. 23). Minor’s statement acknowledges the
considerable variation in how governance functions within individual campuses.
Much scholarship has been dedicated to understanding as well as rethinking the concept
of shared governance (Birnbaum, 1988, 1989; 1991a,b, 2004; Baldwin & Leslie, 2001; Longin,
2002; Minor, 2006; Tierney & Lechuga, 2004; Hamilton, 2004 & 2009). This body of literature
focuses most heavily on faculty governance or the relationships between the faculty and the
administration (Del Favero, 2003; Dill & Helm, 1988; Eckel, 2000; Finsen, 2002; Gayle,
Tewarie, & White, 2003; Johnston, 2003; Mortimer & McConnell, 1979; Mortimer & Sathre,
2006, 2007). Although student governance is discussed in student development and affairs
literature, only a few resources are dedicated to student participation in institutional governance
(Miller & Nadler, 2006; McGrath, 1970). Empirical scholarship dedicated to the governing
board is growing yet remains minimal (Bastedo, 2005; Chait, Holland, & Taylor, 1991, 1996;
Kezar, 2006; Minor, 2008; Pusser, Slaughter, & Thomas, 2006; Tierney, 2004a, 2006;

25

McGuinness, 2005). The bulk of the writing found on governing boards is often anecdotal and
based on individual experiences or institutional experiences (Tierney, 2004b). Most of that work
appears as association reports and occasional papers (e.g. Hill, Green, & Eckel, 2001). The
scarcity of scholarship on governing boards suggests a need for more research on how governing
boards work and who board members rely on as they fulfill their responsibility as institutional
stewards. I now turn to discussing studies related to higher education governing boards.
Studies related to higher education governing boards.
Scholarship on governing boards can be segmented into two categories. The first
category is literature dedicated to governing boards. The other category discusses governing
boards in the larger context of shared governance. In this latter category, governing boards are
one of many constituencies discussed in relationship with other constituencies. This literature
review is intended to focus on the related findings about higher education governing boards that
emerge from scholarship dedicated to the first category.
The study by Chait, Holland, & Taylor (1996), Improving the Performance of Governing
Boards, explores how private boards of trustees can learn to improve their performance. This
book was a transition to practical application from their theory-based work in identifying
competencies of effective governing boards published in The Effective Board of Trustees (1991).
While both studies focused exclusively on private board governance, these two works are
regarded as the most significant and rigorous studies of governing board members at postsecondary institutions (Tierney, 2004).
Chait et al. (1991) found three major trustee competencies. First, dimensions of
governing board characteristics and behaviors influence the effectiveness of boards. These

26

dimensions include contextual, educational, interpersonal, analytical, political, and strategic. For
example in consideration of the contextual dimension of governance, effective boards ensure that
trustees understand and take into account the culture and norms of the organization it governs.
The study suggests that effective boards adapt to the distinctive characteristics of an academic
environment, rely on the institution’s mission, values, and traditions as a guide for decisions, and
reinforce the institution’s core values through decisions. Another illustration is the analytical
dimension. This competency focuses on the board’s capacity to assess complex problems and to
draw upon multiple perspectives to address institutional matters. It is advocated that boards
create an environment conducive to open discussion and welcoming to multiple perspectives.
Second, the authors found a positive relationship between a governing board’s performance and
the institution’s performance. The authors made this determination by relating the six
dimensions of governing board competency to measures of institutional financial health. Lastly,
the authors concluded that trustee self-assessments potentially are not accurate “barometers of a
board’s effectiveness” (1991, p. 2) rather are more likely to be valuable catalysts for discussions
and measures of self-perception.
The authors’ discussion on private board performance (Chait, et al., 1996) focused on
lessons to improve individual trustee competencies, governing board learning and development,
and board processes and cohesion. A primary lesson advocated by the authors is to create
multiple opportunities for trustees to learn about the institution, the relevant profession, and the
larger environment. Essential to these educational programs is varied formats, content clearly
germane to the institution’s priorities, and direct access to constituency perspectives through
collaboration on meaningful tasks. Moreover, effective boards continually reexamine their
behavior and performance in light of institutional strategy. Progress occurs when the chair, the

27

president, or a subset of legitimately empowered trustees assumes responsibility for the way the
board does business and determining whether the board should do business differently as
institutional circumstances change.
Another lesson focused on ensuring the board operates as a cohesive and goal-oriented
entity: “the most competent boards consciously form and assiduously maintain a cohesive unit,
in which the whole is more than the sum of the parts” (Chait, 1996, p. 83). As such, the authors
concluded, a board can be a formidable source of strength for an institution by providing ideas,
insights, solutions, and advocacy. Lastly, the authors advocate that effective board meetings are
essential to improving board performance. Critical to the design and execution of board
meetings are agendas that reflect institutional strategy, structures that support consequential
work, effective leadership, and timely and pertinent information.
In 2006, Kezar expanded on Chait’s and his colleagues work by conducting a national
study to capture elements of public board performance and effectiveness. She identified six
thematic elements of effective public governing boards (leadership/board agenda, culture,
education, external relations, relationships, and structure) and compared these findings to private,
corporate, and non-profit governing board literature. The author’s work found that many of the
same principles of performance for private institutions as defined by Chait et al. (1991, 1996)
applied to public institutions. Kezar (2006) identified a set of elements necessary to facilitate
high performance among public governing boards, many of which overlapped with categories
found by Chait and colleagues. The distinctive areas for public institutions concerned the
political nature of public governing boards, the influence of stakeholder interests on leadership
and agenda setting, and the impact of governance layers.

28

Scholarship also has focused on the selection of public governing boards. The Center for
Higher Education Policy Analysis in 2004 conducted a study on the selection and appointment of
public college and university boards. Two sets of criteria are essential: one for individual
nominees and the second for the board as a whole. The report advocates that prior to selection or
appointment, individual boards members should be able to (a) demonstrate a commitment to
public education; (b) show record of public or community service; (c) express knowledge of
complex organizations and academic institutions; (d) articulate, understand, and develop
collaborative leadership; (e) demonstrate a willingness and availability for constructive
engagement; (f) commit to open-minded, non-partisan decision making; and (g) illustrate a
record of integrity and civic virtue. The report recommends that the governor or nominating
committee should consider the diversity, unique skills or competencies, as well as
complementary skills and perspectives represented on the board.
As part of the research team, Minor (2008) considered the various appointment processes
in relationship to the performance of state higher education systems. He measured performance
based on four categories of the Measuring Up 2004 report - participation, affordability,
completion, and benefits. To reduce the variation in appointment process as well as state
governance structures, Minor compared governing board selection across three dimensions:
restrictions, qualifications, and evidence of scrutiny as well as categorized governance structures.
His study concluded that the top performing state higher education systems rely more heavily on
the use of qualifications and scrutiny of potential trustees. Among the five bottom-performing
states, he found virtually no evidence of qualifications or methods to scrutinize the
appropriateness of candidates. Minor noted that “[i]n each of the top-performing states there is
substantial statewide coordination of public higher education. Four of the five top-performing

29

states have coordinating bodies” (2008, p. 841).
In writing about states’ roles and relationship to higher education, McGuinness (2005)
found that states differ in the overall performance of the state system, financing policies,
authority and responsibility of state structures, and legal specifications for budgeting and
financing between states and institutions. McGuinness’ work provided a strong foundation for
understanding the context which public governing boards work.
The AGB publishes a variety of literature on higher education governance ranging from
board basic manuals/guides (e.g., Effective Governing Boards: A Guide for Members of
Governing Boards of Public Colleges, Universities, and Systems, 2010) to research studies on
aspects of governance (e.g., Faculty, Governing Boards, and Institutional Governance, 2009).
Plus on a monthly basis the AGB publishes Trusteeship. This magazine is dedicated to
examining contemporary topics related to higher education governance. Contributing articles
often come from institutional leaders, state higher education officials, and academic scholars.
The AGB also endorses statements (approved by AGB board of directors) that provide
principles for higher education governance. These documents are not prescriptive in nature.
Rather they provide a foundation for discussions on policies and practices of good governance.
Often these documents offer questions for governing boards to consider and illustrative
examples. These works reflect the voice of AGB, higher education leaders, and member
institutions and establish a tone for higher education governance to aspire to achieve high
performance. Two most recent publications include: Association of Governing Boards of
Universities and Colleges Statement on Board Responsibility for Institutional Governance
(2010) and AGB Statement on Board Accountability (2007). In addition, the senior scholars and
consultants of AGB who have experience in the field of higher education often write on topics

30

related to governance (e.g. Kerr & Gade, 1989; Morrill, 2010; Bornstein, 2010).
To this point, the literature I reviewed focused on structure and performance. Salient and
applicable to my study is literature that examines the theoretical frameworks used to study
governance. The following section reviews the common frameworks used to examine
governance and creates a foundation to introduce social network analysis as a new framework for
examining how governing boards work.
Frameworks to study governance.
Little literature on higher education governance uses theoretical frameworks to study
governance. Over the last forty years, a majority of scholarship has been dedicated to traditional
structural theories (Tierney, 2004b; Kezar & Eckel, 2004). Most recently three major reviews of
shared governance literature indicate what has been learned from the study of governance and
expose the theoretical gaps in studying higher education governance.
Writing in the Higher Education: Handbook of Theory and Research, Tierney implies
that recommendations to improve governance are hindered by the lack of theoretical frameworks
and often are “based on intuition rather than a logically nuanced framework” (2004, p. 86). He
encourages future scholarship to go beyond the four frames through which governance has been
examined – historical, structural, constituency-focuses, and research-based. He advocates
exploration of governance through communication or as he states the “codes through which
governance gets enacted” (2004, p. 125).
Kezar’s & Eckel’s (2004) synthesis of literature on governance indicated that scholarship
has focused on structural conditions with some dedication to political and cultural frameworks.
Scholars who examine governance through structural theories emphasize the importance of

31

authority, roles, procedures, and entities responsible for decision-making. Human relations,
cultural, and social cogitation theories remain underutilized frameworks for the study of
governance. Kezar and Eckel recommend focusing on the role of human dynamics in college
and university governance. This recommendation is consistent with that of Baldridge (1971),
Cohen and March (1974/1986), and Birnbaum (1988) all of whom emphasized the importance of
human dynamics in organizations to higher education research. These foundational works found
that decision-making in higher education institutions is not always rational; it is frequently
political. In addition, decision-making involves the dynamic interaction of both structure and
people. However, this older scholarship limited its focus to administrator-faculty relationships; it
did not apply these concepts to institutional governing boards.
A recently published New Directions for Higher Education highlights the need to study
governance using different theoretical perspectives (Tierney & Lechuga, 2004). Kaplan (2004)
challenged the usefulness of governing structures in explaining administrative actions and
cautioned against overemphasizing the importance of structure. Kezar (2004) argued that a key
to improve governance lies in developing internal leadership and relationships rather than in
changing the structure of shared governance. Pope (2004) introduced the concept of trust as a
new conceptual way to investigate governance in colleges and universities. Although Tierney
and Minor (2004) claim that structures have a place in studying governance, they also support
Kaplan’s argument that governance structures are not always central to effectiveness. Tierney
and Minor suggest that communication among relevant actors is essential to effective
governance.
Although these three reviews advocate for scholarship to embrace new frameworks to
study governance, some scholars disagree and argue that structures are critical to meet calls for

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state accountability (Lingenfelter, 2004; Mingle, 2005; MacTaggart & Mingle, 2002). These
scholars advocate that higher education can establish and advance a higher education policy
agenda, hold institutions accountable for performance, holistically rethink funding and student
aid, and ensure greater alignment between higher education and state economic needs.
The application of social network analysis as a framework to study higher education
governing boards seems one way to combine calls to explore the human relationships in
governance in the context of existing governance structures. I now provide a foundational
introduction to social network analysis and discuss relevant studies of social networks.
Social Network Analysis
In this section I first discuss the history of social network analysis. Next I describe social
network analysis as a tool to study relationships and explain core concepts of social network
analysis. Finally, I provide an overview of relevant social network studies.
History of social network analysis.
The broad field of network science focuses on the study of relational structures in a wide
range of physical and social phenomena (Borgatti, et al., 2009; Butts, 2008; Hansen,
Schneiderman, & Smith, 2011). Social network analysis is the application of network science to
the formal study of systems of people with an emphasis on representing, measuring, and
modeling human relationships (Butts, 2008).
Two seminal theoreticians laid the groundwork for what became social network theory:
Auguste Comte in the 1800s and Georg Simmel in the early 1900s. Comte coined the term
sociology with the goal of uncovering the laws of society. This goal, he argued, required both
theory and systematic observation as a means to view society in terms of the interconnections

33

among social actors (Freeman, 2004). Comte claimed that society was more than simply a group
of people and imagined society as a network of relationships of reciprocal influence (Hansen, et
al., 2011). Perhaps the most explicitly structural perspective adopted by any of the late
nineteenth and early twentieth century social thinkers was displayed in the work of Georg
Simmel. Simmel said, “[s]ociety exists where a number of individuals enter into interaction”
(cited in Freeman, 2004, p. 15). In making this comment, Simmel considered sociology
exclusively as the study of the patterning of interactions. And Simmel’s student, Leopold von
Wiese, went even further and talked in contemporary terms about a “system of relations” and a
“network of lines between men” (Freeman, 2004, p. 16).
The two sociologists saw patterns of social ties as the main focus of sociology in contrast
to the study of individuals and their attributes. Moreover, both scholars defined society using the
kinds of structural perspective and terms found today in social network analysis (Freeman,
2004). The concept of connected actions has remained a core belief of the social sciences and
underlies modern social network analysis (Breiger, 2004). As leading social network analysts
put it, social network analysis is grounded in a belief that “the social world is found in
interactions rather than in an aggregation of individuals” (Marin & Wellman, 2010, p. 4).
Efforts to create a systematic language to record social relationships started in the
twentieth century (Scott, 2000). With foundational roots in graph theory (Euler in 1736) and in
the later development of the sociometry/sociogram (Moreno in 1934), scholars built on these
concepts to capture the nonrandom connections that occur among groups of people (Freeman,
2004). For example, teams at Harvard University worked to advance social network analysis.
Lead by W. Lloyd Warner and Elton Mayo, a group explored interpersonal relationships in the
workplace (See, Western Electric Wiring study by Roethlisberger and Dickson (1939)). At the

34

same time, Quine, Chapple and Arensberg collaborated to develop computational tools to
describe a system of relations quantitatively (Freeman, 2004). Over time, the approach by these
scholars at Harvard was based on a structural perspective, involved the collection of empirical
data, used graphic imagery, and followed explicit mathematical models. Collectively these
advancements represent all of the features of contemporary social network analysis (Breiger,
2004; Wellman, 1988). However, the surge of scholarship by teams of collaborators from
sociology, psychology, anthropology, and mathematics that occurred in the 1930s was then
followed by a period of stagnation (Freeman, 2004; Hansen, et al., 2011).
Combining prior social network research with the advancements in mathematical graph
theory and the development of random graphs, Milgram (1967) explored the idea of small world
networks by conducting research on the randomness of connections between any two people.
The study became known as “Six Degrees of Separation” and, despite its modern day popularity,
it is likely that Mark Granovetter's articles on "The Strength of Weak Ties" (1973) and
"Threshold Models of Collective Behavior" (1978) were the first to ignite public fascination with
social networks and the spread of ideas. Focusing on employment and how people discover new
job opportunities, Granovetter (1973) found that job news passed through connections that were
not the closest and most intense relationships. An individual’s “weak ties” brought news from
distant parts of the social network to which “strong ties” did not have access because they
occupied such a similar place in the network of the individual job seeker (Granovetter, 1973).
This finding demonstrated that weak ties are particularly useful in acquiring novel information.
Moreover, weak ties are less intense and less costly to maintain in terms of time and attention.
Granovetter (1978) advanced models of collective behavior based on behavioral
thresholds. The models are valuable to understanding situations when the outcome does not

35

seem intuitively consistent: “By explaining paradoxical outcomes as the result of aggregation
processes, threshold models take the ‘strangeness’ often associated with collective behavior out
of the heads of actors and put it into the dynamics of situations” (Granovetter, 1978, p. 1442).
This threshold model of social behavior was later popularized by Malcom Gladwell's (2000) best
selling The Tipping Point.
In his work Gladwell (2000, p. 33) described three agents of change in the tipping points
of epidemics. Gladwell describes change agents as connectors, mavens, and salesmen. In
Gladwell’s words connectors “span many different worlds” and have the ability to provide links
(2000, p. 49). Mavens are sources of new information and have the ability to start epidemics
because of their knowledge, social skills, and ability to communicate (2000, p. 69). Salesmen
are individuals with powerful negotiation skills and described by Gladwell as “persuaders”.
Presently, the application of social network analysis is widely found throughout a range
of disciplines including sociology, anthropology, communications, computer science, education,
economics, physics, management, medicine, political science, public health, psychology,
biology, and the humanities. One prominent commentator on the history of social scientific
thought and on contemporary development writes that “network sociology is doing the very
thing that early sociologists and anthologists saw as crucial – the mapping of the relations that
create social structures” (Turner, 1991, p. 571). For example, communications researchers study
social media to evaluate the diversity of technologies that support social interaction such as
asynchronous (email) and synchronous (chat/IM) conversation, world wide web, collaborative
authoring (wikki/google docs), blogs and podcasts (WordPress, Twitter), social sharing
(YouTube), social network services (Facebook), online markets (eBay), and virtual worlds
(World of Warcraft) (Hansen, et al., 2011).

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Researchers across multiple disciplines have developed through a sizable body of
empirical work a coherent set of characteristics and principles to guide social network analysis
(Wellman, 1988). In addition, the field is supported by a professional association for social
network analysts, International Network for Social Network Analysis (“INSNA”), formed in
1997 by Wellman. The association hosts an annual “Sunbelt” conference and is involved in the
publication of three professional journals (Freeman, 2004).
Having just provided an overview of the history of social network analysis, let me now
turn to discussing social network analysis as a tool to study relationships.
Social network analysis as a paradigm.
The study of social networks is grounded in the belief that relationships are the building
blocks of the social world. As an analytical tool social network analysis examines the complex
relations and patterns of connections among individuals by mapping interactions, calculating
measures of the network as a whole, and by determining positions of individuals within the
network (Marin & Wellman, 2010; Wasserman & Faust, 1994). As the prominent social
network analyst Barry Wellman states, “[structural analysis] is a comprehensive paradigmatic
way of taking social structure seriously by studying directly how patterns of ties allocate
resources in a social system” (Wellman, 1988, p. 20). In making this comment, Wellman argues
that the strength of social network analysis lies in its integrated application of theoretical
concepts, intellectual unity about the characteristics of structural analysis, and ways of collecting
and analyzing data (Wellman, 1988). The following components of this section review key
terminology helpful to understanding social network analysis, types of social networks, levels of
analysis applied to social network research, and ways to represent social networks (including

37

software alternatives).
Key terminology and language.
Social network analysts use specialized terminology for describing the structure and
contents of networks (Hanneman & Riddle, 2005; Marsden, 1990; Scott, 2000). Formally
defined by Wasserman and Faust (1994), social networks are a set of network members
(commonly referenced as nodes/vertices/actors) tied by one or more types of relations
(commonly referenced as edges/ties/relations). An actor is represented in a network diagram by
a node, or point in space. A tie, the relational connection or linkage between two actors, is
represented by a line connecting the two nodes. Appendix A provides a glossary of terms for
reference.
Network analysis focuses on the relations among actors, not on individual actors and their
attributes. Actors are usually not selected with traditional independent probability sampling.
Rather, network studies are much more likely to include all of the actors within some boundary
(Hanneman & Riddle, 2005). Although not necessary for network analysis, collecting attribute
data that describes each actor can add insights to the analysis and visualization of networks
(Hansen, et al., 2011).
The boundaries of the populations studied by network analysts are of three main types
(Laumann et al., 1992). According to Hanneman & Riddle (2005), the most common boundaries
are those imposed or created by the actors themselves. As such, “social network studies often
draw the boundaries around a population that is known, a priori, to be a network” (Hanneman &
Riddle, 2005, Chp 1). In essence these networks are naturally occurring formal clusters of
actors. Alternatively, a network analyst might take a more event-based approach to defining
population boundaries (Laumann, et al., 1992). In this instance a social network analyst might
38

have reason to suspect that networks exist, but the entity being studied is an abstract concept
(Hanneman & Riddle, 2005). For example, an analyst might impose a geographic boundary in
studying a set of actors who live near each other. Lastly, boundaries may be based on small set
of actors within a population of interest and then expanded to include other actors sharing
particular types of relations. Laumann et al. (1992) defined this type as a relational-based
approach to boundary specification. In this study I used the first approach where the formal
members of the governing board defined the network boundary.
Ties represent the variety of relations among actors. The two major types of connections
are undirected ties and directed ties. An undirected tie (symmetric) represents the existence of a
relation between two actors. No origin or destination is necessary in these mutual relationships.
Moreover, these ties cannot exist unless they are reciprocated. On the other hand, directed ties
(asymmetric) have clear origins and destinations. It is not necessary for directed ties to be
reciprocated. For example, if actor A claims a relationship with actor B, an arrow is drawn
between the nodes pointing to actor B. If actor B also claims a relationship with A, then the
arrow would be bi-directional and represent an undirected tie (see, Figure 2.1).
Figure 2.1
Illustration of Directed and Undirected Ties

Note. Arrows indicate the direction of the tie.
Borgatti et al. (2009) identified four broad categories of relations – similarities, social
relations, interactions, and flows. Similarities are location, membership, and attribute ties
39

between nodes. Social relations are ties between nodes based on roles (kinship, friend,
competitor of) as well as affective (likes, hates) or cognitive (knows about) dispositions.
Interactions are behavior-based ties (speak to, help) between nodes. Flows are relations based on
exchanges or transfers (resources, information, influence) between nodes. Similarities and social
relations are considered undirected ties, while interactions and flows are directed ties. In this
study I asked board members to identify individuals they draw upon to inform their role as a
governing board member. My goal was to capture interaction and flow relationships and
represent these ties in a directed graph.
Different types of data can represent ties. The simplest type is an unweighted binary tie
(the tie exists or does not exist). In contrast, a weighted tie includes values associated with each
tie that indicates the strength or frequency of a relation (interval data) (Hansen, et al., 2011).
Valued data (such as frequency of communication) gives information on the strength of an
existing tie. For example, if A sent 10 emails to B last week and only 1 to C, it is possible to
conclude that last week A was more strongly connected to B than C. Hanneman and Riddle
claim, “even though it is a good idea to measure relationship intensity at the most refined level
possible, most network analysis does not operate at this level. The most powerful insights of
network analysis, and many of the mathematical and graphical tools used by network analysts
were developed for simple graphs (i.e. binary, undirected)” (2005, Chp 1).
Ultimately, social network analysis involves the examination and comparison of ties at
many levels: between two nodes (also called a dyad), among and between clusters of nodes (also
called cliques), and among all nodes included in the network as shown in Figure 2.2. The
structure of a network can influence outcomes for individual actors because the position of a
node in the network can provide both opportunities and constraints (Burt, 1992). Moreover,

40

changes in the pattern of relationships among actors can impact the structure of the whole
network (Frank & Fahrbach, 1999; Frank, 1996).
Figure 2.2
Illustration of a Whole Network

Note. Arrows indicate the direction of the tie. Adapted from Hanneman, Robert A. and Mark
Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California,
Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/ )

41

Some social network analyses use matrix algebra to describe relationships when the data
are too complex to portray patterns in a sociogram. Networks can be represented in this
mathematical format and calculations performed to summarize the information on the sociogram.
Figure 2.3 is the mathematical representation of Figure 2.2.
Figure 2.3
Example of a Network Matrix

Note. This matrix represents a binary network – either a tie exists (value = 1) or not (value = 0).
Adapted from Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network
methods. Riverside, CA: University of California, Riverside (published in digital form at
http://faculty.ucr.edu/~hanneman/ )
Types of networks.
Two important dimensions define the type of network. The first is whole versus ego
networks. The second is one-mode versus two-mode networks. Whole networks examine the
social structure by focusing on all actors and frequently analyze multiple relations within the
network (see, Figure 2.2). Alternatively, ego networks focus on one actor (the ego) and the
relations with the ego (the alter) (Marin & Wellman, 2010). Ego networks can be extracted from
whole networks by selecting a focal actor and examining the alters connected to the focal actor
(Burt, 1992, 2004, 2005) (see, Figure 2.4).

42

Figure 2.4
Illustrations of Ego-centric Networks

Note. Two illustrative ego networks. The one on the left contains many structural holes; the one
on the right contains few. Adapted from Borgatti, S.P., Mehra, A., Brass, D. and Labianca, G.
(2009). “Network Analysis in the Social Sciences.” Science. Vol. 323. no. 5916, Feb 13, pp. 892
– 895.
In one-mode networks everyone could potentially be connected. Researchers studying
whole networks most frequently collect data on a single type of node and analyze one-mode
networks. Alternatively two-mode networks examine multiple types of nodes. For example, in
these affiliation networks researchers examine events and attendees or organizations and
membership (Marin & Wellman, 2010).
Levels of analysis.
Two distinct approaches to social network analysis exist (Hanneman & Riddle, 2005;
Marin & Wellman, 2010). As explained these scholars, the mathematical approaches to network
analysis tend to treat the data as deterministic. That is, they tend to regard the measured
relationships and relationship strengths as accurately reflecting the actual status of the network.
Mathematical approaches also tend to assume that the observations are not a random sample of

43

some larger population and do not have inferential statistical qualities; rather, the observations
are usually regarded as the population of interest.
In contrast statistical analysts tend to regard the particular scores on relationship strengths
as stochastic. Statistical analysts also tend to view a particular set of network data as a sample of
a larger population of such networks with the potential to make generalizations and conduct
hypothesis testing. However, this approach is limited when there is no plausible way of
identifying populations and drawing independent random samples by probability methods.
These distinctions are more than of academic interest. The choice of approach affects
both the design and analysis of social network studies. Most common is the use of mathematical
approaches to social network data with a focus on descriptive statistics. The application of
inferential statistics to social network data is more recent and is a growing in its application
(Wasserman & Faust, 1994).
In the mathematical approach scholars use a variety of network metrics to measure
properties of the network as a whole as well as calculate measures for each actor in the network.
Several metrics describe networks as a whole (Marsden, 1990; Wasserman & Faust, 1994).
Scholars often examine the density of the network. It is a quantitative way to measure the
interconnectedness of all the actors and assess concepts such as cohesion and membership
(Hansen, et al., 2011). By using a set of centrality measures, scholars can describe a variety of
ways an individual actor is or is not central to a whole network (Freeman, 1979). Other metrics
integrate attribute data with network data to examine homogeneity and composition of the
network (Marin & Wellman, 2009).
Individual actor metrics often identify the individuals’ position in the network (Marin &
Wellman, 2009). Commonly these measures include degree centrality, betweenness centralities,

44

closeness centrality, and eigenvector centrality (Freeman, 1979; Wasserman & Faust, 1994).
Degree centrality is a count of the total number of connections linked to an actor. As a measure
it does not differentiate between quantity and quality. For directed networks an in-degree and
out-degree centrality measure is calculated for each actor. Betweenness centralities is a measure
of how often an actor lies on the shortest path between other actors. Scholars often reference this
as a bridge score for boundary spanners. As a measure it provides insight into how removing an
actor from a network would disrupt connections between other actors in the network.
Additionally, this measure can identify structural holes in a network where two or more actors
fail to connect. Closeness centrality captures the shortest-path length between an actor and every
other actor in the network. A high closeness centrality score means that an actor is directly or
closely connected to another actor. A low closeness centrality score often indicates actors in
peripheral network locations who require many connections to reach other actors in the network.
The eigenvector centrality measure is a more sophisticated view of centrality. The measure
allows scholars to assess an actor with a few highly connected relations.
Network representation.
Contemporary social network research represents a move from vague network metaphors
to sophisticated representations of central elements of social structure (Breiger, 2004). A notable
indication of this change is the development of several software applications to analyze as well
as visually represent social network data. As stated by Butts, “modern social network analysis is
a computationally intensive affair” (2008, p. 1).
Huisman & van Duijin (2005) reviewed twenty-seven software programs used for the
analysis of social networks. They found the most frequent applications included data
manipulation, data visualization techniques, and data analysis focused on descriptive network
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statistics, procedure-based network analysis, and statistical modeling of networks. Freeman
(2000) illustrates some of the newest procedures for producing web-based pictures that allow
viewers to interact with the network data, using visual input to explore a variety of analytical
models of their structural properties. Regardless of the technique “the tools for analyzing and
visualizing networks have demanded significant technical skill and often mastery of
programming languages” (Hansen et al., 2011, p. 47).
For my study I selected to use the UCINET program. Similar to many social network
analysis software programs, UCINET is a free, open-source application. A team of social
network scholars (Freeman, Borgatti, & Everett) developed the software. This program was
meant to be accessible while at the same time providing rich analysis and visualization
(Hanneman & Riddle, 2005). My rationale for selecting UCINET was two fold. The software is
compatible with SPSS Statistics, which made it possible to combine social network analysis with
other data manipulations. In addition UCINET is one of the few software packages to combine
statistical analysis with graphical tools.
In summary, social network analysis is a means to study relationships and interactions.
As a grounded analytical tool, social network analysis is used in a variety of disciplines. The
various analytical approaches share a common terminology and language. Social network
scholarship either focuses on examining whole or ego-centric networks. Network metrics allow
for scholars to describe individual actors within networks as well as networks as a whole.
Analysis is supported by many software applications.
Relevant Studies of Social Networks
Since 1970 there has been an exponential growth of social network research and

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publications (Borgatti & Foster, 2003). In the field of higher education social network
scholarship has focused on faculty networks with an emphasis on understanding academic
success and research productivity (Finkelstein, 1982; Fries-Britt, 2000; Gerstick, Bartunek, &
Dutton, 2000; Hitchcock, Bland, Hekelman, & Blumenthal, 1995; Leahey, 2007; McDowell &
Smith, 1992; O’Leary & Mitchell, 1990; Rawlings & McFarland, 2010). With the exception of
Pusser’s, Slaughter’s, & Thomas’ (2006) study of public higher education governing board
members interlocks with corporate governing boards, no research exists that applies social
network analysis or theory directly to post-secondary governing boards. Three theoretical
constructs may be relevant to the study of governing board social networks: social capitol,
organizational research, and board interlocks.
Social capital research.
As detailed in reviews by Alder & Kwon (2002), Lin (2001), and Burt (2005), much
research focuses on the concept of social capital. In the most general terms, the concept is about
the value of connections. As stated by Alejandro Portes “[t]he consensus is growing in the
literature that social capital stands for the ability of actors to secure benefits by virtue of
membership in social networks or other social structures” (1998, p. 7).
Three major theories conceptualize social capital: weak-tie theory (Granovetter, 1973),
structural hole theory (Burt, 1992), and social-resource theory (Lin, Ensel & Vaughn, 1981).
Weak-tie theory focuses on the characteristics of the tie between actors. Structural hole theory
emphasizes the bridging properties between clusters of actors. Social-resource theory focuses on
the characteristics of the actors within the network not the nature of the tie or the pattern of ties
among actors.

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Burt’s (1992) book on structural holes directed attention to the structure of an actor’s ego
network. Burt equates social capital with the lack of ties among an actor’s alters, a condition he
names structural holes. He argues that the spanning of structural holes provides the mechanism
relating weak ties to positive outcomes in Granovetter’s (1973) strength of weak ties theory.
Burt’s view contrasts with Coleman’s (1990) view of social capital, which calls for a dense egonetwork in which ego’s alters are able to coordinate with each other to help the ego. Coleman’s
view is similar to that of Putnam (2000) and others who define a group’s social capital in terms
of broad cross-cutting interconnections among all group members. Consequently, views about
optimal network structures vary by theoretical perspective about social capital.
Regardless of the theory employed, social capital research often focuses on relating an
individual’s network to significant outcomes such as power (Brass & Burkhardt, 1993a,b),
leadership (Pastor, Meindl & Mayo, 2002), mobility (Seibert, Kraimer & Liden, 2001; Seidel,
Polzer & Stewart, 2000), employment (Crowell, 2004; Fernandez, Castilla & Moore, 2000;
Krackhardt & Porter, 1985), individual and team performance (Sparrowe, Liden, Wayne &
Kraimer, 2001; Tsai, 2001), and entrepreneurship (Renzulli, Aldrich & Moody, 2000).
A related line of scholarship reverses the usual logic of social capital and examines the
negative consequences of social capital. This scholarship discusses the “dark side” of social ties
that trap actors into maladaptive situations or facilitate undesirable behavior (Gargiulo &
Benassi, 1999; Gulati & Westphal, 1999; Portes & Landolt, 1996).
Organizational research.
Some research uses social networks to examine organizations (Brass & Burkhardt,
1993b). As discussed by Frank (1996), the structure of interactions and the pattern of influence

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in an organization can be characterized by a map of interactions within and between members.
Organizational network studies focus on either selection or influence occurring within the
network. Selection studies examine how actors choose others with whom they interact.
Influence studies examine how actor’s interactions affect their beliefs and behaviors. Both
dynamics have been shown to affect organizational behavior.
In studies of organization learning social network scholars have designed models to
measure knowledge held by individuals and groups (Borgatti & Carboni, 2007) as well as
models of information seeking (Borgatti & Cross, 2003). Frank advocates for understanding the
social context of learning: “[b]y studying the relations among the participants in schooling, we
can begin to understand the processes through which individuals are affected by, and partially
construct, schools as organizations and institutions” (1998, p. 201). Frank (1996, 2005) also
showed that teacher networks directly affect the educational process and are a key element of
schools as social organizations.
The study of informal networks and their impact on organizational performance is
another important line of inquiry (Cross, Parker, & Borgatti, 2002). Hatala (2006) argues that
social network analysis can advance the field of human resources by enabling researchers to
analyze the interactions between individuals and their environment. Social network studies also
examine organizational culture. White (1992) considered discursive “narratives” and “stories” to
be fundamental to structural pursuits, writing that “stories describe the ties in networks” and that
“a social network is a network of meanings” (pp. 65, 67). Frank and Fahrbach (1999) discussed
the role each individual plays in generating organizational culture. Their work combines
“balance theory and information theory to specify models of influence (changes in actors’
sentiments as a function of interactions) and selection (changes in the pattern of interaction as a

49

function of actors’ sentiments)” (1999, p. 252). These authors claim that social networks form
the basis for organizational culture.
Social networks have been shown to influence organizational adaption and change.
Kraatz (1998) examined the role of inter-organizational networks in shaping adaptive change at
colleges. Frank, Krause, & Penuel (2010) studied how the distribution and flow of knowledge
through intra-organizational networks affects organizational change. Frank and colleagues found
that successful organizational change depends in part on information flows from subgroups
whose members possess high quality knowledge. In a study of teachers, Darling-Hammond &
McLaughlin (1995) concluded that teachers are more likely to innovate when they interact with
other innovators. Rogers (1995) described successful “diffusion of innovations” as dependent on
the patterns of connections in a social network.
Corporate board interlocks.
Another development in social network literature has been the empirical research on
corporate board interlocks. Mizruchi (1996) provides an excellent review of this scholarship
dedicated to exploring the ties among organizations created by having a member of one
corporate board member sitting on the board of another. Early corporate board interlock
research focused on resource dependence and class perspectives which viewed interlocks as a
means to (a) manage organizational dependencies (Pfeffer & Salancik, 1978) and (b) maintain
power and control for social elites (Useem, 1984). Although the primary objective in both
research streams was identifying the causes of interlock ties, some of this early research used
interlocks to predict organizational behaviors (Mizruchi, 1996).
In recent years, the focus of this research has shifted toward viewing corporate board

50

interlocks as a means by which organizations reduce uncertainties and share information about
acceptable and effective corporate practices. Scholars have used board interlocks to explain
corporate acquisition behavior, the adoption of organizational structures, CEO pay premiums,
and joint venture formation (Borgatti & Foster, 2003). Several studies highlighted the benefits of
interlocks in reducing uncertainty (Borgatti & Foster, 2003). One development in this literature,
paralleling developments in the social capital literature, is that researchers are beginning to study
the negative effects of certain types of interlocks (Davis & Greve, 1997; Gulati & Westphal,
1999; Haunschild & Beckman, 1998).
Pusser, Slaughter, and Thomas (2006) conducted one of the few applications of board
interlocks to higher education governance in their study of trustee networks. They examined
trustee interlocks (individuals who simultaneously sit on multiple governing boards) at
prominent research universities and explored the implications of these relations with universitybusiness relationships. The study concluded that private institutions governing board members
are more likely to be connected to powerful publicly held corporations than public institution
governing board members. Moreover, private governing board members interlocks were with a
wider variety of industry sectors. Finally, governing board members from a variety of private
institutions often were members of corporate boards thereby creating opportunities for conflicts
of interest. This pattern was not evident with public governing board members. The authors
acknowledge that the study did not explain how interlocks impact organizational performance
nor address what flows through the network. However, this study made an important
contribution in identifying governing board interlocks and advocating for an agenda of research
exploring “the broader institutional and political-economic dynamics that set the context in
which networks of trustees shape institutional policies” (Pusser et al., 2006, p. 770).

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Summary of Literature Review
The current financial crisis as well as the cumulative effects of decades of declining public
investment in higher education substantially affects public and private higher education
institutions. Even with a widespread commitment to shared governance, higher education
institutions have substantial variation in structures and in institutional responses to the current
fiscal conditions. Studies of higher education governing boards typically focus on structure and
performance. Research has yet to explore in any depth the impact of higher education governing
board members as social actors. The application of social network analysis as a framework to
study higher education governing boards is one way to combine calls to explore the human
relationships in governance in the context of existing governance structures. Next I present the
research design, sampling plan, instrumentation, process to collect and analyze data, and
limitations of my study.

52

CHAPTER 3
Study Design
This chapter describes how I conducted the study. After reintroducing the purpose of the
current study, I discuss my research design, sampling framework and steps, and data collection
approach. Then, I explain the instrumentation and data analysis procedures. I conclude by
detailing the limitations of my study.
The purpose of the current study was to identify the social network patterns of governing
board members at public institutions and explore the broad range of interactions that can
influence governing boards’ networks. I sought to determine whether identifiable social
networks for public higher education governing board members exist, to develop an overall
picture of public higher education governing board networks, and to understand the internal and
external factors impacting governing boards’ social networks during times of fiscal crisis.
Research Design
My study focused on the role of social networks in public higher education governing
boards. During the current fiscal crisis I assumed that institutional policy decisions are crucial to
institutional survival and functioning, and that the context makes it easier for governing board
members to reflect on the diversity of actors whom they call upon to inform their role as an
institutional leader. The premise was that an understanding of these social networks would
better enable researchers to understand governing boards.
Since governing boards are not well understood in general and have not been examined
through the lens of social network analysis, the study would be exploratory for the population
(Tierney, 2004b). However, the technique of social network analysis is a well-developed
53

approach and sophisticated analytical tool (Marin & Wellman, 2010; Scott, 2000).
Sampling Framework
The sampling framework was driven by two significant considerations. First was to
capture potentially important institutional variations in public governing boards through the
selection of distinct types of institutions from states transferring fiscal burdens onto postsecondary education. As such, I limited the universe to states with individual institution level
boards because I wanted to associate governing board social networks with actions related to that
institution. I also wanted to focus on states with the greatest likelihood of the financial crisis
affecting the operation of public higher education institutions in that state. Recall that I used this
criterion because of the possibility that it would make the identification of board social networks
more obvious than in less turbulent times. Finally, I wanted to look at institutional variation by
studying a teaching-oriented public institution and a research-oriented public university in the
same state to see whether or not institutional type differences were related to board social
networks.
The second consideration was to meet the methodological requirements of social network
analysis. Critical to whole network analysis is high participation rates (Marsden, 1990, 1993).
High participation allows for a complete mapping of the network. The assumption is often
asserted that network analysis is intolerant of missing data. However, scholars have tested this
assumption and identified conditions under which network measures were likely to be reliable
(Borgatti, Carley, & Krackhardt, 2006; Marsden, 1990 & 1993). Borgatti, Carley, & Krackhardt
examined this important practical question: “is it reasonable to compute centrality indices when
we know that the data contain errors?” (2006, 134). The scholars concluded that measures of

54

centrality tested were quite robust under small amounts of error (such as 10% and under). More
importantly the authors note “accuracy not only declines with increasing error, but does so
predictably and monotonically” (2006, 134). The implication of this finding is that knowing the
rate and type of error in the data collection process can allow for the creation of error bounds on
the metrics constructed from the observed data.
I wish to note here that I decided to exclude any institution with publicly-elected
governing board members from the study. In particular, publicly-elected governing boards (e.g.
election year cycle) could potentially influence the mapping of the networks in a manner that
distorts the more typical social network relationships of board members.
To take into account these sampling considerations, I used a blend of criterion sampling
and judgment/purposeful sampling (Miles & Huberman, 1994; Patton, 2002). Patton defines
criterion sampling as a technique that “involves selecting cases that meet some predetermined
criterion of importance” (2001, p. 238). A judgment/purposeful sample depends on the
researcher’s practical knowledge of the research area along with relevant knowledge of the
population. My goal was to have a total sample size of at least four complete public governing
boards from two types of institutions: research-oriented and teaching-oriented.

55

Figure 3.1
Sampling Framework Flow Chart
32 States
excluded
from the
study

50 States

17 States
with
institutions
governed by
an
institutional
level
governing
board

2 States with
limited fiscal
impact on
higher
education
15 States
with fiscal
impact on
higher
education

9 States
excluded
from study
6 States with
Public
Research and
Master
instituions
governed by
an
instituional
level
governing
boards

10 VH
Research
Universities
30 S,M,L
Public
Masters
Insitutions

In this study, the first step was to select a sample of states. Drawing on McGuiness’
(2003) categorization of state higher education governance structures (see, Table 3.1) and
conversations with experts in the field of higher education governance and policy, I identified
states with public post secondary institutions governed locally by an institutional board. This
generated a possible universe of 17 states.

56

Table 3.1
States with Institutional Level Governing Boards for 4-year Institutions
Higher Education Governance Structure Category
State Coordinating / Institutional Board
! Single statewide coordinating board. Institutional level governing boards
for several colleges and universities. Individual governing boards for
community/technical colleges. Multi-campus governing board for
universities and 2-year campuses.
! Single statewide coordinating board. Institutional level governing boards
for several colleges and universities. Individual governing boards for
community/technical colleges. Multi-campus governing board for
universities.
! Single statewide coordinating board. State level governing boards for
multi-campus systems, state level governing or coordinating boards for
community/technical colleges, and institutional level governing boards
for some universities.
State Coordinating / Mixed Institutional Boards
! Single statewide coordinating board. Each public university is locally
governed by a governing board. State governing boards for community
colleges either govern the colleges or coordinate locally governed
community colleges.

State
AR, NM,
OH, OK,
WV
MD, MO,
NJ
AL, CO, IL,
IN, SC, TX

KY, VA,
WA

Although financial issues confront all states, the current study focused on states that
transferred a larger portion of the financial burden onto public higher education institutions. The
rationale for focusing on states that have transferred financial burdens from the state to its public
colleges and universities is the potential for such a shift to increase the engagement of governing
board members and possibly make board members more aware of the social networks they rely
upon as institutional leaders. I assessed the impact of the recent fiscal crisis on these 17 states
and determined how identifiable shortfalls in state fiscal support influenced higher education
funding (see, Table 3.2). Using the Fiscal Survey of the States report by the National Governors
Association, I determined that of the 17 states with locally governed institutional boards all but

57

three states (Arkansas, Ohio, and Washington) reported mid-year cuts to higher education in
fiscal year 2010. Arkansas and Washington reported mid-year budget cuts for fiscal year 2010
but those cuts were not transferred to higher education budgets. Accordingly, I removed
Arkansas and Washington from the sampling framework. Ohio did not report any mid-year
budget cuts for fiscal year 2010. However, the state did have a $1,180.7 million budget gap in
fiscal year 2009 (NCSL, 2009) and fiscal year 2009 required the use of the state’s stabilization
(rainy day) fund (NGA Fiscal Survey of States, 2009). At the end of the year the balance of the
fund was exhausted. I selected to keep Ohio in the study since the state does appear to have been
adversely affected by the recent fiscal crisis. Moreover, the fiscal impacts to the state potentially
may have an impact on state funding of higher education.
Table 3.2
Percentage of Mid-Year Higher Education Cuts for States in Sampling Framework
State Higher Education Governance
Structure Category
State Coordinating / Institutional Board
Arkansas
New Mexico
Ohio
Oklahoma
West Virginia
Maryland
Missouri
New Jersey
Alabama
Colorado
Illinois
Indiana
South Carolina
Texas
State Coordinating / Mixed Institutional Boards
Kentucky
Virginia
Washington

58

% of Higher Education Cuts
Mid-Year 2010
Mid-Year 2011
0.00%
9.57%
13.25%
10.31%
11.68%
3.62%
3.39%
19.18%
47.36%
1.78%
31.00%
9.36%
28.22%
9.91%
20.51%
0.00%

16.10%

26.27%
0.00%
2.50%
48.59%

1.63%

I further narrowed the potential pool of states for the study by including only those states
that had public research universities and public masters level institutions governed by
autonomous institutional governing boards. The result was a sampling framework of six states:
New Mexico, Ohio, West Virginia, New Jersey, Kentucky, and Virginia. The remaining nine
states were excluded from the study because in the state higher education governance structure a
multi-campus system governing board not an institutional board governed the research
universities.
The second step in the study’s sampling framework was to select institutions within each
of these six states. Utilizing the ‘Basic Classification Descriptions’ of the Carnegie
Classification of Institutions of Higher Education I identified public research universities (very
high research activity) and public masters institutions (small, medium, and large programs) that
existed in each state. The first represents a strong research-oriented institution, the latter a more
teaching-oriented institution. As shown in Table 3.3, a total of 10 very high research activity
public universities and 30 small, medium, and large program public masters level institutions
existed in the 6 states.
Table 3.3
Number of Public Research and Masters Level Institutions in Sample States
State

Public Research (VH)

Public Masters (S, M, L)
Kentucky
2
5
New Jersey
1
10
New Mexico
1
4
Ohio
2
1
Virginia
3
7
West Virginia
1*
3
Total
10
30
Note: * A Very High Research Activity University does not exist in West Virginia. As
such the High Research Activity University in the state was selected.

59

The next step was to determine access to and cooperation by governing board members in
potential participating institutions. The board secretaries1 at the Association of Governing
Boards National Conference on Trusteeship (March 2010) recommended a strategy of going
through the board secretary to gain access to board members. The board secretary’s assessment
of institutional interest and board members participation was critical for data collection and
obtaining high response rates. I found that board secretaries could indicate the likelihood of
participation of each board member as well as the best way to collect data from each board
member (e.g., e-survey, telephone interview). An important criterion in sample selection was the
support by a board secretary in assisting with board member participation in the study.
Sampling Steps
This section details the steps I took to select the governing boards for the study. Figure
3.2 illustrates the overall scheme of governing board selection and participation.
With the goal of including a governing board from at least one public research institution
and one public masters institution from the same state, I elected to first contact the ten public
research universities within the six states about participation in the study. Only after a public
research university board agreed to participate did I approach the board secretary’s at public
masters institutions within the state.
Initially the board secretary at four public research universities in four states indicated a
strong likelihood of participation by the governing board. The remaining public research
institutions declined to participate or never responded to the invitation. Of the four interested
1

Throughout this paper I use the title board secretary or secretary to represent the institutional
employee who works directly with the governing board. Note that some institutions use the term
board professional.

60

Figure 3.2
Flow of Governing Board Participation in Study

3 Accepted
Kentucky
New Jersey
New
Mexico
Ohio
Virginia
West
Virginia

2 Accepted
10 Research
Universities

5 Declined

4 Masters

1 Refused
0 No
Response

3 No
Response

Note: As a result of the “Declined” and “No Response” by 8 public research universities, 26
masters level institutions were excluded from sample selection and were not contacted for
participation.
research institutions, one elected not to participate after the board secretary consulted with board
members. A second institution dropped out because the board members never followed up on
the initial interest expressed by the board secretary. So I ended up with two public research
universities from two states (State A and State B).
Once I identified the two public research universities I sought a public masters institution
from each of the two states in which the research universities were located. I contacted four
public masters institutions, three from State B and one from State A. With the support of the
AGB and state higher education executive officers (“SHEEO”) three of these institutions agreed
to participate. Only two are included in the study’s results since the participation rate for one

61

public masters was extraordinarily low. I detail the data collection process for participating
institutions below.

Data Collection
To ensure gathering high quality data I employed a variety of strategies. Essential to my
study was my initial contact with the board secretary at each institution. A successful interaction
enabled me to begin to determine the interest by the institution. The support for my study by the
AGB gave me credibility and was a significant factor that advanced these initial inquiries.
Based on a small group discussion with board secretaries (March 2010) I received counsel on
how best to invite institutions to participate. From these dialogues I learned it was critical for
the board secretaries to understand the intention and scope of my study. I prepared and sent a
statement that communicated AGB’s endorsement, the intention of the study, and what I
expected from the board in terms of participation in data collection the board’s commitment to
provide data. Once the board secretary received the information about the study, she or he
discussed possible participation with the President and Board Chair. If the institution was
receptive to participate in the study, I worked with the board secretary to learn how to best to
collect data from individual board members. I submitted data collection forms to the board
secretary for distribution to board members. I also worked with the board secretary to send
follow-up reminders.
Another critical tactic was the design of the survey instrument. The aim of the survey
was to capture governing boards’ social networks and factors influencing governing boards’
networks, learn about governing board decision making processes, and understand how the
governing board functions in the context of the current fiscal crisis. Previous experience with

62

collecting data from similar role groups showed the need for the instrument to be concise and
relevant (Alberbach & Rockman, 2002; Berry, 2002; Goldstein, 2002; Stephens, 2007).
After developing the draft instrument (see the following section for details) I conducted
pilot investigations. I asked a board secretary from a public research institution not included in
the study’s sample and an AGB research staff member to review the draft survey instrument for
both meaning and conciseness. I also consulted social network scholars to verify the fit between
questions asked and the type of data required for social network analysis. In addition, at the
AGB National Meeting I conducted pilot-tests of the draft survey with governing board members
followed by debriefing sessions with board secretaries to discuss both the content and how best
to distribute surveys to board members. In the end I developed a survey with 15 items that took
10-15 minutes to complete. I also developed guidelines to assist board members with definitions
of key terms. Based on the recommendations of the AGB and board secretaries I either
distributed the instrument electronically or gave it to board members in person at a board
meeting. I also provided the option for board members to speak with me directly about the study
and offered to collect responses to the survey instrument through a structured interview.
Lastly, I took into account ethical considerations related to social network research
(Borgatti & Molina, 2005; Kadushin, 2005; Klovdahl, 2005). Social network analysis is
predicated on obtaining the identities of the participants and the identities of those individuals
nominated as ties to develop the sociogram: “the collection of names of either individuals or
social units is not incidental to the research but its very point” (Kadushin, 2005, p.141).
Anonymity, a common strategy used in social research, was not possible since participant names
were necessary to gather network data. Accordingly I promised the institutional leaders and
governing board members that I would use pseudonyms in my dissertation to protect

63

confidentiality. This approach is consistent with recommendations made by Klovdahl (2005),
Borgatti & Molina (2003), and Kadushin (2005).
My initial strategy relied on the board secretary to disseminate surveys along with letters
of support from AGB, the university president, and the board chair. This strategy was ineffective.
It became clear that board members paid little attention to board issues when not in their formal
board role at meetings. Accordingly I changed data collection strategies. At board meetings
either the board secretary or myself distributed surveys, waited for board members to fill them
out, and gathered completed surveys. This process extended my data collection process since
public governing board meetings are held on average seven times a year (AGB Report on Public
Governance, 2010). These meetings have full agendas, which required negotiation with the
board secretary to find space for my research on the agenda. In general the most effective data
collection strategy started with my introduction to the board (either by the board secretary or
myself), the distribution of hard-copy surveys, and often the collection of data on the spot. In
some cases the board members took the forms home and mailed responses back in a selfaddressed stamped envelope. This strategy worked because either the board secretary on my
behalf or I was able to address board members in their formal board roles. This strategy
substantially increased response rates. Follow-up was essential to attempt to get data from
complete boards (or at least a large majority of members). In some cases I was permitted to
contact board members directly. In other cases the board secretary transmitted the follow-up
request. Table 3.4 describes the response rate by institutions.

64

Table 3.4
Governing Board Response Rates by Institution
Institution

Masters I (M-I)
Masters II (M-II)
Research I (R-I)
Research II (R-II)

Number of
Board
Members
11
12
17
18

Number of Participation
Respondents
Rate
11
8
15
8

100%
66.6%
88.2%
44.4%

Instrumentation
The aim of the survey was threefold: 1) to capture information about governing boards’
social networks; 2) to learn about factors influencing the governing board social networks; and 3)
gain insight into how the governing board functions in the context of the current fiscal crisis. I
used previous social network surveys (Frank, Zhao & Borman, 2004) and assessments of board
performance (Chait et al., 1996) to develop a series of survey items about governing board
networks and factors influencing decision-making during the current financial crisis. Social
network analysis requires collection of a standard set of questions about the relations between
group members. The items I collected are relatively standard and widely applied. To help me
develop the study’s social network questions I used Frank et al.’s (2004) technology practices
survey as a model. In addition, I consulted the numerous handouts, presentations, and tutorials
on network data collection, with an emphasis on surveys from Borgatti’s website. In a similar
vein Chait et al. (1996) developed a self-assessment instrument based on six dimensions of board
effectiveness. Using a behavioral focus the series of questions measure the board’s performance
on specific behaviors association with effective trusteeship, pinpoints areas that a board needs to
strengthen, and offers suggestions to remedy areas of relative weakness. As a tool it provides

65

boards the information to move from self-evaluation to self-improvement. Chait et al.’s work
provided a complementary guide for the survey questions about governing board functions.
These standard sources—for social networks and for board functions—served as the basis for the
core content of the draft survey instrument.
For the social network analysis I asked board members to identify sources of knowledge
they draw upon in their role as a governing board member. The goal was to capture knowledge
sources both in the form of human relationships and information materials. I asked board
members to identify individuals and their role function with whom board members most
frequently discussed institutional policies and strategies. I asked board members to consider
individuals both internal and external to the institution. I provided a list of role options to help
generate thinking about personal networks as recommended by Marsden: “Self-reports of the
presence or absence of social ties are the most common method used to gather network data.
Most often such data are obtained with single-item questions that ask a respondent to enumerate
those individuals with whom he or she … has direct ties of a specified kind” (Marsden, 1990). It
is important to note when a board member indicated the board (not a specific board member) as a
tie, I included every board member individually in the matrix.
I asked board members to identify information sources (e.g. governing board meeting
materials) they consult when considering institutional policies. To gather data relevant for
subsequent statistical comparisons I included items about the backgrounds of the board
members. These items included demographic information, years of service on the board,
committee assignments and leadership roles on the board, professional history and current
professional position, and frequency of on-campus activities outside of formal board meetings.
The items also included prior experience in governance and professional affiliation/expertise

66

because past studies (Chait et al., 1991, 1996) found them related to overall board performance.
Another section of the survey focused on capturing board members perceptions of how
the governing board reaches decisions. I asked board members to select between 1) board
members are uncommitted to a particular institutional policy decision and adopt the
recommendation of the administration and 2) board members have established opinions on
institutional policies and during the board meetings interests are negotiated. I followed that item
with a question about variation in decision-making processes. I asked board members to rate on
a five-point Likert scale a series of statements about the relative importance of stakeholders,
governance structures, and governing board members professional affiliations and relationships
in the governing board decision making process.
Lastly, the survey addressed governance during a financial crisis. I asked board members
whether or not the governing board took more deliberate actions and engaged more actively in
decisions because of the implications of the financial crisis for the future of the institution. I
asked board members using a five-point Likert scale to rate the effects of the financial crisis on
board strategies and openness to innovation, access to knowledge, and alignment with state
higher education policies and recommendations. Appendix B shows the complete survey
instrument.
Data Analysis
In my study the unit of analysis is the individual governing board. My goal was to
determine whether identifiable social networks for public higher education governing boards
exist. If so, my next goal was to describe the nature of these networks and assess the factors
affecting how the governing boards function during a time of fiscal crisis.

67

To preserve the confidentiality of the board members and the institution, I present data on
the institutions and characteristics of the individual board members collectively across all four
participating governing boards. Using SPSS I calculated mean scores for descriptive information
such as the number of board members, appointment terms and mechanisms, gender and ethic
composition of the board, average years of service, range of occupations represented, and
frequency of participation in board meetings as well as on-campus engagements.
The data on governing board members’ perceptions of board functioning are presented
both collectively and by individual institution. Across institutions I calculated mean scores for
how governing boards approached decisions during this time of fiscal crisis and the sources of
information governing board members drew upon to inform their roles as institutional leaders.
In addition I present board members’ perceptions of how the governing board functions for each
institution. Governing board members attitudes were captured on a 5-point Likert scale. From
my initial assessment of the data distribution, I elected to convert the scale into a 3-point scale
with 1 representing an agreed attitude, 0 representing a neutral attitude, and -1 representing a
disagree attitude.
Finally, I present a social network analysis for each institution. Social networks can be
analyzed at multiple levels. To begin I present a sociogram2 as well as calculate the density and
geodesic distance of the whole network. The density of a binary network is simply the proportion
of all possible ties that are actually present. The density of a network offers insights into such
phenomena as the speed at which information diffuses among the actors and the extent to which

2

The sociogram is created from a network matrix. In the matrix the rows represent the source
and the columns represent the receiver. Appendix C lists the numerical coding used for the
development of each matrix. Note that when the respondent listed the role function “governing
board” on the survey I in turn coded each individual board member as tie to that respondent.
68

actors have high levels of social capital and/or social constraint. The geodesic distance is the
number of relations in the shortest possible path from one actor to another (Burt, 1976; Doriean,
1974).
Next, I describe the connectedness and distance of the whole network based on individual
actors. These basic properties of social networks have important consequences for this study.
More connections often mean that individuals are exposed to a greater volume and more diverse
sources of information. Highly connected individuals may be more influential, and may be more
influenced by others. Differences among whole networks can be consequential as well. More
cohesive networks may be better able to mobilize their resources, and may be better able to bring
multiple and diverse perspectives to bear to solve problems. As stated by Hanneman & Riddle,
“[t]he extent to which individuals are connected to others, and the extent to which the network as
a whole is integrated are two sides of the same coin” (2005, Chapter 7).
For example, in any network there are (k * k-1) unique ordered pairs of actors (that is AB
is different from BA, and leaving aside self-ties), where k is the number of actors. Moreover, the
number of actors places an upper limit on the number of connections that each individual can
have (k-1). As a result the number of logically possible relationships grows exponentially as the
number of actors increases. Examining the distribution of connected individual actors provides
insight into the social structure of the network. In this study the data is asymmetric (directed
ties). As such results can be distinguished between ties being sent and ties being received.
Looking at the density for each row (source) and for each column (receiver) can explain how
actors are embedded in the overall density of the network.
After describing the whole network, I then examine the centrality of each individual actor
in the network. There are many measures of centrality of a node in a sociogram. Degree,

69

closeness, and betweenness measures are foundational in the field of network analysis. Their
prominence within the field stems from the fact that they all have strong yet distinct theoretical
underpinnings and that they are frequently used for empirical analysis of social systems (Marin
& Wellman, 2010; Wasserman & Faust, 1994).
Using the Freeman Degree Centrality calculation I report in-degree and out-degree
centrality measures. Another means to determine an actor’s centrality is to calculate a closeness
score. Using a reach calculation I determined how close actors are to one another. Some actors
may be able to reach most other members of the network with little effort. Other actors may
have difficulty being heard since they are not well connected. Lastly, calculating the
betweenness measure determines the number of times an actor occurs on a geodesic and can
offer insight into information control.
I depended on the geodesic scores as evidence of social networks among the whole
governing board. This score indicates the number of ties necessary to connect a governing board
member to any actor in the network. A low number indicates a tightly connected network. A
high number is in indication of a loosely connected or potentially non-existent network. Further
evidence of social networks was determined by examining the range of variance in individual
actor centrality measures. Small variance and standard deviations indicate that the network
frequently nominated the same actor. Alternatively, actors with sizable variance and stand
deviations associated to centrality scores are evidence against the existence of central actors and
an identifiable social network.
Ultimately, this analysis focuses on the descriptive and relational data about governing
boards’ social networks. The primary focus is on the composition of the network and how the
networks vary by institution. I also make comparisons across networks of differing size using

70

normalized scores.
Limitations
Although careful steps were taken to ensure that the data I collected and subsequently
analyzed were reflective of complete/whole governing boards networks and their perceptions of
how the governing board functions during financial crisis, there are several limitations that are
important to note.
In a study of higher education governing boards, two significant limitations emerge. First
is access to governing board members. While the sunshine laws make all public governing board
meetings open, the result is that board activity is largely ceremonial and does not offer insight
into the inner workings of boards (Kezar, 2006). Access to governing board members directly is
often constrained as a result of crowded agendas during board meetings and retreats, political
reasons advanced by institutional leadership, and board members holding professional positions
that make them frequently inaccessible. Scholars often note the challenge in directly
investigating board members (Chait, et al., 1993; Kezar, 2006, Tierney, 2004b). As such
empirical studies of governing boards is limited (Teirney, 2004b).
A second potential limitation of the study is the trustworthiness of individual responses.
Qualitative studies of elites, which may include governing boards, are frequently used in political
science (Alberbach & Rockman, 2002; Berry, 2002; Goldstein, 2002; Stephens, 2007). This
methodology uses interviews with experts in the field who have in-depth knowledge and
experience. One significant limitation of this technique is the potential lack of honesty and
candor of the responses. Moreover, the study is based on the perceptions of individual governing
board members. Applied to this study, governing board members may be guarded in their

71

responses and the study is susceptible to individual bias.
Social network analysis also has limitations. Social network studies require high response
rates to map a network accurately (Marsden, 1990). In a study of public governing boards that
on average consist of nine members (AGB Board Composition Report, 2004) obtaining high
responses requires orchestration at some level. My sample selection and data collection process
(engagement of AGB in institution selection, using board secretaries to access board members
and determine how best to gain board members participation, conforming data collection (survey
or interview) to obtain high response rates, and using a short survey instrument) addressed this
potential limitation. Network mapping can also be impaired if participants primarily indicate
role functions instead of individuals in their network. In this study I had to restrict the network
mapping to role groups. Overall the findings are limited to the institutions included in the study.
Whether or not the findings/results are generalizable across higher education governance boards,
systems, or states is unknown. To increase the ability to generalize my conclusions, my sample
included two complete governing boards within each category of my sampling matrix.
Ultimately the goal of the study is to learn if social networks have an impact on higher education
governance and begin to understand the complexity of these networks.
In Chapter Four I present an analysis for each institution in this study. In the analysis I
provide a description of the institutions and the board members, a summary of governing board
members attitudes about how the board functions, and a whole network social network analysis.

72

CHAPTER 4
Results
This chapter details the results of the study. To preserve the confidentiality of the board
members and their institutions, I report results for all four participating institutional governing
boards in aggregate form. However, I discuss the governing board members’ perceptions about
how the board functions and the social network analysis by institution.
Out of 58 possible respondents (total number of board members for all four institutions),
42 responded to the survey. The overall response rate was 72.4% percent. However, the
response rates for each individual governing board ranged from 44.4% to 100%. This response
rate pattern has implications for the analysis and interpretations of the social networks. It is
important to note that voting students, staff, and faculty representatives are included in the data.
Institution and Governing Board Characteristics
The Research I and Research II are large research-intensive institutions for their
respective states. Masters I and Masters II are both comprehensive institutions. Masters I is set
in an urban area with a strong research focus; Masters II is located in a rural location offering a
wide range of baccalaureate degrees and a few graduate programs. The average board
membership is 11.5 with a variance of 3.32. (Mean = 11.5, SD = 3.32). The governance across
all four boards is primarily vested in lay citizen members ranging from a low of 9 to a high of 16
in individual boards. The Governor appoints the lay members for all four boards; however,
Masters II and Research I also require the consent of the state Senate. All four institutions
include student representation on the board. For two institutions board membership also includes

73

faculty and staff who are selected by the governing structures of their respective constituency.
The average board member term is 5.7 years (SD 3.00). The lay citizen members serve terms on
average for 6.7 years (SD 2.5) while faculty, student, staff, and other members serve terms on
average of 1.8 years (SD 0.38).
Based on survey responses, the current composition of all four boards is 80% male and
20% female. A majority of the board members are Caucasian (85%) with the remainder of the
board members self-identifying as Black/African American (10%) and Asian/Pacific Islander
(3%). One board member declined to respond to the item about race/ethnicity. The professional
occupations of the board cut across a variety of disciplines as indicated in Table 4.1. Two
professional backgrounds predominate. Many board members come from the education
profession, not surprising because of inclusion of students, academic staff, and faculty
appointments to the boards. Board members are also frequently in senior level executive
positions (48.7%) within the private sector or retired (17.1%).
The experience of the board members (as determined by number of years of service)
across the four institutions ranges from one year to more than six years with an average of 2.8
years (SD 1.79). More than half of the board members (56.1%) are on campus at least once a
week for meetings, events, ceremonies, or athletics. Board members indicated attending on
average seven (SD 2.74) board meetings a year.
Governing Board Members Perceptions
This section presents governing board members perceptions about how the board
functions and about how governing board members in their institutional leadership role identify
sources of information.

74

Table 4.1
Governing Board Members’ Occupations
Occupation

Frequency

Accounting
Construction / Home
Improvement
Consulting
Education
Engineering / Architecture
Finance / Banking / Insurance
Food Service
Government / Military
Healthcare / Medical
Legal
Media / Printing / Publishing
Mining
Non-profit
Pharmaceutical / Chemical
Research / Science
Real Estate
Retail
Business / Professional Services
Other
Total

Percent
1
1

2.4
2.4

2
9
1
2
1
2
2
7
1
1
1
1
1
1
2
1
4
41

4.9
22.0
2.4
4.9
2.4
4.9
4.9
17.1
2.4
2.4
2.4
2.4
2.4
2.4
4.9
2.4
9.8
100.0

The governing board members indicated that the recent global fiscal crisis represented a
unique historical period where board decisions were more likely to significantly affect the future
of the institution. Almost unanimously the board members across all four institutions indicated
that during this time of financial crisis the governing board was taking more deliberate actions
and engaging more actively in decisions than in previous years (see, Table 4.2).

75

Table 4.2
Engagement by Governing Board Members during Financial Crisis

Institution
Masters I
Masters II
Research I
Research II

Governing board are taking more deliberate actions
and engaged more actively in decisions during the
financial crisis
Frequency
Mean
SD
N
Yes
No
11
0
1.000
.0000
11
8
0
1.000
.0000
8
14
1
.9333
.25820
15
8
0
1.000
.0000
8

Another commonality across all four institutions was that a majority of respondents (73.2%)
indicated that board members bring to board meetings their own opinions on institutional
policies. These policy positions serve as the basis for negotiations during board meetings (see
Figure 4.1). This finding is contrary to the common assumption that governing board members
adopt the recommendations of the administration and are uncommitted to particular institutional
policies. However, board members indicated that this process varied by issue (see Table 4.3).
The most intensive negotiations center on issues with legal, financial, or employment
implications3. As indicated by one board member, “[t]he less dollars involved, the less the
negotiation”. One way to interpret this statement is that policy decisions with significant
monetary implications either on revenues or expenditures are deliberated on more intensely by
governing boards.

3

Items listed by governing board members included tuition, budgeting and finance, labor
contracts, collective bargaining, competitive bidding, conflict resolution, research, athletics,
curriculum, and student issues.

76

Figure 4.1
Governing Board Members’ Policy Position

26.80%

73.20%

Uncommitted, Adopt recommendation of administration
Established opinions, Interests negotiated

Note. N=41, Mean = .7317, SD = .44857.
Table 4.3
Variance in Governing Board Members’ Policy Position

Institution
Masters I
Masters II
Research I
Research II

Does the issue affect governing board members’
policy position?
Frequency
Mean
SD
N
Yes
No
10
1
.9091
.30151
11
8
0
1.000
.0000
8
10
4
.7143
.46881
14
4
3
.5714
.53452
7

Table 4.4 identifies the information sources the governing board members draw upon
when considering institutional policies. The mean scores represent the percentage of board
members indicating that a particular source is consulted. Across all four institutions the
predominate source of information is correspondence with the President. Beyond the President,
the board members indicated that the Chief Financial Officer (Mean = .8333) of the institution

77

and the governing board meeting materials (Mean = .8810) are important information sources.
However, direct correspondence with the Provost (Mean = .7619) of the institution and
Chancellor (Mean = .7619) of the state higher education system also appear to be important
sources of knowledge.
Table 4.4
Governing Board Information Sources: Percentage Indicating “Yes”
Information Sources
Correspondence from Secretary of the Board
Correspondence from President
Correspondence from the Provost
Correspondence from the Chief Financial Officer
Correspondence from Faculty Senate
Correspondence from State Higher Education Chancellor
Correspondence from State Higher Education
Commissions/Councils
Board Meeting Materials
Reports from State Government Offices
National Higher Education Association Reports
National Media on Higher Education
Local Newspapers
Others
Note. N=42

Mean
.5952
1.0000
.7619
.8333
.3333
.7619
.2143

SD
.49680
.00000
.43108
.37720
.47712
.43108
.41530

.8810
.2381
.2619
.5238
.3810
.1429

.32777
.43108
.44500
.50549
.49151
.35417

Table 4.5 details the governing board members’ perceptions of how the board functions
by each institution. A positive mean score indicates an affirmative response to the item.
Conversely a negative mean score indicates disagreement with the item.
Across all four institutions there appears to be strong agreement that the professional
backgrounds of governing board members impact decisions of the board. In addition,
differences of opinion in board decisions are more often settled by discussion than by vote. This
finding is consistent with board members’ viewing board meetings as negotiating sessions. All
four governing boards indicated that the multiple layers of governance impact institutional

78

policy. Nevertheless, during this financial crisis board members found that institutional policies
were not strongly aligned with recommendations from the state higher education systems.
Although respondents thought the fiscal crisis was a unique period of institutional
decision-making there was little consensus about the importance of different sources of
knowledge. Moreover, board members did not indicate a greater likelihood of using creative
institutional practices or policies to address continuing declining resources.
Table 4.5
Governing Board Members’ Perceptions
Variable
Governing board stakeholders have an
effect on governing board’s decisions.

Multiple layers of governance impact
institutional policies.
Governing board members’ professional
backgrounds impact their decisions on the
board.
Governing board members’ relationships
inform and influence their decisions on the
board.
Differences of opinion in board decisions
are more often settled by discussion than by
vote.
To address the financial crisis the governing
board is encouraging the development of
specific institutional policies.

Institution
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II

79

Mean
.8182
.8750
.5000
.8571
.8000
1.000
.8667
.8571
1.000
.6250
1.000
1.000
.5455
.7500
.9333
.8571
1.000
.7500
1.000
.8750
.9091
.5000
.4000
.7500

SD
.40452
.35355
.51887
.37796
.42164
.00000
.35187
.37796
.00000
.74402
.00000
.00000
.68755
.70711
.25820
.37796
.0000
.46291
.0000
.35355
.30151
.75593
.63246
.70711

N
11
8
14
7
10
8
15
7
11
8
15
7
11
8
15
7
11
8
15
8
11
8
15
8

Table 4.5 (cont’d)
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II
M-I
M-II
R-I
R-II

Our board has adequate sources of
knowledge to address institutional dilemmas
presented by the financial crisis
When considering continued declining state
appropriations, the board often tries to
generate creative approaches or solutions to
close budget gaps or manage costs.
The board consults the faculty when
considering cost saving strategies that affect
the academic curriculum and degree
programs.
When considering institutional policies to
address the financial crisis the board is
alignment with recommendation from the
State Higher Education Commission,
Council, or Coordinating System.

.4545
.7500
.8000
.6250
.9091
1.000
.4667
.8750
.3636
.5000
.6667
-.3750
.4545
.5000
.6667
.5000

.52223
.70711
.41404
.51755
.30151
.0000
.63994
.35355
.50452
.53452
.48795
.74402
.68755
.53452
.48795
.53452

11
8
15
8
11
8
15
8
11
8
15
8
11
8
15
8

In sum, public higher education governance is primarily vested in lay citizens. The
composition frequently is white males who have professional backgrounds in the private sector
as senior level executives although many also have training in educational professions. Board
members bring policy positions to their role as institutional leaders and at board meetings policy
positions are negotiated primarily through discussion. Moreover, professional backgrounds
influence board member’s decisions. Board members across all four institutions indicated that
the recent global fiscal crisis represented a unique historical period where board decisions were
more likely to affect the future of the institution. Almost unanimously the board members across
all four institutions indicated that during this time of financial crisis the governing board was
taking more deliberate actions and engaging more actively in decisions than in previous years.
However, there was little consensus about the importance of sources of knowledge in informing
the boards’ actions. Communication from the President of the institution emerged as the only

80

main source of knowledge depended upon by all four boards.
Social Network Analysis of Governing Boards
The following section provides a social network analysis for each governing board. I
present the results by type of institution, starting with the two masters-level institutions and then
with the two research universities.
Using a variety of metrics I calculated the connectedness of the whole network as well as
individual actor centrality. In a directed network, statistics on the rows explain the role that each
actor plays as a source of ties. The sum of the connections from the actor to others is called the
out-degree of the point. The statistics for in-degree (column data of network matrix) makes it
possible to identify the actors as receivers of information or as ties to the actors in the network
(Hanneman & Riddle, 2005; Marin & Wellman, 2010).
Of greatest interest is the in-degree data. Across all four governing boards these
measures frequently pointed to the centrality of the same actors and had small variance and
standard deviations. Examining the Freeman graph centralization measure for the networks as a
whole expresses the degree of variance in a network as a percentage of a perfectly connected
network of the same size. In directed networks, the measure is calculated for both directions.
Table 4.6 clearly demonstrates that all four governing board networks are more homogenous
with regard to in-degree than with out-degree network ties. This essentially says that each
governing board members consults a variety of individuals as sources of knowledge (outdegree); however, a selection of network actors are frequently nominated by governing board
members (in-degree). These actors’ in-degree metric measures demonstrate how central they are
to the network.

81

Table 4.6
Freeman Graph Centralization Measures for Governing Boards

Masters I
Masters II
Research I
Research II

Out-Degree
58.790%
60.124%
51.222%
70.273%

In-Degree
17.958%
17.355%
20.222%
13.199%

Masters Institutions
Masters I.
The concepts of density and centralization refer to differing aspects of the overall
compactness of the graph. Density describes the general level of cohesion in a graph;
centralization describes the extent to which this cohesion is organized around particular focal
actors. They are complementary measures. Masters I governing board has an overall network
matrix density of 0.1322 (SD 0.3388). Only 13% of all possible ties (73) are present. However,
for each pair of actors the number of edges on the shortest path between them is 1.559 (average
geodesic distance). This finding indicates that most governing board members are connected to
the whole network either directly or through a mutual actor.
The overall Freeman graph centralization measure for the network as a whole indicates
an out-degree graph centralization of 58.790% with the in-degree graph centralization of
17.958%. These measures describe the population as a whole and express the variance in the
observed network as a percentage of a perfect star network4 of the same size. In the current case,
there is a substantial amount of variation among the board members (out-degree data). That is,

4

In a star network, all the actors but one have degree of one, and the "star" has a degree of the
number of actors, less one.
82

the power of individual board members varies rather substantially, which means that positional
advantages are unequally distributed in this network. By contrast the in-degree graph
centralization indicates far greater cohesion around a select group of actors. Ultimately these
actors have positional advantages within the network.
The sociogram for Masters I (Figure 4.2) depicts the location of the actors in the
governing board network. The network graph illustrates the centrality of actors and provides an
initial visual representation of the various roles and clustering in a network such as the core
individuals in the network and which actors act as boundary spanners. Actors on the periphery
of the network can also be important. Peripheral actors are connected to networks that are not
currently mapped. As a result these actors can be important sources of new information not
available inside the network. Examining Masters I it is evident that actors 4 (Deans), 5
(Executive Assistant to President), 7 (Faculty Senate), 9 (General Counsel), and 11 (Legislative
Office) are on the outskirts of network. However, each is directly connected to a board member
that appears to be at the core of the network.
Examining the connectedness of Masters I governing board, Table 4.7 depicts that actors
116, 118, and 133 are tied to many other actors and actors 132, 136, 109, 131, 119, and 134 are
connected to a smaller portion of the network. It appears that the remaining actors in Masters I
governing board network are not sources of relationships or information. However, this
interpretation would be inaccurate. Since only the governing board members were surveyed the
out-degree data for non-board members is unknown. Actors 116, 118, and 133 have a higher
potential to be influential. The remaining actors, however, will be influential if they are
connected to the "right" other actors. As such there is variation in the roles that these actors play
as sources of information.

83

Figure 4.2
Masters I Sociogram

84

As demonstrated by the in-degree network statistics for Masters I, there is less variation
in information receiving. Of interest is that actors 109 (Chair), 13 (President), 15 (Provost) are
very high receivers of information in the network (see, Table 4.8).
Table 4.7
Out-degree Statistics for Masters I Governing Board Members
Actor
116
118
133
132
136
109
131
119
134
13
18
135
127
3
15
4
9
17
8
6
2
5
7
11

Mean
0.6960
0.5650
0.5220
0.3480
0.2610
0.2170
0.2170
0.1740
0.1740
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000

SD
0.4600
0.4960
0.5000
0.4760
0.4390
0.4120
0.4120
0.3790
0.3790
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000

Sum
Variance
16.0000
0.2120
13.0000
0.2460
12.0000
0.2500
8.0000
0.2270
6.0000
0.1930
5.0000
0.1700
5.0000
0.1700
4.0000
0.1440
4.0000
0.1440
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000

85

Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Max
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

N
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23

N Miss
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Table 4.8
In-degree Statistics for Masters I Governing Board Members
Actor
Mean
SD
Sum
Variance
Min.
Max.
N

133
0.087
0.282
2
0.079
0
1
23

109
0.304
0.46
7
0.212
0
1
23

13
0.304
0.46
7
0.212
0
1
23

18
0.13
0.337
3
0.113
0
1
23

135
0.13
0.337
3
0.113
0
1
23

131
0.13
0.337
3
0.113
0
1
23

119
0.174
0.379
4
0.144
0
1
23

132
0.13
0.337
3
0.113
0
1
23

118
0.13
0.337
3
0.113
0
1
23

134
0.13
0.337
3
0.113
0
1
23

116
0.13
0.337
3
0.113
0
1
23

127
0.13
0.337
3
0.113
0
1
23

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

136
0.174
0.379
4
0.144
0
1
23

3
0.174
0.379
4
0.144
0
1
23

15
0.261
0.439
6
0.193
0
1
23

4
0.043
0.204
1
0.042
0
1
23

9
0.043
0.204
1
0.042
0
1
23

17
0.174
0.379
4
0.144
0
1
23

8
0.087
0.282
2
0.079
0
1
23

6
2
0.087 0.087
0.282 0.282
2
2
0.079 0.079
0
0
1
1
23
23

5
0.043
0.204
1
0.042
0
1
23

7
0.043
0.204
1
0.042
0
1
23

11
0.043
0.204
1
0.042
0
1
23

86

Centrality can be measured by three approaches -- degree, closeness, and betweenness.
Each approach describes the locations of individual actors in the network based on different
definitions of what it means to be central. The degree centrality measures for Masters I (see,
Table 4.9) show that on average the actors have a degree of 3.042. This indicates that the
number of direct connections an actor has in the network is on average to three other actors.
Actors 116, 118, and 133 have the greatest out-degrees, and might be regarded as the most
influential (although this measure does not take into account to whom they are sending
information). Actors 109 (Chair), 13 (President), and 15 (Provost) have the greatest in-degrees.
That a majority of the network shares ties with these three would potentially indicate their
importance as a resource.
The range of out-degree relationships is larger (minimum and maximum) than that of indegree. There is substantially more variability across the actors in out-degree than in-degree
(standard deviations and variances). It appears that the Masters I governing board network is
more homogeneous with regard to in-degree (prominence) than with regard to out-degree
(influence).
Table 4.9
Masters I: Freeman’s Degree Centrality Measures for each Actor and Overall Descriptive
Statistics
Actor

OutDegree
116
118
133
132
136
131
109
134
119

InDegree
16
13
12
8
6
5
5
4
4

3
3
2
3
4
3
7
3
4

87

NrmOutDeg
69.565
56.522
52.174
34.783
26.087
21.739
21.739
17.391
17.391

NrmInDeg
13.043
13.043
8.696
13.043
17.391
13.043
30.435
13.043
17.391

Table 4.9 (cont’d)
13
135
127
18
3
15
4
9
17
8
6
2
5
7
11
Mean
SD
Sum
Variance
Min.
Max.
N

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.042
4.695
73
22.04
0
16
24

7
3
3
3
4
6
1
1
4
2
2
2
1
1
1
3.042
1.695
73
2.873
1
7
24

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
13.225
20.412
317.391
416.634
0
69.565
24

30.435
13.043
13.043
13.043
17.391
26.087
4.348
4.348
17.391
8.696
8.696
8.696
4.348
4.348
4.348
13.225
7.37
317.391
54.315
4.348
30.435
24

Degree centrality measures are limited because they only take into account an actor’s
immediate ties rather than indirect ties to all others. Closeness centrality measures emphasize
the distance of an actor to all others in the network by focusing on the distance from each actor
to all others. Researchers have shown that networks have horizons over which we cannot see nor
influence (Burt, 1992; Freidkin, 1983). These scholars propose that key connections in networks
are one and two steps away; on rare occasions even three steps away. As such it is important to
know the actors in the network neighborhood and who one can reach.
Examining the reach centrality (one measure of closeness) I calculate how close each
actor is to all others in the network. For Masters I, the out and in-reach centrality measures are
in alignment with that of the degree centrality measures (see, Table 4.10). In other words,
governing board members 116, 118, and 133 have the greatest outward reach and actors 109

88

(Chair), 13 (President), and 15 (Provost) are reached out to the most by the network. However,
actors 3 (Chief Financial Officer) and 119 (governing board member) join actors 109 (Chair), 13
(President), and 15 (Provost) with high in-reach centrality measures. It appears that the Masters
I governing board commonly depends upon the Board Chair, President, Provost, and Chief
Financial Officer as sources of information.
Table 4.10
Masters I: Reach Centrality Measures
Actor
OutdwReach IndwReach
116
20.500
4.500
118
19.000
4.500
133
18.500
4.000
132
16.000
4.500
136
13.833
4.500
131
8.000
6.000
109
7.667
5.500
134
7.000
5.000
119
6.000
8.500
13
1.000
9.000
135
1.000
5.000
127
1.000
5.000
18
1.000
5.333
3
1.000
7.500
15
1.000
8.500
4
1.000
4.167
9
1.000
3.833
17
1.000
6.500
8
1.000
4.500
6
1.000
4.500
2
1.000
6.333
5
1.000
5.833
7
1.000
3.833
11
1.000
4.667
Mean
5.48
5.48
SD
6.69
1.49
Sum
131.50
131.50
Variance
44.71
2.22
Min.
1.00
3.83
Max.
20.50
9.00
N
24.0
24.0

nOutdwReach
0.854
0.792
0.771
0.667
0.576
0.333
0.319
0.292
0.250
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.042
0.23
0.28
5.48
0.08
0.04
0.85
24.0

89

nIndwReach
0.188
0.188
0.167
0.188
0.188
0.250
0.229
0.208
0.354
0.375
0.208
0.208
0.222
0.313
0.354
0.174
0.160
0.271
0.188
0.188
0.264
0.243
0.160
0.194
0.23
0.06
5.48
0.00
0.16
0.38
24.0

Betweenness centrality views an actor as being in a favored position to the extent that the
actor falls on the geodesic paths between other pairs of actors in the network. Adding up, for
each actor, the proportion of times that they are "between" other actors in the network I obtained
another measure of actor centrality. This measure can be expressed as a percentage of the
maximum possible betweenness of an actor. The results for Masters I governing board network
are shown in Table 4.11.
In the Masters I governing board network there is much variation in actor betweenness
(from zero to 20.976). There is also variation (SD = 6.043) relative to a mean betweenness of
3.375. The overall network centralization is relatively low (3.63%). This finding makes sense
because previous results show that a majority of all connections can be made in this network
without the aid of any intermediary. As a result there cannot be a high level of "betweenness."
Of interest in the betweenness measure is that both actor 109 (Chair) and 136 join actors 116 and
118 as being more structurally central to the network whereas actor 133 becomes far less central.
Table 4.11
Masters I: Freeman Betweenness Centrality Measures
Actor
Betweenness nBetweenness
116
20.976
4.145
109
16.726
3.306
118
13.667
2.701
136
12.726
2.515
131
6.226
1.23
132
5.726
1.132
119
2.643
0.522
134
1.5
0.296
133
0.81
0.16
13
0
0
135
0
0
127
0
0
18
0
0
3
0
0

90

Table 4.11 (cont’d)
15
0
4
0
9
0
17
0
8
0
6
0
2
0
5
0
7
0
11
0
Mean
3.375
SD
6.043
Sum
81.000
Variance
36.524
Min
0.000
Max
20.976
N
24.000

0
0
0
0
0
0
0
0
0
0
0.667
1.194
16.008
1.427
0.000
4.145
24.000

Masters II
The network analysis for Masters II governing board is based on a response rate of
66.6%. This response rate is slightly below the response rate indicated by scholars to have
genuine representation of the network and results must be interpreted somewhat cautiously. The
board has an overall network matrix density of 0.1522 (SD 0.3592) and represents 15% of all
possible ties (77). Similar to Masters I, most governing board members are connected to the
whole network directly or through a mutual actor (average geodesic distance = 1.529). The
Freeman graph centralization measure for the network as a whole indicates an out-degree
centralization of 60.124% and an in-degree centralization of 17.355%. As with Masters I these
results indicate the strong cohesion around a focal set of in-degree actors. Additionally, the
sociogram (Figure 4.3) shows that many board members are structural cores of the network
along with actor 13, the President of the institution. The peripheral actors in the network are
actors 2 (board secretary), 11 (legislative office), 17 (State Higher Education System
Chancellor), 10 (Governor), and 14 (professional colleagues).
91

Figure 4.3
Masters II Sociogram

Four governing board members (139, 152, 154, 129) have the largest out-degree
connections. These board members indicated the greatest number of other actors as sources of
information. The President (13) has the greatest in-degree measure among the network and
appears to be a central receiver of relationships. See Table 4.12 for Out-Degree Statistics and
Table 4.13 for In-Degree Statistics for Masters II. This finding is supported by the degree
centrality measures (see, Table 4.14). The range of out-degree is greater than in-degree, and

92

there is less variability across the actor’s in-degree measure.
Table 4.12
Out-degree Statistics for Masters II Governing Board Members
Actor
128
13
5
137
15
18
2
129
153
3
138
152
168
154
139
169
170
171
7
14
10
11
17

Mean
0.273
0
0
0.318
0
0
0
0.591
0.182
0
0.182
0.636
0
0.591
0.727
0
0
0
0
0
0
0
0

SD
0.445
0
0
0.466
0
0
0
0.492
0.386
0
0.386
0.481
0
0.492
0.445
0
0
0
0
0
0
0
0

Sum
6
0
0
7
0
0
0
13
4
0
4
14
0
13
16
0
0
0
0
0
0
0
0

Variance
0.198
0
0
0.217
0
0
0
0.242
0.149
0
0.149
0.231
0
0.242
0.198
0
0
0
0
0
0
0
0

93

Min.

Max.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

N
1
0
0
1
0
0
0
1
1
0
1
1
0
1
1
0
0
0
0
0
0
0
0

22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22

N Miss
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Table 4.13
In-degree Statistics for Masters II Governing Board Members
Actor
Mean
SD
Sum
Variance
Min.
Max.
N

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

128
0.227
0.419
5
0.176
0
1
22

168
0.182
0.386
4
0.149
0
1
22

13
0.318
0.466
7
0.217
0
1
22

5
0.182
0.386
4
0.149
0
1
22

137
0.227
0.419
5
0.176
0
1
22

154
0.136
0.343
3
0.118
0
1
22

139
0.182
0.386
4
0.149
0
1
22

169
0.182
0.386
4
0.149
0
1
22

15
0.136
0.343
3
0.118
0
1
22

170
0.182
0.386
4
0.149
0
1
22

18
0.091
0.287
2
0.083
0
1
22

171
0.182
0.386
4
0.149
0
1
22

94

2
0.045
0.208
1
0.043
0
1
22

7
0.091
0.287
2
0.083
0
1
22

129
0.136
0.343
3
0.118
0
1
22

14
0.045
0.208
1
0.043
0
1
22

153
0.227
0.419
5
0.176
0
1
22

10
0.045
0.208
1
0.043
0
1
22

3
0.182
0.386
4
0.149
0
1
22

11
0.045
0.208
1
0.043
0
1
22

138
0.227
0.419
5
0.176
0
1
22

17
0.045
0.208
1
0.043
0
1
22

152
0.182
0.386
4
0.149
0
1
22

Table 4.14
Masters II: Freeman’s Degree Centrality Measures for each Actor and Overall Descriptive
Statistics
Actor

OutDegree
139
152
154
129
137
128
153
138
13
15
18
2
168
5
3
169
170
171
7
14
10
11
17

Mean
SD
Sum
Variance
Min.
Max.
N

16
14
13
13
7
6
4
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.348
5.313
77
28.227
0
16
23

InDegree
4
4
3
3
5
5
5
5
7
3
2
1
4
4
4
4
4
4
2
1
1
1
1
3.348
1.605
77
2.575
1
7
23

NrmOutDeg
NrmInDeg
72.727
18.182
63.636
18.182
59.091
13.636
59.091
13.636
31.818
22.727
27.273
22.727
18.182
22.727
18.182
22.727
0
31.818
0
13.636
0
9.091
0
4.545
0
18.182
0
18.182
0
18.182
0
18.182
0
18.182
0
18.182
0
9.091
0
4.545
0
4.545
0
4.545
0
4.545
15.217
15.217
24.15
7.294
350
350
583.199
53.196
0
4.545
72.727
31.818
23
23

The reach centrality (see, Table 4.15) for Masters II indicates the distance each actor is to all
other actors in the network. Both the out and in-reach centrality measures reinforce the degree
centrality measures; no additional insights are evident. Inward degree and reach measures
indicate at this institution that the President (13) is central to the network.

95

Table 4.15
Masters II: Reach Centrality Measures
Actor
OutdwReach IndwReach nOutdwReach nIndwReach
139
20
5.5
0.87
0.239
152
19
5.5
0.826
0.239
154
18.5
4.833
0.804
0.21
129
18.5
4.833
0.804
0.21
137
15.5
6
0.674
0.261
128
13.167
6
0.572
0.261
153
5
6.5
0.217
0.283
138
5
6.5
0.217
0.283
13
1
8.5
0.043
0.37
15
1
6
0.043
0.261
18
1
5
0.043
0.217
2
1
4.5
0.043
0.196
168
1
5.833
0.043
0.254
5
1
6.5
0.043
0.283
3
1
6.833
0.043
0.297
169
1
5.833
0.043
0.254
170
1
5.833
0.043
0.254
171
1
5.833
0.043
0.254
7
1
5
0.043
0.217
14
1
4.833
0.043
0.21
10
1
4.333
0.043
0.188
11
1
4.333
0.043
0.188
17
1
4.833
0.043
0.21
Mean
5.64
5.64
0.25
0.25
SD
7.2
0.94
0.31
0.04
Sum
129.67
129.67
5.64
5.64
Variance
51.87
0.89
0.1
0
Min.
1
4.33
0.04
0.19
Max.
20
8.5
0.87
0.37
N
23
23
23
23
The overall network centralization is 3.53% and reflects a connected network of actors.
The betweenness centrality measures provide some new information about the network (Table
4.16). In the other centrality measures actor 137 (Chair) has moderate out and in-degree
measures. Moreover, the Chair’s reach measure was not as strong as other actors in the network.
However, the betweenness score (18.833) indicates that the Chair of Masters II governing board

96

has a greater degree of centrality—the Chair falls on the greatest number of paths between other
pairs of actors in the network. Actors 154 and 129 (board members), considered central to the
network using the previous measures, have the lowest betweenness scores.
Table 4.16
Masters II: Freeman Betweenness Centrality Measures
Actor
137
139
128
152
153
138
154
129
13
15
18
2
168
5
3
169
170
171
7
14
10
11
17
Mean
Std Dev
Sum
Variance
Min.
Max.
N

Betweenness
nBetweeness
18.833
4.076
18.533
4.012
9.367
2.027
9.367
2.027
8.7
1.883
8.2
1.775
0.5
0.108
0.5
0.108
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.217
0.696
5.82
1.26
74
16.017
33.869
1.587
0
0
18.833
4.076
23
23

97

Research Institutions
Research I.
88.2% of board members responded, more than adequate for social network analyses.
The overall network matrix density is 0.170 (SD = 0.3765). The total possible ties in this
network are 159 and the average geodesic distance is 1.460. Similar to the two masters level
institutional boards, Research I’s governing board is strongly connected. Figure 4.4 provides a
sociogram of the network. Among the peripheral actors of great interest is the inclusion of actor
10 (Governor). Ultimately the Governor of State B is a source of information for both Masters II
and Research I governing board networks.
A significant number of the Research I governing board members indicated the collective
board in their networks. As a result, the sociogram reflects many relationships among the board
members and the out-degree statistics for seven board members (113, 146, 155, 147, 145, 120,
123) is large (see, Table 4.17).
The in-degree measures tell a similar story to that of the other governing boards.
Different actors are high receivers of information. As is true for Masters I and Masters II, the
President (13) is a central in-degree actor. Many in the network also nominated the Chair (111)
as a strong resource. Both the Research I President and governing board Chair share the same
in-degree score of eleven (see, Table 4.18).
Examining the degree centrality measure for Research I (see, Table 4.19), on the average
the actors have a degree of 5.129. The range of out-degree is larger than that of in-degree and
there is less variability among in-degree than out-degree. The network is more homogenous for
in-degree network members (President of the institution and Chair of governing board) than for
out-degree network members. Overall the Freeman graph centralization measures for the

98

network as a whole indicate an out-degree graph centralization of 51.222% and in-degree graph
centralization of 20.222%.
Figure 4.4
Research I Sociogram

99

Table 4.17
Out-degree Statistics for Research I Governing Board Members
Actor
113
146
155
147
145
120
123
112
122
149
111
150
117
148
151
1
2
3
5
6
7
8
10
13
14
15
17
18
156
157
168

Mean
0.667
0.633
0.633
0.567
0.533
0.5
0.5
0.233
0.233
0.2
0.167
0.133
0.1
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

SD
0.471
0.482
0.482
0.496
0.499
0.5
0.5
0.423
0.423
0.4
0.373
0.34
0.3
0.3
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Sum
Variance
20
0.222
19
0.232
19
0.232
17
0.246
16
0.249
15
0.25
15
0.25
7
0.179
7
0.179
6
0.16
5
0.139
4
0.116
3
0.09
3
0.09
3
0.09
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

100

Min.

Max.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

N
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30

N Miss
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Table 4.18
In-Degree Statistics for Research I Governing Board Network
Actor
Mean
SD
Sum
Variance
Min.
Max.
N

111
0.367
0.482
11
0.232
0
1
30

13
0.367
0.482
11
0.232
0
1
30

5
0.133
0.34
4
0.116
0
1
30

1
0.067
0.249
2
0.062
0
1
30

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

7
0.067
0.249
2
0.062
0
1
30

8
0.033
0.18
1
0.032
0
1
30

117
0.233
0.423
7
0.179
0
1
30

145
0.2
0.4
6
0.16
0
1
30

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

122
0.233
0.423
7
0.179
0
1
30

150
0.233
0.423
7
0.179
0
1
30

151
0.233
0.423
7
0.179
0
1
30

17
0.1
0.3
3
0.09
0
1
30

2
0.1
0.3
3
0.09
0
1
30
147
0.2
0.4
6
0.16
0
1
30
15
0.133
0.34
4
0.116
0
1
30

3
0.133
0.34
4
0.116
0
1
30

112
0.233
0.423
7
0.179
0
1
30

168
0.033
0.18
1
0.032
0
1
30

146
0.233
0.423
7
0.179
0
1
30

10
0.033
0.18
1
0.032
0
1
30

6
0.133
0.34
4
0.116
0
1
30

113
0.2
0.4
6
0.16
0
1
30

156
0.233
0.423
7
0.179
0
1
30

123
0.2
0.4
6
0.16
0
1
30

148
0.233
0.423
7
0.179
0
1
30

120
0.2
0.4
6
0.16
0
1
30

149
0.233
0.423
7
0.179
0
1
30

155
0.2
0.4
6
0.16
0
1
30

157
0.233
0.423
7
0.179
0
1
30

18
0.033
0.18
1
0.032
0
1
30

101

14
0.033
0.18
1
0.032
0
1
30

Table 4.19
Research I: Freeman’s Degree Centrality Measures for each Actor and Overall Descriptive
Statistics
Actor
OutDegree
113
20
155
19
146
19
147
17
145
16
123
15
120
15
112
7
122
7
149
6
111
5
150
4
151
3
148
3
117
3
8
0
13
0
156
0
5
0
7
0
3
0
1
0
168
0
157
0
10
0
6
0
2
0
17
0
15
0
18
0
14
0
Mean
5.129
Std Dev
6.964
Sum
159
Variance
48.499
Min.
0
Max.
20
N
31

InDegree NrmOutDeg
6
66.667
6
63.333
7
63.333
6
56.667
6
53.333
6
50
6
50
7
23.333
7
23.333
7
20
11
16.667
7
13.333
7
10
7
10
7
10
1
0
11
0
7
0
4
0
2
0
4
0
2
0
1
0
7
0
1
0
4
0
3
0
3
0
4
0
1
0
1
0
5.129
17.097
2.709
23.214
159
530
7.338
538.883
1
0
11
66.667
31
31

102

NrmInDeg
20
20
23.333
20
20
20
20
23.333
23.333
23.333
36.667
23.333
23.333
23.333
23.333
3.333
36.667
23.333
13.333
6.667
13.333
6.667
3.333
23.333
3.333
13.333
10
10
13.333
3.333
3.333
17.097
9.03
530
81.535
3.333
36.667
31

The reach centrality measures (Table 4.20) for the Research I governing board indicate
how close each actor is to all others in the network. The outward reach measures reinforce the
results based on the degree centrality measures. The inward reach measures reinforce the
centrality of the President and Board Chair to the network with inward reach scores of 14 and 13
respectively. However, four other actors are identified as easily accessible in the network – the
Executive Assistant to the President (5), the Chief Financial Officer (3), the Board Secretary (2),
and the Athletic Director (1). Correspondingly, each has an inward reach score of 10.5, 10, 9.5
and 9. In essence these scores represent the number of board members who nominated this
selection of actors.
Table 4.20
Research I: Reach Centrality Measures
Actor
OutdwReach IndwReach nOutdwReach
113
26
7.5
0.839
155
25.5
7.5
0.823
146
25.5
8
0.823
147
24.5
7.5
0.79
145
24
7.5
0.774
123
23.5
7.5
0.758
120
23.5
7.5
0.758
112
18.667
8
0.602
122
8.5
8.5
0.274
149
7.5
8.5
0.242
150
6
8.5
0.194
111
6
13
0.194
148
6
8.5
0.194
117
6
8.5
0.194
150
5
8.5
0.161
8
1
5.333
0.032
13
1
14
0.032
156
1
8.5
0.032
5
1
10.5
0.032
7
1
6.333
0.032
3
1
10
0.032
1
1
9
0.032
168
1
5.5
0.032

103

nIndwReach
0.242
0.242
0.258
0.242
0.242
0.242
0.242
0.258
0.274
0.274
0.274
0.419
0.274
0.274
0.274
0.172
0.452
0.274
0.339
0.204
0.323
0.29
0.177

Table 4.20 (cont’d)
157
1
10
1
6
1
2
1
17
1
15
1
18
1
14
1
Mean
8.13
SD
9.62
Sum
252.17
Variance
92.52
Min.
1
Max.
26
N
31

8.5
5.5
7
9.5
7.833
8.5
5.833
5.333
8.13
1.91
252.17
3.64
5.33
14
31

0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.26
0.31
8.13
0.1
0.03
0.84
31

0.274
0.177
0.226
0.306
0.253
0.274
0.188
0.172
0.26
0.06
8.13
0
0.17
0.45
31

The betweenness measures for Research I do not reveal much new information. Since in
the network is strongly connected, actors can readily connect without an intermediary (see, Table
4.21).
Table 4.21
Research I: Freeman Betweenness Centrality Measure
Actor
Betweenness nBetweenness
146
24.848
2.856
111
23.098
2.655
112
20.064
2.306
113
12.064
1.387
122
11.814
1.358
150
11.798
1.356
155
8.564
0.984
149
8.314
0.956
151
3.23
0.371
117
3.23
0.371
148
3.23
0.371
147
0.564
0.065
145
0.182
0.021
1
0
0
8
0
0
5
0
0
13
0
0
156
0
0

104

Table 4.21 (cont’d)
3
0
7
0
120
0
123
0
168
0
157
0
10
0
6
0
2
0
17
0
15
0
18
0
14
0
Mean
4.226
SD
7.181
Sum
131
Variance
51.56
Min.
0
Max.
24.848
N
31

0
0
0
0
0
0
0
0
0
0
0
0
0
0.486
0.825
15.057
0.681
0
2.856
31

Research II.
The modest response rate by Research II governing board (44.4%) limits interpretability.
Even so social network scholars have found measures of centrality remain robust under
conditions of error, including low marginal response rates (Borgatti, Carley, & Krackhardt,
2006). Based on the responses, the overall network matrix density is 0.0793 (SD .2702). This
represents a very small percentage (7%) of all the possible ties (69). The average geodesic
distance is 1.250. This score indicates most governing board members are connected to the
whole network either directly or through a mutual actor. The sociogram (Figure 4.5) illustrates
the network data collected by the few participating board members. The number of peripheral
actors connected to the network by only three board members is particularly interesting. The
Research II board is obviously infused with a great amount of information from networks beyond
the board.

105

The out-degree centrality measures for Research II indicate that board members 141 and
140 are connected to a large portion of the network. Since only a small portion of board
members responded it is unclear if other board members may be more connected (see, Table
4.22). The in-degree statistics provided more useful information. The data suggest that the
President (13) and Board Secretary (2) are recipients of the greatest number of connections (see,
Table 4.23). This finding is consistent with the prior three governing board network analysis.
Figure 4.5
The Research II Sociogram

106

Table 4.22
Out-degree Statistics for Research II Governing Board Members
Actor
141
140
121
126
144
143
124
1
2
3
5
6
8
12
13
14
15
17
18
142
158
159
160
161
162
163
164
165
166
167

Mean
0.759
0.69
0.276
0.207
0.207
0.138
0.103
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

SD
0.428
0.463
0.447
0.405
0.405
0.345
0.305
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Sum
Variance
22
0.183
20
0.214
8
0.2
6
0.164
6
0.164
4
0.119
3
0.093
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

107

Min.

Max.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

N
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29

N Miss
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Table 4.23
In-degree Statistics for Research II Governing Board Network
Actor
Mean
SD
Sum
Variance
Min.
Max.
N

121
0.069
0.253
2
0.064
0
1
29

18
0.034
0.182
1
0.033
0
1
29

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

126
0.069
0.253
2
0.064
0
1
29

6
0.034
0.182
1
0.033
0
1
29

Actor
Mean
SD
Sum
Variance
Min.
Max.
N

165
0.069
0.253
2
0.064
0
1
29

141
0.034
0.182
1
0.033
0
1
29

144
0.103
0.305
3
0.093
0
1
29 29
5
0.034
0.182
1
0.033
0
1
29
166
0.069
0.253
2
0.064
0
1
29

2
0.207
0.405
6
0.164
0
1

14
0.034
0.182
1
0.033
0
1
29
167
0.069
0.253
2
0.064
0
1
29

143
0.138
0.345
4
0.119
0
1
29
140
0.034
0.182
1
0.033
0
1
29

3
0.138
0.345
4
0.119
0
1
29
158
0.069
0.253
2
0.064
0
1
29

142
0.069
0.253
2
0.064
0
1
29

108

1
0.034
0.182
1
0.033
0
1
29

17
0.034
0.182
1
0.033
0
1
29
159
0.069
0.253
2
0.064
0
1
29

15
0.172
0.378
5
0.143
0
1
29
160
0.103
0.305
3
0.093
0
1
29

8
0.034
0.182
1
0.033
0
1
29

161
0.069
0.253
2
0.064
0
1
29

124
0.069
0.253
2
0.064
0
1
29

162
0.069
0.253
2
0.064
0
1
29

13
0.207
0.405
6
0.164
0
1
29

163
0.069
0.253
2
0.064
0
1
29

164
0.103
0.305
3
0.093
0
1
29

12
0.069
0.253
2
0.064
0
1
29

The average degree centrality measure for the governing board members is 2.300. As is
true for the other three governing boards the out-degree range is larger than that of in-degree.
Moreover, the variability is greater with out-degree relationships than in-degree. These data
suggest that the social network of Research II displays a similar pattern to the prior institutions.
The in-degree data are more concentrated on a select few actors with small variance (President
(13), Provost (15), and Board Secretary (2)). Table 4.24 represents the degree centrality
measures for Research II Governing Board. Overall the Freeman graph centralization measure
for the network as a whole indicates an out-degree graph centralization of 70.273% and an indegree graph centralization of 13.199%.
Table 4.24
Research II: Freeman’s Degree Centrality Measures for each Actor and Overall Descriptive
Statistics
Actor
OutDegree
141
22
140
20
121
8
144
6
126
6
143
4
124
3
15
0
8
0
17
0
2
0
12
0
3
0
6
0
5
0
14
0
18
0
158
0
159
0
160
0
161
0

InDegree NrmOutDeg
1
75.862
1
68.966
2
27.586
3
20.69
2
20.69
4
13.793
2
10.345
5
0
1
0
1
0
6
0
2
0
4
0
1
0
1
0
1
0
1
0
2
0
2
0
3
0
2
0

109

NrmInDeg
3.448
3.448
6.897
10.345
6.897
13.793
6.897
17.241
3.448
3.448
20.69
6.897
13.793
3.448
3.448
3.448
3.448
6.897
6.897
10.345
6.897

Table 4.24 (cont’d)
162
0
163
0
164
0
165
0
13
0
166
0
167
0
142
0
1
0
Mean
2.3
SD
5.435
Sum
69
Variance
29.543
Min.
0
Max.
22
N
30

2
2
3
2
6
2
2
2
1
2.3
1.394
69
1.943
1
6
30

0
0
0
0
0
0
0
0
0
7.931
18.743
237.931
351.288
0
75.862
30

6.897
6.897
10.345
6.897
20.69
6.897
6.897
6.897
3.448
7.931
4.807
237.931
23.107
3.448
20.69
30

The reach centrality scores for Research II governing board parallel the out-degree
centrality measures. Also, the inward reach measures reinforce the centrality of the President
and board secretary to the network with each having an inward reach score of 7.5. However,
board member 124 appears as a central actor with a reach score of 6.5. Table 4.25 presents the
reach centrality scores for the Research II governing board.
Table 4.25
Research II: Reach Centrality Measures
Actor
141
140
121
144
126
124
143
124
8
17
2
12
3

OutdwReach IndwReach nOutdwReach nIndwReach
26.5
2
0.883
0.067
25.5
2
0.85
0.067
11
3
0.367
0.1
7
4
0.233
0.133
7
3
0.233
0.1
5.5
3
0.183
0.1
5
5
0.167
0.167
1
6.5
0.033
0.217
1
3
0.033
0.1
1
3
0.033
0.1
1
7.5
0.033
0.25
1
3.5
0.033
0.117
1
6
0.033
0.2

110

Table 4.25 (cont’d)
6
1
5
1
14
1
18
1
158
1
159
1
160
1
161
1
162
1
163
1
164
1
165
1
13
1
166
1
167
1
142
1
1
1
Mean
3.68
SD
6.44
Sum
110.5
Variance
41.42
Min.
1
Max.
26.5
N
30

3
3
3
3
3
3
4.5
3
3
3
4.5
3
7.5
3
3
3
3.5
3.68
1.41
110.5
1.99
2
7.5
30

0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.033
0.12
0.21
3.68
0.05
0.03
0.88
30

0.1
0.1
0.1
0.1
0.1
0.1
0.15
0.1
0.1
0.1
0.15
0.1
0.25
0.1
0.1
0.1
0.117
0.12
0.05
3.68
0
0.07
0.25
30

Similar to the findings for the other three governing boards, the betweenness scores do
not reveal new information about the network (see, Table 4.26).
Table 4.26
Research II: Freeman Betweenness Centrality Measure
Actor
Betweenness
121
6.25
126
6
144
5.75
143
3.75
141
0.75
124
0.5
18
0
15
0
8
0
17
0
2
0

nBetweenness
0.770
0.739
0.708
0.462
0.092
0.062
0
0
0
0
0

111

Table 4.26 (cont’d)
12
0
3
0
6
0
5
0
14
0
140
0
158
0
159
0
160
0
161
0
162
0
163
0
164
0
165
0
13
0
166
0
167
0
142
0
1
0
Mean
0.767
SD
1.874
Sum
23
Variance
3.512
Min.
0
Max.
6.25
N
30

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.094
0.231
2.833
0.053
0
0.77
30

Summary of Results
In sum, the average governing board membership in this study was 11.5 with lay citizens
representing a majority. Faculty, staff, and student representatives were also included. The
average governing board member’s term was 5.7 years. On the governing boards males
significantly out numbered females. Males represented 80% of governing board membership.
The professional occupations of board members were concentrated in senior executive level
positions within the private sector and the field of education. The average years of service on the
governing board was 2.8 years by board members. Moreover, board members attended on
average seven board meetings each calendar year.

112

The governing board members’ perceptions on how the board functions is concentrated
on four primary findings. First, board members unanimously indicated that the recent financial
crisis has created a unique period for higher education. Moreover, during this time of financial
crisis governing boards are taking more deliberate actions and engaged more actively in
decisions than in previous years. This finding reinforced my assumption that the fiscal crisis is
impacting higher education governance. Second, governing board members have established
policy positions that are negotiated. Governing board members view board meeting as time to
discuss and negotiate policy disagreements. In a related way the third common perception by
governing board members is that the professional backgrounds of board members impact board
members decisions on the board. Fourth, communication from the President of the institution
emerged as the most constant source of information for governing board members. Beyond, the
President, the board members indicated that communication from the Chief Financial Officer is
an important information source. Communication from the Chief Financial Officer is potentially
explained by the strong influence of the financial crisis on institutional policy decisions.
The social network analysis demonstrated that identifiable social networks did in fact
exist for each governing board. Furthermore, commonalities in the social networks are present
across the four participating institutional governing boards. Let me first briefly summarize the
results for each institution. Then I will present the common patterns in the governing boards’
social network.
Examining the set of centrality measures, it is evident that at Maters’ I the most central
actors are the Chair of the governing board as well as the President and Provost of the institution.
These actors have the greatest number of in-degree connections. The Chief Financial Officer
becomes an additional central actor when examining the reach centrality scores. This measure is

113

a mechanism to measure the shortest-path length between actors in the network. As a result the
Chief Financial Officer while not a high recipient of ties is connected to a large portion of the
whole network via short-paths. The peripheral actors in the Masters I governing board social
network are Deans, the Executive Assistant to the President, Faculty Senate, General Counsel,
and state legislative office.
Of the centrality measures the President of Masters II is frequently most central actor in
the governing board network. The one exception is the betweenness centrality measure. As a
measure it provides insight into how removing an actor from a network would disrupt
connections between other actors in the network. For Masters II, the Chair of the governing
board falls on the greatest number of paths between other pairs of actors. As a result the Chair is
central to the network as a boundary spanner. The peripheral actors are frequently external to the
institution and include the state legislative office, state higher education system chancellor,
Governor, and professional colleagues of board members. The one peripheral actor internal to
the institution is the secretary of the governing board.
At Research I the President and Chair of the governing board are the greatest recipient of
in-degree relationships and structurally central to the network. A number of additional actors
(Executive Assistant to President, Chief Financial Officer, Board Secretary, and Athletic
Director) are central to the network under the reach centrality measure. These actors are
accessible to the governing board via short-paths. The peripheral actors of the network consist of
the Governor, professional colleagues, and spouse/family members.
The President, Board Secretary and Provost at Research II are central to the governing
board social network. The governing board members nominated a large number of peripheral
actors with some internal and others external to the institution. These actors include students,

114

athletic director, faculty members, professional colleagues, spouse/family members, and state
higher education system chancellor.
Commonalities in the governing board social networks exist at two levels. At one level is
to examine the networks as a whole. The other is to study the network by determining positions
of individuals within the network. A common pattern among the governing board networks as a
whole is the high level of connectedness (see, geodesic scores). The governing board networks
are comprised of many short-paths. As a result governing board members are only one or two
ties away from all network members. Another frequent feature of the whole networks was the
presence of peripheral actors.
The commonalities that emerged in examining the individual actors in the governing
board networks are from the in-degree data. The in-degree data indentifies the actors in the
network most frequently consulted by the governing board members. Utilizing the normative
scores allows for comparison across all four institutions. It is evident that the governing board
members most frequently depend upon the President of the institution. The Chair of the
governing board was frequently the actor with the highest betweenness score for each board.
This finding emphasizes the important role the Chair plays in connecting the governing board
network. Removal of the Chair would disrupt connections between other actors in the network.
Additionally using the reach centrality scores both the President and Chief Financial Officer are
comparable sources of information for all four institutions’ governing board members. These
individuals are accessible to governing board members through short network paths.
Collectively, it appears that the President and Chief Financial Officer of the institution and the
Chair of the governing board are central to the governing board networks in this study. The
cohesion around this particular set of actors demonstrates that a majority of governing board

115

members share ties with these individuals. Moreover, it emphasizes the positional advantage
these actors have in the network.
In Chapter 5 I use these results to answer each research question and situate these
findings in established literature. I also propose how research, practice, and policy can be
advanced based on the study’s findings.

116

CHAPTER 5
In this final chapter, I offer answers each research question. Next, I discuss the findings
of the current study by situating them in existing research. I conclude by discussing implications
for research, practice, and policy.
A defining feature of American higher education is the provision for authority over the
institution by an external governing board consisting of lay members of the public (Thelin,
2004). Studies of higher education governing boards typically focus on structure and
performance (Kezar & Eckel, 2004; Kezar, 2006; McGuinness, 2003; Minor, 2006; Tierney,
2004b) as well as assume governing boards endorse institutional policies created under the
leadership of the executive officer (Mintzberg, 1979). Research has yet to explore in any depth
the impact of higher education governing board members as social actors.
Situated in the current financial crisis and a trend of declining public investment in higher
education (Fairweather, 2009, 2006; NACUBO/NCSE, 2010; Weerts & Ronca, 2006; Zumeta,
2006), the current study examines the social network patterns of governing board members at
public institutions and expands the knowledge of how governing boards work. The study sought
to determine whether identifiable social networks for public higher education governing board
members exist, to develop an overall picture of public higher education governing board
networks, and to understand the internal and external factors impacting governing boards’ social
networks during times of fiscal crisis.
This study draws upon the technique of social network analysis as a means to understand
how public governing boards work. As an analytical tool, social network analysis examines the
relations and patterns of relations among actors by mapping interactions and relationships (Marin
117

& Wellman, 2010; Wasserman & Faust, 1994).
From August 2010 through January 2011, I collected data from public post-secondary
governing boards to examine the social network patterns of governing board members at public
institutions and explored the broad range of interactions that can influence governing boards’
networks. I used a survey instrument to capture information about governing boards’ social
networks; to learn about factors influencing the governing board social networks; and to gain
insight into how the governing board functions in the context of the current fiscal crisis.
Research Questions
An examination of governing boards at four public post-secondary institutions alone is
hardly a basis for generalization about higher education governance, but it does suggest certain
conclusions. Below I use these results to answer each research question in turn.
Are there identifiable social networks for public higher education governing boards?
The results indicate identifiable social networks for public higher education governing
boards. All four governing boards are tightly connected (see, Table 5.1). The geodesic scores
for each institution indicate that the whole network can be reached with relative ease. To put it
Table 5.1
Governing Board Networks Geodesic Distance
Institution
Masters I
Masters II
Research I
Research II

Geodesic Distance
1.559
1.529
1.460
1.250

another way, the governing board members know one another directly or through a mutual actor.
Moreover, the governing board members are connected to all the actors in the network via short

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paths.
The President at each institution was a strong recipient of relationships (in-degree
measure). The normative in-degree scores5 across all four boards are close (Masters I = 30.435;
Masters II = 31.818; Research I = 36.667; Research II = 20.69). Another similarity is that the
Chair of the board was frequently the actor with the highest betweenness score for each board.
The level of betweenness was not comparable across all four boards; however, the Chair was a
critical connector for actors within the networks.
Each of the public governing boards also had peripheral actors that were connected to the
structural core of the network. In other words, a common feature of the governing board
networks is the infusion of new information from a broad spectrum of actors beyond the board
members. Some of the peripheral actors are internal to the institution; however, frequently the
actors are external community members. Common across two or more governing boards were
the legislative office, Executive Assistant to the President, State Higher Education System
Chancellor, professional colleagues, students, family/spouse, and Governor. For example in
State B, the Governor is a peripheral actor in both the research and masters level institutions.
If so, how much variation exists in the social networks across governing board members
and governing boards as a collective team?
Individual governing board members’ networks had a considerable amount of variation.
The overall out-degree graph centralization measures as well as each of the individual actor
outward centrality measures demonstrate that the centrality of individual board members varies

5

The standardized measures for the centrality measures allows for comparisons across networks
of different sizes and densities (expresses data as a percentage of the number of actors in the
network, less one (ego)).

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rather substantially. Ultimately across all four governing boards the positional advantages of
board members is unequally distributed in the network.
Alternatively, the governing board networks had noteworthy cohesion around inward
data. As a collective team the governing boards frequently relied on common sources of
information as well as individual actors. For example, the reach centrality scores pointed to
select group of actors within each network. Examining the standardized reach scores for each
institution the data suggests that both the President and Chief Financial Officer are comparable
sources of information for all four governing boards. Table 5.2 presents the standardized reach
centrality scores for these two network members. Moreover, this finding aligns with the mean
scores representing the high percentage of board members indicating that communication from
the President and Chief Financial Officer are consulted as sources of information. At the same
time the governing board networks are also infused with new information from a diversity of
peripheral actors.
Table 5.2
Standardize Reach Scores for Institutional Chief Financial Officers and Presidents
Institution
Masters I
Masters II
Research I
Research II

Standardize Reach Scores
Chief Financial Officer
President
.313
.375
.297
.370
.323
.452
.200
.250

To what extent do internal and external factors impact governing boards’ social networks?
My goal in this section is to identify common forces identified by all four governing
boards as influencing board networks. I begin by discussing the common internal factors
followed by frequently noted external factors.
All four governing boards indicated that board members have established policy positions

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that they bring to their role. In addition, their professional backgrounds were frequently listed as
influencing their role as institutional leaders. Communication from the President, Chief
Financial Officer, Provost, and Chancellor of the State Higher Education system were indicated
by boards to be sources of knowledge most frequently consulted in their role. Interestingly,
across all four boards, the President was highest in-degree receiver of relationships. In contrast,
the State Higher Education System Chancellor was often a peripheral network actor typically
interacting through a tie to a board member central to the network. Beyond individuals, the
source of knowledge most commonly relied upon to by the boards are board meeting materials.
These materials are prepared by institutional leadership and provide a filtered source of
information.
The most striking external factor was the almost unanimous agreement that the current
global financial crisis affected how governing boards function. All four governing boards
indicated that the recent global fiscal crisis represented a unique historical period where board
decisions were more likely to affect the future of the institution significantly. During this time of
financial crisis the governing board was taking more deliberate actions and engaging more
actively in decisions than in previous years. In addition, the governing boards frequently
indicated that the multiple layers of governance impacted how the board works.
Discussion of Findings
The following section discusses the findings of the current study in the context of
established scholarship. I begin by presenting the composition and characteristics of the
governing boards in the study. Next I discuss the governing board members’ perceptions of how
the board functions inclusive of both internal and external factors. Finally, I describe the
characteristics of the governing board social networks.

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Governing Board Composition and Characteristics
The four governing boards in this study are similar in several ways to the general profile of
public post-secondary governing boards. In comparison to the data reported by the AGB 2010
Survey of Public Colleges and Universities Governing Boards, the governing boards in the
current study are comparable across a number of dimensions.
Size, composition, and method of appointment are points of comparisons. The governing
boards were comparable in size. Using a historical survey base ranging from 1986 to 2010, the
AGB study revealed that the average number of voting board members remained relatively stable
over the years, at about 11 or 12. In a parallel fashion the average size of the participating
governing boards in the current study was 11.5. Across all four boards governance is primarily
vested in lay citizen members ranging from a low of 9 to a high of 16 in individual boards. The
Governor appoints the lay members for all four boards; however, Masters II and Research I also
require the consent of the state Senate. This is consistent with national trends for gubernatorial
appointment and legislative confirmation of public governing boards: “The majority of members
of most public governing boards (77%) were appointed by the governor, 60 percent with
confirmation by the state legislature and 17 percent without; 5 percent of boards were elected, 3
percent were appointed by legislatures, and 15 percent were selected in a combination of ways or
in some other manner” (AGB, 2010).
All four institutions included student representation on the board. For two institutions
board membership also includes faculty members and staff selected by the governing structures
of their respective constituencies. As reported by the AGB in 2010, 50.3 percent of public
college boards included at least one student as a voting member of the board, and 28.2 percent
included at least one nonvoting student member. Of public colleges, universities, and systems,

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13.3 percent included at least one faculty member as a voting board member, and 9.7 percent
included a nonvoting faculty member. In addition, 7.2 percent included at least one staff member
as a voting board member, and 3.6 percent included one or more nonvoting staff.
Based on survey responses, the current composition of all four participating governing
boards is 80% male and 20% female. This is consistent with the AGB report that indicated men
outnumbered women on governing boards by more than two to one in 2010, 71.6 to 28.4 percent.
In addition, 23.1 percent of board members were racial and ethnic minorities, including 15.8
percent African Americans or Blacks, 4.1 percent Hispanics and Latinos, 2.1 percent Asians and
Pacific Islanders, 0.7 percent American Indians and Alaskan Natives, and 0.4 percent other
races; 74.3 percent were White non-Hispanic and 2.6 percent unknown races. Minority trustees
increased from 21.3 percent in 2004 to 23.1 percent in 2010. In the current study a majority of
the board members are Caucasian (85%) with the remainder of the board members selfidentifying as Black/African American (10%) and Asian/Pacific Islander (3%).
This sample was also similar to national averages in the length of their terms and extent
of participation. On average public board members typically serve six years for a single term.
The length of a single term for the current study’s governing board members was 5.7 years. The
lay citizen members serve terms on average for 6.7 years while faculty members, students, staff,
and other members serve terms on average of 1.8 years. The AGB study also revealed that
public boards met on average six or seven times per year. Board members in this study attended
on average seven board meetings a year.
In the current study board members are frequently in senior level executive positions
(48.7%) within the private sector or field of education (22.0%). The professional background of
half (49.4%) of board members of public colleges and universities in 2010 was business as

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reported by the AGB.
In examining this data my conclusion then is that the participating governing boards in
the current study provided representative sample on key demographic characteristics including
size, composition, gender and ethnicity, occupation, and service. I now turn to discussing
governing board members’ perceptions on how the board functions.
Governing Board Members Perceptions
The perceptions by governing board members in this study about how the board functions
frequently is consistent with findings found in contemporary research on governance. The
following section first focuses on exploring the implications of fiscal instability on the
governance of higher education institutions. Next, the effect on institutional governance by the
policy positions and professional backgrounds of governing board members is examined. Lastly,
is an acknowledgement about the impact by the multiple layers of governance found in public
higher education.
The finding that the current global financial crisis affects how governing boards function
is consistent with previous research on the effect of the long-term decline in state funding for
post-secondary education, tuition increases, and decreasing endowment returns on higher
education institutions (Fairweather, 2009; Keller, 2009; Long, 2010; NACUBO/NCSE, 2010;
Weerts & Ronca, 2006; Zumeta, 2006). Even so the results of this study show that the effects of
the recent fiscal crisis on governing boards goes well beyond what board members have seen in
the past (including declining state financial support). All four governing boards in this study
indicated that the recent global financial crisis represented a unique historical period where board
decisions were more likely to affect the future of the institution significantly. During this time of
financial crisis respondents indicated that governing boards took more deliberate actions and

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engaged more actively in decisions than in previous years. This finding is consistent with both
theoretically-based arguments by Chaffee (2010) and Wellman (2009) as well as by survey
results from the APLU study Coping Strategies of Public Universities During The Economic
Recession of 2009: Results of a Survey on the Impact of the Financial Crisis on University
Campuses where respondents indicated the use of both short-term and long-term budget cutting
and revenue enhancing strategies (Keller, 2009). Governing boards in this study seemed aware
of the gravity of their decisions for the future of the institution. However, governing boards
appear uncertain about the resources that might best guide them in their institutional leadership
role during this period of fiscal turbulence.
To understand this dilemma better I turn to work carried out by Chait and his colleagues
(1991). Chait et al. found the contextual, analytical, and strategic dimensions of governance
critical to effective governing board performance. These dimensions of governance focus on the
distinctive characteristics and culture of the college’s environment. Moreover, governing board
members who have an appreciation for the academic context rely on the institution’s mission,
values, and traditions as a guide for decisions. Chait and colleagues (1991, 1996) advocate for
governing board members to recognize the complexity and subtleties in the issues faced by the
board and to develop policy responses the fit the institutional context. Ultimately this
recommendation requires the board to embrace an institutional outlook when addressing
problems and search for concrete information from multiple sources to develop solutions.
One important contribution of this study is that board members bring policy positions
with them to the role and negotiate with other members on the basis of those policy positions.
This finding contrasts with some existing research on governing boards. Although the political
nature of board functioning has long been acknowledged (Birnbaum, 1988; Chait, Holland &

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Taylor, 1991), Chait et al.’s (1991) finding that effective boards attempt to minimize conflict and
avoid win-lose situations does not sufficiently portray the amount of negotiation and potential
conflict on boards found in this study even when the board is functioning effectively. In this
study it appeared board member disagreements are common and that such disagreements did not
necessarily adversely affect cohesion. It is quite possible that the finding from this study
conflicts with previous studies because of the nature of the 2008 fiscal crisis, particularly how
boards had to seek a variety of possible solutions without the option to sit passively by or to
follow historical precedent.
Another related finding from this study is the impact of governing board members
professional backgrounds on the decision making of the governing board. Consistent with
research (Chait et al., 1996) boards in this study depended upon the professional expertise and
affiliations of board members to solve problems for the institution. In this context the
professional backgrounds of board members is useful in understanding how boards work and
who they rely on for information. As described by Chait et al. (1996) on one hand having board
members contribute outside their area of expertise seems beneficial for board cohesion. In
particular, boards whose members feel confident in asking questions outside their personal area
of expertise often encourage new lines of inquiry and creativity (Chait et al., 1996). Boards with
members who stick solely to their area of expertise may not fully explore the governance and
fiscal solutions if the board does not contain members from a wide variety of backgrounds (Chait
et al., 1996). Again this finding may be related to boards having to find a resolution to the
problems created by the financial crisis and found that considering a wide array of possible
solutions from different perspectives increased their effectiveness.
A nominal finding from this study is the effect of the multiple layers of governance on

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how the governing boards function. This finding is consistent with previous research articulating
the unique and complex governance structures in public higher education systems (Kezar, 2006;
McGuinness, 2003, 2005; Minor, 2006). As indicated by Kezar (2006) effective public
governing boards coordinate with the governor’s and legislature’s strategic plans and
communicate regularly with state leaders. The four governing boards in this study receive
communication directly from the state higher education officer. Moreover, the governing
boards’ social networks commonly included the legislative office, state higher education system
chancellor, and governor. Although these external actors were often found on the periphery of
the network, their impact is not necessarily minimal. Rather the governing boards’ connection to
state-level actors infuses new information into the board and links the board to a broader
network.
Governing Board Social Network Characteristics
Public higher education governing boards appear to have identifiable social networks.
Moreover, commonalities in the governing board social networks exist. The geodesic scores for
all four participating governing boards indicate that the boards are tightly connected. As such
the governing board members know one another directly or through a mutual actor. Moreover,
the governing board members are connected to all the actors in the network via short paths. This
finding of strong cohesion combined with board composition resonates with scholars who
articulate the importance of understanding the influence of individual behavior in organizations
(Frank, 1996). Moreover, the finding lends itself to considering governing boards as collective
teams that have the potential to build successful collaborations to address institutional challenges
(Gray, 1989).
Strongly connected networks potentially are ineffective because actors can become

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trapped (Gargiulo & Benassi, 1999; Gulati & Westphal, 1999; Portes & Landolt, 1996). It
appears that governing boards counter this problem in two ways. First, this study concluded that
governing board members do have dissenting policy positions that are negotiated. As such it
appears alternative perspectives are encouraged and the possible negative consequences of strong
cohesion are offset. Second, governing board networks are infused with new information from a
diversity of peripheral actors. Each of the public governing boards had peripheral actors that
were connected to the structural core of the network. This finding is consistent with Kezar’s
(2006) scholarship that discusses the important influence stakeholders have on public higher
education governing boards. Moreover, this finding is consistent with Burt’s (1992) explanation
that weak ties can generate positive outcomes. Peripheral actors in governing board social
networks potentially enable institutional change and adaptation (Christensen & Horn; 2011;
Frank, Zhao, & Borman, 2004; Handy, 2002).
Results from this study show that the President and Chief Financial Officer are equally
important sources of information for all four governing boards. Board members primarily
depend upon communication from the President and Chief Financial Officer for information.
Other individuals frequently consulted by the board are the Provost and Chancellor of the State
Higher Education system. Tierney and Minor (2004) claim that communication among relevant
actors is essential to effective governance. The findings of this study indicate that the most
relevant actors to the governing boards are the President and Chief Financial Officer. Governing
board members dependence upon the President and Chief Financial Officer for guidance in
institutional decisions and strategy is often acknowledged (Chait et al., 1991, 1996; Kezar,
2006). Furthermore this finding supports research that cites the comfort and expertise board
members have with financial issues and frequent reliance financial performance indicators to

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guide decisions (Chait et al., 1996). It is unclear from this study whether governing boards’
reliance on the President and Chief Financial Officer for information increased during the
present financial crisis.
Utilizing the normative social network metric scores, it is evident that the governing
board members most frequently depend upon the President of the institution. The in-degree data
for the President commonly indicated that he/she was a central actor. Additionally using the
reach centrality scores both the President and Chief Financial Officer are comparable sources of
information for all four institutions’ governing board members. These individuals are accessible
to governing board members through short network paths. This finding is consistent with
scholars who frequently discuss the integral relationships among the governing board and senior
level administrative leaders (AGB, 2006, 2010; Birnbaum, 1988; Chait et al., 1991, 1996;
MacTaggart & Mingle, 2002). Frank, Krasue, & Penuel (2010) support this interpretation -successful organizational change depends in part on information flows from network members
who possess high quality knowledge.
The high betweenness score for the Chair of the Board in all four institutions indicated
the Chair is the primary connector or boundary spanner for actors within the networks. This
finding is consistent with research on the importance of boundary spanners to connect and link
subgroups within the organization (Burt, 1976; Cross, Parker & Borgatti, 2002; Frank, 1996;
Marin & Wellman, 2010; Rawlings & McFarland, 2010).
In summary, it appears that the President and Chief Financial Officer of the institution
and the Chair of the governing board are central to the governing board networks in this study.
The cohesion around this particular set of actors demonstrates that a majority of governing board
members share ties with these individuals. Moreover, it emphasizes the positional advantage

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these actors have in the network and ultimately the governance of the institution.
Implications for Research, Practice, and Policy
A primary reason for conducting this study was to help inform future research, practice,
and policy relating to higher education governance. The following section discusses
implications for researchers, institutional governing boards, and the Association of Governing
Boards of Universities and Colleges.
Researchers
An empirical question guiding this study was whether or not identifiable social networks
existed in higher education governing boards. The results of this study positively indicated the
presence of social networks. Even more striking was the common features in the social networks
across the four governing boards. I acknowledge that an examination of governing boards at
four public post-secondary institutions alone is hardly a basis for generalization. However, the
findings of the current study suggest that the application of social network analysis to higher
education governance offers new insights to understand how governing boards work.
The current study supports the continued usage of social network analysis as a means to
understand how governing boards work. As such, a variety of social network analysis research
agendas emerge. Of interest is to understand more fully the knowledge and resources drawn
upon by the set of central actors in the governing board networks. As central actors in the
network they possess positional advantages. One interesting approach would be to employ an
ego analysis for central actors (e.g. President, Board Chair, Chief Financial Officer). This type
of study would develop networks for central actors in higher education governing boards and
enable scholars to understand the influences upon these actors (Burt, 2004, 2005; Marin &

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Wellman, 2010). Applied to a single institution it would offer a deeper understanding of the
impacts on the governing board social networks. Applied to a larger sample of actors in a similar
role function, an ego analysis would allow for comparisons of resources depended upon by a set
of actors from a variety of institutions. Furthermore, an ego analysis may be advantageous since
it overcomes some of the limitations imposed by a whole network study (Hanneman & Riddle,
2005; Marsden, 1990). Alternatively, by conducting interviews with these central actors,
researchers could develop a deeper understanding of the role they play in governing board
networks. This qualitative approach would be a means to develop case studies for specific
governing boards.
Additionally, having addressed the empirical question of the existence of social networks,
more complex social network analysis potentially could be advantageous. Social network
analysis could be applied to further understand specific institutional policy decisions made by
governing boards (Frank, 1996, 2005; Kraatz, 1998). In this case social network analysis could
measure how important and influential certain types of information or resources are in the
governing board network (Borgatti & Carboni, 2007) as it relates to specific policies. For
example, data could be collected on who governing board members are speaking to about a
financial policy and an academic policy. These networks could then be compared.
Attribute data about governing board members could be applied to social network
analysis for further conclusions about the network (Marin & Wellman, 2010). For example,
governing board members professional affiliations could be overlaid on the governing board
network. This type of study would potentially provide further explanations of ties and
relationships found among the network.
Social network analysis studies could be designed for other governing board structures

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such as private higher education institutions, elected public higher education governing boards,
or state system governing boards.
Beyond the application of a new theoretical framework to studying higher education
governance, the current study also offers contributions to established educational research. One
finding worthy of further consideration is that governing board members bring established policy
positions to their institutional leadership role. These policy positions could potentially be the
result of the formal appointment process, basis, or personal experience. Moreover, board
members use governing board meetings to negotiate policy positions. This finding contrasts with
some existing research on governing boards that indicate that governing boards typically act in
bureaucratic, hierarchical ways (Birnbaum, 1988; Chait, Holland, & Taylor, 1991; Mintzberg,
1979). It is possible that this finding conflicts with previous studies because of the unique nature
of the current fiscal crisis and the necessity by boards to develop creative solutions to pressing
challenges. More research is warranted to understand if the pattern of intensive negotiations
centered on issues with legal, financial, or employment implications held true prior to 2008
because state contributions to public post-secondary education have been declining for a
significant period of time (Zumeta, 2006).

Another question remaining at the end of the current study to research are strategies and
practices for navigating the financial environment. This study concluded that the current
environment is requiring board members to be more engaged than in previous time periods.
However, board members are unclear about the importance of different sources of information to
guide them in their institutional role. Exploring policies and practices used by governing boards
to navigate these turbulent financial times would be of benefit to Presidents who are most

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frequently consulted by governing board members for guidance in their institutional leadership
role.
Examining how governing boards function the data suggestions a few interesting research
alternatives. Governing Boards Members’ Perceptions (see, Table 4.5) suggests some
differences between Masters and Research institutions. Future research could explore if there are
significant differences and what are the policy implications. Additionally, the only negative
score was by Research II in assessing the usage of faculty by the board when considering cost
saving strategies that affect the academic curriculum and degree programs. However, the
Provost for Research II was a high receiver of relationship in the social network. It would be
interesting for future research to examine the extent to which the governing board views the
Provost as the voice of the faculty.
Institutional Governing Boards
The contributions of the current study to higher education governance are worth noting.
One important finding is that the recent financial crisis on governing boards represents a unique
historical period that is requiring governing boards to take more deliberate actions and engage
more actively in decisions than in the past. However, this study indicates that governing boards
appear uncertain about the resources that might best guide them in their institutional leadership
role during this period of financial turbulence. This study also concluded that the President is the
most common source of information for governing boards. Moreover, the Chair of the governing
board is frequently the boundary spanner among the governing board network. Taken together
these findings support the integral role both the President and Chair have in leading the
governing board. From a social network perspective these actors are within the “horizon” of
having informal influence on the governing board as an organization (Friedkin, 1983).

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Additionally, higher education scholars (Chait, 1991; Kezar, 2006; AGB, 2006) frequently
advocate that governance performance is enhanced by successful relationships between the
President and Chair.
Governing boards could potentially benefit from increased self-awareness about their
own social networks. An understanding of who the board is depending on for information to
inform policy decisions or deferring institutional management is important. A board’s awareness
of this information provides insight into how the board operates and can be a foundation for
evaluating organizational practices and assessing the need for change.
During this time of financial turbulence, this study demonstrated the need for Presidents
and governing board Chairs to distribute timely and succinct information to governing board
members that can spur creative solutions to current institutional challenges. Alternatively some
scholars advocate that financial crisis will require disruptive innovation to initiate change
(Christensen & Horn, 2011). Linking this approach to the current study, governing boards would
be wise to consider the advocacy by peripheral actors in the network.
Another implication for institutional governance is the finding that board members use
board meetings to negotiate established policy positions. With public higher education
governance subject to sunshine laws this finding supports creating alternative space for board
members to discuss dissenting opinions. Time designated for board members to negotiate
alternative policy perspectives and share viewpoints of peripheral actors of the governing board
network will potentially avoid the “dark side” of social cohesion (Gargiulo & Benassi, 1999;
Gulati & Westphal, 1999; Portes & Landolt, 1996). Moreover, this process will hopefully enable
boards to generate creative solutions to institutional challenges posed by the current financial
crisis and established trend of declining state support. As concluded by Kezar (2006), a unique

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element requiring attention by public higher education governing boards to achieve high
performance is attention to external stakeholders.
Another finding of the current study was the impact of governing board members
professional backgrounds on how the board functioned. With a majority of public higher
education governing boards appointed by the Governor (AGB, 2010) the study concludes that the
appointment process of governing board members may benefit from considering industry sectors
of future board members. This finding is consistent with Chait et al.’s work (1991, 1996) that
advocates for drawing upon the expertise of board members. In addition, this policy implication
is in alignment with a small movement by six states to establish screening and nominating
committees to vet potential governing board candidates on merit before advancing them to the
Governor (Novak, 2009).
Several states are considering proposals to restructure or strengthen public higher
education governance as a result of economic turbulence and greater calls for institutional
accountability. As noted by Novak, “public-policy developments during 2008 and early 2009 and
those expected over the next two years have raised the bar on expectations for greater board
engagement” (2009, p. 16). The results of the current study support this trend and clearly
illustrated that the current financial environment is generating more active governing boards. As
a result, governing board members need to be prepared to engage deeply in the critical issues
facing higher education.
Association of Governing Boards of Universities and Colleges
The AGB is a national association that serves governing boards and leaders of
universities, colleges, and systems through the dissemination of research and publications as well
as delivery of programs. In acknowledging the strong work of the AGB, my intention in this

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section is to put forward a few thoughts about services the AGB could offer member institutions
based on the results of the current study.
The data suggests that governing board members are unclear about the most timely and
relevant sources of information to guide them during this financial crisis. The AGB could
develop more mechanisms to deliver current information to governing boards and institutional
leaders to help institutions navigate the turbulent financial times. Additionally, drawing upon the
knowledge of AGB staff and faculty consultants who regularly engage with higher education
governing boards generate a selection of best practices on how boards function during this
financial crisis as well as strategies governing boards are using to address critical challenges.
These timely lessons could be invaluable for other boards confronting similar challenges.
Additionally, the results of the current study reinforced the central role of the institutional
President. Understanding that numerous higher education associations develop programs for
institutional leaders, the AGB is well situated to develop a specific line of services for
institutional Presidents that focuses on advancing governing board performance. Drawing upon
Chait et al., (1991), the AGB could develop programs that help institutional Presidents advance
the six dimensions of effective governance. Alternatively, the AGB could assist Presidents in
using board self-assessment tools to facilitate organization learning and change.
The data from the current study suggests some significant differences in the perceptions
of how the board functions between governing boards members from different types of
institutions. As the AGB develops programs and services it might consider the varying needs
beyond public and private institutions, but to also to consider institutional type.
The social network analysis reveals interesting information about how governing boards
function. Through the Board Consulting Services the AGB could utilize this analytical tool in

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their consulting engagements to help boards better understand the resources they frequently
depend upon. Potentially this could be powerful and informative information for governing
boards in reflecting on how they function.
Conclusion
This study sought to determine whether identifiable social networks for public higher
education governing board members exist, to develop an overall picture of public higher
education governing board networks, and to understand the internal and external factors
impacting governing boards’ social networks during times of fiscal crisis.
Data collected from four public post-secondary governing boards from two states
demonstrated that identifiable social networks did in fact exist for each governing board.
Commonalities in the social networks were present across the four participating institutional
governing boards. The commonalities that emerged in examining the individual actors in the
governing board networks are from the in-degree data. The in-degree data indentifies the actors
in the network most frequently consulted by the governing board members. Utilizing the
normative scores it is evident that the governing board members most frequently depend upon
the President of the institution. The Chair of the governing board was frequently the actor with
the highest betweenness score for each board. This finding emphasizes the important role the
Chair plays in connecting the governing board network. Additionally using the reach centrality
scores both the President and Chief Financial Officer are comparable sources of information for
all four institutions’ governing board members. These individuals are accessible to governing
board members through short network paths. Collectively, it appears that the President and
Chief Financial Officer of the institution and the Chair of the governing board are central to the
governing board networks in this study. The cohesion around this particular set of actors

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demonstrates that a majority of governing board members share ties with these individuals.
Moreover, it emphasizes the positional advantage these actors have in the network.
The governing board members’ perceptions about how the board functions is
concentrated on four primary findings. First, board members unanimously indicated that the
recent financial crisis has created a unique period for higher education. Moreover, during this
time of financial crisis governing boards are taking more deliberate actions and engaged more
actively in decisions than in previous years. Second, governing board members have established
policy positions that are negotiated. Governing board members view board meeting as time to
discuss and negotiate policy disagreements. Third, the professional backgrounds of board
members impact board members decisions on the board. Fourth, communication from the
President of the institution emerged as the most constant source of information for governing
board members. Beyond, the President, the board members indicated that communication from
the Chief Financial Officer is an important information source.

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APPENDIX A
Glossary of Terms for Social Network Analysis

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Actor: An individual in the network (commonly referenced as actors/nodes/vertices).
Closeness Centrality: Captures the shortest-path length between an actor and every other actor
in the network. Measured by a variety of metrics.
Degree Centrality: A count of the total number of connections linked to an actor. As a
measure it does not differentiate between quantity and quality. For directed networks an indegree and out-degree centrality measure is calculated for each actor.
Freeman Betweenness Centrality: A measure of how often an actor lies on the shortest path
between other actors. As a measure it provides insight into how removing an actor from a
network would disrupt connections between other actors in the network. Additionally, this
measure can identify structural holes in a network where two or more actors fail to connect.
Freeman Graph Centralization: An expression of the degree of variance in a network as a
percentage of a perfectly connected network of the same size. In directed networks, the measure
is calculated for both directions
Geodesic Distance: The number of relations in the shortest possible path from one actor to
another.
Graph Density: The proportion of all possible ties that are actually present.
In-degree: Column data of network matrix. Identifies actors as receivers of information or
relationships.
Network Matrix: A mathematical format to represent the relationships in a sociogram.
Out-degree: Row data of network matrix. Identifies actors as senders of information or
nominators of relationships.
Peripheral Actor: Actors on the fringe of the network and often connected to networks that are
not currently mapped.
Reach Centrality: One measure of closeness that examines how close each actor is to all others
in the network.
Social Network Analysis: An analytical tool used to examine the patterns of relations among
actors by mapping interactions and relationships.
Sociogram: A graph representing the actors and ties in a social network.
Tie: An indication of a relationship between one or more actors (commonly referenced as
edges/ties/relations).

140

APPENDIX B
Survey

141

Public Higher Education Governing Board Survey
I am asking you and other governing board members at your institution to respond to the
following research survey. I am trying to learn about the types of information and information
sources higher education governing board members draw from in their role as an institutional
leader during times of environmental distress, such as the current financial crisis. I will use the
results of the research to capture an overall picture of public higher education governing board
networks and begin to understand factors impacting governing board networks.
Completing this survey indicates your consent as a participant in this study insofar as your
responses will be analyzed. Participating in this study is voluntary and I will keep all data
collected confidential. You may refuse to answer any question and exit the survey at any time.
Although I will protect your confidentiality by using a pseudonym for each institution and
identification numbers for individual governing board members in all publications and written
reports, you or others may be able to discern some of the identities based on reported attributes
of the institution and individuals. Identifying information may be of the form: "Experienced
board members (unnamed) at name ("name" is a pseudonym of the institution) indicate that ... "
Completing this survey should take approximately 10-15 minutes. Please return the survey in
the enclosed self-addressed stamped envelope or take the survey online at
http://www.surveygizmo.com/s3/310335/Public-Higher-Education-Governing-Boards
I appreciate you taking the time to respond to this survey.
Sincerely,
Emily R. Miller
Doctoral Candidate, College of Education, Michigan State University
Phone: 617.869.9314
Email: ermiller@msu.edu
Dr. James Fairweather
Professor, College of Education, Michigan State University
Phone: 517.353.3387
Email: fairwea4@msu.edu

142

Background and Board Membership Information
1. Please provide your name and the name of the institution.
Name: _______________________________________________
Institution: ___________________________________________
2. Please select your professional industry:
____ Accounting

____ Legal

____ Advertising

____ Manufacturing

____ Aerospace / Aviation / Automotive

____ Marketing / Market Research / Public

____ Agriculture / Forestry / Fishing

Relations

____ Biotechnology

____ Media / Printing / Publishing

____ Business Services Hotels, Lodging

____ Mining
____ Non-Profit

Places

____ Pharmaceutical / Chemical

____ Computers Hardware, Desktop
Software

____ Research / Science

____ Communications

____ Real Estate

____ Construction / Home Improvement

____ Retail

____ Consulting

____ Telecommunications

____ Education

____ Utilities

____ Engineering / Architecture

____ Wholesale

____ Entertainment / Recreation

____ Transportation / Distribution

____ Finance / Banking / Insurance

____ Utilities

____ Food Service

____ Business / Professional Services

____ Government / Military

____ Other

____ Healthcare / Medical

____ Don't work

____ Internet

143

3. Please provide your current professional position:
____ Top Level Executive

____ Faculty Member

____ Senor Vice President

____ Professional

____ Vice President

____ Student

____ Director

____ Administrative/Support personnel

____ Manager

____ Unemployed/Retired/Homemaker

4. Gender
____ Male
____ Female
5. Race
____ Asian/Pacific Islander
____ Black/African-American
____ Caucasian
____ Hispanic
____ Native American/Alaska Native
____ Other/Multi-Racial
____ Decline to Respond
6. Please indicate the approximate frequency that you are on-campus for meetings, events,
ceremonies, athletics, others:
____ Daily
____ Once a Week
____ Once a Month
____ 6 - 12 p/year
____ 1 - 5 p/year
7. Please indicate the number of years you have served on this board:
____ 1
____ 5
____ 2

____ 6

____ 3

____ More than 6 years

____ 4
144

8. Please indicate the number of board meetings you participate in during a year:
____ 1
____ 2
____ 3
____ 4
____ 5
____ 6
____ 7
____ 8
____ 9
____ 10
____ 11
____ 12

145

9. Please select all the committees that you serve on as a board member and indicate if you
chair the committee:
Committee
Academic Affairs Committee

Member

Chair

Audit Committee
Building & Grounds
Committee
Committee on Trustees
Compensation Committee
Development Committee
Executive Committee
Finance Committee
Investment Committee
Student Affairs Committee
Other

Sources of Knowledge
This section focuses on your interactions and sources of knowledge, especially with regard to
your role as a governing board member. A diagram of governing board networks will be
created. Those diagrams will not identify anyone by name. Your answers will be anonymous.
10. In your role as a governing board member, please indicate the individuals you most
frequently discuss institutional policies and strategies.
List as many individuals as you wish, indicate their role* as well as the frequency with which
you interact with each person. Names are optional. Please consider individuals both internal and
external to the institution. You do not have to use all the spaces provided.
* Role Options: Athletics Director; Board Member; Board Secretary; Chair of the Board; Chief
Financial Officer of Institution; Executive Assistant to President; Faculty Member; Faculty
Senate; Family; Governor; Governor Office; Legislative Office; National Higher Education
Associations; Personal Friend; President of Institution; Professional Colleague; Provost of
Institution; State System Chancellor; State System Board Member; State Higher Education
Commission/Council Member; Spouse/Partner; Student; Unions; Other

146

Name

Role*

Frequency of Interaction
____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

1.

2.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

3.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

4.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

5.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year
____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

6.

7.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

8.

____ Daily
____ Weekly
____ Monthly
____ Once or twice a year

147

11. In your role as a governing board member, what information sources do you consult
when considering institutional policies. Please select all that apply.
____ Correspondence from Secretary of the Board
____ Correspondence from President of the Institution
____ Correspondence from Provost of the Institution
____ Correspondence from Chief Financial Officer of the Institution
____ Correspondence from Faculty Senate
____ Correspondence from State Higher Education Chancellor
____ Correspondence from State Higher Education Commissions/Councils
____ Reports from State Government Offices
____ National Higher Education Association Reports or Print Materials
____ National Media on Higher Education (e.g. Chronicle, Inside Higher Education)
____ Board meeting materials
____ Local Newspapers
____ Others

Board Decision Making Process
This section focuses on your perception of how the governing board reaches decisions.
12. Please select the process which your governing board most commonly reaches policy
decisions
Board members ...
____ are uncommitted to a particular institutional policy decision and adopt the recommendation
of the administration.
____ have established opinions on institutional policies and during the board meetings interests
are negotiated.
Does this process vary by issue?
____ Yes
____ No
If yes, what issues most commonly do board members have established opinions and require
negotiation: ____________________________________________

148

13. Consider how your governing board functions. To what extent do you agree with the
following statements:
Strongly
Agree

Agree

Neither
Agree or
Disagree

Disagree

Strongly
Disagree

Governing board stakeholders have an
affect on governing boards decisions.
The multiple layers of governance
state coordinating systems, state
higher education commissions, faculty
governance impact institutional
policies.
Governing board members'
professional backgrounds impact their
decisions on the board.
Governing board members'
relationships inform and influence
their decisions on the board.
Differences of opinion in board
decisions are more often settled by
discussion than by vote

Governing in a Financial Crisis
In the fall of 2008 the world economic markets entered a period of significant turbulence, a
reality that when compounded with a long-term steady decline in state funding for public higher
education has intensified the challenges confronting higher education institutions. Often higher
education institutions are using both short-term and long-term budget cutting and revenue
enhancing strategies. Please reflect on how your board is approaching institutional decisionmaking during this time of fiscal crisis.
14. During this time of financial crisis, do you believe your governing board is taking
more deliberate actions and engaged more actively in decisions because of the implications
for the future of the institution.
____ Yes
____ No

149

15. To what extent do you agree with the following statements:
Strongly
Agree

Agree

Neither
Agree or
Disagree

Disagree

To address the financial crisis the
governing board is encouraging the
development of specific institutional
policies.
Our board has adequate sources of
knowledge to address institutional
dilemmas presented by the financial
crisis.
When considering continued declining
state appropriations, the board often
tries to generate creative approaches or
solutions to close budget gaps or
manage costs.
The board consults the faculty when
considering cost saving strategies that
affect the academic curriculum and
degree programs.
When considering institutional policies
to address the financial crisis the board
is in alignment with recommendations
from the State Higher Education
Commission, Council, or Coordinating
System.
Thank you for taking the survey. Your response is very important to my research.

150

Strongly
Disagree

APPENDIX C
Numeric Coding for Social Network Analysis

151

Role

ID Number

Athletics Director
Board Secretary
Chief Financial Officer of Institution
Deans
Executive Assistant to the President
Faculty Member
Faculty Senate
Family/Spouse
General Counsel
Governor
Legislative Office
Other
President of the Institution
Professional Colleague
Provost of the Institution
State Higher Education Commission/Council
State System Chancellor
Students
Masters I Board Members

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
116, 127, 136, 132, 135,
134, 119, 133, 118, 109
(Chair), 131
128, 129, 137, 138, 139,
152, 153 (Chair), 154, 168,
169, 170, 171
111 (Chair), 112, 113, 117,
120, 122, 123, 145, 146,
147, 148, 149, 150, 151,
155, 156, 157, 168 (member
resigned January 2011; 151
was replacement)
121, 124, 126, 140, 141,
142, 143 (Chair), 144, 158,
159, 160, 161, 162, 163,
164, 165, 166, 167

Masters II Board Members
Research I Board Members

Research II Board Members

152

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153

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