+1(978)310-4246 credencewriters@gmail.com

Coding Scholar of Change Video #1

In Weeks 1–4, you observed and created field notes for each of the Walden Scholars of Change videos. Now that you have completed your observations, next you will select one of the Scholars of Change videos to begin the coding process of not only your field notes but also the transcript of the video you downloaded..

To prepare for this Discussion:

Choose one of the three social change literature review articles found in this week’s Learning Resources and review the article in detail.

Explore the Walden Social Change website and locate an additional document, video, or webpage that will inform your understanding of the meaning of positive social change. Reflect on any additional sources you find.

Next, write field notes based on the information you gathered from the Walden social change website and any other documents or websites that might inform your changing impressions about the meaning of positive social change.

Finally, review the media programs related to coding and consider how you will use this information to support this Discussion. Note: In your Excel Video Coding template there is a tab for your website data. Use this tab to place your content and codes for the website.

Prepare a brief explanation of your understanding of the meaning of positive social change thus far. Refer to the additional sources you have reviewed this week, and comment on how they are shaping your experience. Use the data you gathered from your analytic memo to support your explanation.                                                                                                                                                                                                                                                                                Resources

-Saldaña, J. (2021).

The coding manual for qualitative researchers

(4th ed.). Sage Publications.

Chapter 1, “An Introduction to Codes and Coding” (pp. 3–23)

Chapter 3, “Writing Analytic Memos About Narrative and Visual Data” (pp. 57–84)

Ravitch, S. M., & Carl, N. M. (2021).

Qualitative research: Bridging the conceptual, theoretical, and methodological

(2nd ed.) Sage Publications.

Chapter 8, “An Integrative Approach to Data Analysis” (pp. 233–252)

Chapter 9, “Methods and Processes of Data Analysis (pp. 254–294)

Rubin, H. J., & Rubin, I. S. (2012).

Qualitative interviewing: The art of hearing data

(3rd ed.). Thousand Oaks, CA: Sage Publications.

Chapter 12, “Data Analysis in the Responsive Interviewing Model” (pp. 189–211)

Introduction to Coding
Introduction to Coding
Program Transcript
SUSAN MARCUS: Hello. My name is Dr. Susan Marcus, and this is an introduction to
coding qualitative data. Before we get started, I’m going to give you a definition and
some visuals of what we mean by coding qualitative data. A code in qualitative inquiry is
most often a word or a short phrase that symbolically assigns a summative, a summary,
a salient or essence-capturing attribute, for some portion of language, or visual data.
So what does that mean? With these certain words, short words or phrases, we’re trying
to capture a meaning that’s been attributed to, or contributed to, by another source. So
the process of coding means we identify distinctive features of a piece of text, and see if
there are similar features to other pieces of text from other sources.
What this also means is you can code just about anything. You can code transcripts
from videos. You can code transcripts from written, or phone, or live, interviews. You
can also code observations of a field experience. Or code observations of a photo.
So once you get anything that you have observed, and want to include, in your
qualitative data analysis into a language-based form, typically in the form of a transcript
in a word processing document, you can then start the process of coding. What we’re
doing is we’re looking for patterns, similarities in features, similarities in order of
presentation, similarities of context, similarities in meaning.
So what these marbles represent are the different thoughts, and feelings, and
experiences, each person has about being in nature. And my quest as a qualitative
researcher to see if I can understand each individual’s experience, and then look for
shared meaning across those experiences. So here’s person A. And each type of
experience they share is noted by a different marble.
And as a qualitative researcher beginning to code, I say, hmm, maybe I can first
organize them according to a distinctive feature. Let’s try color. And as I’m organizing
the marbles, I see, well, some of the colors are really distinct, and some of them are not
quite as distinct as I thought. But I’m going to group them together anyway. So in a
sense, I’ve created a code for this person according to color.
Now I’m going to do it with the next person’s– marble’s– experiences. And I’m going to
organize them and sort them so that they line up with, to the best that I can surmise, the
preceding person. And I’ll do the same for the other two individuals. And as I’m doing
this, I’m also reflecting in my mind– but if I was doing this as a qualitative study, I would
be taking notes, writing memos– about the choices I was making about where to group,
or where to put, which marble with which group.
So, obviously, color is a really easy way to sort. We could sort on size. We could sort on
clarity. We could sort on whether some of the marbles were colored, or solid, or cat’s
eyes. And voila. So I’ve sorted, I’ve coded each individual’s experiences by color. And
© 2016-2021 Walden University, LLC
Introduction to Coding
now, as a qualitative researcher, I want to group these experiences– and again, we’ll do
it by color just for the purposes of illustration– into larger patterns to see if there are
similarities across these different individuals.
And one of the things, for example, I would note, is that while most of the group share
this experience, indicated by the red marbles in color, this person has a similar kind of
experience, but it’s not quite the same. So as a qualitative researcher, I would make a
note– I’m grouping these codes together into a category that I could call red. Noting that
some of the codes are approximations, but not identical, to the final category. And then I
can do that with the other marbles as well.
So again, you can see that I have made groups of like objects some of them– Oh,
here’s another. Look at this. I had one set of objects over here, but they actually can go
over here. And I also have a couple of discrepancies that don’t really fit in any particular
category. And so as a qualitative researcher, I also have to make a decision. Do I want
to force, or try and make, every bit of information fit into a category, or do I want to use
these as what we call discrepant cases, to explore what these individual items or codes
So I hope that helps illustrate what we mean by the process of coding. Taking individual
bits of information, grouping them. I could also try grouping them with another approach.
For example, some of these marbles have two different colors. So I could also see what
pattern emerges if I take marbles with two different colors and put them all in one group.
And marbles with single colors and another group. Looks different.
So that’s the idea of approaching coding from different perspectives. That is, if you code
just one way, you get one picture. But if you code taking another approach, you may get
an entirely different picture.
The other point I’d like to share with you is the choice of doing manual coding. What we
mean by manual coding is using basic word processing and spreadsheet tools to move
bits of data around in order to create codes, categories, and themes.
The other alternative, is computer-assisted qualitative data analysis software. There are
many, many choices available. And of course, the advantage of using a computer
application is that you have a bit more efficiency. They have lots of great visual displays,
and other ways to manipulate data. The challenge is, most of these software programs
have a very steep learning curve. So you’re learning both how to code qualitative data
and learning a software program.
The other issue is that because there are so many different kinds of programs to choose
from, you, as you become a more experienced qualitative researcher, and if you choose
to go on and do a qualitative dissertation, you may, after looking at different programs,
develop your own preference. Or your chair may have a preference for which one to
use. You’ll have a chance to work with smaller data sets in this course, and so using
© 2016-2021 Walden University, LLC
Introduction to Coding
Excel and Word are more than acceptable. And, towards the end of the course, you’ll
also have a chance to explore software alternatives.
The other thing you’ll need to do is create a notebook. It can be in electronic form, like a
Word document, or an actual notebook for handwriting notes. In qualitative research, we
call these memos. Which contain your reflections, your thoughts, your descriptions of
your process, of going from the data sources, the transcripts, to codes, to categories,
and writing up your results.
As a final note, I just want to encourage you to use this as an opportunity to explore,
and develop new skills, and consider whether or not this type of research is something
that you would like to pursue for your dissertation. The act of qualitative data analysis
can be laborious, intensive, and repetitive. But it’s also the opportunity for discovery, for
something new, that’s been generated by your participants, for the data that you’ve
collected, and perhaps even the opportunity to discover something about yourself.
© 2016-2021 Walden University, LLC
Adrianna Kezar
Higher Education Change and
Social Networks: A Review of Research
This article reviews literature on the potential for understanding higher education change
processes through social network analysis (SNA). In this article, the main tenets of SNA
are reviewed and, in conjunction with organizational theory, are applied to higher education change to develop a set of hypotheses that can be tested in future research.
Social network analysis provides strong confirmation of the inseparability of
fundamental planned change and social networks. Schemata are embedded in
communities and emerge and are maintained through interpersonal interactions. Lasting change does not result from plans, blueprints, and events. Rather
the changes must be appropriated by the participants and incorporated into
their patterns of interaction. It is through the interaction of the participants that
the social system is able to arrive at a new network of relations and new way of
operating. (Mohrman, Tenkasi, & Mohrman, 2003, p. 321)
We live in a time where there is the potential for people to be increasingly connected through formal and informal networks; technology has
made these types of social networks more common and accessible.’
Social media such as Facebook, in particular, has connected people
in vastly different countries that otherwise would not have contact;
it makes networks more commonplace within our modem world. We
Adrianna Kezar is a Professor in the Rossier School of Education at the University of
Southern California: kezar@usc.edu.
The Journal of Higher Education, Vol. 85, No. 1 (January/February)
Copyright O 2014 by The Ohio State University
The Journal of Higher Education
need only think about the various political changes in Syria, Egypt, and
Libya to see the power of social networks/media for creating change.
Social networks are defined as people loosely connected through some
form of interdependencies such as values, preferences, goals, ideas, or
people (Wasserman & Faust, 1994). Social networks can serve many
functions such as social support, knowledge, and change.
Recently, policy makers and foundations have begun to capitalize on
the potential of networks to create education reforms (Lumina Foundation, 2010). Leaders who have attempted to address complex problems
realize that multiple stakeholders need to be engaged and that these
problems are multifaceted and require quite different expertise and
groups to address them (Spillane, Healey, & Chong, 2010). The Lumina
Foundation, for example, is investing in a variety of statewide and national networks to help improve access to higher education. The Lumina Foundation recognizes that the goal of increasing access in higher
education to 60% by 2025 will not be met unless a variety of groups
are connected and work together (Lumina Foundation, 2010). Networks
have also been used to scale up change in science disciplines. The National Science Foundation has invested in networks to connect STEM
faculty to solve complex problems, improve the nature of undergraduate teaching and learning, and help transform K-12 teachers’ knowledge and preparation so they can increase the pipeline into science
(Fairweather, 2009). It would seem obvious that social relationships are
fundamental to creating change, but the multidisciplinary research literature on change, as well as the higher education field, specifically,
does not refiect this most basic assumption.
Even though social networks have become part of our daily consciousness and several visible, national higher education projects utilize
networks, there is little research in higher education on the way networks create change or can be used towards change, particularly in the
U.S. higher education literature^ (Hartley, 2009b; Kezar, 2001, 2005).
Within the higher education literature, social network analysis (SNA)
has been used primarily to examine issues of access and success among
college students, particularly around the development of social or cultural capital through networks of peers, school personnel, and their family (Heck, Price, & Thomas, 2004; Mayer & Puller, 2008; Skahill, 2003;
Teranishi & Briscoe, 2006; Thomas, 2000; Tiemey, Corwin, & Colyar,
2005). These studies have examined how social networks within high
schools or colleges help to retain students by connecting them to needed
information and support.
SNA has also been used to study technology and information sciences
and, to a lesser degree, institutional and research collaborations. In
Higher Education Change and Social Networks
these areas, topics include sense of community of online learners (Otte
& Rousseau, 2002; Traud, Kelsic, Mucha, & Porter, 2011); citation
analysis to see connections among researchers (Marion, Garfield, Hargens, Lievrouw, White, & Wilson, 2003; Sharma & Urs, 2008; White,
2003); efforts to map collaborations among colleges (Larivi, Gingras,
& Archambault, 2006); more specific research collaborations (Balconi
& Laboranti, 2006; McMillan, 2008; Rogers, Bozeman, & Chompalov,
2001); student activism (Biddix & Park, 2008; Crossley, 2008); and corporate partners of higher education associations (Metcalfe, 2006). The
few studies of student activism are the most directly related to this article in that they begin to hint at the power of SNA for understanding
change. Therefore, SNA is used in higher education research but only
for a limited set of topics. Yet, it is also a burgeoning area that is being
recognized as important for studying various phenomenon that focus on
social relationships and collaborations. In this article, I argue for its application to change and reform—a critical and important challenge facing most postsecondary institutions.^ The main objective of this article
is to conduct a multidisciplinary review of the literature on social network theory and apply it the problem of understanding change in higher
education. As part of this objective, I also demonstrate the synergy of
SNA with long-used organizational change theories. And lastly, the application of SNA is used to develop a research agenda. As the article
will demonstrate, the combining of organizational and social network
theories remains a major gap in the literature. The article raises broad
questions about how the enterprise of higher education can and does
engage in change and how researchers’ current approach to studying
change misses many significant change processes and dynamics.
In the next sections, I review the main tenets of SNA and compare it
to other theories that have been applied to the study of change in higher
education, specifically focused on networks or groups of people. Next,
I provide an overview of some of the main findings from the multidisciplinary research base on SNA as it relates to change, but largely applied outside institutional/organizational settings. I draw on examples
from institutional settings where possible, as this provides a more direct analogy for applying SNA in higher education. Then I present how
these concepts from SNA can be applied to higher education change
and develop a set of hypotheses that can be tested in future research,
developing a formal research agenda. In addition to contributing to the
literature by applying this theory and its concepts to higher education
and offering up an extensive research agenda, I also offer a unique perspective—describing the ways that SNA and organizational theories can
and should be combined. One of the main messages in this review is
The Journal of Higher Education
that networks, while an important new area for study in their own right,
will likely be more fruitful for understanding change in higher education when examined in conjunction with organizational theory. Studies
of networks within other institutions (e.g., hospitals, schools) find that
networks are poorly understood when the context (societal, organizational, and more local-unit level) within which they exist is ignored.”
I offer this insight from having reviewed literature across various disciplines and settings that study institutions where the issue of missing
context has repeatedly emerged.
Social Network Analysis:
Basic Assumptions and How It Relates to Change
Basic Assumptions
As one reviews the multidisciplinary literature base on SNA, it is
clear that social network analysis works against the grain of much ofthe
change literature; it challenges assumptions about the meaningfulness
of organizational boundaries and forms, looking instead at how ideas,
information, resources, and influence flow across what are normally
conceptualized as more rigid boundaries and forms (Scott, 1991).^ SNA
challenges the underlying belief that the formal organization or social
system has the most dominant impact on individuals and their choices
(Daly, 2010c).^ It suggests that informal networks of relationships have
a significant impact on whether individuals decide to engage in change
or reform behavior. Networks also challenge the notion that overarching
norms (i.e., society, organizations, institutions) are the only impact on
behavior; instead, important close peers or even distant contacts can impact choices and attitude (Kilduff & Tsai, 2003). Most higher education
change literature views departments, schools/ colleges, or state systems
as the natural unit or target for change processes and analysis rather
than networks or relationships (Kezar, 2001). SNA describes more fluid
relationships that cross these boundaries—looking, for example, at collaboratives, online communities, or informal collectives. This research
also takes a decidedly non-authoritative and hierarchical approach to
thinking about social systems and how they operate, by examining all
people at any level or within any unit (Daly, 2010a), whereas organizational theory often privileges those in positions of authority in terms of
analysis. The theory and methodology of social network analysis also
attempt to look at the dynamic interactions between formal structures
and informal relationships, examining participants’ peers, friends, and
Higher Education Change and Social Networks
Before describing key insights related to change and social networks, I present basic notions about SNA and I refer the reader to the
Appendix for more detail about terms. Networks can be tight or loose:
Tighter networks are often denser and smaller, while looser networks
can be quite complex, composed of all people across a variety of units,
organizations, and countries (Borgatti & Cross, 2003; Kilduff & Tsai,
2003). Networks can also be formal or informal: Formal networks have
more structure related to communication and interaction, whereas informal networks are less organized and have little in the way of structure to support communication and interaction (Kilduff & Tsai, 2003;
Scott, 1991). Researchers who study networks often examine the nodes
(people) and ties (relationships with people). Through description and
analysis of the nodes and ties, researchers are able to predict certain
outcomes (e.g., social capital, knowledge, or change) or processes (e.g.,
learning, information sharing) within the network.
Relationship to Change
Diffusion of innovation is an outcome in many different studies of
social networks, which is why SNA has been applied to the study of
change processes in more recent years (Rogers, 2003; Valente, 1995).
In addition, many studies have linked the existence of social networks
with the success of change initiatives, suggesting a strong correlation
between these processes (Daly, 2010c; Hartley, 2009a, 2009b).^ As Daly
(2010a) has noted, the social network perspective provides proof that
“relationships within a system matter to enacting change” (p. 2). He
goes on to note that all reforms may begin as ideas or visions but that
they eventually need to be engaged by people who work in social structures and relationships. Therefore, webs of relationships are often the
chief determinant of how well and quickly change efforts take hold, diffuse, and are sustained (Daly, 2010a).
Researchers have identified several key ways that social networks
lead to change. First, social networks offer a set of mechanisms that
enable change—through communication systems, knowledge transfer, alteration of schema or mindset, shaping of attitudes, increasing of
problem-solving, and accountability (Ahuja, 2000; Borgatti & Foster,
2003; Kraatz, 1998; McGrath & Krackhardt, 2003; Szulanski, 1996;
Wasserman & Faust, 1994). ^ Second, two outcomes of social networks
have been related to change—learning and social capital (Borgatti &
Foster, 2003; Burt, 2000; Kilduff & Tsai, 2003; Tenkasi & Chesmore,
2003). Many researchers have found a strong linkage between learning
and social networks, and learning has been strongly linked to changes
in behavior (Tenkasi & Chesmore, 2003). Networks also provide social
The Journal of Higher Education
capital that facilitates the change process (Burt, 2000). While different
definitions of social capital exist, underlying most of the theoretical discussions is the assumption that social capital is the resources embedded in social relations and social stmcture, which can be mobilized by
an actor to increase the likelihood of success in purposive action (Daly
& Finnigan, 2008). This resource can vary from knowledge about how
the organization works to influence to finances. Third, change often involves risk-taking that can be less problematic if it is done collectively
rather than individually (Valente, 1995). If one knows many of their
peers are going to engage in the same behavior, then one is more likely
to also engage in this behavior (Rogers, 2003; Valente, 1995).
While this is not an exhaustive list, these are some of the most commonly identified areas that link social networks and change and demonstrate why networks facilitate reform. An example of this process might
help: Faculty who participate in a STEM reform network gain access
to the latest research from cognitive science about how students leam,
which helps them to have discussions that change their view on pedagogical approaches. While they now want to change their pedagogy,
they lack the confidence (and feel it is too risky) to enact the change or
skill to alter their teaching. Through network presentations from faculty who have changed their approach, they gain the confidence (seems
less risky) and the skills to rethink their practices. Even though no one
at their own campus is engaged in these new teaching techniques, they
can contact members of the network for support, guidance, and even accountability to maintain the change.
The most often noted source for understanding social networks and
change is Rogers’ (2003) diffusion of innovation model that examines
communication channels and how ideas are transmitted. Rogers’ work
emerged out of a variety of studies that examined the diffusion of innovation, mostly outside of organizational contexts.’ It may be that
because his work was located beyond the formal organizational setting, many researchers did not originally see the potential for studying
changes within formal organizations and institutions like colleges. Yet,
Rogers’ work has been applied within educational contexts, particularly
to examine the diffiision of technology use (Solem, 2000; White, 2001).
The focus was on individual uptake of various forms of technology on
college campuses but rarely applied to other change processes.
How Social Network Analysis Compares to Other Theories of
Change in Higher Education
Certainly collections of people and a human dimension to change
are alluded to in organizational theories of change in higher educa-
Higher Education Change and Social Networks
tion. For example, management science models of change focused on
strategic planning refer to broad buy-in and participation in decisionmaking around reforms, suggesting that plans without the involvement
and ownership of employees will not be successful (Keller, 1983). Political models of change examine how certain groups try to assert their
interests and develop agendas for change (Baldridge, Curtis, Ecker, &
Riley, 1977; Clark, 1983; Kezar, 2001). As coalitions are a form of network, some of the political models of change address the impact of networks (through examination of coalitions) and the way people influence
change processes (Baldridge et al, 1977; Clark, 1983; Kezar, 2001). Political theories of change focus on groups often left out of management
science studies of change—those with less power and authority—and
how they try to combat changes by those with more authority. Political
theories also move outside of campuses, examining community groups
and external influences that might shape change. In many ways, SNA
builds on political theories but focuses more on informal and less structured groups than coalitions.
Social cognition theories of change also describe the power of social interactions for creating change (Gioia & Thomas, 1996; Kezar,
2001; Wenger, 1998). Social cognition models examine how mental
processes and mental models shape the ability of people to engage in
a particular change initiative (Kezar, 2001; Wenger, 1998). Learning
communities and communities of practice evolved from social cognition approaches to change and rely heavily on networks of people coming together around a shared interest to develop professionally (Kezar,
2001). Communities of practice are organic networks that evolve from
people engaged in similar work and may cut across institutions (e.g.,
student affairs practitioners) (Wenger, 1998). Learning communities are
formalized, typically organizationally situated or constructed (often not
extending beyond its boundaries) and therefore less organic (Wenger,
1998). Also learning communities are typically created with the goal of
shifting mindsets and are not existing networks (such as communities of
practice) that are part of the fabric of existing social interactions—the
focus of social network analysis (Kezar, 2001). Both learning communities and communities of practice have been harnessed to create changes
such as pedagogical and curricular reforms of higher education (Micomonaco & Austin, 2010).
So the idea of social relationships being significant to creating change
is not foreign to the higher education change literature and builds off
this earlier work from management science, political models, and social cognition approaches to change. Yet the emphasis in SNA is slightly
different (more informal groups, organic, and moving beyond organizational boundaries). Also, it suggests that social relationships are more
The Journal of Higher Education
central or pivotal to change efforts than these earlier models/theories
suggest, as they instead foreground planning, agenda setting, or schema
development. Yet, as this review will continue to emphasize, the social
relationships already identified in organizational theories of change
should not be ignored or forgotten as we shape studies of change with
a focus on social networks. These insights are best paired with what we
learn from SNA.
Key Insights from Research on Social Network Analysis
Related to Change
Next, I review some of the key insights that have been identified in
research using SNA to understand change processes. To inform this
review, I draw on the multidisciplinary research conducted in sociology, organizational theory, psychology, education, anthropology, public
policy, and business. Yet, I rely heavily on K-12 literature since this
is a more parallel setting than corporations or industry for understanding social networks and change in higher education. While research has
been conducted in higher education, much of this has not been applied
to change. However, when examples from higher education exist, I also
bring these in to inform the article. Furthermore, I focus more on studies
using SNA conducted in formal organizational settings, as they likely
have greater translatability to higher education than studies of farmers
or entrepreneurs (Valente, 1995).’° The following key insights are reviewed: structure of ties; organic versus artificially created networks;
diversity and homogeneity of ties; central actors and opinion leaders;
expressive and instrumental functions; trust; subgroups; connectedness;
nature of interactions; leadership; and organizational elements, such as
structured networks, teams, prescriptive versus flexible polices, hierarchy, and formal leaders. These concepts are reviewed because they
are the most often cited and used concepts related to change and social
Structure of Ties for Creating Change
Varying types of social structure achieve different change outcomes.
Strong ties are most useful for communication of tacit, nonroutine, and
complex knowledge, such as teaching and learning; weak or less dense
networks are better suited for communication of simple and routine information such as basic information sharing (Nelson, 1989; Tenkasi &
Chesmore, 2003). Strong ties are characterized by three defining characteristics: frequent interaction, an extended history, and intimacy or
Higher Education Change and Social Networks
mutual confiding between the parties (Kraatz, 1998). Most studies of
change find strong ties more conducive to deep or complex changes
(Balkundi & Harrison, 2006; Tenkasi & Chesmore, 2003). Strong ties
are also more likely to promote in-depth, two-way communication and
exchange of detailed information. For example, strong ties among people within the organization tend to foster change more than weak ties
because change is typically nonroutine and usually involves more complex thinking. One drawback of strong ties is that they may foster less
diverse or novel information and ideas (Tenkasi & Chesmore, 2003).
Weak links have the advantage of requiring little time and effort but
often have enormous dividends in terms of information and knowledge
gained and can lead to lack of insularity of ideas (Granvetter, 1973).
Weak ties are characterized by distance and infrequent relationships that
may be casual, less intimate, and nonreciprocal in nature. However, for
diffusion of ideas and public information, weak ties can be extremely
helpful. Also, for obtaining ideas for change, weak links can provide
important external ideas that promote a more robust change idea. Weak
links have also been identified with helping foster innovation in interorganizational collaborations (Tsai, 2002). Weak links usually result
in more ideas being introduced into the network because there may be
more diverse people and because people do not interact all the time, so
they do not have set schema or norms for their interaction and therefore may be more open to new ideas. Thus, there may be times and circumstances where weak links are important for creating specific types
of change or in certain phases of the change process. An example of
the importance of understanding the strength of ties finding in education is Cobum’s and Russell’s (2008) study of math curricular reform
that found strong ties were critical to reshaping teacher pedagogical approaches seen as nonroutine and complex.
Organic Versus Artificially Created Networks
Existing relationships are more influential than relationships created
as part of a change initiative (Cobum & Russell, 2008; Cole & Weinbaum, 2010). Therefore, the more that change agents can build upon
existing relationships for a change process, the more likely they are
to be successful with implementing the change. This is not to suggest
that learning communities or other communities created for innovation
cannot work but that they have proven less successful than an existing community where trust and familiarity already exist (Moolenaar
& Sleegers, 2010). A variety of studies have found that if social networks already exist within organizations or groups, they are much better able to engage in change processes and reforms. In organizations
The Journal of Higher Education
lacking these networks, the reform efforts more likely fail (Atteberry
& Bryk, 2010; Cobum & Russell, 2008; Cole & Weinbaum, 2010). For
example, Cobum, Choi, & Mata (2010) demonstrated that a National
Science Foundation school initiative helped expand teachers’ networks
through structured meetings and regular contacts among a broad network. However, they found that as the project waned, people went back
to their smaller, less diverse networks that they connected with prior
to the initiative. This suggests that unless structures are put in place to
sustain networks, people tend to retreat to their more familiar existing
relationships even though they found the broader, constructed network
important for increasing their knowledge and expertise and met for several years.
Kezar and Lester (2009) found that change efforts were much more
successful when existing campus networks were tapped than when new
ones were created for a change initiative. Campuses that use their centers for teaching and learning, for example, to create networks of faculty with similar interests on an ongoing basis were much more likely
to be able to implement changes around assessment, engaging pedagogies, interdisciplinarity, or responding to diverse students than establishing new networks. The time and effort put into creating the social
network prior to the change initiative allowed for more authentic engagement of the proposed change. When groups spend considerable
time developing trust, relationships and familiarity, and do not get to
the change process itself for an extended period of time, they become
frustrated and often leave the group before learning and other key elements related change could occur.
Diversity of Ties and Change Possibilities
The notion of diversity of ties (ties that span multiple knowledge
sources or cut across structural holes) has been demonstrated to allow
access to information not available within the immediate network (Borgatti & Foster, 2003; Moody & White, 2003). So a diversity of ties can
facilitate change by accessing new information that might help overcome or solve a problem related to a change initiative. The concept of
diverse ties is also called heterophily (Granovetter, 1973). Networks
that are more diverse tend to create complex ideas, yet the diversity
might slow down the change processes because of poor or difficult communication. Furthermore, diversity can lead to the network dissolving
or splitting due to poor interpersonal connections. The propensity for
people to develop ties with individuals that are more similar to themselves (homophily) can help speed up information flow around the
change but might also result in a narrower set of ideas that can have
a negative impact on change. Studies find that people gravitate toward
Higher Education Change and Social Networks
homophily rather than heterophily of ties (Borgatti & Foster, 2003).
Yet, the importance of heterophily is indicated by others. For example,
Daly and Finnigan (2008) found that complex school reform efforts like
No Child Left Behind are advanced by a heterohily of ties as different
views, stakeholders, and interests are important to broad, multifaceted
reforms. Crossley’s (2008) research on student activism also indicates
that heterophily of ties can enhance the ability to engage and overcome
politics on campus.
Central Actors and Opinion Leaders
Social network analysis can help researchers to identify central actors—individuals that have the most ties to other actors in an organization or a network (Cross & Parker, 2004; Freemen, 1979). Because of
their central location, these individuals have more access to information and knowledge, have a better ability to communicate throughout
the system, and are likely to have great influence within the network
(Freeman, 1979; Reagans & McEvily, 2003). SNA allows researchers
to identify people who are more peripheral or are isolated and help create ways to make them more central if necessary within the network in
order to enable change. An example of this finding in education is Daly
and Finnigan’s (2008) study identifying how site administrators (responsible for curriculum reform related to No Child Left Behind) were
typically on the periphery and disconnected from other principals and
core staff, which prevented reforms from taking place.
In addition, the literature points to the importance of opinion leaders who are people that individuals say would influence their choices
and attitudes in the network (Valente, 1995). People often wait to adopt
a change until the opinion leader has adopted it.’^ For example, doctors adopted a new dmg once an opinion leader they were familiar with
had used it (Valente, 1995). Centrality of the network also affects the
possibilities for change. Networks can be said to be centralized when
there is closeness between individuals and there are a lot of people in
between, often described as “centrality betweenness” (Szulanski, 1996).
Essentially these measures describe a dense network, and this is typically more beneficial to change (Valente, 1995). An example of this phenomenon is Tsai’s (2002) study in which units that are more innovative occupy a central network position that provides them access to new
Expressive Versus Instrumental
Networks can serve expressive or instrumental functions (Kilduff &
Krackhardt, 2008; Wasserman & Faust, 1994). Expressive networks are
typically developed as a result of non-work-related relationships and
The Journal of Higher Education
are more social and friendship based. They may develop within organizations, but they are focused on friendship. Expressive networks tend
to be strong and carry a great deal of social support. Instrumental networks are created so that people seek information and resources with a
particular professional purpose in mind (Kilduff & Krackhardt, 2008;
Wasserman & Faust, 1994). Instrumental networks tend to be weak
and based on seeking advice or expertise. Both expressive and instrumental networks can be used to create change. Expressive networks are
more helpful for influencing people’s attitudes or a change in mindset,
whereas instrumental networks are helpful for disseminating information and introducing people to new ideas. These relationships are also
noted as kinship (expressive) versus role (instrumental). Both of these
types of networks can create normative pressures for reform. In education, Cole and Weinbaum (2010) found expressive ties were more effective in impacting teacher attitudes towards reform than instrumental
One of the primary characteristics explored throughout social networks is the concept of relational trust (Kilduff & Krackhardt, 2008;
Moolenaar & Sleegers, 2010; Scott, 1991). Relationships are fundamentally built on the notion of trust, and whether or not relationships
are sustained and move forward is also often based on issues of trust.
Therefore, it is not surprising that network analysis has examined the
notion of trust as it relates to change processes. Relational trust is defined as exchanges among members of the community and the reciprocal understandings about the obligations and expectations inherent in
their roles (Bryk & Schneider, 2002). Change often entails taking risks,
and people are more likely to take risks when they trust the individuals who are asking them to engage in risk-taking behavior. As Cobum
and Russell (2008) note: “Trust enables organizational change by moderating the uncertainty and vulnerability that can accompany change”
(p. 207). A quantitative study by Moolenaar & Sleegers (2010) investigated social networks among 775 educators at 53 schools where an
educational innovation had recently been implemented. The study investigated various characteristics of social networks, including density,
nodes, and reciprocity, and hypothesized that trust within this network
would lead to an innovative climate in schools. They hypothesized that
those without trust would have a less innovative school climate and be
less open to change. They found a strong relationship between trust and
the development of an innovative climate that would be open to change.
They also found that dense networks helped facilitate trust among
Higher Education Change and Social Networks
teachers. Dense ties are often created through subgroups. Studies of research collaboration in higher education suggest the importance of expressive ties for building richer research collaborations (Larivi, Gingras,
& Archambault, 2006).
Researchers have also examined subgroups that emerge within larger
networks—for example, cliques and their role in information flow
and influence within the larger network (Kilduff & Krackhardt, 2008;
Nelson, 1989). In particular, densely connected subgroups have been
identified as important to reform efforts by enabling information flow,
changing attitudes, and creating resources necessary for change. Subgroups are often important to the development of innovative ideas and
problem-solving that move change forward. When the formal organization creates mechanisms for leveraging subgroups and information flow
between them, the formal organization can foster greater change (Finnigan & Daly, 2010). Subgroups (e.g., affinity group by interest, informal lunch groups) are often based on expressive ties and have greater
trust since they are smaller parts of the overall network. Thus, they
can be capitalized on for many different purposes to facilitate change
since they contain the properties most facultative of change (Finnigan
& Daly, 2010). Within education settings, Daly (2010a, 2010b) suggests
that leveraging and connecting subgroups is one of the key principles
for creating change and reform in education. Subgroups are where attitudes can be changed, problems solved, and strong influence exerted.
Studies of college collaborations and researcher collaboration in higher
education suggest that subgroups help enhance and advance the networks and help them to better accomplish their goals (Larivi, Gingras,
& Archambault, 2006; McMillan, 2008).
Another line of research related to subgroups is structural holes, liaisons, and bridging (Ahuja, 2000; Burt, 1992; Wellman & Berkowitz,
1988). All of these concepts relate to linkages within the system. Bridging often happens when an individual is a member of one subgroup and
then becomes a member of another subgroup. A liaison is an individual
who is a member of two subgroups, so he or she can facilitate communication and idea exchange. Structural holes are subgroups that are not
connected in any way and represent potential opportunities for creating greater density if the structural hole can be filled in by liaisons or
bridging. Finnigan and Daly (2010) demonstrated how a lack of bridging individuals (between principals and central office staff) resulted in
a core-periphery structure that prevented communication, information
exchange, and ultimately change in school reform.
The Journal of Higher Education
Connectedness Encourages Change
If people in the network have a great deal of contact (or connectedness) with the innovation, they are also more likely to undergo change
(Cross & Parker, 2004; Honig, 2006; Valente, 1995). Connectedness is a
measure of how much exposure an individual receives to the innovation.
Individuals surrounded by many people who have adopted the change,
even if others throughout their profession have not adopted the change,
will be more likely to alter their behavior (Valente, 1995). Therefore,
change processes that have people interact and connect often to innovators can facilitate change. Cobum and Russell (2008) found that
teachers that had frequent professional development, interaction with a
coach, and interactions with other adopting teachers were much more
likely to change themselves.
Nature of Interaction:
Ongoing, Rich and Meaningful, and Non-Hierarchical
More recent studies have examined the quality and nature of interactions and shown how they are linked to sense-making and schema
change essential for learning.’^ Cobum and Russell’s (2008) study of
teachers enacting mathematics reform demonstrated that sense-making
is necessary for teachers to enact the reform and that networks that do
not allow for deep, ongoing interaction are unlikely to result in change.
In a similar view, Mohrman, Tenkasim, and Mohrman (2003) found that
one-way, hierarchical communication within the networks prevented
grappling with information and sense-making and eventually led to less
leaming within the network, stifling change. In contrast two-way knowledge sharing allows for schema adjustments through mutual interaction
and leads to greater learning. Another study identified the importance of
rich dialogue to learning within the network and identified conflictual
discussion as counteractive to leaming and network relationships (Tenkasi & Chesmore, 2003). Lastly, in higher education, Kezar and Lester
(2011) showed how campus networks that focused strongly on network
relationships through forging interpersonal conflict resolution, creating
common schema, honing communication strategies, and fostering internal leadership were better able to create changes.
Related to the nature ofthe interactions is the composition ofthe network. Maroulis and Gomez (2008) note:
The composition ofthe network refers to the characteristics and resources of
the people in the network. Differences in network composition can lead to
differences in student performance due to direct influence, information, or
assistance from others in one’s network, (p. 1910)
Higher Education Change and Social Networks
Therefore, the qualities of network members need specific examination
because they shape outcomes. Network composition has been the focus
of studies looking at social influence and how particular people might
be more influential (related to the notion of opinion leaders, already
Role of Formal Organizational Leaders
Some research also looks at those in leadership roles and the way that
they are networked or not to support innovations (Mullen & Kochan,
2000; Spillane, Healey, & Chong, 2010). If leaders have greater connection to meaningful external organizations (district offices or national
associations), they are often more likely to be able to foster and support
changes by providing innovative ideas and potentially having more influence and resources to support innovations (Finnigan & Daly, 2010).
Leaders with weak and sparse ties are unable to support large-scale and
complex changes because they lack resources, employment, and information necessary to support such changes. The focus on leadership also
starts to connect networks to the formal organization.
Organizational Impact on Networks
Organizations can also impact the way networks operate, and this is
a recent area of research within education, business, and medicine. We
know very little about how organizational contexts shape individual factors that influence tie formation or relationship development (Cobum,
Choi, & Mata, 2010; Kilduff & Tsai, 2003). Cobum, Choi, and Mata
(2010) have noted that social network researchers tend to focus on the
organic nature of networks and not look at the ways that the organization could influence or support networks. Yet, emerging literature suggests the importance of interaction between organizations and networks.
A set of studies has examined how organizational structures and
culture shape networks. Mohrman, Tenkasi, and Mohrman (2003)
identified how prescriptive and inflexible policies within organizations prevent dense networks that create greater information flow and
knowledge transfer from forming. Furthermore, they identified how
effective change implementation is better achieved by simultaneous,
organization-wide, and local self-design networks than by simply cascading the change through the organization’s hierarchical network linkages. Hierarchical networks rely on one-way information flow but do
not dismpt existing schema, restrict communication and information
flow, prevent leaming, and have people operate in prescriptive rather
than creative ways. Unfortunately, many leaders within organizations
attempt to link networks using the formal hierarchical structure and
The Journal of Higher Education
change processes rather than enabling them. This research supports
cross-functional, team-level networks to overcome the limitations of
hierarchical structures. Organizations that have very strict and formal
hierarchies often make it difficult for networks to form and work together. For example, more hierarchical relationships that are established between district offices and schools because of No Child Left
Behind have created relationships of distrust between district offices
and schools that have broken down networks that formerly operated to
facilitate change (Finnigan & Daly, 2009). Additionally, Coburn and
Russell (2008) have shown that the way districts allocated resources
around coaching impacted the depth of interaction among teachers and
network outcomes. Also, school leaders impacted the ideas and conversations within teacher networks. School policies also impacted teachers’ trust and openness to the school reform. Cobum and Russell (2008)
concluded that their study shows evidence that organizational policies
and structures impact network formation and interactions, which eventually shape outcomes such as change processes.
Other studies conducted that examine networks in relationship to the
organization find that dense ties between levels in an organization (e.g.,
school versus the district), or units within a large organization (e.g., finance versus marketing), or between different organizations all further
important network functions such as communication, gaining novel
knowledge, and innovation (Kogut & Zander, 1996; Tsai, 2002). Researchers continuously find that dense ties within units lead to important
outcomes (Tsai, 2002). Researchers also have identified that interorganizational collaboration allows companies to increase their knowledge,
leading to learning and facilitating innovation, which creates competitive advantages (Kogut & Zander, 1996; Tsai, 2002).
Organizations Intentionally Influencing Networks
Not only do organizations unintentionally shape networks, but organizations can also attempt to influence network creation and direction.”’
Organizations can purposefully influence networks by creating interorganizational linkages or structures to promote interaction (Reagans
& McEvily, 2003; Tilly, 2005; Tsai, 2002). Cross-functional teams are
one way that businesses have helped create networks within otherwise
siloed organizations (Tsai, 2002). Within education, Coburn, Choi, and
Mata’s (2010) study shows how national initiatives and organizations
can create networks that lead to change through stmctured interactions,
establishing instrumental ties and directing teachers to expertise within
a new network. Their study shows that district policy positively influenced social networks by creating structures, requirements, and focus
Higher Education Change and Social Networks
that helped create ties that were beneficial to change. But they emphasize that when support structures are removed by policy entities or organizations, they then interrupt the networks that have been formed, and
these networks may not be sustained.’^ Moolenaar and Sleegers (2010)
also showed that districts can establish organizational structures—such
as teams and learning communities—to help support networks and innovation. Daly (2010a) also points out that reform efforts often target
resources on professional development (individual focus), leadership,
and incentives and typically ignore the social capital of organizations and how network structures and relationships should be devoted
resources to facilitate change. Rather than investing in individual incentives or professional development, research from SNA suggests
the value of investing in the development of social network structures
within organizations for reform.
Implications of Findings on Social Network Analysis for a
Higher Education Research Agenda
It should be noted that the most fundamental shift in a future research
agenda is to alter the focus of change research from the campus (organization) as the only analytic unit to the network (or network in combination with the campus). This would suggest a range of new objects of
study: internal, on-campus networks; networks that connect or bridge
campuses, such as alliances and consortia; and off-campus formal (e.g.,
disciplinary societies) and informal (e.g., online) networks that have
little or no connection to campus boundaries. In the future research
agenda described below, all these new objects of study will be noted
in relationship to specific hypothesis that emerge from overlaying key
concepts from SNA.
There are several key areas (e.g., strong and weak ties) in higher education that seem important to study given the findings from SNA. As I
review each area of future research, I examine it in relationship to existing research about higher education—noting characteristic networks
like disciplinary societies or professional groups—and relevant organizational theory (as we see that organizations can influence networks)
and establish some hypotheses about how these phenomena may need
to be considered or studied, given earlier studies on colleges and universities. Some concepts reviewed in the last section will be discussed
simultaneously (diversity of ties and subgroups) rather than separately
for space purposes and because the concepts meaningfully overlap.
Also, since there is little known about informal types of networks, there
The Journal of Higher Education
are fewer assumptions I can bring to how those operate, but this is an
important area for future research. While these hypotheses will be presented as generalizations,”” I acknowledge the variability of campus
sectors and cultures as identified by authors such as Berquist (2007),
Birnbaum (1988), Kezar (2001), and Tiemey (1988). Concepts like trust
that seem deeply shaped by institutional context will be noted; other
areas may be more amenable to some level of generalization. Recent
research from SNA using a qualitative approach suggests the need to
think about network associations as loose guides that are investigated in
local contexts with specific populations.
Examination of Social Network Structures:
Strong and Weak Ties and Connectedness
Given that strong ties are important for creating change, we know
very little about the existing ties on campuses and whether or not campuses are well-positioned for change. One might suspect that the difficulty encountered by many change initiatives on campuses might mean
that weak ties exist. It may be that strong ties are challenging to create
on college campuses since faculty are often not on campus, work from
home and travel, and often having few regular interactions with other
faculty or staff (Burgan, 2006). Non-tenure-track faculty are working at
multiple campuses and increasing in number, making up two-thirds of
the academy (Kezar & Sam, 2010). Department chairs and other administrators are often isolated and broken up into different siloed schools
and units (Kezar & Lester, 2009). Also, given the many external networks that campus staff are likely to be part of—disciplinary areas, professional groups, and local and regional communities—ties might be
weak and diffuse (Kezar, 2001). Clearly, this differs by campus context, as small or rural campuses may have capacity for strong ties, for
Each of these organizational characteristics structure campuses so
that collaboration is difficult, and this might also mean strong ties are a
challenge. These same characteristics make connectedness a challenge,
as frequent interaction may be uncommon and the opportunity to introduce the innovation to people is rare. Also, research demonstrates that
low-conflict organizations are much more likely to have strong ties than
high-conflict organizations (Nelson, 1989). Campuses are often places
of conflict, and faculty see conflict as part of their socialization. We also
know that trust is low within campuses and is critical to building strong
networks (Tiemey, 2006). Campuses seem a challenging environment
for strong ties and this assumption/hypothesis needs investigation. The
lack of hierarchy on some campuses (or with certain groups—faculty
Higher Education Change and Social Networks
being given some authority on certain issues) might suggest that knowledge sharing occurs through networks and that at least weak ties might
allow strong information flow that can lead to some outcomes associated with change (Tsai, 2002). Also, the nature ofthe initiative or kinds
of relationships that can be developed within certain on-campus groups
may create stronger ties. For example, if I have several colleagues that
are interested in a similar pedagogical technique, we might form a
strong tie; or, if others in my network are in similar circumstances, such
as being faculty of color or early-career faculty, we might form stronger
Off-campus networks based on shared interests might be capable
of creating stronger ties. While distance, lack of regular interaction,
and size may be detrimental to strong ties in the short run, individuals who remain affiliated with off-campus and online networks may develop strong ties of affinity that may be levers for change in the academy (Wenger, 1998). External groups that cross boundaries, such as the
World Bank and Organisation for Economic Co-operation and Development (OECD), even though they have weak ties to campus, may have a
significant impact because of their ability to share and spread globally.
Based on these findings, the following hypotheses are derived:
1. Hypothesis: On-campus networks are characterized by weak and diffuse
ties, and this prevents change from occurring locally.
2. Hypothesis: The lack of trust, conflict, autonomy, and/or disconnection
of faculty and siloed units will create weak links for on-campus networks, making certain outcomes and processes a challenge.
3. Hypothesis: Higher education stakeholders benefit from off-campus networks, such as disciplinary societies and professional groups, that foster
weak and, in some instances, strong ties that might be capitalized on
campus for change.
4. Hypothesis: Off-campus networks (e.g., disciplinary or professional
groups) that create strong ties can be mobilized for cross-sector or enterprise-level changes in the academy.
5. Hypothesis: External groups (e.g., OECD) that have a global network
and reach can impact change even with weak ties.
Longevity of Ties and Organic Versus Artificial Networks
One might assume that because campuses (and as a result, disciplinary groups/professional organizations) have many long-term employees that there are more opportunities for individuals to be connected
through long-term ties. Also long-term employment may facilitate organic networks, and so there may be less of a need to artificially constmct networks to facilitate change. Historically, higher education likely
11 o
The Journal of Higher Education
had longevity of ties and many organic networks. Yet with the trends
toward non-tenure-track faculty, staff layoffs, and reductions, campuses
may be losing long-term, existing networks. Or non-tenure-track faculty
may create networks that are not campus-based but which cross boundaries to support each other. Such examples already exist, such as the
New Faculty Majority and academic unions. Will the trend toward nontenure-track faculty create less longevity of ties in disciplinary societies
as well, or will new organizations/networks be formed such as the New
Faculty Majority? We need studies about campus networks in terms of
longevity, changes in staffing pattems, the impact on on- and off-campus networks, and the degree to which this might differ for groups on
campus—faculty, student affairs, business affairs, or by different disciplines. Perhaps because campuses and disciplines vary so much, it will
be important for studies to be conducted at a local level. Also it will be
important to look in new places for emerging networks that will be different from traditional ones represented through disciplinary societies
and professional organizations.
Another implication and line of research is to examine constmcted,
not organic, networks. What are the best ways organizations can enhance or build networks on an ongoing basis that will support changes
in the higher education sector and on college campuses? Studies have
found that a critical competency for leaders is networking across units
and divisions in order to develop relationships so that they can connect
people and create stronger ties (Tenkasi & Chesmore, 2003). Given
change is an ongoing phenomenon, leaders may not want to wait until
they propose a major change initiative to think about network development. Instead, effective leaders are likely those that see relationship and
network development as connected to creating stronger ties for change.
There is limited leadership research that examines the way campus leaders create stronger ties between people on campus. As noted earlier,
political theories examine how leaders build alliances and coalitions,
and scientific management theories describe leaders’ efforts to include
people on teams for planning processes that may end up creating relationships (Baldridge et al., 1977; Clark, 1983; Kezar, 2001). Most college campuses today would say that they are in the flux of a variety of
change processes and likely could benefit from constmcted networks,
yet we do not know much about how to create them.
Networks are also being formed locally, regionally, and nationally
to support change. There are hundreds of online communities that
are connecting people on meaningful issues and changes they wish to
make in higher education (see, for example, the New Faculty Majority,
http://www.newfacultymajority.info, and the National Association for
Higher Education Change and Social Networks
Student Personal Administrators Knowledge Communities, http://www.
naspa.org). Next, there are more structured and sometimes more formal networks being formed regionally and within communities to create changes in higher education (see the NERCHE Think Tanks, http://
www.nerche.org). An example of the structured networks is Project Kaleidoscope (http://www.pkal.org/), which brings together faculty interested in STEM undergraduate reform. It is more structured in that it offers formal workshops, conferences, and times for face-to-face interaction. The process of interaction is more prescribed and targeted. There
are also external networks that formally interact with campuses—for
example, alliances and consortia—that have been the subject of little if
any study (for example, ACL, http://www.national-acl.com/, and Connect, http://www.connectsemass.org/). Because people volunteer for
these various communities, even though they are constructed, they will
operate more like organic networks than constructed networks. Most
constructed networks studied have been within formal organizations,
such as cross-functional teams or learning communities. Based on
these findings, the following hypotheses are derived:
1. Hypothesis: Because of their historic trend toward long-term employment, there will be some networks that have longevity of ties that can
be levers for change (but there is need to test whether and how they are
used). Longevity of ties is becoming increasingly uncommon on college
campuses, and this will be a less likely lever for change in the future.
2. Hypothesis: Even though some organic networks exist, because they are
likely to be weak (even though they are long-term), higher education
institutions may need to artificially construct or support networking to
facilitate change.
3. Hypothesis: Leadership can foster networks by creating structures to
support network formation that can enable change.
4. Hypothesis: Online and informal constructed communities will operate like organic communities. As communities become more formalized and attached to organizations (such as CIRTL, http://www.cirtl.
net/), they will lose the advantages (trust, expressive ties) of organic
Diversity of Ties and Subgroups
Organizational theory also suggests that given the diversity of stakeholders and groups in higher education by discipline or unit and division (alumni affairs vs. business), higher education is likely to have
significant heterophily and have many diverse ideas flowing that can
lead to change. However, the diversity may lead to difficulty in trust
formation. Yet the many subgroups and committees on campus might
The Journal of Higher Education
help overcome the downsides of heterophily as people have a chance
to work in small groups on an ongoing basis. It may be that committees as artificial network structures are not developing the types of relationships important to change. We need to study and test the roles of
committees and other meaningful subgroups (that operate like teams
within organizations) and their ability to create new ties, build trust,
allow information flow, and further other network development aimed
at change.
Off-campus networks are likely to experience much more homophily.
For example, disciplinary societies bring together people with similar
interests, as do professional organizations for student or business officers. These networks often have smaller affinity or subgroups that can
create expressive ties and trust. Formal networks off-campus will be a
place where significant change may be leveraged. In addition to these
more formal organizations, less formal organizations can be online but
also take a physical form through regional and national movements for
diversity, sustainability, and innovative teaching and learning that provide points of similar interest within and across campuses. There may
be more levers for change within these non-campus-based networks that
can draw on homophily for change.
Global international networks and groups like the International Monetary Fund, World Bank, and World Trade Organization will create
a sense of homophily by developing a sense of common interest and
agenda across campuses and groups worldwide (Rhoades & Slaughter,
2004). Furthermore, the worldwide neoliberal agenda advanced through
groups like the John Olin Foundation will create networks that are homopholous and where information readily flows and potentially creates changes worldwide at a more rapid pace. Organizations such as the
World Bank create policies and practices such as GATT (General Agreement on Tariffs and Trade) that have impacts on educational organizations and support the flow and interchange between formerly isolated
campuses. Yet there will be countervailing forces from non-governmental agencies (NGOs) that often have more local connections and networks that attempt to foster more heterophily. Based on these findings,
the following hypotheses are derived:
1. Hypothesis: The diversity of ties will allow greater flow of ne-w ideas
into higher education, particularly on-campus where multiple and diverse groups are brought together.
2. Hypothesis: The presence of committees, task forces, and subgroups onor off- campus can provide a mechanism to overcome the downsides of
heterophily if designed to do so.
Higher Education Change and Social Networks
3. Hypothesis: Deep changes may be best addressed through off-campus
networks (both formal and informal) that bring together like-minded individuals and where trust and expressive ties can be developed.
4. Hypothesis: Global networks will create greater homophily among campuses in vastly different regions leading to changes that cross national
boundaries and greater flow of ideas. Yet NGOs will provide a countervailing heterophily.
Central Actors and Opinion Leaders
Organizational theory suggests that if higher education institutions
are loosely coupled and place less emphasis on formal authority, central
actors may be less important than opinion leaders for campus networks
(Kezar, 2001). Furthermore, campuses are expert organizations where
people do not necessarily react and change based on pure influence but
evaluate the veracity of the individual more. Opinion leaders may be
likely to have a greater impact. Yet campuses are becoming increasingly
hierarchical in structure due to neoliberalism,” and this might make
central actors more important in the future. In off-campus networks,
opinion leaders are also more likely to have an impact as leadership
within disciplinary societies or even professional groups rotates very
regularly. Central actors may be less common within formal hierarchy
but may be influential in informal networks (on- and off-campus) created around interests like LGBT support or environmentalism. Based on
these findings, the following hypotheses are derived:
1. Hypothesis: Opinion leaders will be more influential than central actors
in many higher education settings, including on-campus networks and
off-campus formal networks like disciplinary societies.
2. Hypothesis: In more bureaucratic and hierarchical campuses (some
community colleges or campuses affected by neoliberalism), central actors will play a more critical role in change.
3. Hypothesis: For informal networks of interest, central actors likely play
a more key role in change than opinion leaders.
Expressive Versus Instrumental Functions
As noted earlier, organizational theory demonstrates that higher education employees tend to be long-term—faculty may be on a campus
their entire career, staff often are long-term, and even non-tenure trackfaculty tend to stay employed at institutions fairly long-term (averaging 7 years) (Kezar & Sam, 2010 ). This long-term employment (historically at least) would lend itself to expressive ties that help facilitate
change. Yet the lack of interaction of many groups (particularly across
groups) may mean that over time they develop only instrumental ties
The Journal of Higher Education
based on their role rather than friendship ties. This likely differs by
campus context as well. Small liberal arts colleges are likely to have
more expressive ties develop because ofthe community culture and orientation, whereas large urban research universities may have commuting staff and faculty that interact less and have a more bureaucratic feel.
Within formal off-campus networks (disciplinary and professional), expressive ties are likely more significant than instrumental ties, as people
do not usually interact in relationship to an organizational role within
these type of networks but instead interact based on common interests
that can spark a friendship. As noted in other sections, higher education professionals are increasingly connecting informally, based on interests through local communities or online. These informal networks
are likely to be expressive, as they often will not share common roles
and affiliation will be based on interests. Based on these findings, the
following hypotheses are derived:
1. Hypothesis: Expressive/instrumental networks are more likely to be leveraged within certain campus contexts (small and communal vs. large
and bureaucratic) based on their culture and institutional type.
2. Hypothesis: Expressive ties are more likely to be fostered in both formal and informal off-campus networks.
In today’s context, where campuses are under a great deal of threat
and pressure based on declining funds, trust between groups on many
campuses is likely low. Research from organizational theory describe
the strains between faculty and administrators based on administrators
centralizing decision-making, violating shared governance, and hiring non-tenure-track faculty that erode tenure (Burgan, 2006; Rhoades,
1996). The heterophily of stakeholders previously mentioned generally
creates a context where tmst may be low as people feel a lack of similarity to others in the network. Yet,we know that tmst will also vary
by campus context based on communication, transparency, and history
(Tiemey, 2006). Off-campus networks are more likely to be able to develop tmst than on-campus networks. There are fewer decisions made
within off-campus networks that would likely alienate members. Based
on these findings, the following hypotheses are derived:
1. Hypothesis: Campuses that exhibit less trust will likely hinder network
formation—particularly around instrumental ties.
2. Hypothesis: Trust is likely to be high in off-campus networks and therefore may be a stronger lever for certain types of changes.
Higher Education Change and Social Networks
3. Hypothesis: Campus leaders that create trust will be more likely to enhance networks and foster greater change.
Interactions and Sense-Making
The findings about how the nature of the interactions between people
in the network deeply influencing the outcomes has important implications for college campuses. Academic debate (embraced by faculty) is
often counter to dialogue and openness necessary for positive network
interactions. It may be that colleges and universities face a particular
challenge around networks because dialogue necessary for learning
and change is difficult to create. Organizational theory highlights how
higher education invests little in fostering interpersonal interactions;
human resource management has a background role (particularly with
faculty), and minimal training occurs related to conflict management,
managing groups or teams, and communication (Kezar, 2001). Leadership and management training occurs within certain campuses, but it
is not a routine feature of higher education. This lack of development
around interpersonal interactions is likely to impact network development and success. Based on these findings, the following hypotheses are
1. Hypothesis: Higher education networks will be characterized by many
barriers and obstacles largely due to poorly developed group interactions that result from the lack of adequate training for interpersonal
skills to work in groups.
2. Hypothesis: Higher education settings that offering training in interpersonal interactions and leadership and that foster collégial dialogue are
more likely to have networks that can be capitalized on for change.
Implications for Policy and Practice
It is important to also examine the implications of this research
agenda for higher education policy and practice. In the past, policy
makers and practitioners have not looked to broader networks for creating changes in the higher education sector. As noted in the introduction,
a few funders such as the Lumina Foundation and the National Science
Foundation, based on emerging research described in this article, are
beginning to examine the potential of networks for creating broader and
scaled-up changes. However, there is very limited research at this point
to guide policy and action. Because most of the research that exists
has been developed outside of higher education, we do not know if the
same network design principles will be important or if they will unfold
The Journal of Higher Education
uniquely within higher education context, which is why the proposed
research is so important to guide practice.
In the research agenda described above, I suggest some of the ways
that policy makers and practitioners should be aware of unique aspects
of higher education that might shape how they create, use, fund, and
sustain networks. For example, the diversity of ties will allow for complex ideas to emerge through networks, but these diversity ties will also
make change processes slower, so they may need to decide if innovation
or expediency is more important to their effort. Also, campuses tend to
have low levels of tmst, which make network building more difficult.
Therefore, policy makers and practitioners might leverage off campus
networks where more tmst exists to create change. Another example
is that policy makers and practitioners might want to utilize opinion
leaders to create changes, as they will have a stronger impact in many
higher education settings.
Policy makers and practitioners need to look quite broadly at the
plethora of different networks (professional organizations, disciplinary,
consortia, online, communities of practice, etc.) that exist and identify
those that best serve their purposes. If policy makers are aimed at creating broad level sector changes, they may want to mobilize off-campus
networks that have greater reach across the sector rather than on-campus networks, for example. This is just a set of examples of the implications of the research agenda described in the article, and as we have
more research to guide policy and practice, the field will have even better guidance. For now, scholars can combine insights from organizational and social network theory to develop some hypotheses (and use
the ones above in the research agenda) to create studies that can provide
evidence to inform practice and policy.
Ultimately, scholars have focused change research more on the formal, internal organizational structures and ignored networks. We need
to balance an organizational perspective with more attention to networks and social relationships. Yet we need to also combine the organizational perspective, as studies of networks related to institutions demonstrate the importance of these settings in shaping the network. Both
approaches are needed to understand how change occurs within postsecondary education. Network analysis has usually failed to take into
account the formal organization and how it might be impacting change
or can help strengthen networks. Higher education takes place in an institutional context, and to ignore this context is also problematic.
Higher Education Change and Social Networks
Similarly, researchers studying formal organizations have typically
ignored social networks and their informal leaders that can create social capital. These researchers’ findings result in organizational investment in individual development rather than investments in important social networks. There are many networks that go beyond organizational
boundaries and networks that are largely outside of or only impacted
in a limited way by organizations (disciplinary networks and online
communities are examples). Thus scholars also need to be open to new
concepts that expand our notion of relationships, such as online communities. Research on learning communities and communities of practice (Wenger, 1998) is a way that the two perspectives (organizational
and network) are coming together in more recent years, yet there are so
many other opportunities to study the links between networks and organizations—many offered within this article.
‘ Whether people actually are more connected and networked remains a debated
question. See Putnam (2000) for a critique of whether people are more connected.
^ A few examples exist of social network analysis applied to change in higher education—for an example, see Pusser, Slaughter, and Thomas (2006).
‘ The focus of this article is on ways social network analysis can be used to create changes in postsecondary institutions—but this cuts across many different areas, including faculty development, pedagogical and curricular innovations, leadership, and
decision-making. So while I use the label change and reform, the suggestions apply to a
broader set of phenomenon within higher education than this label may suggest.
” There is a danger that organizational theory will dominate and overwhelm the assumptions of networks, which are about seeing beyond organizational boundaries, particularly as organizational theory has been a major lens in higher education. These two
perspectives need to be examined equally and balanced.
* Attention to social network analysis began in the 1930s, but a significant number of
studies were not conducted until the mid-1990s. Thus research has just started to expand
in the last decade and a half (Daly, 2010b).
‘ Given social network analysis examines the network level, changes are focused on
the individual and group impact, and these findings may or may not be applicable for organizational changes/impact. So in the article, when referring to the change impact, research goes back and forth between these varying levels—individual and organizational.
‘ However, it should be noted that most studies do not control for a variety of other
factors that could be operating other than the social networks. The prevalence of the
studies with similar conclusions is suggestive of a relationship.
* It should be noted that poorly formed (e.g., no trust, few ties, many structural holes)
networks may not serve these purposes, so the relationship is not always beneficial or
advantageous towards a given change initiative. Certain features that will be described
below can help harness these benefits.
‘ A related area of literature to social network analysis is social movement literature, which also emphasizes networks and is also outside formal organizational settings
(Hartley, 2009b; Meyerson, 2003).
The Journal of Higher Education
‘” It is important to note that much of the literature on social network analysis uses
a more positivistic and quantitative approach stemming from matrix algebra and graph
theory to formalize principles from social psychological concepts such as groups (Daly,
2010a, 2010b). Social network analysis is typically associated with this quantitative
work using specialized software such as UCINET. Quantitative studies were helpful for
understanding the outcomes of networks that emerged as some of the first central questions about their role in change. Yet the principles of social network analysis can also
be used in a more constructivist and qualitative approach, but this has been done less
often (communities of practice, for example, represent an important way for this type
of qualitative analysis to be conducted). Therefore, most of the studies described focus
on quantitative social network analysis, as this makes up the majority of the existing
literature. Also, because of the methodology used, much of the literature describes networks but is less focused on how networks are formed, how they can be sustained, and
other questions more easily answered through qualitative methods. More recent literature on communities of practice has addressed these issues and can be used as a model
for thinking about new qualitative approaches to social network analysis (Lave, 1988;
Wenger, 1998; Wenger, McDermott, & Snyder, 2002). Ideally, more mixed methods/
paradigm studies would be conducted that help examine network outcomes and design
as well as formation and sustaining.
” There are some more cutting-edge items of scholarship (see, for example. Moody,
McFarland, & Bender-deMoll, 2005).
‘^ Within social movement literature, this is often described as a charismatic leader
(Hartley, 2009b). There are many similar concepts and interconnections between the
social movement and social analysis literatures.
‘^ Recent qualitative social network analysis studies have been looking more at what
flows (e.g., character of information) between nodes or nature of interactions (e.g., degree of trust). The quantitative approach to early social network analysis did not allow
for understanding the fluidity of the networks and the interactions within them. This is
an emerging and important new line of research.
”’ However, it should be noted that very little research has been conducted to help us
understand the way networks can be intentionally created or formed to help facilitate
change processes.
” One might interpret this finding to reinforce the importance of organic networks
that do not need organizational supports for achieving outcomes.
” Another problem with the generalized hypothesis of social network analysis is outlined by Mohrman, Tenkasi, and Mohrman (2003) from studies using SNA across a host
of organizational types/disciplines. They show that structural properties of networks
reveal only the potential for action and, unfortunately, have been used too much as a
structural determinant rather than examining possibilities that may exist. Kilduff and
Tsai (2003) offer the following example of the problem with dependence on structural
analysis and overly deterministic generalizations. In trying to understand why physicians adopt new technology, social network analysis suggests that competition between
rivals within a network typically explains why they adopt the new technology. However,
through more careful discussions with the doctors, they found out that friendship, communication, and empathy were the reasons they were using it and that competition was
less likely to explain the adoption. More attention to individual agency and a variation
of reasons for choices is suggested by examples like this, where prior theory is not just
placed on individual actors. Given the multitude of actors within higher education and
the differing amounts of power and authority, agency, and context, it is important to
keep in mind and understand this. Thus, future research across all fields should be aware
Higher Education Change and Social Networks
of these problems of overgeneralization—and, given the higher education context, this
is an issue to pay particular attention to. Network associations should be studied in multiple contexts and among different actors to identify whether they are sound.
” Neoliberalism refers to an overarching philosophy of privatization of public institutions and the adoption of private sector and corporate approaches to management.
Ahuja, G. (2000). Collaboration networks: Structural holes, and innovation: A longitudinal
study. Administrative Science Quarterly, 45(3), 425^55.
Atteberry, A., & Bryk, A. (2010). Centrality, connection, and commitment: The role of
social networks in a school based literacy initiative. In A. Daly (Ed.), Social network
theory (pp. 51-76). Cambridge, MA: Harvard Education Press.
Balconi, M., & Laboranti, A. (2006). University-industry interactions in applied research:
The case of microelectronics. Research Policy, i5(10), 1616-1630.
Baldridge, J. V, Curtis, D. V., Ecker, G. R, & Riley, G. L. (1977). Alternative models of
governance in higher education. In G. L. Riley & J. V. Baldridge (Eds.), Governing
academic organizations. Berkley: McCutchan.
Balkundi, P., & Harrison, D. (2006). Ties, leaders, and time in teams: Strong inference
about network structures effects on team viability and performance. The Academy of
Management Journal, 49(), 49-68.
Berquist, W. (2007). The six cultures ofthe academy. San Francisco: Jossey Bass.
Biddix, J. R, & Park, H. W. (2008). Online networks of student protest: The case ofthe
living wage campaign. New Media & Society, 10(6), 871-891.
Birnbaum, R. (1988). How colleges work. San Francisco: Jossey Bass.
Borgatti, S., & Cross, R. (2003). A relationship view of information seeking and leaming
in social networks. Management Science, 49(4), 432^45.
Borgatti, S., & Foster., P. (2003). The new paradigm of organizational research: A review
and typology. Journal of Management, 29(6), 991-1013.
Bryk, A., & Schneider, B. (2002) Trust in schools: A core resource for school improvement. New York: Russell Sage Foundation.
Burgan, M. (2006). What happened to faculty governance. Baltimore: Johns Hopkins University Press.
Burt, R. (1992). Structural holes: The structure of competition. Cambridge, MA: Harvard
University Press.
Burt, R. (2000). The network structure of social capital. In R. Sutton & B. Staw (Eds.),
Research in organizational behavior (pp. 1-83), Greenwich, CT: JAI Press.
Clark, B. R. (1983). The contradictions of change in academic systems. Higher Education,
Cobum, C , Choi, L., & Matta, W. (2010). “I would go to her because her mind is math”:
Network formation in the context of a district based mathematics reform. In A. Daly
(Ed.), Social network theory (pp. 33-51). Cambridge, MA: Harvard Education Press.
Cobum, C , & Russell, J. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30(3), 203-235.
The Journal of Higher Education
Cole, R., & Weinbaum, E. (2010). Change in attitude: Peer inñuence in high school reform. In A. Daly (Ed.), Social network theory (pp. 77-96). Cambridge, MA: Harvard
Education Press.
Cross, R., & Parker, A. (2004). The hidden power of social networks: Understanding how
work really gets done in organization. Cambridge, MA: Harvard Business School Press.
Crossley, N. (2008). Social networks and student activism: On the politicising effect of
campus connections. The Sociological Review, 56(1), 18-38.
Daly, A. (2010a). Mapping the terrain: Social network theory and educational change. In
A. Daly (Ed.), Social network theory (pp. 1-17). Cambridge, MA: Harvard Education
Daly, A. (2010b). Surveying the terrain ahead: Social network theory and educational
change. In A. Daly (Ed.), Social network theory (pp. 259-274). Cambridge, MA: Harvard Education Press.
Daly, A. (Ed.). (2010c). Social network theory. Cambridge, MA: Harvard Education Press.
Daly, A., & Finnigan, K. (2008). A bridge between worlds: Understanding network structure to understand change strategy. Journal of Educational Change, 11(2), 111-138.
Fairweather, J. (2009). Linking evidence and promising practices in science, technology,
engineering and mathematics (STEM) undergraduate education. Paper for the National
academies National Research Council board of Science Education.
Finnigan K., & Daly, A. (2010). Leaming at a system level: Ties between principals of
low-performing schools and central ofñce leaders. In A. Daly (Ed.), Social network
theory (pp. 179-196). Cambridge, MA: Harvard Educatioti Press.
Freemen, C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.
Gioia, D. A., Schultz, M., & Corley, K. G. (2000). Organizational identity, image, and
adaptive instability. The Academy of Management Journal, 25(1), 63-81.
Gioia, D. A., & Thomas, J. B. (1996). Identity, image, and issue interpretation: Sensemaking during strategic change in academia. Administrative Science Quarterly, 41,
Granvetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78,
Hartley, M. (2009a) Reclaiming the democratic purpose of american higher education:
Tracing the trajectory of the civic engagement movement. Learning and Teaching, 2(3),
Hartley, M. (2009b). Leading grassroots change in the academy: Strategic and ideological
adaptation in the civic engagement movement. Journal of Change Management, 9(3),
Heck, R., Price, C, & Thomas, S. (2004). Tracks as emergent structures: A network analysis of student differentiation in a high school. American Journal of Education, 110(4),
Honig, M. (2006). Street level bureaucracy revisited: Frontline district central office administrators as boundary spanners in education policy implementation. Educational
Evaluation and Policy Analysis, 28(4), 357-383.
Keller, G. (1983). Shaping an academic strategy. In G. Keller (Ed.), Academic strategy:
The management revolution in American higher education. Baltimore: Johns Hopkins
University Press.
Higher Education Change and Social Networks
Kezar, A. (2001). Understanding and facilitating organizational change in the 27″ Century: Recent research and conceptualizations. Washington, DC: ASHE-ERIC Higher
Education Reports.
Kezar, A. (2005). Redesigning for collaboration with higher education institutions: An
exploration into the developmental process. Research in Higher Education, 46(1), 831860.
Kezar, A., & Lester, J. (2009). Organizing for collaboration in higher education: A guide
for campus leaders. San Francisco: Jossey Bass.
Kezar, A., & Lester, J. (2011 ). Enhancing campus capacityfor leadership: An examination
of grassroots leaders. Palo Alto, CA: Stanford Press.
Kezar, A., & Sam. C. (2010). Understanding the new majority : Contingentfaculty in higher education (Vol. I). ASHE Higher Education Report Series. San Francisco: Jossey
Kilduff, M., & Krackhardt, D. (2008). Interpersonal networks in organizations: Cognition, personality, dynamics, and culture. Cambridge, UK: Cambridge University Press.
Kilduff, M., & Tsai, W. (2003). Social networks and organizations. Thousand Oaks, CA:
Kogut, B., & Zander, U. (1996). What firms do? Coordination, identity and learning. Organization Science, 7(5), 502-527.
Kraatz, M. (1998). Learning by association? Interorganizational networks and adaptation
to environmental change. Academy of Management Journal, 41(6), 621-643.
Ladvire, V, Gingras, Y., & Archambault, É. (2006). Canadian collaboration networks: A
comparative analysis of the natural sciences, social sciences and the humanities. Scientometrics, 6S(3), 519-533.
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life.
New York: Cambridge University Press.
Linton, F. (2006). The development of social network analysis. Vancouver, BC: Empirical
Lumina Foundation, (2010). Lessons: When networks build a platform students step up.
Indianapolis, IN: Lumina Foundation Press.
Marion, L. S., Garfield, E., Hargens, L. L., Lievrouw, L. A., White, H. D., & Wilson, C.
S. (2003). Social network analysis and citation network analysis: Complementary approaches to the study of scientific communication. Sponsored by SIG MET. Proceedings of the American Society for Information Science and Technology, 40(1), 486-487.
Maroulis, S., & Gomez, L. M. (2008). Does “connectedness” matter? Evidence from asocial network analysis within a small school reform. Teachers College Record, 110(9),
Mayer, A., & Puller, S. L. (2008). The old boy (and girl) network: Social network formation on university campuses. Journal of Public Economics, 92(1), 329-347.
McGrath, C , & Birackhardt, D. (2003). Network conditions for organizational change.
Journal ofApplied Behavioral Science, 39(3), 304-336.
McMillan, G. S. (2008). Mapping the invisible colleges of R&D management. R&D Management, 5S(1), 69-83.
Metcalfe, A. S. (2006). The corporate partners of higher education associations: A social
network analysis. Industry and Innovation, 13(4), 459^79.
The Journal of Higher Education
Meyerson, D. (2003). Tempered radicals: How everyday leaders inspire change at work.
Boston: Harvard Business School Press.
Mohrman, S., Tenkasi, R., & Micomonaco, J. P., & Austin, A. E. (November, 2010). Examining inter-institutional learning communities: The Center for the Integration of
Research, Teaching, and Learning as a collaboration for institutional learning. Paper
presented at the Annual Meeting of the Association for the Study of Higher Education
(ASHE). Indianapolis, IN.
Mohrman, A. (2003). The role of networks in fundamental organizational change. The
Journal of Applied Behavioral Science, 39(3), 301-323.
Moody, J., McFarland, D., & Bender-deMoll, S. (2005). Dynamic network visualization.
American Journal of Sociology, 110(4), 1206-1241.
Moody, J., & White, D. R. (2003). Structural cohesion and embeddedness: A hierarchical
concept of social groups. American Sociological Review, 68(), 103-127.
Moolenaar, N., & Sleegers, P. (2010). Social networks, trust, and innovation: The role of
relationships in supporting an innovative climate in Dutch schools. In A. Daly (Ed.),
Social network theory (pp. 97-114). Cambridge, MA: Harvard Education Press.
Mullen, C, & Kochan, F. (2000). Creating a collaborative leadership network: An organic
view of change. International Journal of Leadership in Education, 3(3), 183-200.
Nelson, R. (1989). The strength of strong ties: Social networks and intergroup conflict in
organizations. The Academy of Management Journal, 32(2), 377-401.
Nohria, N., & Eccles, R. (1992). Networks in organizations (2″” ed.). Boston: Harvard
Business Press.
Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy. Journal of
Information Science, 28, 441^51.
Pema, L. W., Rowan-Kenyon, H. T., Thomas, S. L., Bell, A., Anderson, R., & Li, C.
(2008). The role of college counseling in shaping college opportunity: Variations across
high schools. The Review of Higher Education, 31(2), 131-159.
Pusser, B., Slaughter, S., & Thomas, S. (2006). Playing the board game: An empirical
analysis of university trustee and corporate board interlocks. The Journal of Higher
Education, 77(5), 747-775
Putnam, R. (2000). Bowling alone: The collapse and revival of american community. New
York: Simon & Schuster.
Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240-267.
Rhoades, G. (1996). Reorganizing the faculty workforce for flexibility: Part-time professional labor. The Journal of Higher Education, 67(6), 626-659.
Rhoades, G., & Slaughter, S. (2004). Academic capitalism. Baltimore: Johns Hopkins University Press.
Rogers, E. (2003). Diffusion of innovations. New York: Simon and Schuster.
Rogers, J. D., Bozeman, B., & Chompalov, I. (2001). Obstacles and opportunities in the
application of network analysis to the evaluation of R&D. Research Evaluation, 10(3),
Scott, J. (1991). Social network analysis. London: Sage.
Higher Education Change and Social Networks
Sharma, M., & Urs, S. R. (2008, October). Network dynamics of scholarship: A social
network analysis of digital library community. In Proceedings of the 2nd PhD Workshop
on Information and Knowledge Management (pp. 101-104). New York: ACM.
Skahill, M. (2003). The role of social support network in college persistence among freshman students. Journal of College Student Retention, 4(10), 39-52.
Solem, M. N. (2000). Differential adoption of internet-based teaching practices in college
geography. Journal of Geography, 99, 219-227.
Spillane, J., Healey, K., & Chong, M. (2010). Leading and managing instruction: Fonrial
and informal aspects of the elementary school organization. In A. Daly (Ed.), Social
network theory (pp. 129-158). Cambridge, MA: Harvard Education Press.
Szulanski, G. (1996). Internal stickiness: Impediments to the transfer of best practices
within the firm. Strategic Management Journal, 17, 21-43.
Tenkasi, R., & Chesmore, M. (2003). Social networks and planned change: The impact of
strong ties on effective change implementation and use. Journal of Applied Behavioral
Science, 39(3),2U-3QQ.
Teranishi, R., & Briscoe, K. (2006). Social capital and the racial stratification of college
opportunity. In J. C. Smart (Ed.), Higher education: Handbook of theory and research
(Vol. 21, pp. 591-614). Dordrecht, The Netherlands: Springer.
Thomas, S. (2000). Ties that bind: A social network approach to understanding student
integration and persistence. The Journal of Higher Education, 71(5), 591-615.
Tiemey, W. (1988). Organizational culture in higher education. The Journal of Higher
Education, 59(1), 2-2.
Tiemey, W. (2006). Trust and the public good. New York: Peter Lang.
Tiemey, W. G., Corwin, Z., & Colyar, J. (2005). Preparing for college: Nine elements of
effective outreach. Albany, NY: State University of New York Press.
Tilly, C. (2005). Identities, boundaries, and social ties. Boulder, CO: Paradigm Press.
Traud, A. L., Kelsic, E. D., Mucha, P. J., & Porter, M. A. (2011). Comparing community
structure to characteristics in online collegiate social networks. SIAM Review, 53(3),
Tsai, W. (2002). Social structure of competition within a multiunit organization: Coordination, competition, and intra-organizational knowledge sharing. Organization Science,
13(2), 179-190.
Valente, T. (1995). Network models of the diffusion of innovations. Cresskill, NJ: Hampton
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
Cambridge, UK: Cambridge University Press.
Wellman, B., & Berkowitz, S. (Eds.). (1988). Social structures: A network approach.
Cambridge, UK: Cambridge University Press.
Wenger, E. (1998). Communities of practice: Leaming as a social system. Systems Thinker,
Wenger, E., McDermott, R., & Snyder, W. (2002) Cultivat…
Purchase answer to see full

error: Content is protected !!