social change

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 educa- tion 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 interac-

tions. 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 increas- ingly 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: [email protected].

The Journal of Higher Education, Vol. 85, No. 1 (January/February) Copyright O 2014 by The Ohio State University

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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 Founda- tion, 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 na- tional networks to help improve access to higher education. The Lu- mina 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 Na- tional Science Foundation has invested in networks to connect STEM faculty to solve complex problems, improve the nature of undergradu- ate teaching and learning, and help transform K-12 teachers' knowl- edge 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 lit- erature 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 con- sciousness and several visible, national higher education projects utilize networks, there is little research in higher education on the way net- works 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 cul- tural capital through networks of peers, school personnel, and their fam- ily (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

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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, Har- gens, 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 cor- porate partners of higher education associations (Metcalfe, 2006). The few studies of student activism are the most directly related to this ar- ticle 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 ap- plication to change and reform—a critical and important challenge fac- ing most postsecondary institutions.^ The main objective of this article is to conduct a multidisciplinary review of the literature on social net- work 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 ap- plication 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 multidis- ciplinary research base on SNA as it relates to change, but largely ap- plied outside institutional/organizational settings. I draw on examples from institutional settings where possible, as this provides a more di- rect 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 per- spective—describing the ways that SNA and organizational theories can and should be combined. One of the main messages in this review is

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that networks, while an important new area for study in their own right, will likely be more fruitful for understanding change in higher educa- tion 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, organiza- tional, and more local-unit level) within which they exist is ignored.'' I offer this insight from having reviewed literature across various dis- ciplines 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 im- pact 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 col- laboratives, 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 organiza- tional 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 colleagues.

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Before describing key insights related to change and social net- works, 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 in- formal networks are less organized and have little in the way of struc- ture 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 struc- tures and relationships. Therefore, webs of relationships are often the chief determinant of how well and quickly change efforts take hold, dif- fuse, 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 trans- fer, 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

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capital that facilitates the change process (Burt, 2000). While different definitions of social capital exist, underlying most of the theoretical dis- cussions is the assumption that social capital is the resources embed- ded 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 in- volves 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 com- monly identified areas that link social networks and change and demon- strate 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 ped- agogical 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 fac- ulty 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 ac- countability 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 in- novation, mostly outside of organizational contexts.' It may be that because his work was located beyond the formal organizational set- ting, 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-

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tion. For example, management science models of change focused on strategic planning refer to broad buy-in and participation in decision- making around reforms, suggesting that plans without the involvement and ownership of employees will not be successful (Keller, 1983). Po- litical 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 net- work, some of the political models of change address the impact of net- works (through examination of coalitions) and the way people influence change processes (Baldridge et a l , 1977; Clark, 1983; Kezar, 2001). Po- litical 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 struc- tured groups than coalitions.

Social cognition theories of change also describe the power of so- cial 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 cogni- tion approaches to change and rely heavily on networks of people com- ing 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 communi- ties and communities of practice have been harnessed to create changes such as pedagogical and curricular reforms of higher education (Mico- monaco & 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 so- cial cognition approaches to change. Yet the emphasis in SNA is slightly different (more informal groups, organic, and moving beyond organiza- tional boundaries). Also, it suggests that social relationships are more

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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 sociol- ogy, 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 understand- ing 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 re- viewed: 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, hier- archy, and formal leaders. These concepts are reviewed because they are the most often cited and used concepts related to change and social networks."

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 in- formation such as basic information sharing (Nelson, 1989; Tenkasi & Chesmore, 2003). Strong ties are characterized by three defining char- acteristics: frequent interaction, an extended history, and intimacy or

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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 peo- ple within the organization tend to foster change more than weak ties because change is typically nonroutine and usually involves more com- plex 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 inter- organizational 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 there- fore may be more open to new ideas. Thus, there may be times and cir- cumstances 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 educa- tion is Cobum's and Russell's (2008) study of math curricular reform that found strong ties were critical to reshaping teacher pedagogical ap- proaches 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 & Wein- baum, 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 exist- ing community where trust and familiarity already exist (Moolenaar & Sleegers, 2010). A variety of studies have found that if social net- works already exist within organizations or groups, they are much bet- ter able to engage in change processes and reforms. In organizations

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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 net- work. 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 sev- eral 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 cen- ters for teaching and learning, for example, to create networks of fac- ulty with similar interests on an ongoing basis were much more likely to be able to implement changes around assessment, engaging pedago- gies, interdisciplinarity, or responding to diverse students than estab- lishing new networks. The time and effort put into creating the social network prior to the change initiative allowed for more authentic en- gagement 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 ele- ments 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 (Bor- gatti & Foster, 2003; Moody & White, 2003). So a diversity of ties can facilitate change by accessing new information that might help over- come 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 com- munication. 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 them- selves (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

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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 ac- tors—individuals that have the most ties to other actors in an organiza- tion or a network (Cross & Parker, 2004; Freemen, 1979). Because of their central location, these individuals have more access to informa- tion 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 cre- ate 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 (re- sponsible 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 lead- ers 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, doc- tors 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 typi- cally more beneficial to change (Valente, 1995). An example of this phe- nomenon is Tsai's (2002) study in which units that are more innova- tive occupy a central network position that provides them access to new knowledge.

Expressive Versus Instrumental Functions

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

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are more social and friendship based. They may develop within orga- nizations, but they are focused on friendship. Expressive networks tend to be strong and carry a great deal of social support. Instrumental net- works 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 instru- mental 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 informa- tion 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 educa- tion, Cole and Weinbaum (2010) found expressive ties were more ef- fective in impacting teacher attitudes towards reform than instrumental networks.

Trust

One of the primary characteristics explored throughout social net- works is the concept of relational trust (Kilduff & Krackhardt, 2008; Moolenaar & Sleegers, 2010; Scott, 1991). Relationships are funda- mentally 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 de- fined as exchanges among members of the community and the recipro- cal 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 individu- als who are asking them to engage in risk-taking behavior. As Cobum and Russell (2008) note: "Trust enables organizational change by mod- erating the uncertainty and vulnerability that can accompany change" (p. 207). A quantitative study by Moolenaar & Sleegers (2010) inves- tigated social networks among 775 educators at 53 schools where an educational innovation had recently been implemented. The study in- vestigated 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

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teachers. Dense ties are often created through subgroups. Studies of re- search collaboration in higher education suggest the importance of ex- pressive ties for building richer research collaborations (Larivi, Gingras, & Archambault, 2006).

Subgroups

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. Sub- groups are often important to the development of innovative ideas and problem-solving that move change forward. When the formal organiza- tion creates mechanisms for leveraging subgroups and information flow between them, the formal organization can foster greater change (Finni- gan & Daly, 2010). Subgroups (e.g., affinity group by interest, infor- mal 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 at- titudes 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 net- works and help them to better accomplish their goals (Larivi, Gingras, & Archambault, 2006; McMillan, 2008).

Another line of research related to subgroups is structural holes, li- aisons, and bridging (Ahuja, 2000; Burt, 1992; Wellman & Berkowitz, 1988). All of these concepts relate to linkages within the system. Bridg- ing 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 commu- nication and idea exchange. Structural holes are subgroups that are not connected in any way and represent potential opportunities for creat- ing greater density if the structural hole can be filled in by liaisons or bridging. Finnigan and Daly (2010) demonstrated how a lack of bridg- ing individuals (between principals and central office staff) resulted in a core-periphery structure that prevented communication, information exchange, and ultimately change in school reform.

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Connectedness Encourages Change

If people in the network have a great deal of contact (or connected- ness) 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 in- novators 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 inter- actions 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 knowl- edge 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 (Ten- kasi & 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 inter- nal leadership were better able to create changes.

Related to the nature ofthe interactions is the composition ofthe net- work. 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)

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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 discussed).

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 con- nection 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 in- fluence 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 infor- mation 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 fac- tors 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 organiza- tion could influence or support networks. Yet, emerging literature sug- gests 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 organiza- tions 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 cas- cading the change through the organization's hierarchical network link- ages. 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

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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 to- gether. For example, more hierarchical relationships that are estab- lished 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 conver- sations within teacher networks. School policies also impacted teach- ers' 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 even- tually 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., fi- nance versus marketing), or between different organizations all further important network functions such as communication, gaining novel knowledge, and innovation (Kogut & Zander, 1996; Tsai, 2002). Re- searchers continuously find that dense ties within units lead to important outcomes (Tsai, 2002). Researchers also have identified that interorga- nizational collaboration allows companies to increase their knowledge, leading to learning and facilitating innovation, which creates competi- tive advantages (Kogut & Zander, 1996; Tsai, 2002).

Organizations Intentionally Influencing Networks

Not only do organizations unintentionally shape networks, but orga- nizations can also attempt to influence network creation and direction.''' Organizations can purposefully influence networks by creating inter- organizational 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 influ- enced social networks by creating structures, requirements, and focus

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that helped create ties that were beneficial to change. But they empha- size that when support structures are removed by policy entities or orga- nizations, 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 in- novation. 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 organiza- tions and how network structures and relationships should be devoted resources to facilitate change. Rather than investing in individual in- centives 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 (orga- nization) as the only analytic unit to the network (or network in combi- nation 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 edu- cation that seem important to study given the findings from SNA. As I review each area of future research, I examine it in relationship to ex- isting research about higher education—noting characteristic networks like disciplinary societies or professional groups—and relevant orga- nizational 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 uni- versities. 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

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are fewer assumptions I can bring to how those operate, but this is an important area for future research. While these hypotheses will be pre- sented 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 cam- puses are well-positioned for change. One might suspect that the diffi- culty 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 admin- istrators are often isolated and broken up into different siloed schools and units (Kezar & Lester, 2009). Also, given the many external net- works that campus staff are likely to be part of—disciplinary areas, pro- fessional groups, and local and regional communities—ties might be weak and diffuse (Kezar, 2001). Clearly, this differs by campus con- text, as small or rural campuses may have capacity for strong ties, for example.

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 intro- duce 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

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being given some authority on certain issues) might suggest that knowl- edge sharing occurs through networks and that at least weak ties might allow strong information flow that can lead to some outcomes associ- ated 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 bonds.

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, individu- als who remain affiliated with off-campus and online networks may de- velop strong ties of affinity that may be levers for change in the acad- emy (Wenger, 1998). External groups that cross boundaries, such as the World Bank and Organisation for Economic Co-operation and Develop- ment (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 net- works, making certain outcomes and processes a challenge.

3. Hypothesis: Higher education stakeholders benefit from off-campus net- works, 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 en- terprise-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, disciplin- ary groups/professional organizations) have many long-term employ- ees that there are more opportunities for individuals to be connected through long-term ties. Also long-term employment may facilitate or- ganic networks, and so there may be less of a need to artificially con- stmct networks to facilitate change. Historically, higher education likely

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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 bound- aries to support each other. Such examples already exist, such as the New Faculty Majority and academic unions. Will the trend toward non- tenure-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-cam- pus networks, and the degree to which this might differ for groups on campus—faculty, student affairs, business affairs, or by different disci- plines. 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 dif- ferent 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 en- hance 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 develop- ment. 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 lead- ers 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 rela- tionships (Baldridge et al., 1977; Clark, 1983; Kezar, 2001). Most col- lege 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

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Student Personal Administrators Knowledge Communities, http://www. naspa.org). Next, there are more structured and sometimes more for- mal networks being formed regionally and within communities to cre- ate changes in higher education (see the NERCHE Think Tanks, http:// www.nerche.org). An example of the structured networks is Project Ka- leidoscope (http://www.pkal.org/), which brings together faculty inter- ested in STEM undergraduate reform. It is more structured in that it of- fers formal workshops, conferences, and times for face-to-face interac- tion. 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 Con- nect, 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 employ- ment, 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 oper- ate like organic communities. As communities become more formal- ized and attached to organizations (such as CIRTL, http://www.cirtl. net/), they will lose the advantages (trust, expressive ties) of organic networks.

Diversity of Ties and Subgroups

Organizational theory also suggests that given the diversity of stake- holders and groups in higher education by discipline or unit and divi- sion (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

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help overcome the downsides of heterophily as people have a chance to work in small groups on an ongoing basis. It may be that commit- tees as artificial network structures are not developing the types of re- lationships 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 offi- cers. 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 pro- vide 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 Mon- etary 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 ho- mopholous and where information readily flows and potentially cre- ates changes worldwide at a more rapid pace. Organizations such as the World Bank create policies and practices such as GATT (General Agree- ment on Tariffs and Trade) that have impacts on educational organiza- tions and support the flow and interchange between formerly isolated campuses. Yet there will be countervailing forces from non-governmen- tal agencies (NGOs) that often have more local connections and net- works 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 di- verse groups are brought together.

2. Hypothesis: The presence of committees, task forces, and subgroups on- or off- campus can provide a mechanism to overcome the downsides of heterophily if designed to do so.

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3. Hypothesis: Deep changes may be best addressed through off-campus networks (both formal and informal) that bring together like-minded in- dividuals and where trust and expressive ties can be developed.

4. Hypothesis: Global networks will create greater homophily among cam- puses in vastly different regions leading to changes that cross national boundaries and greater flow of ideas. Yet NGOs will provide a counter- vailing 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) cre- ated 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 ac- tors 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 edu- cation employees tend to be long-term—faculty may be on a campus their entire career, staff often are long-term, and even non-tenure track- faculty tend to stay employed at institutions fairly long-term (averag- ing 7 years) (Kezar & Sam, 2010 ). This long-term employment (his- torically 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

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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 ori- entation, whereas large urban research universities may have commut- ing staff and faculty that interact less and have a more bureaucratic feel. Within formal off-campus networks (disciplinary and professional), ex- pressive 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 educa- tion professionals are increasingly connecting informally, based on in- terests 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 lev- eraged 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 for- mal and informal off-campus networks.

Trust

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 hir- ing 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 simi- larity 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 de- velop 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 there- fore may be a stronger lever for certain types of changes.

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3. Hypothesis: Campus leaders that create trust will be more likely to en- hance 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 implica- tions 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). Leader- ship 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 develop- ment and success. Based on these findings, the following hypotheses are derived:

1. Hypothesis: Higher education networks will be characterized by many barriers and obstacles largely due to poorly developed group interac- tions that result from the lack of adequate training for interpersonal skills to work in groups.

2. Hypothesis: Higher education settings that offering training in interper- sonal 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 creat- ing 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

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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 com- plex 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 creat- ing broad level sector changes, they may want to mobilize off-campus networks that have greater reach across the sector rather than on-cam- pus networks, for example. This is just a set of examples of the impli- cations of the research agenda described in the article, and as we have more research to guide policy and practice, the field will have even bet- ter guidance. For now, scholars can combine insights from organiza- tional 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.

Conclusion

Ultimately, scholars have focused change research more on the for- mal, internal organizational structures and ignored networks. We need to balance an organizational perspective with more attention to net- works and social relationships. Yet we need to also combine the organi- zational perspective, as studies of networks related to institutions dem- onstrate the importance of these settings in shaping the network. Both approaches are needed to understand how change occurs within post- secondary 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 in- stitutional context, and to ignore this context is also problematic.

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Similarly, researchers studying formal organizations have typically ignored social networks and their informal leaders that can create so- cial capital. These researchers' findings result in organizational invest- ment in individual development rather than investments in important so- cial 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 com- munities. Research on learning communities and communities of prac- tice (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 orga- nizations—many offered within this article.

Notes

' 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 educa- tion—for an example, see Pusser, Slaughter, and Thomas (2006).

' The focus of this article is on ways social network analysis can be used to cre- ate changes in postsecondary institutions—but this cuts across many different areas, in- cluding 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 as- sumptions of networks, which are about seeing beyond organizational boundaries, par- ticularly 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 or- ganizational changes/impact. So in the article, when referring to the change impact, re- search 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 litera- ture, which also emphasizes networks and is also outside formal organizational settings (Hartley, 2009b; Meyerson, 2003).

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'" 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 ques- tions 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 net- works 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 litera- ture 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., de- gree 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 out- lined 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 physi- cians 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, com- munication, 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

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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 mul- tiple contexts and among different actors to identify whether they are sound.

" Neoliberalism refers to an overarching philosophy of privatization of public in- stitutions and the adoption of private sector and corporate approaches to management.

References

Ahuja, G. (2000). Collaboration networks: Structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 4 2 5 ^ 5 5 .

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), 4 3 2 ^ 4 5 .

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 improve- ment. New York: Russell Sage Foundation.

Burgan, M. (2006). What happened to faculty governance. Baltimore: Johns Hopkins Uni- versity 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, 101-116.

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. Educa- tional Evaluation and Policy Analysis, 30(3), 203-235.

120 The Journal of Higher Education

Cole, R., & Weinbaum, E. (2010). Change in attitude: Peer inñuence in high school re- form. 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 Press.

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: Har- vard 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 struc- ture 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 Net- works, 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: Sense- making during strategic change in academia. Administrative Science Quarterly, 41, 3 7 0 ^ 0 3 .

Granvetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360-1380.

Hartley, M. (2009a) Reclaiming the democratic purpose of american higher education: Tracing the trajectory of the civic engagement movement. Learning and Teaching, 2(3), 11-30.

Hartley, M. (2009b). Leading grassroots change in the academy: Strategic and ideological adaptation in the civic engagement movement. Journal of Change Management, 9(3), 323-338.

Heck, R., Price, C , & Thomas, S. (2004). Tracks as emergent structures: A network analy- sis of student differentiation in a high school. American Journal of Education, 110(4), 321-353.

Honig, M. (2006). Street level bureaucracy revisited: Frontline district central office ad- ministrators 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 121

Kezar, A. (2001). Understanding and facilitating organizational change in the 27" Cen- tury: 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), 8 3 1 - 860.

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 capacity for leadership: An examination of grassroots leaders. Palo Alto, CA: Stanford Press.

Kezar, A., & Sam. C. (2010). Understanding the new majority : Contingent faculty in high- er education (Vol. I). ASHE Higher Education Report Series. San Francisco: Jossey Bass.

Kilduff, M., & Krackhardt, D. (2008). Interpersonal networks in organizations: Cogni- tion, personality, dynamics, and culture. Cambridge, UK: Cambridge University Press.

Kilduff, M., & Tsai, W. (2003). Social networks and organizations. Thousand Oaks, CA: Sage.

Kogut, B., & Zander, U. (1996). What firms do? Coordination, identity and learning. Or- ganization 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. Scien- tometrics, 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 Press.

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 ap- proaches to the study of scientific communication. Sponsored by SIG MET. Proceed- ings of the American Society for Information Science and Technology, 40(1), 486-487.

Maroulis, S., & Gomez, L. M. (2008). Does "connectedness" matter? Evidence from aso- cial network analysis within a small school reform. Teachers College Record, 110(9), 1901-1929.

Mayer, A., & Puller, S. L. (2008). The old boy (and girl) network: Social network forma- tion on university campuses. Journal of Public Economics, 92(1), 329-347.

McGrath, C , & Birackhardt, D. (2003). Network conditions for organizational change. Journal of Applied Behavioral Science, 39(3), 304-336.

McMillan, G. S. (2008). Mapping the invisible colleges of R&D management. R&D Man- agement, 5S(1), 69-83.

Metcalfe, A. S. (2006). The corporate partners of higher education associations: A social network analysis. Industry and Innovation, 13(4), 4 5 9 ^ 7 9 .

122 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). Ex- amining 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, 4 4 1 ^ 5 1 .

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 ef- fects of cohesion and range. Administrative Science Quarterly, 48(2), 240-267.

Rhoades, G. (1996). Reorganizing the faculty workforce for flexibility: Part-time profes- sional labor. The Journal of Higher Education, 67(6), 626-659.

Rhoades, G., & Slaughter, S. (2004). Academic capitalism. Baltimore: Johns Hopkins Uni- versity 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), 161-172.

Scott, J. (1991). Social network analysis. London: Sage.

Higher Education Change and Social Networks 123

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 fresh- man 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), 526-543.

Tsai, W. (2002). Social structure of competition within a multiunit organization: Coordina- tion, 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 Press.

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, 9(5).

Wenger, E., McDermott, R., & Snyder, W. (2002) Cultivating communities of practice: A guide to managing knowledge. Cambridge, MA: Harvard Business School Press.

124 The Journal of Higher Education

White, H. D. (2003). Pathfinder networks and author cocitation analysis: A remapping of paradigmatic information scientists. Journal ofthe American Society for Information Science and Technology, 54(5), 4 2 3 ^ 3 4 .

White, M. D. (2001). Diffusion of an innovation: Digital reference service in Camegie Foundation Master's (Comprehensive) Academic Institution Libraries. Journal of Aca- demic Librarians hip, 27, 173-187.

APPENDIX

Network Terms

Term Definition

Tie/connection

Flow

Outcomes

Density

Structure

Nodes

Tight or dense ties

Loose ties

Formal network

Informal network

Strong ties

Weak ties

Homophily

Heterophily

Central actors

Opinion leaders

Expressive tie

Instrumental or functional tie

Structural holes

Liaisons/bridging

Connectedness

Relationship among people; conduits for the now of interpersonal resources

The content that goes between people in the network

Result of flows, ties, and interactions

Number of ties across a node or network structure

Pattern of connections among parties

Individuals/people in the network

A network with many ties among people

A network with few ties among people

Has many structures in place and organization to support

Has few structures in place and organization to support

Network members have frequent interaction, an extended history, and intimacy.

Network members have infrequent interaction, more limited history, and lack intimacy.

People are attracted to people with similar interests and backgrounds.

People are less likely to interact with people with different interests and back- grounds.

Individuals who have the most ties to other actors in the network

People who individuals say would influence their choices and attitudes

Based on friendship or kinship

Based on role or position

Gaps in the network between groups

Individuals that span different subgroups

The degree to which individuals are exposed to an innovation through the network

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