RSCH 8310 Walden University Positive Social Change Discussion

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Mathematics

RSCH 8310

Walden University

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Data Sources for a Qualitative Study

Websites and social media sites can provide important sources of data that will help expand your understanding of the stakeholders who are connected to the phenomena you are exploring.

Walden has devoted an entire website to making visible its actions and activities about social change. At the social change website, you will find videos, annual reports, text, and images. As you explore this data source, consider what text, images, and reports you would like to include as part of your data analysis exercise.

To prepare for this Discussion:

  • Choose one of the three social change literature review articles found in this week’s Learning Resources and review the article in detail.
  • Explore the Walden Social Change website and locate an additional document, video, or webpage that will inform your understanding of the meaning of positive social change. Reflect on any additional sources you find.
  • Next, write field notes based on the information you gathered from the Walden social change website and any other documents or websites that might inform your changing impressions about the meaning of positive social change.
  • Finally, review the media programs related to coding and consider how you will use this information to support this Discussion. Note: In your Excel Video Coding template there is a tab for your website data. Use this tab to place your content and codes for the website.

By Day 4

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

Be sure to support your main post and response post with reference to the week’s Learning Resources and other scholarly evidence in APA style.

Required Media

Laureate Education (Producer). (2016). Introduction to coding [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 10 minutes.
In this media program, Dr. Susan Marcus, Core Research Faculty with the School of Psychology at Walden University, introduces you to the world of coding using Word or Excel documents. In this first video, you will learn how to organize your data.
Laureate Education (Producer). (2016). From content to coding [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 12 minutes.
In this media program, Dr. Susan Marcus, Core Research Faculty with the School of Psychology at Walden University, introduces coding and how to move from content to codes. This video focuses on what Saldaña (2016) calls “first cycle” coding. Three different approaches are presented. Analytic memos will also be discussed.

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Adrianna Kezar Higher Education Change and Social Networks: A Review of Research This article reviews literature on the potential for understanding higher education change processes through social network analysis (SNA). In this article, the main tenets of SNA are reviewed and, in conjunction with organizational theory, are applied to higher education change to develop a set of hypotheses that can be tested in future research. Social network analysis provides strong confirmation of the inseparability of fundamental planned change and social networks. Schemata are embedded in communities and emerge and are maintained through interpersonal interactions. Lasting change does not result from plans, blueprints, and events. Rather the changes must be appropriated by the participants and incorporated into their patterns of interaction. It is through the interaction of the participants that the social system is able to arrive at a new network of relations and new way of operating. (Mohrman, Tenkasi, & Mohrman, 2003, p. 321) We live in a time where there is the potential for people to be increasingly connected through formal and informal networks; technology has made these types of social networks more common and accessible.' Social media such as Facebook, in particular, has connected people in vastly different countries that otherwise would not have contact; it makes networks more commonplace within our modem world. We Adrianna Kezar is a Professor in the Rossier School of Education at the University of Southern California: kezar@usc.edu. The Journal of Higher Education, Vol. 85, No. 1 (January/February) Copyright O 2014 by The Ohio State University 92 The Journal of Higher Education need only think about the various political changes in Syria, Egypt, and Libya to see the power of social networks/media for creating change. Social networks are defined as people loosely connected through some form of interdependencies such as values, preferences, goals, ideas, or people (Wasserman & Faust, 1994). Social networks can serve many functions such as social support, knowledge, and change. Recently, policy makers and foundations have begun to capitalize on the potential of networks to create education reforms (Lumina Foundation, 2010). Leaders who have attempted to address complex problems realize that multiple stakeholders need to be engaged and that these problems are multifaceted and require quite different expertise and groups to address them (Spillane, Healey, & Chong, 2010). The Lumina Foundation, for example, is investing in a variety of statewide and national networks to help improve access to higher education. The Lumina Foundation recognizes that the goal of increasing access in higher education to 60% by 2025 will not be met unless a variety of groups are connected and work together (Lumina Foundation, 2010). Networks have also been used to scale up change in science disciplines. The National Science Foundation has invested in networks to connect STEM faculty to solve complex problems, improve the nature of undergraduate teaching and learning, and help transform K-12 teachers' knowledge and preparation so they can increase the pipeline into science (Fairweather, 2009). It would seem obvious that social relationships are fundamental to creating change, but the multidisciplinary research literature on change, as well as the higher education field, specifically, does not refiect this most basic assumption. Even though social networks have become part of our daily consciousness and several visible, national higher education projects utilize networks, there is little research in higher education on the way networks create change or can be used towards change, particularly in the U.S. higher education literature^ (Hartley, 2009b; Kezar, 2001, 2005). Within the higher education literature, social network analysis (SNA) has been used primarily to examine issues of access and success among college students, particularly around the development of social or cultural capital through networks of peers, school personnel, and their family (Heck, Price, & Thomas, 2004; Mayer & Puller, 2008; Skahill, 2003; Teranishi & Briscoe, 2006; Thomas, 2000; Tiemey, Corwin, & Colyar, 2005). These studies have examined how social networks within high schools or colleges help to retain students by connecting them to needed information and support. SNA has also been used to study technology and information sciences and, to a lesser degree, institutional and research collaborations. In Higher Education Change and Social Networks 93 these areas, topics include sense of community of online learners (Otte & Rousseau, 2002; Traud, Kelsic, Mucha, & Porter, 2011); citation analysis to see connections among researchers (Marion, Garfield, Hargens, Lievrouw, White, & Wilson, 2003; Sharma & Urs, 2008; White, 2003); efforts to map collaborations among colleges (Larivi, Gingras, & Archambault, 2006); more specific research collaborations (Balconi & Laboranti, 2006; McMillan, 2008; Rogers, Bozeman, & Chompalov, 2001); student activism (Biddix & Park, 2008; Crossley, 2008); and corporate partners of higher education associations (Metcalfe, 2006). The few studies of student activism are the most directly related to this article in that they begin to hint at the power of SNA for understanding change. Therefore, SNA is used in higher education research but only for a limited set of topics. Yet, it is also a burgeoning area that is being recognized as important for studying various phenomenon that focus on social relationships and collaborations. In this article, I argue for its application to change and reform—a critical and important challenge facing most postsecondary institutions.^ The main objective of this article is to conduct a multidisciplinary review of the literature on social network theory and apply it the problem of understanding change in higher education. As part of this objective, I also demonstrate the synergy of SNA with long-used organizational change theories. And lastly, the application of SNA is used to develop a research agenda. As the article will demonstrate, the combining of organizational and social network theories remains a major gap in the literature. The article raises broad questions about how the enterprise of higher education can and does engage in change and how researchers' current approach to studying change misses many significant change processes and dynamics. In the next sections, I review the main tenets of SNA and compare it to other theories that have been applied to the study of change in higher education, specifically focused on networks or groups of people. Next, I provide an overview of some of the main findings from the multidisciplinary research base on SNA as it relates to change, but largely applied outside institutional/organizational settings. I draw on examples from institutional settings where possible, as this provides a more direct analogy for applying SNA in higher education. Then I present how these concepts from SNA can be applied to higher education change and develop a set of hypotheses that can be tested in future research, developing a formal research agenda. In addition to contributing to the literature by applying this theory and its concepts to higher education and offering up an extensive research agenda, I also offer a unique perspective—describing the ways that SNA and organizational theories can and should be combined. One of the main messages in this review is 94 The Journal of Higher Education that networks, while an important new area for study in their own right, will likely be more fruitful for understanding change in higher education when examined in conjunction with organizational theory. Studies of networks within other institutions (e.g., hospitals, schools) find that networks are poorly understood when the context (societal, organizational, and more local-unit level) within which they exist is ignored.'' I offer this insight from having reviewed literature across various disciplines and settings that study institutions where the issue of missing context has repeatedly emerged. Social Network Analysis: Basic Assumptions and How It Relates to Change Basic Assumptions As one reviews the multidisciplinary literature base on SNA, it is clear that social network analysis works against the grain of much ofthe change literature; it challenges assumptions about the meaningfulness of organizational boundaries and forms, looking instead at how ideas, information, resources, and influence flow across what are normally conceptualized as more rigid boundaries and forms (Scott, 1991).^ SNA challenges the underlying belief that the formal organization or social system has the most dominant impact on individuals and their choices (Daly, 2010c).^ It suggests that informal networks of relationships have a significant impact on whether individuals decide to engage in change or reform behavior. Networks also challenge the notion that overarching norms (i.e., society, organizations, institutions) are the only impact on behavior; instead, important close peers or even distant contacts can impact choices and attitude (Kilduff & Tsai, 2003). Most higher education change literature views departments, schools/ colleges, or state systems as the natural unit or target for change processes and analysis rather than networks or relationships (Kezar, 2001). SNA describes more fluid relationships that cross these boundaries—looking, for example, at collaboratives, online communities, or informal collectives. This research also takes a decidedly non-authoritative and hierarchical approach to thinking about social systems and how they operate, by examining all people at any level or within any unit (Daly, 2010a), whereas organizational theory often privileges those in positions of authority in terms of analysis. The theory and methodology of social network analysis also attempt to look at the dynamic interactions between formal structures and informal relationships, examining participants' peers, friends, and colleagues. Higher Education Change and Social Networks 95 Before describing key insights related to change and social networks, I present basic notions about SNA and I refer the reader to the Appendix for more detail about terms. Networks can be tight or loose: Tighter networks are often denser and smaller, while looser networks can be quite complex, composed of all people across a variety of units, organizations, and countries (Borgatti & Cross, 2003; Kilduff & Tsai, 2003). Networks can also be formal or informal: Formal networks have more structure related to communication and interaction, whereas informal networks are less organized and have little in the way of structure to support communication and interaction (Kilduff & Tsai, 2003; Scott, 1991). Researchers who study networks often examine the nodes (people) and ties (relationships with people). Through description and analysis of the nodes and ties, researchers are able to predict certain outcomes (e.g., social capital, knowledge, or change) or processes (e.g., learning, information sharing) within the network. Relationship to Change Diffusion of innovation is an outcome in many different studies of social networks, which is why SNA has been applied to the study of change processes in more recent years (Rogers, 2003; Valente, 1995). In addition, many studies have linked the existence of social networks with the success of change initiatives, suggesting a strong correlation between these processes (Daly, 2010c; Hartley, 2009a, 2009b).^ As Daly (2010a) has noted, the social network perspective provides proof that "relationships within a system matter to enacting change" (p. 2). He goes on to note that all reforms may begin as ideas or visions but that they eventually need to be engaged by people who work in social structures and relationships. Therefore, webs of relationships are often the chief determinant of how well and quickly change efforts take hold, diffuse, and are sustained (Daly, 2010a). Researchers have identified several key ways that social networks lead to change. First, social networks offer a set of mechanisms that enable change—through communication systems, knowledge transfer, alteration of schema or mindset, shaping of attitudes, increasing of problem-solving, and accountability (Ahuja, 2000; Borgatti & Foster, 2003; Kraatz, 1998; McGrath & Krackhardt, 2003; Szulanski, 1996; Wasserman & Faust, 1994). ^ Second, two outcomes of social networks have been related to change—learning and social capital (Borgatti & Foster, 2003; Burt, 2000; Kilduff & Tsai, 2003; Tenkasi & Chesmore, 2003). Many researchers have found a strong linkage between learning and social networks, and learning has been strongly linked to changes in behavior (Tenkasi & Chesmore, 2003). Networks also provide social 96 The Journal of Higher Education capital that facilitates the change process (Burt, 2000). While different definitions of social capital exist, underlying most of the theoretical discussions is the assumption that social capital is the resources embedded in social relations and social stmcture, which can be mobilized by an actor to increase the likelihood of success in purposive action (Daly & Finnigan, 2008). This resource can vary from knowledge about how the organization works to influence to finances. Third, change often involves risk-taking that can be less problematic if it is done collectively rather than individually (Valente, 1995). If one knows many of their peers are going to engage in the same behavior, then one is more likely to also engage in this behavior (Rogers, 2003; Valente, 1995). While this is not an exhaustive list, these are some of the most commonly identified areas that link social networks and change and demonstrate why networks facilitate reform. An example of this process might help: Faculty who participate in a STEM reform network gain access to the latest research from cognitive science about how students leam, which helps them to have discussions that change their view on pedagogical approaches. While they now want to change their pedagogy, they lack the confidence (and feel it is too risky) to enact the change or skill to alter their teaching. Through network presentations from faculty who have changed their approach, they gain the confidence (seems less risky) and the skills to rethink their practices. Even though no one at their own campus is engaged in these new teaching techniques, they can contact members of the network for support, guidance, and even accountability to maintain the change. The most often noted source for understanding social networks and change is Rogers' (2003) diffusion of innovation model that examines communication channels and how ideas are transmitted. Rogers' work emerged out of a variety of studies that examined the diffusion of innovation, mostly outside of organizational contexts.' It may be that because his work was located beyond the formal organizational setting, many researchers did not originally see the potential for studying changes within formal organizations and institutions like colleges. Yet, Rogers' work has been applied within educational contexts, particularly to examine the diffiision of technology use (Solem, 2000; White, 2001). The focus was on individual uptake of various forms of technology on college campuses but rarely applied to other change processes. How Social Network Analysis Compares to Other Theories of Change in Higher Education Certainly collections of people and a human dimension to change are alluded to in organizational theories of change in higher educa- Higher Education Change and Social Networks 97 tion. For example, management science models of change focused on strategic planning refer to broad buy-in and participation in decisionmaking around reforms, suggesting that plans without the involvement and ownership of employees will not be successful (Keller, 1983). Political models of change examine how certain groups try to assert their interests and develop agendas for change (Baldridge, Curtis, Ecker, & Riley, 1977; Clark, 1983; Kezar, 2001). As coalitions are a form of network, some of the political models of change address the impact of networks (through examination of coalitions) and the way people influence change processes (Baldridge et al, 1977; Clark, 1983; Kezar, 2001). Political theories of change focus on groups often left out of management science studies of change—those with less power and authority—and how they try to combat changes by those with more authority. Political theories also move outside of campuses, examining community groups and external influences that might shape change. In many ways, SNA builds on political theories but focuses more on informal and less structured groups than coalitions. Social cognition theories of change also describe the power of social interactions for creating change (Gioia & Thomas, 1996; Kezar, 2001; Wenger, 1998). Social cognition models examine how mental processes and mental models shape the ability of people to engage in a particular change initiative (Kezar, 2001; Wenger, 1998). Learning communities and communities of practice evolved from social cognition approaches to change and rely heavily on networks of people coming together around a shared interest to develop professionally (Kezar, 2001). Communities of practice are organic networks that evolve from people engaged in similar work and may cut across institutions (e.g., student affairs practitioners) (Wenger, 1998). Learning communities are formalized, typically organizationally situated or constructed (often not extending beyond its boundaries) and therefore less organic (Wenger, 1998). Also learning communities are typically created with the goal of shifting mindsets and are not existing networks (such as communities of practice) that are part of the fabric of existing social interactions—the focus of social network analysis (Kezar, 2001). Both learning communities and communities of practice have been harnessed to create changes such as pedagogical and curricular reforms of higher education (Micomonaco & Austin, 2010). So the idea of social relationships being significant to creating change is not foreign to the higher education change literature and builds off this earlier work from management science, political models, and social cognition approaches to change. Yet the emphasis in SNA is slightly different (more informal groups, organic, and moving beyond organizational boundaries). Also, it suggests that social relationships are more 98 The Journal of Higher Education central or pivotal to change efforts than these earlier models/theories suggest, as they instead foreground planning, agenda setting, or schema development. Yet, as this review will continue to emphasize, the social relationships already identified in organizational theories of change should not be ignored or forgotten as we shape studies of change with a focus on social networks. These insights are best paired with what we learn from SNA. Key Insights from Research on Social Network Analysis Related to Change Next, I review some of the key insights that have been identified in research using SNA to understand change processes. To inform this review, I draw on the multidisciplinary research conducted in sociology, organizational theory, psychology, education, anthropology, public policy, and business. Yet, I rely heavily on K-12 literature since this is a more parallel setting than corporations or industry for understanding social networks and change in higher education. While research has been conducted in higher education, much of this has not been applied to change. However, when examples from higher education exist, I also bring these in to inform the article. Furthermore, I focus more on studies using SNA conducted in formal organizational settings, as they likely have greater translatability to higher education than studies of farmers or entrepreneurs (Valente, 1995).'° The following key insights are reviewed: structure of ties; organic versus artificially created networks; diversity and homogeneity of ties; central actors and opinion leaders; expressive and instrumental functions; trust; subgroups; connectedness; nature of interactions; leadership; and organizational elements, such as structured networks, teams, prescriptive versus flexible polices, hierarchy, and formal leaders. These concepts are reviewed because they are the most often cited and used concepts related to change and social 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 information such as basic information sharing (Nelson, 1989; Tenkasi & Chesmore, 2003). Strong ties are characterized by three defining characteristics: frequent interaction, an extended history, and intimacy or Higher Education Change and Social Networks 99 mutual confiding between the parties (Kraatz, 1998). Most studies of change find strong ties more conducive to deep or complex changes (Balkundi & Harrison, 2006; Tenkasi & Chesmore, 2003). Strong ties are also more likely to promote in-depth, two-way communication and exchange of detailed information. For example, strong ties among people within the organization tend to foster change more than weak ties because change is typically nonroutine and usually involves more complex thinking. One drawback of strong ties is that they may foster less diverse or novel information and ideas (Tenkasi & Chesmore, 2003). Weak links have the advantage of requiring little time and effort but often have enormous dividends in terms of information and knowledge gained and can lead to lack of insularity of ideas (Granvetter, 1973). Weak ties are characterized by distance and infrequent relationships that may be casual, less intimate, and nonreciprocal in nature. However, for diffusion of ideas and public information, weak ties can be extremely helpful. Also, for obtaining ideas for change, weak links can provide important external ideas that promote a more robust change idea. Weak links have also been identified with helping foster innovation in interorganizational collaborations (Tsai, 2002). Weak links usually result in more ideas being introduced into the network because there may be more diverse people and because people do not interact all the time, so they do not have set schema or norms for their interaction and therefore may be more open to new ideas. Thus, there may be times and circumstances where weak links are important for creating specific types of change or in certain phases of the change process. An example of the importance of understanding the strength of ties finding in education is Cobum's and Russell's (2008) study of math curricular reform that found strong ties were critical to reshaping teacher pedagogical approaches seen as nonroutine and complex. Organic Versus Artificially Created Networks Existing relationships are more influential than relationships created as part of a change initiative (Cobum & Russell, 2008; Cole & Weinbaum, 2010). Therefore, the more that change agents can build upon existing relationships for a change process, the more likely they are to be successful with implementing the change. This is not to suggest that learning communities or other communities created for innovation cannot work but that they have proven less successful than an existing community where trust and familiarity already exist (Moolenaar & Sleegers, 2010). A variety of studies have found that if social networks already exist within organizations or groups, they are much better able to engage in change processes and reforms. In organizations 100 The Journal of Higher Education lacking these networks, the reform efforts more likely fail (Atteberry & Bryk, 2010; Cobum & Russell, 2008; Cole & Weinbaum, 2010). For example, Cobum, Choi, & Mata (2010) demonstrated that a National Science Foundation school initiative helped expand teachers' networks through structured meetings and regular contacts among a broad network. However, they found that as the project waned, people went back to their smaller, less diverse networks that they connected with prior to the initiative. This suggests that unless structures are put in place to sustain networks, people tend to retreat to their more familiar existing relationships even though they found the broader, constructed network important for increasing their knowledge and expertise and met for several years. Kezar and Lester (2009) found that change efforts were much more successful when existing campus networks were tapped than when new ones were created for a change initiative. Campuses that use their centers for teaching and learning, for example, to create networks of faculty with similar interests on an ongoing basis were much more likely to be able to implement changes around assessment, engaging pedagogies, interdisciplinarity, or responding to diverse students than establishing new networks. The time and effort put into creating the social network prior to the change initiative allowed for more authentic engagement of the proposed change. When groups spend considerable time developing trust, relationships and familiarity, and do not get to the change process itself for an extended period of time, they become frustrated and often leave the group before learning and other key elements related change could occur. Diversity of Ties and Change Possibilities The notion of diversity of ties (ties that span multiple knowledge sources or cut across structural holes) has been demonstrated to allow access to information not available within the immediate network (Borgatti & Foster, 2003; Moody & White, 2003). So a diversity of ties can facilitate change by accessing new information that might help overcome or solve a problem related to a change initiative. The concept of diverse ties is also called heterophily (Granovetter, 1973). Networks that are more diverse tend to create complex ideas, yet the diversity might slow down the change processes because of poor or difficult communication. Furthermore, diversity can lead to the network dissolving or splitting due to poor interpersonal connections. The propensity for people to develop ties with individuals that are more similar to themselves (homophily) can help speed up information flow around the change but might also result in a narrower set of ideas that can have a negative impact on change. Studies find that people gravitate toward Higher Education Change and Social Networks 101 homophily rather than heterophily of ties (Borgatti & Foster, 2003). Yet, the importance of heterophily is indicated by others. For example, Daly and Finnigan (2008) found that complex school reform efforts like No Child Left Behind are advanced by a heterohily of ties as different views, stakeholders, and interests are important to broad, multifaceted reforms. Crossley's (2008) research on student activism also indicates that heterophily of ties can enhance the ability to engage and overcome politics on campus. Central Actors and Opinion Leaders Social network analysis can help researchers to identify central actors—individuals that have the most ties to other actors in an organization or a network (Cross & Parker, 2004; Freemen, 1979). Because of their central location, these individuals have more access to information and knowledge, have a better ability to communicate throughout the system, and are likely to have great influence within the network (Freeman, 1979; Reagans & McEvily, 2003). SNA allows researchers to identify people who are more peripheral or are isolated and help create ways to make them more central if necessary within the network in order to enable change. An example of this finding in education is Daly and Finnigan's (2008) study identifying how site administrators (responsible for curriculum reform related to No Child Left Behind) were typically on the periphery and disconnected from other principals and core staff, which prevented reforms from taking place. In addition, the literature points to the importance of opinion leaders who are people that individuals say would influence their choices and attitudes in the network (Valente, 1995). People often wait to adopt a change until the opinion leader has adopted it.'^ For example, doctors adopted a new dmg once an opinion leader they were familiar with had used it (Valente, 1995). Centrality of the network also affects the possibilities for change. Networks can be said to be centralized when there is closeness between individuals and there are a lot of people in between, often described as "centrality betweenness" (Szulanski, 1996). Essentially these measures describe a dense network, and this is typically more beneficial to change (Valente, 1995). An example of this phenomenon is Tsai's (2002) study in which units that are more innovative occupy a central network position that provides them access to new 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 102 The Journal of Higher Education are more social and friendship based. They may develop within organizations, but they are focused on friendship. Expressive networks tend to be strong and carry a great deal of social support. Instrumental networks are created so that people seek information and resources with a particular professional purpose in mind (Kilduff & Krackhardt, 2008; Wasserman & Faust, 1994). Instrumental networks tend to be weak and based on seeking advice or expertise. Both expressive and instrumental networks can be used to create change. Expressive networks are more helpful for influencing people's attitudes or a change in mindset, whereas instrumental networks are helpful for disseminating information and introducing people to new ideas. These relationships are also noted as kinship (expressive) versus role (instrumental). Both of these types of networks can create normative pressures for reform. In education, Cole and Weinbaum (2010) found expressive ties were more effective in impacting teacher attitudes towards reform than instrumental networks. Trust One of the primary characteristics explored throughout social networks is the concept of relational trust (Kilduff & Krackhardt, 2008; Moolenaar & Sleegers, 2010; Scott, 1991). Relationships are fundamentally built on the notion of trust, and whether or not relationships are sustained and move forward is also often based on issues of trust. Therefore, it is not surprising that network analysis has examined the notion of trust as it relates to change processes. Relational trust is defined as exchanges among members of the community and the reciprocal understandings about the obligations and expectations inherent in their roles (Bryk & Schneider, 2002). Change often entails taking risks, and people are more likely to take risks when they trust the individuals who are asking them to engage in risk-taking behavior. As Cobum and Russell (2008) note: "Trust enables organizational change by moderating the uncertainty and vulnerability that can accompany change" (p. 207). A quantitative study by Moolenaar & Sleegers (2010) investigated social networks among 775 educators at 53 schools where an educational innovation had recently been implemented. The study investigated various characteristics of social networks, including density, nodes, and reciprocity, and hypothesized that trust within this network would lead to an innovative climate in schools. They hypothesized that those without trust would have a less innovative school climate and be less open to change. They found a strong relationship between trust and the development of an innovative climate that would be open to change. They also found that dense networks helped facilitate trust among Higher Education Change and Social Networks 103 teachers. Dense ties are often created through subgroups. Studies of research collaboration in higher education suggest the importance of expressive ties for building richer research collaborations (Larivi, Gingras, & Archambault, 2006). 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. Subgroups are often important to the development of innovative ideas and problem-solving that move change forward. When the formal organization creates mechanisms for leveraging subgroups and information flow between them, the formal organization can foster greater change (Finnigan & Daly, 2010). Subgroups (e.g., affinity group by interest, informal lunch groups) are often based on expressive ties and have greater trust since they are smaller parts of the overall network. Thus, they can be capitalized on for many different purposes to facilitate change since they contain the properties most facultative of change (Finnigan & Daly, 2010). Within education settings, Daly (2010a, 2010b) suggests that leveraging and connecting subgroups is one of the key principles for creating change and reform in education. Subgroups are where attitudes can be changed, problems solved, and strong influence exerted. Studies of college collaborations and researcher collaboration in higher education suggest that subgroups help enhance and advance the networks and help them to better accomplish their goals (Larivi, Gingras, & Archambault, 2006; McMillan, 2008). Another line of research related to subgroups is structural holes, liaisons, and bridging (Ahuja, 2000; Burt, 1992; Wellman & Berkowitz, 1988). All of these concepts relate to linkages within the system. Bridging often happens when an individual is a member of one subgroup and then becomes a member of another subgroup. A liaison is an individual who is a member of two subgroups, so he or she can facilitate communication and idea exchange. Structural holes are subgroups that are not connected in any way and represent potential opportunities for creating greater density if the structural hole can be filled in by liaisons or bridging. Finnigan and Daly (2010) demonstrated how a lack of bridging individuals (between principals and central office staff) resulted in a core-periphery structure that prevented communication, information exchange, and ultimately change in school reform. 104 The Journal of Higher Education Connectedness Encourages Change If people in the network have a great deal of contact (or connectedness) with the innovation, they are also more likely to undergo change (Cross & Parker, 2004; Honig, 2006; Valente, 1995). Connectedness is a measure of how much exposure an individual receives to the innovation. Individuals surrounded by many people who have adopted the change, even if others throughout their profession have not adopted the change, will be more likely to alter their behavior (Valente, 1995). Therefore, change processes that have people interact and connect often to innovators can facilitate change. Cobum and Russell (2008) found that teachers that had frequent professional development, interaction with a coach, and interactions with other adopting teachers were much more likely to change themselves. Nature of Interaction: Ongoing, Rich and Meaningful, and Non-Hierarchical More recent studies have examined the quality and nature of interactions and shown how they are linked to sense-making and schema change essential for learning.'^ Cobum and Russell's (2008) study of teachers enacting mathematics reform demonstrated that sense-making is necessary for teachers to enact the reform and that networks that do not allow for deep, ongoing interaction are unlikely to result in change. In a similar view, Mohrman, Tenkasim, and Mohrman (2003) found that one-way, hierarchical communication within the networks prevented grappling with information and sense-making and eventually led to less leaming within the network, stifling change. In contrast two-way knowledge sharing allows for schema adjustments through mutual interaction and leads to greater learning. Another study identified the importance of rich dialogue to learning within the network and identified conflictual discussion as counteractive to leaming and network relationships (Tenkasi & Chesmore, 2003). Lastly, in higher education, Kezar and Lester (2011) showed how campus networks that focused strongly on network relationships through forging interpersonal conflict resolution, creating common schema, honing communication strategies, and fostering internal leadership were better able to create changes. Related to the nature ofthe interactions is the composition ofthe network. Maroulis and Gomez (2008) note: The composition ofthe network refers to the characteristics and resources of the people in the network. Differences in network composition can lead to differences in student performance due to direct influence, information, or assistance from others in one's network, (p. 1910) Higher Education Change and Social Networks 105 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 connection to meaningful external organizations (district offices or national associations), they are often more likely to be able to foster and support changes by providing innovative ideas and potentially having more influence and resources to support innovations (Finnigan & Daly, 2010). Leaders with weak and sparse ties are unable to support large-scale and complex changes because they lack resources, employment, and information necessary to support such changes. The focus on leadership also starts to connect networks to the formal organization. Organizational Impact on Networks Organizations can also impact the way networks operate, and this is a recent area of research within education, business, and medicine. We know very little about how organizational contexts shape individual factors that influence tie formation or relationship development (Cobum, Choi, & Mata, 2010; Kilduff & Tsai, 2003). Cobum, Choi, and Mata (2010) have noted that social network researchers tend to focus on the organic nature of networks and not look at the ways that the organization could influence or support networks. Yet, emerging literature suggests the importance of interaction between organizations and networks. A set of studies has examined how organizational structures and culture shape networks. Mohrman, Tenkasi, and Mohrman (2003) identified how prescriptive and inflexible policies within organizations prevent dense networks that create greater information flow and knowledge transfer from forming. Furthermore, they identified how effective change implementation is better achieved by simultaneous, organization-wide, and local self-design networks than by simply cascading the change through the organization's hierarchical network linkages. Hierarchical networks rely on one-way information flow but do not dismpt existing schema, restrict communication and information flow, prevent leaming, and have people operate in prescriptive rather than creative ways. Unfortunately, many leaders within organizations attempt to link networks using the formal hierarchical structure and 106 The Journal of Higher Education change processes rather than enabling them. This research supports cross-functional, team-level networks to overcome the limitations of hierarchical structures. Organizations that have very strict and formal hierarchies often make it difficult for networks to form and work together. For example, more hierarchical relationships that are established between district offices and schools because of No Child Left Behind have created relationships of distrust between district offices and schools that have broken down networks that formerly operated to facilitate change (Finnigan & Daly, 2009). Additionally, Coburn and Russell (2008) have shown that the way districts allocated resources around coaching impacted the depth of interaction among teachers and network outcomes. Also, school leaders impacted the ideas and conversations within teacher networks. School policies also impacted teachers' trust and openness to the school reform. Cobum and Russell (2008) concluded that their study shows evidence that organizational policies and structures impact network formation and interactions, which eventually shape outcomes such as change processes. Other studies conducted that examine networks in relationship to the organization find that dense ties between levels in an organization (e.g., school versus the district), or units within a large organization (e.g., finance versus marketing), or between different organizations all further important network functions such as communication, gaining novel knowledge, and innovation (Kogut & Zander, 1996; Tsai, 2002). Researchers continuously find that dense ties within units lead to important outcomes (Tsai, 2002). Researchers also have identified that interorganizational collaboration allows companies to increase their knowledge, leading to learning and facilitating innovation, which creates competitive advantages (Kogut & Zander, 1996; Tsai, 2002). Organizations Intentionally Influencing Networks Not only do organizations unintentionally shape networks, but organizations can also attempt to influence network creation and direction.''' Organizations can purposefully influence networks by creating interorganizational linkages or structures to promote interaction (Reagans & McEvily, 2003; Tilly, 2005; Tsai, 2002). Cross-functional teams are one way that businesses have helped create networks within otherwise siloed organizations (Tsai, 2002). Within education, Coburn, Choi, and Mata's (2010) study shows how national initiatives and organizations can create networks that lead to change through stmctured interactions, establishing instrumental ties and directing teachers to expertise within a new network. Their study shows that district policy positively influenced social networks by creating structures, requirements, and focus Higher Education Change and Social Networks 107 that helped create ties that were beneficial to change. But they emphasize that when support structures are removed by policy entities or organizations, they then interrupt the networks that have been formed, and these networks may not be sustained.'^ Moolenaar and Sleegers (2010) also showed that districts can establish organizational structures—such as teams and learning communities—to help support networks and innovation. Daly (2010a) also points out that reform efforts often target resources on professional development (individual focus), leadership, and incentives and typically ignore the social capital of organizations and how network structures and relationships should be devoted resources to facilitate change. Rather than investing in individual incentives or professional development, research from SNA suggests the value of investing in the development of social network structures within organizations for reform. Implications of Findings on Social Network Analysis for a Higher Education Research Agenda It should be noted that the most fundamental shift in a future research agenda is to alter the focus of change research from the campus (organization) as the only analytic unit to the network (or network in combination with the campus). This would suggest a range of new objects of study: internal, on-campus networks; networks that connect or bridge campuses, such as alliances and consortia; and off-campus formal (e.g., disciplinary societies) and informal (e.g., online) networks that have little or no connection to campus boundaries. In the future research agenda described below, all these new objects of study will be noted in relationship to specific hypothesis that emerge from overlaying key concepts from SNA. There are several key areas (e.g., strong and weak ties) in higher education that seem important to study given the findings from SNA. As I review each area of future research, I examine it in relationship to existing research about higher education—noting characteristic networks like disciplinary societies or professional groups—and relevant organizational theory (as we see that organizations can influence networks) and establish some hypotheses about how these phenomena may need to be considered or studied, given earlier studies on colleges and universities. Some concepts reviewed in the last section will be discussed simultaneously (diversity of ties and subgroups) rather than separately for space purposes and because the concepts meaningfully overlap. Also, since there is little known about informal types of networks, there 108 The Journal of Higher Education are fewer assumptions I can bring to how those operate, but this is an important area for future research. While these hypotheses will be presented as generalizations,"" I acknowledge the variability of campus sectors and cultures as identified by authors such as Berquist (2007), Birnbaum (1988), Kezar (2001), and Tiemey (1988). Concepts like trust that seem deeply shaped by institutional context will be noted; other areas may be more amenable to some level of generalization. Recent research from SNA using a qualitative approach suggests the need to think about network associations as loose guides that are investigated in local contexts with specific populations. Examination of Social Network Structures: Strong and Weak Ties and Connectedness Given that strong ties are important for creating change, we know very little about the existing ties on campuses and whether or not campuses are well-positioned for change. One might suspect that the difficulty encountered by many change initiatives on campuses might mean that weak ties exist. It may be that strong ties are challenging to create on college campuses since faculty are often not on campus, work from home and travel, and often having few regular interactions with other faculty or staff (Burgan, 2006). Non-tenure-track faculty are working at multiple campuses and increasing in number, making up two-thirds of the academy (Kezar & Sam, 2010). Department chairs and other administrators are often isolated and broken up into different siloed schools and units (Kezar & Lester, 2009). Also, given the many external networks that campus staff are likely to be part of—disciplinary areas, professional groups, and local and regional communities—ties might be weak and diffuse (Kezar, 2001). Clearly, this differs by campus context, as small or rural campuses may have capacity for strong ties, for 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 introduce the innovation to people is rare. Also, research demonstrates that low-conflict organizations are much more likely to have strong ties than high-conflict organizations (Nelson, 1989). Campuses are often places of conflict, and faculty see conflict as part of their socialization. We also know that trust is low within campuses and is critical to building strong networks (Tiemey, 2006). Campuses seem a challenging environment for strong ties and this assumption/hypothesis needs investigation. The lack of hierarchy on some campuses (or with certain groups—faculty Higher Education Change and Social Networks 109 being given some authority on certain issues) might suggest that knowledge sharing occurs through networks and that at least weak ties might allow strong information flow that can lead to some outcomes associated with change (Tsai, 2002). Also, the nature ofthe initiative or kinds of relationships that can be developed within certain on-campus groups may create stronger ties. For example, if I have several colleagues that are interested in a similar pedagogical technique, we might form a strong tie; or, if others in my network are in similar circumstances, such as being faculty of color or early-career faculty, we might form stronger 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, individuals who remain affiliated with off-campus and online networks may develop strong ties of affinity that may be levers for change in the academy (Wenger, 1998). External groups that cross boundaries, such as the World Bank and Organisation for Economic Co-operation and Development (OECD), even though they have weak ties to campus, may have a significant impact because of their ability to share and spread globally. Based on these findings, the following hypotheses are derived: 1. Hypothesis: On-campus networks are characterized by weak and diffuse ties, and this prevents change from occurring locally. 2. Hypothesis: The lack of trust, conflict, autonomy, and/or disconnection of faculty and siloed units will create weak links for on-campus networks, making certain outcomes and processes a challenge. 3. Hypothesis: Higher education stakeholders benefit from off-campus networks, such as disciplinary societies and professional groups, that foster weak and, in some instances, strong ties that might be capitalized on campus for change. 4. Hypothesis: Off-campus networks (e.g., disciplinary or professional groups) that create strong ties can be mobilized for cross-sector or enterprise-level changes in the academy. 5. Hypothesis: External groups (e.g., OECD) that have a global network and reach can impact change even with weak ties. Longevity of Ties and Organic Versus Artificial Networks One might assume that because campuses (and as a result, disciplinary groups/professional organizations) have many long-term employees that there are more opportunities for individuals to be connected through long-term ties. Also long-term employment may facilitate organic networks, and so there may be less of a need to artificially constmct networks to facilitate change. Historically, higher education likely 11 o The Journal of Higher Education had longevity of ties and many organic networks. Yet with the trends toward non-tenure-track faculty, staff layoffs, and reductions, campuses may be losing long-term, existing networks. Or non-tenure-track faculty may create networks that are not campus-based but which cross boundaries to support each other. Such examples already exist, such as the New Faculty Majority and academic unions. Will the trend toward nontenure-track faculty create less longevity of ties in disciplinary societies as well, or will new organizations/networks be formed such as the New Faculty Majority? We need studies about campus networks in terms of longevity, changes in staffing pattems, the impact on on- and off-campus networks, and the degree to which this might differ for groups on campus—faculty, student affairs, business affairs, or by different disciplines. Perhaps because campuses and disciplines vary so much, it will be important for studies to be conducted at a local level. Also it will be important to look in new places for emerging networks that will be different from traditional ones represented through disciplinary societies and professional organizations. Another implication and line of research is to examine constmcted, not organic, networks. What are the best ways organizations can enhance or build networks on an ongoing basis that will support changes in the higher education sector and on college campuses? Studies have found that a critical competency for leaders is networking across units and divisions in order to develop relationships so that they can connect people and create stronger ties (Tenkasi & Chesmore, 2003). Given change is an ongoing phenomenon, leaders may not want to wait until they propose a major change initiative to think about network development. Instead, effective leaders are likely those that see relationship and network development as connected to creating stronger ties for change. There is limited leadership research that examines the way campus leaders create stronger ties between people on campus. As noted earlier, political theories examine how leaders build alliances and coalitions, and scientific management theories describe leaders' efforts to include people on teams for planning processes that may end up creating relationships (Baldridge et al., 1977; Clark, 1983; Kezar, 2001). Most college campuses today would say that they are in the flux of a variety of change processes and likely could benefit from constmcted networks, yet we do not know much about how to create them. Networks are also being formed locally, regionally, and nationally to support change. There are hundreds of online communities that are connecting people on meaningful issues and changes they wish to make in higher education (see, for example, the New Faculty Majority, http://www.newfacultymajority.info, and the National Association for Higher Education Change and Social Networks 111 Student Personal Administrators Knowledge Communities, http://www. naspa.org). Next, there are more structured and sometimes more formal networks being formed regionally and within communities to create changes in higher education (see the NERCHE Think Tanks, http:// www.nerche.org). An example of the structured networks is Project Kaleidoscope (http://www.pkal.org/), which brings together faculty interested in STEM undergraduate reform. It is more structured in that it offers formal workshops, conferences, and times for face-to-face interaction. The process of interaction is more prescribed and targeted. There are also external networks that formally interact with campuses—for example, alliances and consortia—that have been the subject of little if any study (for example, ACL, http://www.national-acl.com/, and Connect, http://www.connectsemass.org/). Because people volunteer for these various communities, even though they are constructed, they will operate more like organic networks than constructed networks. Most constructed networks studied have been within formal organizations, such as cross-functional teams or learning communities. Based on these findings, the following hypotheses are derived: 1. Hypothesis: Because of their historic trend toward long-term employment, there will be some networks that have longevity of ties that can be levers for change (but there is need to test whether and how they are used). Longevity of ties is becoming increasingly uncommon on college campuses, and this will be a less likely lever for change in the future. 2. Hypothesis: Even though some organic networks exist, because they are likely to be weak (even though they are long-term), higher education institutions may need to artificially construct or support networking to facilitate change. 3. Hypothesis: Leadership can foster networks by creating structures to support network formation that can enable change. 4. Hypothesis: Online and informal constructed communities will operate like organic communities. As communities become more formalized and attached to organizations (such as CIRTL, http://www.cirtl. net/), they will lose the advantages (trust, expressive ties) of organic networks. Diversity of Ties and Subgroups Organizational theory also suggests that given the diversity of stakeholders and groups in higher education by discipline or unit and division (alumni affairs vs. business), higher education is likely to have significant heterophily and have many diverse ideas flowing that can lead to change. However, the diversity may lead to difficulty in trust formation. Yet the many subgroups and committees on campus might 112 The Journal of Higher Education help overcome the downsides of heterophily as people have a chance to work in small groups on an ongoing basis. It may be that committees as artificial network structures are not developing the types of relationships important to change. We need to study and test the roles of committees and other meaningful subgroups (that operate like teams within organizations) and their ability to create new ties, build trust, allow information flow, and further other network development aimed at change. Off-campus networks are likely to experience much more homophily. For example, disciplinary societies bring together people with similar interests, as do professional organizations for student or business officers. These networks often have smaller affinity or subgroups that can create expressive ties and trust. Formal networks off-campus will be a place where significant change may be leveraged. In addition to these more formal organizations, less formal organizations can be online but also take a physical form through regional and national movements for diversity, sustainability, and innovative teaching and learning that provide points of similar interest within and across campuses. There may be more levers for change within these non-campus-based networks that can draw on homophily for change. Global international networks and groups like the International Monetary Fund, World Bank, and World Trade Organization will create a sense of homophily by developing a sense of common interest and agenda across campuses and groups worldwide (Rhoades & Slaughter, 2004). Furthermore, the worldwide neoliberal agenda advanced through groups like the John Olin Foundation will create networks that are homopholous and where information readily flows and potentially creates changes worldwide at a more rapid pace. Organizations such as the World Bank create policies and practices such as GATT (General Agreement on Tariffs and Trade) that have impacts on educational organizations and support the flow and interchange between formerly isolated campuses. Yet there will be countervailing forces from non-governmental agencies (NGOs) that often have more local connections and networks that attempt to foster more heterophily. Based on these findings, the following hypotheses are derived: 1. Hypothesis: The diversity of ties will allow greater flow of ne-w ideas into higher education, particularly on-campus where multiple and diverse groups are brought together. 2. Hypothesis: The presence of committees, task forces, and subgroups onor off- campus can provide a mechanism to overcome the downsides of heterophily if designed to do so. Higher Education Change and Social Networks 113 3. Hypothesis: Deep changes may be best addressed through off-campus networks (both formal and informal) that bring together like-minded individuals and where trust and expressive ties can be developed. 4. Hypothesis: Global networks will create greater homophily among campuses in vastly different regions leading to changes that cross national boundaries and greater flow of ideas. Yet NGOs will provide a countervailing heterophily. Central Actors and Opinion Leaders Organizational theory suggests that if higher education institutions are loosely coupled and place less emphasis on formal authority, central actors may be less important than opinion leaders for campus networks (Kezar, 2001). Furthermore, campuses are expert organizations where people do not necessarily react and change based on pure influence but evaluate the veracity of the individual more. Opinion leaders may be likely to have a greater impact. Yet campuses are becoming increasingly hierarchical in structure due to neoliberalism," and this might make central actors more important in the future. In off-campus networks, opinion leaders are also more likely to have an impact as leadership within disciplinary societies or even professional groups rotates very regularly. Central actors may be less common within formal hierarchy but may be influential in informal networks (on- and off-campus) created around interests like LGBT support or environmentalism. Based on these findings, the following hypotheses are derived: 1. Hypothesis: Opinion leaders will be more influential than central actors in many higher education settings, including on-campus networks and off-campus formal networks like disciplinary societies. 2. Hypothesis: In more bureaucratic and hierarchical campuses (some community colleges or campuses affected by neoliberalism), central actors will play a more critical role in change. 3. Hypothesis: For informal networks of interest, central actors likely play a more key role in change than opinion leaders. Expressive Versus Instrumental Functions As noted earlier, organizational theory demonstrates that higher education employees tend to be long-term—faculty may be on a campus their entire career, staff often are long-term, and even non-tenure trackfaculty tend to stay employed at institutions fairly long-term (averaging 7 years) (Kezar & Sam, 2010 ). This long-term employment (historically at least) would lend itself to expressive ties that help facilitate change. Yet the lack of interaction of many groups (particularly across groups) may mean that over time they develop only instrumental ties 114 The Journal of Higher Education based on their role rather than friendship ties. This likely differs by campus context as well. Small liberal arts colleges are likely to have more expressive ties develop because ofthe community culture and orientation, whereas large urban research universities may have commuting staff and faculty that interact less and have a more bureaucratic feel. Within formal off-campus networks (disciplinary and professional), expressive ties are likely more significant than instrumental ties, as people do not usually interact in relationship to an organizational role within these type of networks but instead interact based on common interests that can spark a friendship. As noted in other sections, higher education professionals are increasingly connecting informally, based on interests through local communities or online. These informal networks are likely to be expressive, as they often will not share common roles and affiliation will be based on interests. Based on these findings, the following hypotheses are derived: 1. Hypothesis: Expressive/instrumental networks are more likely to be leveraged within certain campus contexts (small and communal vs. large and bureaucratic) based on their culture and institutional type. 2. Hypothesis: Expressive ties are more likely to be fostered in both formal and informal off-campus networks. 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 hiring non-tenure-track faculty that erode tenure (Burgan, 2006; Rhoades, 1996). The heterophily of stakeholders previously mentioned generally creates a context where tmst may be low as people feel a lack of similarity to others in the network. Yet,we know that tmst will also vary by campus context based on communication, transparency, and history (Tiemey, 2006). Off-campus networks are more likely to be able to develop tmst than on-campus networks. There are fewer decisions made within off-campus networks that would likely alienate members. Based on these findings, the following hypotheses are derived: 1. Hypothesis: Campuses that exhibit less trust will likely hinder network formation—particularly around instrumental ties. 2. Hypothesis: Trust is likely to be high in off-campus networks and therefore may be a stronger lever for certain types of changes. Higher Education Change and Social Networks 115 3. Hypothesis: Campus leaders that create trust will be more likely to enhance networks and foster greater change. Interactions and Sense-Making The findings about how the nature of the interactions between people in the network deeply influencing the outcomes has important implications for college campuses. Academic debate (embraced by faculty) is often counter to dialogue and openness necessary for positive network interactions. It may be that colleges and universities face a particular challenge around networks because dialogue necessary for learning and change is difficult to create. Organizational theory highlights how higher education invests little in fostering interpersonal interactions; human resource management has a background role (particularly with faculty), and minimal training occurs related to conflict management, managing groups or teams, and communication (Kezar, 2001). Leadership and management training occurs within certain campuses, but it is not a routine feature of higher education. This lack of development around interpersonal interactions is likely to impact network development and success. Based on these findings, the following hypotheses are derived: 1. Hypothesis: Higher education networks will be characterized by many barriers and obstacles largely due to poorly developed group interactions that result from the lack of adequate training for interpersonal skills to work in groups. 2. Hypothesis: Higher education settings that offering training in interpersonal interactions and leadership and that foster collégial dialogue are more likely to have networks that can be capitalized on for change. Implications for Policy and Practice It is important to also examine the implications of this research agenda for higher education policy and practice. In the past, policy makers and practitioners have not looked to broader networks for creating changes in the higher education sector. As noted in the introduction, a few funders such as the Lumina Foundation and the National Science Foundation, based on emerging research described in this article, are beginning to examine the potential of networks for creating broader and scaled-up changes. However, there is very limited research at this point to guide policy and action. Because most of the research that exists has been developed outside of higher education, we do not know if the same network design principles will be important or if they will unfold 116 The Journal of Higher Education uniquely within higher education context, which is why the proposed research is so important to guide practice. In the research agenda described above, I suggest some of the ways that policy makers and practitioners should be aware of unique aspects of higher education that might shape how they create, use, fund, and sustain networks. For example, the diversity of ties will allow for complex ideas to emerge through networks, but these diversity ties will also make change processes slower, so they may need to decide if innovation or expediency is more important to their effort. Also, campuses tend to have low levels of tmst, which make network building more difficult. Therefore, policy makers and practitioners might leverage off campus networks where more tmst exists to create change. Another example is that policy makers and practitioners might want to utilize opinion leaders to create changes, as they will have a stronger impact in many higher education settings. Policy makers and practitioners need to look quite broadly at the plethora of different networks (professional organizations, disciplinary, consortia, online, communities of practice, etc.) that exist and identify those that best serve their purposes. If policy makers are aimed at creating broad level sector changes, they may want to mobilize off-campus networks that have greater reach across the sector rather than on-campus networks, for example. This is just a set of examples of the implications of the research agenda described in the article, and as we have more research to guide policy and practice, the field will have even better guidance. For now, scholars can combine insights from organizational and social network theory to develop some hypotheses (and use the ones above in the research agenda) to create studies that can provide evidence to inform practice and policy. Conclusion Ultimately, scholars have focused change research more on the formal, internal organizational structures and ignored networks. We need to balance an organizational perspective with more attention to networks and social relationships. Yet we need to also combine the organizational perspective, as studies of networks related to institutions demonstrate the importance of these settings in shaping the network. Both approaches are needed to understand how change occurs within postsecondary education. Network analysis has usually failed to take into account the formal organization and how it might be impacting change or can help strengthen networks. Higher education takes place in an institutional context, and to ignore this context is also problematic. Higher Education Change and Social Networks 117 Similarly, researchers studying formal organizations have typically ignored social networks and their informal leaders that can create social capital. These researchers' findings result in organizational investment in individual development rather than investments in important social networks. There are many networks that go beyond organizational boundaries and networks that are largely outside of or only impacted in a limited way by organizations (disciplinary networks and online communities are examples). Thus scholars also need to be open to new concepts that expand our notion of relationships, such as online communities. Research on learning communities and communities of practice (Wenger, 1998) is a way that the two perspectives (organizational and network) are coming together in more recent years, yet there are so many other opportunities to study the links between networks and organizations—many offered within this article. 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 education—for an example, see Pusser, Slaughter, and Thomas (2006). ' The focus of this article is on ways social network analysis can be used to create changes in postsecondary institutions—but this cuts across many different areas, including faculty development, pedagogical and curricular innovations, leadership, and decision-making. So while I use the label change and reform, the suggestions apply to a broader set of phenomenon within higher education than this label may suggest. '' There is a danger that organizational theory will dominate and overwhelm the assumptions of networks, which are about seeing beyond organizational boundaries, particularly as organizational theory has been a major lens in higher education. These two perspectives need to be examined equally and balanced. * Attention to social network analysis began in the 1930s, but a significant number of studies were not conducted until the mid-1990s. Thus research has just started to expand in the last decade and a half (Daly, 2010b). ' Given social network analysis examines the network level, changes are focused on the individual and group impact, and these findings may or may not be applicable for organizational changes/impact. So in the article, when referring to the change impact, research goes back and forth between these varying levels—individual and organizational. ' However, it should be noted that most studies do not control for a variety of other factors that could be operating other than the social networks. The prevalence of the studies with similar conclusions is suggestive of a relationship. * It should be noted that poorly formed (e.g., no trust, few ties, many structural holes) networks may not serve these purposes, so the relationship is not always beneficial or advantageous towards a given change initiative. Certain features that will be described below can help harness these benefits. ' A related area of literature to social network analysis is social movement literature, which also emphasizes networks and is also outside formal organizational settings (Hartley, 2009b; Meyerson, 2003). 118 The Journal of Higher Education '" It is important to note that much of the literature on social network analysis uses a more positivistic and quantitative approach stemming from matrix algebra and graph theory to formalize principles from social psychological concepts such as groups (Daly, 2010a, 2010b). Social network analysis is typically associated with this quantitative work using specialized software such as UCINET. Quantitative studies were helpful for understanding the outcomes of networks that emerged as some of the first central questions about their role in change. Yet the principles of social network analysis can also be used in a more constructivist and qualitative approach, but this has been done less often (communities of practice, for example, represent an important way for this type of qualitative analysis to be conducted). Therefore, most of the studies described focus on quantitative social network analysis, as this makes up the majority of the existing literature. Also, because of the methodology used, much of the literature describes networks but is less focused on how networks are formed, how they can be sustained, and other questions more easily answered through qualitative methods. More recent literature on communities of practice has addressed these issues and can be used as a model for thinking about new qualitative approaches to social network analysis (Lave, 1988; Wenger, 1998; Wenger, McDermott, & Snyder, 2002). Ideally, more mixed methods/ paradigm studies would be conducted that help examine network outcomes and design as well as formation and sustaining. " There are some more cutting-edge items of scholarship (see, for example. Moody, McFarland, & Bender-deMoll, 2005). '^ Within social movement literature, this is often described as a charismatic leader (Hartley, 2009b). There are many similar concepts and interconnections between the social movement and social analysis literatures. '^ Recent qualitative social network analysis studies have been looking more at what flows (e.g., character of information) between nodes or nature of interactions (e.g., degree of trust). The quantitative approach to early social network analysis did not allow for understanding the fluidity of the networks and the interactions within them. This is an emerging and important new line of research. ''' However, it should be noted that very little research has been conducted to help us understand the way networks can be intentionally created or formed to help facilitate change processes. " One might interpret this finding to reinforce the importance of organic networks that do not need organizational supports for achieving outcomes. " Another problem with the generalized hypothesis of social network analysis is outlined by Mohrman, Tenkasi, and Mohrman (2003) from studies using SNA across a host of organizational types/disciplines. They show that structural properties of networks reveal only the potential for action and, unfortunately, have been used too much as a structural determinant rather than examining possibilities that may exist. Kilduff and Tsai (2003) offer the following example of the problem with dependence on structural analysis and overly deterministic generalizations. In trying to understand why physicians adopt new technology, social network analysis suggests that competition between rivals within a network typically explains why they adopt the new technology. However, through more careful discussions with the doctors, they found out that friendship, communication, and empathy were the reasons they were using it and that competition was less likely to explain the adoption. More attention to individual agency and a variation of reasons for choices is suggested by examples like this, where prior theory is not just placed on individual actors. Given the multitude of actors within higher education and the differing amounts of power and authority, agency, and context, it is important to keep in mind and understand this. Thus, future research across all fields should be aware Higher Education Change and Social Networks 119 of these problems of overgeneralization—and, given the higher education context, this is an issue to pay particular attention to. 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APPENDIX Network Terms Term Definition Tie/connection Relationship among people; conduits for the now of interpersonal resources Flow The content that goes between people in the network Outcomes Result of flows, ties, and interactions Density Number of ties across a node or network structure Structure Pattern of connections among parties Nodes Individuals/people in the network Tight or dense ties A network with many ties among people Loose ties A network with few ties among people Formal network Has many structures in place and organization to support Informal network Has few structures in place and organization to support Strong ties Network members have frequent interaction, an extended history, and intimacy. Weak ties Network members have infrequent interaction, more limited history, and lack intimacy. Homophily People are attracted to people with similar interests and backgrounds. Heterophily People are less likely to interact with people with different interests and backgrounds. Central actors Individuals who have the most ties to other actors in the network Opinion leaders People who individuals say would influence their choices and attitudes Expressive tie Based on friendship or kinship Instrumental or functional tie Based on role or position Structural holes Gaps in the network between groups Liaisons/bridging Individuals that span different subgroups Connectedness The degree to which individuals are exposed to an innovation through the network Copyright of Journal of Higher Education is the property of Ohio State University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Transforming “Apathy Into Movement”:The Role of Prosocial Emotions in Motivating Action for Social Change Emma F. Thomas The Australian National University, Canberra Craig McGarty Murdoch University, Perth, Australia Kenneth I. Mavor The Australian National University, Canberra This article explores the synergies between recent developments in the social identity of helping, and advantaged groups’ prosocial emotion. The authors review the literature on the potential of guilt, sympathy, and outrage to transform advantaged groups’ apathy into positive action. They place this research into a novel framework by exploring the ways these emotions shape group processes to produce action strategies that emphasize either social cohesion or social change. These prosocial emotions have a critical but underrecognized role in creating contexts of in-group inclusion or exclusion, shaping normative content and meaning, and informing group interests. Furthermore, these distinctions provide a useful way of differentiating commonly discussed emotions. The authors conclude that the most “effective” emotion will depend on the context of the inequality but that outrage seems particularly likely to productively shape group processes and social change outcomes. Keywords: I emotion; social identity; helping/prosocial behavior; group processes; morality n 1938, Carl Jung wrote, “There can be no transforming of darkness into light and of apathy into movement without emotion” (p. 32). In this sentence, Jung celebrates the profound role that emotion plays in directing and shaping human behavior. Although individual emotion has long been a mainstay of clinical, personality, and social psychological research (e.g., the work of Ekman et al., 1987; Manstead & Fischer, 2001; Scheff, 1990; Scherer, Schorr, & Johnstone, 2001, to name a few), the advent of intergroup emotions theory (Mackie, Devos, & Smith, 2000; Mackie, Silver, & Smith, 2004; Smith, 1993) signaled increasing interest in the contribution that group-based emotion can add to the study of social phenomena, including prejudice and discrimination (Brown & Hewstone, 2005; Smith, 1993), social harmony and reconciliation (Nadler & Liviatan, 2006), and social and political action (e.g., Iyer, Schmader, & Lickel, 2007; Leach, Iyer, & Pedersen, 2006; van Zomeren, Spears, Leach, & Fischer, 2004; see earlier contributions from relative deprivation theory, Runciman, 1966; Walker & Smith, 2002, for a review). This article concentrates on a specific aspect implied in the Jung quote above: the power of emotion to transform “apathy into movement.” More specifically, this article explores the transformation of an advantaged group’s apathy into movement to promote greater social equality. Following Leach, Snider, and Iyer (2002), we define advantaged groups as those “secure in their position, due to their greater size or control over resources” (p. 137). Thus, the scope of this article is defined by, first, a focus on group emotion and, second, a focus on emotions that advantaged group members experience in relation to other people’s deprivation. We argue that it is in this situation of relative advantage that the power Authors’ Note: The research has been supported in part by the Australian Research Council Discovery Project Grant No. DP0770731. The authors wish to thank Galen Bodenhausen and two anonymous reviewers for their helpful comments on an earlier version of this article. Please address correspondence to Emma F. Thomas, Regulatory Institutions Network, The Australian National University, Canberra, ACT, 0200, Australia; e-mail: emma.thomas@anu.edu.au. PSPR, Vol. 13 No. 4, November 2009 310-333 DOI: 10.1177/1088868309343290 © 2009 by the Society for Personality and Social Psychology, Inc. 310 Thomas et al. / TRANSFORMING APATHY INTO MOVEMENT   311 of emotion to transform apathy to action is most profound—what Nietzsche (quoted in Leach et al., 2002) called “poisoning the consciences of the fortunate” (p. 136). Accordingly, this article explores the various emotional reactions that advantaged groups can have to the disadvantage of others and the potential for these discrete emotions to motivate efforts to achieve greater social equality. We draw on recent developments in the social identity literature that outline the ways that social group memberships shape prosocial behavior (e.g., helping and solidarity; Reicher, Cassidy, Wolpert, Hopkins, & Levine, 2006) to provide a framework for understanding the various prosocial effects of group emotion. Taking a social identity perspective (Tajfel & Turner, 1979; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987; Turner, Oakes, Haslam, & McGarty, 1994), we explore the ways that social identities and emotion can, in combination, profoundly inform perceivers about the social context and shape their reaction to it. ...
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Running Head: DISCUSSION 2

Discussion 2 – Positive Social Change

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DISCUSSION 2
Positive Social Change
In the understanding of Aguinis & Glavas (2012), positive social change may present in
varied structures: organizations and institutions, communities, governments, and individual
relationships. Social change in this aspect, being categorized as the progress achieved within a
community that enables its progress. Thus, it can be drawn that, when the change is good,
necessary products from within mem...


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