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May222013

Applications of Social Network Analysis in Technology Leadership Research

Applications of Social Network Analysis in Technology Leadership Research

Yinying (Helen) Wang

University of Cincinnati

In response to the dearth of technology leadership scholarship (McLeod & Richardson, 2011) and Daly’s (2010) claim that social network analysis has been underused in education research, this article introduces the applications of social network analysis in the discipline of educational technology leadership. Therefore, this article is structured as follows. I begin by introducing the basic concepts in social network analysis. Next, I differentiate social network analysis from conventional statistical analysis. Finally, I explain how to utilize social network analysis in technology leadership research.

What is Social Network Analysis?

Social network analysis, according to Cross and Parker (2004), is a methodological tool detecting the patterns of social structure, analyzing information flow within the network, and uncovering the formation and evolution of network. Building on the conception that “social life is created primarily and most importantly by relations and the patterns formed by these relations” (p. 11), social network analysis uses mathematical models to measure and analyze relational structure, and its influences on both individual behaviors and systemic performance (Martin & Wellman, 2011). From the perspective of social network analysts, nodes (also called actors or vertices) refer to individuals that formulate the network; and ties (also called edges or links) refer to connections between nodes (Prell, 2011). For example, in a friendship network, nodes represent the friends, and ties represent the friendship. Ties can, either be dichotomous as 0 or 1, indicating whether the friendship exist; or be assigned with different weights, indicating the quality of friendship (e.g., acquaintance or close friends).

Social Network Analysis vs Statistical Analysis

Social network analysis distinguishes itself from conventional statistics on three major fronts. First, social network analysis primarily focuses on the absence or presence of relationship and its impact in a network (Wasserman & Faust, 1994); whereas, statisticians measure and analyze the attributes of individuals, such as age, gender, ethnicity, income, and so forth (Martin & Wellman, 2011; Masser, Alvarez, Prosperi, & Mitsova, 2012). This difference leads to the distinctive approaches in data preparation. Specifically, social network analysts build matrices. In Figure 1, the matrix indicates Adam and Cindy are acquaintances, but Adam and Bill are close friends. In contrast, statisticians organize data by subjects, as seen in Figure 2 which displays a snapshot of data file for statistical analysis.

Figure 1 Matrix of a Friendship Network

 

Adam

Bill

Cindy

Adam

0

2

1

Bill

2

0

0

Cindy

1

0

0

 

Figure 2 Snapshot of Data File for Statistical Analysis

 

Age

Gender

Reading score

Adam

10

1

85

Bill

9

1

90

Cindy

10

2

89

 

The second difference resides in fundamental assumption in two analysis approaches. One of the assumptions for statistical analysis is the dependence of observation: the observation of each subject is assumed to be dependent. Social network analysis, however, assumes nodes are interdependent, which reflects the innate nature of network formation and evolution.

Third, social network analysis is conducted at both individual and network level, whereas statistical analysis detects the relationships among variables. At individual level, social network analysis computes centrality measures for each individual, revealing individuals’ influence or importance in the network. At network level, social network analysis detects network structure and examines how individuals’ attributes affect the network formation and evolution. Take the friendship network depicted in Figure 1 as an example. Adam and Bill, Adam and Cindy are friends, respectively; but no friendship exists between Bill and Cindy. According to Granovetter’s (1973) strength of weak ties theory, as time goes by, Bill and Cindy have an increased probability of forming a friendship (also called weak tie in network terminology) because of the shared attributes between Adam and Bill, and Adam and Cindy (e.g., same neighborhood, same hobby, and so forth). It is worth noting that the analysis of the relationship between node attributes and network structure requires two-mode network data (i.e., network data regarding the absence or presence of relationships and the data on node attributes), rather than one-mode data (i.e. network data).

Applying Social Network Analysis in Technology Leadership Scholarship

In this section, I explore the applications of social network analysis in educational technology leadership scholarship in three domains: organizational communication, social capital, and mixed methods research.

Organizational Communication

With the extensive use of information and communication technology in educational leadership, social network analysis can be used to examine the similarities and differences between online relationship and offline relationship. Employing social network analysis, Penuel et al. (2010) revealed the notable variances between formal and informal communication in schools’ organizational change. In the same vein, offline communication might exhibit disparities from the online communication through social network analysis. Taking this idea a step further, researchers can also use social network analysis to address the questions about how online communication within an organization affects offline communication, and vice versa. No existing literature on technology leadership has addressed such questions, but some similar studies have been conducted in other areas. An example is a study using employees’ communication on social media to infer internal organizational structure in six hi-tech companies (Fire, Puzis & Elovici, 2013).

Social Capital and Social Media

Flashback to late 1990s, Lin (1999), a prominent scholar who developed network theory of social capital, predicted the promising role of Internet in creating social capital. According to Lin (1999), social capital is “resources embedded in social structure which are accessed and/or mobilized in purposive actions” (p. 35). In the digital age, social media stands itself out with its social features such as information sharing, collaboration, and creation, when compared to traditional online media which are simply the outlets of gathered information (Goodfellow & Maino, 2010). 

Within in the context of technology leadership scholarship, although Wang (2013) proposed the possibility of using schools’ social media to create social capital, more research are needed to unravel the mechanism of social capital generation process via institutional use of social media. In response to the increasing need of conducting network analysis on social media data, Social Media Research Foundation developed NodeXL (Smith et al, 2010), a free and open-source software for social media data collection and network analysis. Along with other network analysis software programs, including UCINET and Pajek, scholars are well-equipped with research tools to explore the broad implications of social media on social capital in education.

Mixed Methods Research

Social network analysis, as argued by Prell (2011), is a valuable asset in mixed methods research. The variables regarding structural relations generated from social network analysis can be used as independent or dependent variables in statistical analysis. An example is an investigation of the relationships between school principals’ structural position in school social networks, transformational leadership, and schools’ innovative climate conducted by Moolenaar, Daly and Sleegers (2010). In their study, a social network survey was used to collect network data for the social network analysis of principals’ structural position in their schools’ social networks; the data on transformational leadership and innovation climate were collected, respectively, through the instruments established in prior literature. Principals’ centrality measures, computed from social network analysis, were then used as the variables for the subsequent correlation analyses. The findings of Moolenaar et al.’s (2010) study were very illuminating: the more central and connected a principal in the school social network with teachers, the more teachers had positive perception on school’s climate and were willing to take risks in school’s innovation.

In addition to the mixed methods research design of network analysis and quantitative method, researchers also used qualitative method to collect network data for further social network analysis. For example, to study the influential players in state reading policy development, Song and Miskel (2005) first used qualitative data collected from interviews and archives as the source to construct state reading policymaking networks for social network analysis. Through the network metrics of centrality and prestige computed from social network analysis, Song and Miskel (2005) found that government agencies (i.e., offices of governor, education committees in state legislatures, state departments of education, and state boards of education) exerted stronger influences on state reading policy than non-government agencies (i.e., teacher organizations, education associations, higher education institutions, citizens groups, business groups, foundations, think tanks, and the media).

To date, the above introduced mixed methods research design with social network analysis has not been used in technology leadership research. This article, hopefully, provides a fresh eye to approach technology leadership scholarship with a network perspective. 

Conclusion

As a final note, this article serves as weak tie between technology leadership scholarship and social network analysis. The proposed applications of social network analysis in technology leadership in this article, aim to initiate the conversation on technology leadership research. The scope of applying social network analysis in technology leadership scholarship goes far beyond the above three proposed domains. I sincerely invite aspiring researchers in all related areas to join this conversation.

References

Cross, R. L. & Parker, A. (2004). The Hidden power of social networks: Understanding how work really gets done in organizations. Boston, MA: Harvard Business Review Press.

Daly, A. J. (2010). Social network theory and educational change. Cambridge, MA: Harvard Education Press.

Fire, M., Puzis, R., & Elovici, Y. (2013). Organization miming using online social networks. Retrieved from http://arxiv.org/abs/1303.3741

Goodfellow, G. W., & Maino, D. M. (2010). ASCOTech: World Wide Web as easy as 1.0, 2.0, 3.0. Optometric Education, 35(2), 62-63.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. 

Lin, N. (1999). Building a network theory of social capital. Connections, 22, 28-51.

Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. In J. Scott & P. J. Carrington (Eds.), The Sage handbook of social network analysis (pp. 11-25). Thousand Oaks, CA: SAGE Publications Inc.

Masser, A., Alvarez, A. E., Prosperi, D. C., & Mitsova, D. (2012). Comparing metropolitan governance in Germany and the USA: A social network analysis. Proceedings from REAL CORP 2012 Tagungsband. Retrieved from http://www.corp.at/archive/CORP2012_82.pdf

McLeod, S. & Richardson, J. W. (2011). The dearth of technology-related articles in educational leadership scholarship. Journal of School Leadership, 21(2), 216-240.

Moolenaar, N., Daly, A., & Sleegers, P. J. C. (2010). Occupying the principal position: Examining relationships between transformational leadership, social network position, and schools’ innovative climate. Educational Administration Quarterly, 46(5), 623-670.

Penuel, W. R., Riel, M., Joshi, A., Pearlman, L., Kim, C. M., & Frank, K. A. (2010). The alignment of the informal and formal organizational supports for reform: Implications for improving teaching in schools. Educational Administration Quarterly, 46(1), 57-95.

Prell, C. (2011). Social network analysis: History, theory and methodology. London, England: Sage Publications Ltd.

Smith, M., Milic-Frayling, N., Shneiderman, B., Mendes Rodrigues, E., Leskovec, J., & Dunne, C., (2010). NodeXL: A free and open network overview, discovery and exploration add-in for Excel 2007/2010. Retrieved from http://nodexl.codeplex.com/

Song, M., & Miskel, C. G. (2005). Who are the influentials? A cross-state social network analysis of the reading policy domain. Educational Administration Quarterly, 41(1), 7-48.

Wang, Y. (2013, March). Social media in schools: A treasure trove or hot potato? Journal of Cases in Educational Leadership, 16(1), 83 - 91.

Wasserman, S., & Faust, K. (1994) Social network analysis: Methods and applications. New York, NY: Cambridge University Press.

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