Social Network Disruption (MB#4)

Last fall, in conjunction with MSLOC 455, I built a social network graph of the people in my function that captured patterns of advice and knowledge seeking among the network members. My intent was to understand how information flowed between individuals and teams, where there were strong connections, and where there were holes between teams. My thinking was that understanding this network could help effectively disseminate communications about change.

But what happens when that network is disrupted? Since I built the graph last fall, many people have either left the function or left the company entirely, disrupting the structure that I had mapped out. Unfortunately, many of those who have left have not been replaced, so I’m afraid there may be gaps now that did not exist previously.

What happens when employee networks are disrupted? What are the consequences? Are there any ways to mitigate network disruptions caused by exiting employees? That’s the focus of my reading right now, and my comments on some of the research will be the subject of a future blog post.

DOEC Project Progress Report

I detailed my engagement with the work of Rob Cross and my learning about how he has used social network analysis to drive change management in an earlier blog post (Social Network Analysis and Change Management) . In this post I want to take a step back, and outline the intention of the organizational effectiveness project I am working on, and comment on where I am and my next steps.

Back in 2015, my organization administered an organizational health survey, the results of which were shared with functional leadership teams. I was asked in 2016 to lead a team to explore the results on the Change Management portion of the survey. My discovery process utilized round table discussions with a number of groups that represented a broad cross-section of the function, in terms of both level and roles. My team then assembled and analyzed the data collected in these sessions, and distilled the data into a number of themes around change management.  The broad overarching theme was that changes impacting the function were not effectively communicated: round table participants pointed to gaps in timeliness of communication, the communication of intent and benefits, and reporting around progress and achievement of goals.

In response to this, my HR partner and I spent several months in 2017 delivering training intended to build associates’ skills as change agents and leaders. Time well spent, and of great value, but my concern has been that we have not fully addressed some of the key concerns surfaced around change communication. Consequently, as I entered into the Design for Organizational Effectiveness program, I felt I had a ready-made project to explore. My theory, derived from my reading of Rob Cross (noted above) is that social network analysis can be used to facilitate change communication within my organization. I believe that I can use the results of a social network analysis to pull together a team of individuals ideally placed to facilitate the communication of change within my function.

I have spent some time in my fall quarter 2017 class exploring the basic concepts and tools of social network analysis, and I have also invested time in learning an SNA tool, NodeXL. Armed with the results of my discovery round tables and my new tool, I created a survey that I sent out to associates in my function late in 2017, and have used NodeXL to build a social network graph. I have begun but not completed my analysis of my network graph.

I have identified several next steps to flesh out my prototype and  push my project forward:

  • I need to complete my social graph analysis, and assemble my change management team.
  • I have to plan exactly how I am going to use the team.
  • I need to determine the best way to measure effectiveness of the team:
    • Mini-surveys
    • Round tables
    • Another global organizational health survey will take place over the summer, but I hope to have at least some results before then.
  • Finally, I would like to plan out a  couple prototype cycles, if possible, to test my theory and make adjustments.

Fortuitously,  my function just rolled out a substantial change, so when I run a prototype test with my change management team, I will have a change event to which I can draw comparisons. More to come!

Seeking Articles on Social Network Analysis

I’ve spent the last ten days or so furiously “seeking” background research on social network analysis. I have had a basic introduction to the concepts and applications of SNA, and I have a rough idea of how I want to experiment with SNA to drive change management in my organization. Realizing, though, that I have just enough knowledge to be dangerous, I am digging through the research, ransacking libraries and databases,  to build out my understanding of the principles of SNA and how I can use it.

I will readily confess to being overwhelmed: so far, I’ve looked at 57 resources, some broad, some narrowly focused, and I’ve realized how very much I need to learn. I’ve used The Brain application to start building out a simple outline (sense making) of what I have looked at so far:

SNA Outline

I’ve divided the material I’ve reviewed so far into two large areas: basic components of social network analysis (centrality, structural holes, ties, and so on) and applications (Influence, Knowledge Management, and Social Capital being of most interest to me now.)

My critical interest right now lies along the subject of influence, and there is plenty of research covering two different aspects that I want to further explore. The subject of leader influence in networks is covered in articles by Balkundi, Flodgren, and Valente (see references below). Balkundi looks at the impact of team structure and leaders’ networks to understand the impact to team effectiveness. Flodgren looks at local opinion leaders and how they influence medical outcomes. In a similar vein, Valente looks at how opinion leaders can act as gatekeepers for interventions, help change social norms, and accelerate behavior change. I’m also looking at methods for identifying key players, algorithms for which are explored by Borgatti and Kempe.

Review of the Borgatti and Kempe articles is pointing me in the direction I need to go next: before I proceed any further with my project, I need to have a better understanding of centrality, one of the basic concepts of social network analysis. The whole concept of Key Players is based in measures of centrality, so in order to fully understand the former I need to completely ground myself in the latter. Marching orders for the next several days are therefore to take a look at what I have collected so far on the subject of centrality, and get a grip on it. I’ll take it from there!


Balkundi, P., & Harrison, D. A. (2006). Ties, Leaders, and Time in Teams: Strong Inference About Network Structure’s Effects on Team Viability and Performance. Academy of Management Journal, 49(1), 49–68.

Balkundi, P., Kilduff, M., & Harrison, D. A. (2011). Centrality and charisma: Comparing how leader networks and attributions affect team performance. Journal of Applied Psychology, 96(6), 1209–1222.

Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational and Mathematical Organization Theory; Dordrecht, 12(1), 21–34.

Flodgren, G. (2011). Local opinion leaders: Effects on professional practice and health care outcomes.

Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the Spread of Influence Through a Social Network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 137–146). New York, NY, USA: ACM.

Valente, T. W., & Davis, R. L. (1999). Accelerating the Diffusion of Innovations Using Opinion Leaders. The ANNALS of the American Academy of Political and Social Science, 566(1), 55–67.

Valente, T. W., & Pumpuang, P. (2007). Identifying Opinion Leaders to Promote Behavior Change. Health Education & Behavior, 34(6), 881–896.

Response to Social Network Analysis and Diversity & Inclusion

“Has a social network analysis (SNA) been created to link diverse candidates in the tech field?  What if businesses had access to SNA data, would the difficulty with identifying and backfilling strategic roles in the Tech sector, with deeply rooted gender and ethnicity imbalances, be solved?  Can SNA be secret sauce in the talent pipeline?”

via Social Network Analysis and Diversity & Inclusion

Julia, very interesting idea, and I do wonder how something like this would work. I think that individuals certainly have an opportunity to leverage network visualization to aid in their recruiting efforts. Sites like Socilab provide the ability to visualize your LinkedIn network (see mine in the image above). That visualization, in turn, could help you pinpoint individuals in your network whom you could connect to others, or whose connections you could call on when looking to fill a role. Looking at my own network this morning, I was quite surprised by some of my contacts who bridge the gaps between my different network groups. I realized that I will have to spend more time exploring some of these connections.

I’m not sure, though, how businesses would best use network information for recruiting, without being intrusive or violating privacy. We may find ourselves backing in to finding out, now that Microsoft has purchased Linked In. Microsoft has promised to integrate LinkedIn data into Outlook in innovative ways, but I do wonder if, in their eagerness to foster connections, Microsoft may inadvertently risk violating users’ privacy with this sort of integration. It’s a risk we will have to be aware of as we use these software tools.

Social Network Analysis and Change Management

In #msloc455 I’ve been engaged learning how social network analysis (#SNA) can be used not just to examine the flow of knowledge and information across a network, but actually leveraged to drive a change initiative.  Rob Cross and his colleagues have researched and outlined a specific application of social network analysis that is of great interest to me. Cross has studied how leaders can leverage the insights of social network analysis to identify opinion leaders who can help engage and drive others in a change effort. Cross identifies multiple roles (Connector, Expert, Broker, Energizer, and Resister) that provide insight into the different ways that leaders can leverage people in a change effort.  Connectors (those who support many team members in different ways), for example, can help create alignment through their informal leadership and trusted opinions. Brokers, with their ties that bridge organizational boundaries, can help address the need for adjustments that take place during a change initiative. Resisters, who can stall momentum and deenergize colleagues, can be brought on board and engaged to aid change initiatives. The problems that modern organizations face are complex and interconnected, and therefore not easily managed by a strictly top-down management approach. Leaders who can understand and leverage the insights that come from social network analysis can expand their organization’s capacity to manage change.

I am facing a substantial process and systems change within my function, and from my reading of Cross, I know that I can in theory use social network analysis to identify key players to assist in this upcoming change initiative. I have been asked to lead the change management efforts associated with this process implementation, and I have begun to assemble network data to help me understand who the key players are that will be able to facilitate the change. In a diagram I have started building, several key players can be identified as influencers, based on the number of people that turn to them for advice. The diagram illustrates the results of a very simple survey that asked associates to identify those to whom they most frequently turned when they wanted to learn something or had a problem they couldn’t solve. Some individuals, like the function managers, naturally appeared in positions of influence. The network diagram also identified individuals who were unexpectedly revealed to be influential. I’m looking forward to writing more about this over the next several weeks.


Cross, R., Ernst, C., & Pasmore, B. (2013). A bridge too far? How boundary spanning networks drive organizational change and effectiveness. Organizational Dynamics, 42(2), 81–91.

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