Using Social Network Analysis to Evaluate Communities of Practice: A Methodological Journey

1. Using Social Network Analysis to Evaluate Communities of Practice: A Methodological Journey Kathryn Everest Director, Strategy Consulting Jive Software

2. What is Social Network Analysis (SNA)? • A set of methods and metrics that shows how entities interact … • the current patterns of communication, information-sharing, decision-making and innovation within a particular organization or group • Focuses on three elements: • A group of entities (in this case researchers and institutions) and the roles they play • The relationship between the entities • And attributes might be present that are creating a bias in the relationship of the entities • When applied to organizations, it can be referred to as ONA – Organizational Network Analysis • The outcome of an SNA… • helps us to see where collaboration is breaking down, where talent and expertise could be better leveraged, where information is getting bogged down or where opportunities for innovation are being lost • SNA data… • gives us the picture we need to create a set of remedial actions for individuals, influencers and stakeholder to improve productivity, efficiency and innovation

3. The entities, the relationship between entities, and the attributes of the entities are depicted in SNA maps • Social Entities • People (Researchers, Health Professionals, Experts) • Resources (Collaborative spaces, Information repositories) • Relationship • Information sharing • Advice • Collaboration • Trust • Awareness • Access • Attributes • Contributing factors such as location, tenure, specialty, language, funding, culture

4. SNA also provides insights about roles within the network, as well as metrics that allow us to measure where we are and where we want to be • Roles • Central roles – those who many people go to (or could be bottleneck) • Peripheral people - goes to others but no-one goes to them • Boundary Spanners – connects different groups • Brokers – connects many people • Isolate - not connected Network Measures Density = 6% Cohesion = 4 Centrality = 6 Central People DPa(34), CR(29), BB(20), MDo(19), DPr(17) • Density --- Robustness of network (group measure) • Number of connections that exist in the group out of 100% possible in that network • General level of linkage. More points connected means quicker and more accurate information flow • Cohesion --- Ease with which a network can connect • Distance is the shortest path between two people • Aggregate measure at network level reflects average distance between people • Centrality --- Identifies influential people (individual measure) • Number of direct connections that individuals have with others in group • Individuals who have more ties to others may be in more advantaged positions; they may have access to more of the information or knowledge in the network

5. Why do an SNA?

6. Develop “before and after” measurements (e.g. What impact has your community had on the network?) 1: Area 1 (Circle) 2: Area 2 (Square) 3: Area 3 (Up Triangle) 4: Area 4 (Plus) 5: Area 5 (Down Triangle) 1: Geo 1 (Red) 2: Geo 2 (Pink) 3: Geo 3 (Black) 4: Geo 4 (Dark Blue) 5: Geo 5 (Grey) Awareness Network - Density = 4.83% 861 Ties

7. The SNA can provide understand the potential impact losing core researchers to a network Sample hypothesis: Without core XX researchers, collaboration supporting XXX research would be severely affected • Research collaboration with core researchers • Research collaboration without core researchers Example

8. Identify issues and opportunities in the network Note – Removed Gov/Agency and K-12

9. Identify Brokers Consultant • Name • Name • Name • Name • Name • Name Representative • Name • Name • Name • Name • Name • Name • Name • Name • Name • Name • Name Liaison • Name • Name • Name • Name • Name • Name Gatekeeper • Name • Name • Name • Name • Name • Name • Name • Name • Name Liaison Consultant Representative Gatekeeper

10. Measure and identify issues in individual networks

11. Understand the makeup of your network Authority High Involvement 13 11 In-degree Apprentice Low Involvement 16 10 Out-degree Notes: Removed all nodes with a 0 out-degree Higher in-degree and out-degree than the mean Framework Source: Steve Borgatti

12. Provide a perspective of a person and their network Description: Awareness: Awareness describes a person is aware of the knowledge and skills of others in the network. In-degree reflects the number of people who indicated that they believe they are aware of your knowledge and skills. Out-degree reflects the number of people you selected as being aware of their knowledge and skills. The mean represents the mean or median number of the network. SME: SME describes whether a person will call upon another person when looking for expertise related to TOC topics. In-degree reflects the number of people who indicated that they would call upon you for expertise. Out-degree reflects the number of people you selected you would call upon. The mean represents the mean or median number of the network. Betweenness – Betweenness describes how often you fall between two people in the network. The Mean of the network is 51.4 with a maximum value of 533. Comm: Comm (Communications) relates to how many people communicate with each other on a daily, weekly+ (once or more per week), weekly (once a week or less), or monthly (once a month or less). In-degree reflects the number of people who indicated they communicate with you, and out-degree reflects the people you selected. The mean represents the mean or median number of the network. Network Composition by Location: This is analysis looks at location of the people in your network. XX represents the percentage of people in your network who are located in XX, XXX represents the percentage who are located in XXX, and Other represents locations other than XX and XXX. H is the value which represents the heterogeneity of your network. H is a number between 0 and 1 with 1 representing a maximum value. The average result was .46. Centrality Network Composition by Location

13. Example Centrality Network Composition by Location

14. How do you do an SNA? • Gather attribute/demographic and relationship data • Getting the data from public sources / mining data • Survey (bounded and unbounded) • Analyze the data • Pick a tool (I use UCINet, but there are others including NodeXL, Pajek, InFlow just to name a few) And there is a lot to both

15. I use Optimice – http://www.onasurveys.com

16. To use it, you need to register for an Optimice account • There is no charge to create and account, develop and administer a survey • A “free” account however only allows you to download a subset of the data • An account is $75USD per month or $599 per year • You only need to have a valid account when you download data • You can conduct as many surveys as you want, with as many people as you want • You can allow your account to lapse and your data is maintained • They will expunge data on request

17. 1. Log in (after you create your account)

18. 2. Create a new survey

19. 3. About the survey

20. 3. Cont’d

21. Create an introductory email to attach the survey (should reference another communication which puts the project in context) Dear {Name} • What is this project? • Why are you asking these questions? • What will happen to the results? • Will people see how I answered? • When will I hear more? Signed by, To answer the survey please click on this link: {URL}

22. 4. About Respondents – (traditional survey and attribute questions)

23. Respondent List Sun Life Financial SMCS - IBM/SLF Confidential | 14-Jul-14

24. Creating the list of participants Notes: First two columns MUST BE: “Name” and “Email” Create as a CSV file to upload to the survey tool No spaces in the headings Sun Life Financial SMCS - IBM/SLF Confidential | 14-Jul-14

25. 5. About Relationship (Network Questions) – Note: the more names you have, the fewer questions you should consider. Make your questions count!! Checkbox

26. Choice Across and Down

27. Matrix Question

28. Testing the survey

29. Monitor / Test

30. Tracking Progress – Two ways  

31. When the data is collected, download the data

32. Analyze Data

33. What can you do? Way too much to cover, so be focused about what you need Demonstration