SOCIOGRAMS AND SOCIAL NETWORK ANALYSIS

Social network analysis (SNA) is the mapping and measuring of relation- ships and flows between people, groups, organizations, computers, or other information/knowledge processing entities (Krebs, 2000). SNA can map and measure relationships and flows between people, groups, organizations, com- puters, or other information/knowledge processing entities. The nodes in the network are the people and groups, while the links show relationships or flows between the nodes (see Figure 5-2). SNA provides both a visual and a mathe- matical analysis of complex human systems to identify patterns of interaction such as average number of links between people in an organization or com- munity, number of subgroups, information bottlenecks, knowledge brokers, and knowledge hoarders.

Gary Bodam, director of training and development. Once they realize that their willingness to share knowledge affects the bottom line in games, they are more open to making changes in how they operate in the real world, he says. But Thomas & Betts is also using technology to foster knowledge sharing. The company runs an E-learning-management system from ThoughtWare Technologies Inc. that tracks employees’ continuing educa- tion, such as public speaking or engineering. The data is logged in a Systems- Applications-Products human resources system that can be used by managers looking for the best candidates for jobs. As Bodam states, “It’s all become part of the overall knowledge base by which we’ll try to move the organization forward.”

T

HOMAS

& B

ETTS

—Continued

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APPING THE FLOW OF KNOWLEDGE

Knowledge request Knowledge response

Portal

Jack Sue

In the context of knowledge management, social network analysis (SNA) enables relationships between people to be mapped in order to identify knowl- edge flows: from whom do people seek information and knowledge? With whom do they share their information and knowledge? In contrast to an orga- nizational chart that shows formal relationships—who works where and who reports to whom—a social network analysis chart shows informal relation- ships—who knows who and who shares information and knowledge with whom (see Figure 5-3). It therefore allows managers to visualize and under- stand the many relationships that can either facilitate or impede knowledge creation and sharing (Anklam, 2003). Because these relationships are normally invisible, SNA is sometimes referred to as an organizational X ray, showing the real networks that operate underneath the surface organizational structure (Donath, 2002; Freeman, 2004).

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NOWLEDGE FLOW EXAMPLE

Group A

Babette Jack

Heinrich

Group B

Metzger Oedipa

Mucho

Group E

Liz Leamus

George

Group D

April Kurt

Wanda

Kitty Anna

Vronksy Emily

Hugh

Emily and Hugh are ‘hidden’ experts

Group C

Source: Adapted from Krebs, http://www.orgnet.com.

Once social relationships and knowledge flows can be seen, they can be eval- uated and measured. Network theory is sympathetic with systems theory and complexity theory. Social networks are also characterized by a distinctive methodology encompassing techniques for collecting data, statistical analysis, visual representation, and so on. The results of social network analyses can be used at the level of individuals, departments, or organizations to identify infor- mation bottlenecks and to accelerate the flow of knowledge and information across functional and organizational boundaries. A social network should be regarded as a dynamic or moving target and will need to be constructed more than once. For example, the data gathering and analysis process can provide a baseline against which you can then plan and prioritize the appropriate changes and interventions to improve the social connections and knowledge flows within the group or network.

The process of social network analysis typically involves the use of ques- tionnaires and/or interviews to gather information about the relationships between a defined group or network of people. The responses gathered are then mapped using a software tool specifically designed for the purpose. Key stages of the process will typically include:

■ Identifying the network of people to be analyzed (e.g., team, workgroup, department).

■ Clarifying objectives and formulating hypotheses and questions.

■ Developing the survey methodology and designing the questionnaire.

■ Surveying the individuals in the network to identify the relationships and knowledge flows between them.

■ Using a software mapping tool to visually map out the network.

■ Analyzing the map and the problems and opportunities highlighted using interviews and/or workshops.

■ Designing and implementing actions to bring about desired changes.

■ Mapping the network again after a suitable period of time.

In order for SNA maps to be meaningful, it is important to know what infor- mation you need to gather in order to build a relevant picture of your group or network. Good survey design and questionnaire design are therefore key considerations. Questions will be typically based on factors such as:

■ Who knows who and how well?

■ How well do people know each other’s knowledge and skills?

■ Who or what gives people information about xyz?

■ What resources do people use to find information/feedback/ideas/advice about xyz?

■ What resources do people use to share information about xyz?

Although there are quite a number of different SNA tools, there is a need for a user-friendly end-to-end solution that can be applied in a variety of busi- ness settings (Dalkir and Jenkins, 2004). Existing tools have little support, are

usually proprietary, have little track record, and tend to be heavily weighted toward the statistical analysis of data once it has been gathered, with little support for the initial data collection activities.

Community Yellow Pages

All communities are about connections between people, and these connec- tions are often used to develop corporate yellow pages or an expertise loca- tion system. Though initially community based, such expertise locators can eventually be integrated to form a corporatewide yellow pages. Lamont (2003) emphasizes their contribution to organizational learning initiatives such as facilitating mentoring programs, identifying knowledge gaps, and providing both performance support and follow-up to formal training activities. Figures 5-4 and 5-5 illustrate a typical application for a large, distributed European publishing company.

A wide range of software exists for the development of corporate yellow pages (see Table 5–2 for some examples). Most create an initial profile of an individual’s expertise based on an analysis of published documents, question- naires, or interviews, while others focus on e-mails. These are very popular KM applications, and they are often the first KM implementation a company will undertake primarily because they can be developed fairly quickly (on the order of one to two months), and they can provide almost instantaneous ben- efits to individuals, communities, and the organization itself.

Yellow pages, or expertise location systems, were among the earliest KM applications, and they remain one of the best ways to initiate wider-scale knowledge sharing in organizations. Two examples are from Texaco and British Petroleum.

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E

XAMPLE OF A CORPORATE YELLOW PAGES

Directories

Products Projects External Suppliers Publishing Companies Network of Experts

Libraries

Best Practices Library Lessons Learned Stories

Training Modules

Discussion Area Support

Glossary of Terms Frequently Asked Questions Discussion Themes

Project Management Risk Management

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XAMPLE OF A CORPORATE YELLOW PAGES

(

CONTINUED

)

Function Geographic Area Business Area

Content Management Electronic Production Knowledge Management Publishing Management

Network of Experts

Sales Operations Distribution Finance Northeast

West Coast Midwest South Vice President

Director Line Manager Operator

Content Management Jane Dennys Will Jameson

Head Office Regional Office 6

555 434-4564 555 212-3212 Electronic Production

Jan Zariski

Sarah Marxman Regional Office 6

555 212-3233 555 212-3232 Regional Office 6

Expertise

Expertise

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ABLE

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OFTWARE TO

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EVELOP

Y

ELLOW

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AGES OR

E

XPERTISE

L

OCATION

S

YSTEMS

Name Description Website

Kamoon’s Profiles set up by analyzing http://www.kamoon.com/

Connect unstructured repositories to identify documented expertise

AskMe Web-based questionnaire used on http://www.askmecorp.com/

a voluntary basis; can track Q&A to identify any knowledge gaps

Sopheon’s Q&A format, provides answers to http://www.sopheon.com/

Organik questions and then stores the answers in a repository for future reference

Tacit’s Learns about people automatically http://www.tacit.com/

KnowledgeMail through analysis of e-mails as well as document repositories and Lotus Notes databases.

Search results include experts and links to content.

T

EXACO

Texaco’s knowledge management arsenal includes PeopleNet (Gonsalves and Zaino, 2001), a custom-built application that lets employees build a personal profile and post it as a web page on the company’s intranet. The content of the profile does not have to be purely work-related: pictures and hobby lists coexist alongside users’ summaries of their job expertise. The PeopleNet content and the company’s e-mail systems are linked through KnowledgeMail from Tacit Knowledge Systems Inc., which monitors an employee’s e-mail, moving phrases that seem to reflect a person’s expertise on a particular subject into a private profile accessible only to that employee.

The person then chooses which phrases to publish in a public directory to help others distinguish him or her as a potential expert in an area. Someone searching for an expert in marketing crude oil, for example, would get a list of people associated with that phrase; clicking on a name in that list would call up a profile of the person in KnowledgeMail, as well as a link to the person’s PeopleNet profile.

Three hundred people at Texaco have used KnowledgeMail through a pilot program in its first year and a half. It is considered to be a successful KM application. John Old, the company’s director of information, recounts a meeting in which Texaco executives were sharing ideas on knowledge man- agement with a business partner. In demonstrating KnowledgeMail, a col- league typed the word “wireless,” and the top name on the retrieved list was a systems architect who was in the room but had never been identified as someone knowledgeable in wireless technology. “In any large company, there are lots of conversations in e-mail that you’re not aware of, and there are lots of hidden experts,” Old says.

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RITISH

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ETROLEUM

BP’s yellow pages²are entirely bottom up. About 20,000 (of 80,000) have personal pages. It takes about 10 minutes to produce one using a form-filling approach, which contains a self-appraisal of skills and interest. No one vets the content, but people rarely oversell themselves! People who leave BP may still have a page. Every 3 seconds someone makes a connection. The yellow pages are widely embedded in the BP intranet; they are integrated into the search environment and are now a part of how they do business.

Source: Cohen, 1999.