Page 306 -

Knowledge Management Tools 289

Social network analysis (SNA; see http://www.insna.org) is a social science research
tool that dates back to the 1970s and has increasingly become used in KM applications
( Durkheim 1964 , Drucker 1989 , Granovetter 1973 , Lewin 1951 ). Valdis Krebs (2008)
defi nes SNA as the “ mapping and measuring of relationships and fl ows between
people, groups, organizations, computers, or other information/knowledge processing
entities. ” SNA can be used to identify communities and informal networks and to
analyze the knowledge fl ows (i.e., knowledge sharing, communication, and other
interaction) that occur within them ( Brown and Duguid 1991 ). SNA is one of the ways
of identifying experts and expertise to develop an expertise locator system. The basic
steps to develop a survey tool (e.g., a questionnaire) to collect the required data are
to identify network members and their exchange patterns. Next, the data are analyzed
using software such as Pajek (http://www.pajek.com) or UCINET (http://www
.analytictech.com) to identify patterns of interaction and emergent relationships. The
analyzed data can then be used to inform decision-making based on the objectives
( Scott 2000 ), for example, for change management, to establish a baseline in order to
later assess the effects of a technology introduction, or to improve upon the knowledge
fl ow and connections.
The combination of social networking, blogging, wikis, and other related technolo-
gies together defi ne Web 2.0 or the next generation of the web. Web 2.0 is a concept
that began with an interactive conference session between Tim O ’ Reilly and Dale
Dougherty that in turn led to the development of the annual Web 2.0 conference
( O ’ Reilly 2009 ). (http://en.oreilly.com/web2008/public/content/home). They defi ned
Web 2.0 as something without a hard boundary but rather a set of principles that
include:

• The web as a platform
• User control of your own data
• Services instead of packaged software
• An architecture of participation
• Cost-effective scalability
• Re-mixable data sources and data transformations
• Software that rises above the level of single device
• Harnessing of collective intelligence
A popular way of defi ning Web 2.0 is a form of concept analysis — the listing
of examples for both Web 1.0 and Web 2.0. For example, Netscape is an example of
Web 1.0 whereas Google exemplifi es Web 2.0. Microsoft Outlook e-mail is a Web 1.0