Factors Impacting KM
Velocity and Viscosity of Knowledge
“Velocity” of transfer is the speed at which knowledge moves through an organization; how quickly and widely it is disseminated; how quickly people who need the knowledge become aware of it and get access to it challenges also exist in terms of the “viscosity" of KT (Davenport & Prusak, 2000).
Viscosity refers to the richness (or thickness) of the knowledge transferred. For example: How much of what we try to communicate is actually absorbed and used? To what extent does the original knowledge get pared down? Does what was absorbed bear little resemblance to what we tried to transmit and retain little of its original value? Viscosity is influenced by a number of factors, especially the method of transfer. Knowledge transferred by means of a long apprenticeship or mentoring relationship is likely to have high viscosity: the receiver will gained a tremendous amount of detailed and subtle knowledge over time. Knowledge retrieved from an on line database or acquired by reading an article will be much thinner.
According to Davenport & Prusak (2000), both velocity and viscosity are important concerns for knowledge managers in determining how effectively a firm uses its knowledge capital. How quickly can it place knowledge where it can generate value and how much of the knowledge assets are actually getting where they need to go? Because genuine learning is such a deeply human endeavor, and because not only absorbing but accepting new knowledge involves so many personal and psychological factors, velocity and viscosity are often at odds. What enhances velocity may thin viscosity. Most KT efforts strike a compromise between these two factors.
A term closely related to KM is knowledge packaging. This refers to translating and structuring the information into usable knowledge. The concept entails filtering, editing or organizing pieces of knowledge. Packaging is very important because knowledge must be easy to use or employees will not use it and knowledge will not be shared. Myers and Swanborg (1999) identify six steps to ensure successful knowledge packaging:
- Identify specific topics or general domains and then find knowledge that addresses those subjects;
- Segment the audience – this requires identifying the target recipients for the knowledge and sorting them in groups by their respective needs;
- Customize the content – this involves selecting the relevant information for the knowledge base and tailoring it with the appropriate level of detail to each segment;
- Selecting the appropriate format – e.g. paper, electronic, video, multimedia;
- Organizing the content – such as the table of contents; index or search engine; and
- Pilot test the format and content for clarity, usability and overall value (p. 202)
Most organizations rely on computers, intranets and internet for knowledge packaging.
Information technology (IT) has become key to the implementation of KM. It is no coincidence that IT has blossomed at the same time that knowledge is becoming recognized as one of the most valuable organizational assets. Its critical role lies in its ability to support communication, collaboration and those searching for knowledge and information. Computer networks provide the means to break down silos and hierarchical barriers that often inhibit the flow of free-thinking, resulting in the creation of new knowledge. There is a powerful synergistic relationship between KM/KT and technology; that relationship drives increasing returns and increasing sophistication on both fronts. As IT has become our personal desktop tool and our link to each other, access to information and other people’s knowledge has grown exponentially. In turn, we demand even better and more effective IT tools.
Key components of KM information technology systems, as discussed by Turban et al, (2005) include the following:
- Communication – access knowledge and share with others.
- Collaboration – perform group work, synchronous or asynchronous work and ability to collaborate in different place and times.
- Storage and retrieval – capturing, storing and retrieval management of both explicit and tacit knowledge through collaborative systems.
- Supporting technologies to store, retrieve, share, support problem solving and decision making, including:
- Artificial intelligence. These are expert systems which provide “if-then-else’ rules. The systems use natural language processing so searches are intuitive for the user.
- Neural networks. This refers understanding text.
- Fuzzy logic. A form of algebra employing a range of values from "true" to "false" that is used in decision-making with imprecise data, as in artificial intelligence systems.
- Intelligent agents. These are systems that learn how users work and provide assistance, for example: knowledge discovery in databases, which is the process used for searching and extracting information both internal (data and document mining) and external (data warehouses etc). XML allows for extensible mark-up language; enables standardized representation of data and allows collaboration and communications through portals.
- Knowledge discovery within current internal and external databases.
- Customer Resource Management which provides tacit knowledge to users.
- Supply chain management systems (both tacit and explicit knowledge).
- Corporate intranets and extranets (allow knowledge to flow freely in both directions).
Implementing the KM management and supporting systems requires:
- Identifying and integrating system components –e.g. interfaces between networks and databases.
- ‘Know-ware’- These are technology tools that support KM and include collaborative computing tools (such as groupware); knowledge servers; enterprise knowledge portals; document managing systems (which are content management systems); knowledge harvesting tools, search engines and more.
The Limitations of Technology
There are difficulties caused by IT in KM/KT practices. It leads to misconceptions about the differences between information and knowledge. Organizations often store vast amounts of information and/or data and think they are fostering knowledge flow. Constructing an IT infrastructure does not in itself guarantee that employees will use the systems. Technology applications, according to the American Productivity & Quality Center (APQC), do not, in themselves, create a need or demand to change behavior or share knowledge. IT is more an integrator and a disseminator of knowledge, not the keeper of information or the decision-maker. Technology is indispensable to KM in modern organizations; however, the road to effective KM is littered with abandoned “KM solutions” that were really just applications. These vehicles quickly run out of gas, if they start at all. It is critical to select and implement technology as part of a larger, systematic KM change initiative.
Technology can help – but it cannot substitute for interpersonal communication. Technology is an enabler rather than the starting point for a KM/KT effort; a synergistic relationship (O’Dell et al. 1998). In fact, some organizations rely too heavily on IT and not enough on the social aspects of knowledge. There is always a need for creating a shared understanding though networking and social coordination.