Making the Tacit Explicit: The Intangible Assets Monitor in Software
Dr. Charles A. Snyder
Professor
Department of Management
Auburn University
Auburn, AL 36849 –5241 U.S.A.
*e-mail:
and
Larry T. Wilson
President and Chief Science Officer
LearnerFirst, Inc.
The Genesis Center, Suite 201
Birmingham, AL, 35211-6908 U.S.A.
* Corresponding author
Abstract
One of the core principles of the Knowledge Management movement is that of capturing expertise and making it accessible to those in the organization who need it (see, e.g., Amidon, 1997; Stewart, 1997). The process of harvesting the tacit knowledge of the expert and converting it into a form that is available and useful presents some formidable obstacles. We explore the needs and the means to perform the needed activities that will result in a computer-based resource that fulfils those needs.
An example of the process is provided by describing the creation of the Intangible Assets Monitor as conceived by Karl-Erik Sveiby, the expert in this instance and one of the founders of the knowledge management community of practice. The result is a software product developed by LearnerFirst, a company that specializes in harvesting the knowledge of experts and incorporating it in computer-based learning resources. These learning resources encapsulate the tacit knowledge of an expert, placing it in explicit form. The software supports procedural knowledge-based tasks by providing the learner with expert guidance on an as needed basis.
An example of the software generated from the Know-How Harvesting process will be
demonstrated at the conference.
The Knowledge Harvesting Process
Know-how Harvesting is a term applied to the process of eliciting the tacit insights and intuitive knowledge of experts or top performers and converting it into specific, actionable know-how that is easily accessed and used by others. The developer firm, LearnerFirst, formed in 1992, has specialized in this process since its inception. The process is akin to the more familiar Knowledge Engineering activity in the Expert Systems field (see, e.g., Keller, 1987; Wiig, 1990). However, the process differs in its standardized approach and the outcome. Learning support software is the primary output from the process. Also, the intent is different from the expert systems as the software is used to supplement, not replace a decision-maker.
The harvesting process can be applied to virtually any kind of human knowledge that is procedurally actionable. This means that the process is as relevant to the highest level of knowledge as well as to the routine. The resulting software from the process is designed so that an individual can simultaneously understand, learn, perform, and record the performance in a single action. In this sense, it can be classified as an Electronic Performance Support System (EPSS)(See, e.g., Gery, 1991). An EPSS is defined as „…the electronic infrastructure that captures, stores, and distributes individual and corporate knowledge assets throughout an organization, to enable individuals to achieve required levels of performance in the fastest possible time and with a minimum of support from other people“ (Raybold, 1995, p.11). harvesting is described in more detail in a following section.
The Firm and Products
LearnerFirst, Inc. was founded with the aim of producing computer-based products that could augment human intelligence. The conceptual foundation of the company had as its core, the thesis research of the founder. The software is thoroughly grounded in learning theory because it is designed to assist the user (learner) learn from the captured knowledge of an expert while in performance of a process.
Several software products have been developed and marketed by this company. These products have served to verify the know-how harvesting system. There have been dozens of experts whose knowledge has been made accessible through applications that are in daily use in over 4,000 organizations around the world. Thus, the concept has had verification in the marketplace.
What is harvesting?
The harvesting process is proprietary, however it can be described in general terms. During the knowledge engineering process, the expert and the knowledge engineer walk through the process and separate the relevant procedural knowledge from the trivial. The focus is on the procedural or process domain and the organization and sequencing is based on the expertise of the domain expert.
Harvesting is a set of methods for: 1) finding valuable know-how, 2) getting inside the mind of the expert performers to uncover the processes involved, 3) optimizing and deploying the know-how to individuals and teams as software applications, and 4) evaluating and improving applications.
After a top performer or process expert has been identified, LearnerFirst utilizes methods for eliciting and re the expert’s intuitive and explicit knowledge. Then, the expert’s thinking processes are broken down into their elementary components – mental operations and knowledge units – which may be viewed as psychological „atoms“ and „molecules.“ Researchers at LearnerFirst have discovered that performing any intellectual task or solving any problem requires a certain set of elementary knowledge units and operations. These operations and units are generic, domain-neutral, irreducible thinking processes that were brought to light after an extended study of over 300 cases in which the LearnerFirst harvesting process was applied
Information about the thinking process is optimized as specific, actionable know-how that is easily accessed, understood, and applied by target learners/performers. Specifically, the information is communicated to users as guidance and support information via a software application. Guidance is information about how to actually think through the process task. Support information is information to enhance understanding. The software application’s user interface varies in relation to the task that needs to be performed and the user’s individual preferences. Beneath the surface of the user interface, there is a database that successfully holds guidance and support information according to three primary levels of abstraction.
After repeated use of a performance support application, cycles/iterations are evaluated so that improvements can be made. Thus the application is designed so that it can continue to evolve or learn.
Benefits
There are several major benefits of the harvesting system. First, successful thinking is made visible, manageable, useful, and accessible. This means that expertise is available to anyone who needs it, anywhere in the world, when they need it. All that is required is computer access to the software. This can be either in a stand-alone version or via a network such as the Internet. Organizations may wish to make their internal expertise captured by the harvesting process available via Intranets.
Second, decisions can be made faster at lower levels because the expert’s knowledge is available at lower levels. In essence, we can have just-in-time access to the needed expertise.
A third benefit is that work can be properly performed with less supervision and intervention. There are also some important impacts on the individual learner. As the learner progresses, he or she gains in proficiency, confidence, and autonomy. Thus, we are able to improve on the organization’s human capital through increasing competency. This aspect is of great importance because of the leverage effect on the firm’s performance (see Sveiby, 1997 for a discussion of the leverage effect).
Fourth, the organization can use the process to preserve know-how as its most important asset. The nature of the system also allows the individual learner to record experience and learning so that the individual has a continuously updated resource with perfect memory.
Last, the learning resources improve performance in a cost-effective manner. Since the harvesting process is thorough, the captured expertise is usually more complete than that available if the expert were on site. Our experience has revealed that the process of eliciting the tacit knowledge of an expert helps the expert to formalize his/her concepts with a great deal of clarity so that they can be made explicit.
An Example
As an example of the harvesting process output, the first portion of the resource developed with Karl-Erik Sveiby is presented. Sveiby was the first to write about the „Knowledge Company“ in 1986 and has been a leader in the Swedish community of practice surrounding knowledge management and intellectual capital management (Amidon, 1997; Sveiby, 1997). His books and articles are widely referenced and quoted. His concept and taxonomy of Intangible Assets with the components of Customer Capital (External Structure), Human Capital (People’s Competence), and Structural Capital (Internal Structure) are widely known and used by many organizations.
The learning resource under development with Sveiby is the „Intangible Assets Monitor.“ The entire Intangible Assets Monitor includes many features that will be beyond the scope of this presentation. The Invisible Balance Sheet and a simplified version of the monitor will be presented as an example of how the harvesting process is used to generate a learning resource. The software provides individualized guidance for creating and evolving measures of intangible, knowledge-based assets of an organization.
Conclusion
Every firm that undertakes the task of capturing the knowledge of its best performers or that wishes to make expert advice on performance of its key processes available to performers is faced with the problem of harvesting the know-how. The captured knowledge needs to be made available to the individual performer on an as needed basis. The methodology described allows learning resources to be produced that provide the individual with performance oriented expertise. By deploying such software, developed with the aid of the harvesting methodology, a firm can manage its knowledge transfer process as well as remove a significant obstacle to knowledge transfer. The learning resources generated from this process become an important part of a firm’s knowledge management strategy.
References
Amidon, D. M. (1997). The Ken Awakening: Management Strategies for Knowledge Innovation, Boston: Butterworth-Heinemann.
Gery, G. (1991). Electronic Performance Support Systems: How and Why to Remake the Workplace Through the Strategic Application of Technology, Boston: Weingarten.
Keller, R. (1987) Expert System Technology: Development & Application, Englewood Cliffs, NJ: Yourdon Press.
Raybold, B. (1995). Performance Support Engineering: An Emerging Development Methodology for Enabling Organizational Learning. Performance Improvement Quarterly, (8:1), 7-22.
Snyder, C. A. & Wilson, L.T. (1997). Individual Learning: The Foundation of Knowledge Management, Proceedings, The Austin Knowledge Management Dialog, Austin, TX.
Stewart, T. A. (1997). Brain power: Who Owns it…How They Profit From It, Fortune, (135:5), 104-110.
Sveiby, K.E. (1997). The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, San Francisco: Berrett-Koehler.
Wiig, K. (1990). Expert Systems: A Manager’s Guide, Schema.