Running head: KNOWLEDGE 1

Knowledge Elicitation Strategy

Jeffery Oxendine

Kent State University

Expertise Management

Instructors:  Brian Moon, MSc

December 5, 2013

KNOWLEDGE 2

Building a Knowledge Elicitation Strategy

When building a knowledge elicitation strategy and business case, one of the first steps is

to determine why you're pursuing knowledge elicitation strategy, in the first place. What

problems is the organization trying to solve, and what advantages will knowledge elicitation

provide? The next step is to take a look at the organization's strategic goals and talk to

executives about what issues they see are keep in for knowledge elicitation across the enterprise.

For example, if the executives are concerned about the organization being vulnerable to

knowledge loss due to retirements, mergers, or downsizing, the knowledge elicitation strategy

may need to focus on approaches to capture and retain that critical knowledge. But if the

organization is expanding, it may make more sense to focus on knowledge elicitation efforts that

enhance the capabilities and shortens the learning curve for hires and their expertise. No matter

what the organization's knowledge goals are, the knowledge elicitation strategy being

implemented must be linked to targeted objectives and aligned with the organization’s overall

strategic direction and incorporated into the enterprise Knowledge Management program

(APQC, 2012)

Leveraging the Knowledge Management Program

The knowledge elicitation and knowledge retention strategy needs to become a part of

organizations overall knowledge management program which will assist in identify the

knowledge resources that are at risk and must be retained, and then implement specific initiatives

so as to keep these resources in the firm. Like most other knowledge management related

processes and strategies, success depends upon successful knowledge sharing and having a

knowledge sharing & learning organizational culture (Frost, 2013).

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Incorporating the knowledge elicitation strategy into the overall knowledge management

program will assist in identifying and assessing core competencies. A knowledge map can be

developed that maps out key competencies, while possibly linking them directly to specific core

products. Then, an evaluation can be conducted that assesses what knowledge is available and

what knowledge needs to captured. Leveraging the knowledge management program will be

crucial in identifying where the key knowledge is located, including the tacit expertise and

knowledge embedded in products, routines, etc., as well as identifying the knowledge gaps

(Frost, 2013).

Create a Plan of Action

Once the currently knowledge management program has been leverage and the purpose

for the knowledge elicitation has been established, the next step is to articulate a business case.

A good business case answers the same who, what, when, where, why, and how questions that

characterize all informative writing. Explain exactly what the knowledge elicitation strategy is

proposed to do, why it's important, and how it will get accomplished. List the people and

resources involved, describe the benefits and risks, and lay out a timeline with clear milestones.

The executives and other leaders will be more likely to support the proposal if it is back it up

with solid data and realistic estimates (APQC, 2012).

The business case should emphasize how the organization will profit from knowledge

elicitation, sharing and collaboration. The business case should not make vague claims or

enumerating every potential benefit of knowledge management, but should be honed in on the

specific goals and problems identified earlier. The business case must emphasize how the

elicited knowledge will be used, not just how it will be captured and shared. No matter how

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much knowledge an organization documents, it does not benefit until that knowledge is used to

innovate, improve products and services, reduce costs, and shorten cycle times. When possible,

developing hard numbers can make the business case more compelling, and being sure to assign

dollar values to the inputs, the outputs expected, and the projected impact of the knowledge

elicitation on productivity and revenue (APQC, 2012).

Types of Elicited Knowledge

This knowledge elicitation strategy will focus on six primary six types of knowledge.

The first three types are the basic knowledge that an organization has in terms of performing its

business processes. The latter three provide communicating, understanding and learning of

knowledge in order to use it. The types are:

Descriptive knowledge is information about the past, present, future, or hypothetical

states of relevance concerned with knowing what.

Procedural knowledge is concerned with knowing how and specifies step-by-step

procedures for how tasks are accomplished.

Reasoning knowledge is concerned with knowing why, evaluating conclusions that are

valid for set of circumstances.

Presentation knowledge facilitates communication and it is concerned with the method of

delivery of knowledge.

Linguistics knowledge interprets communication once it has been received.

Assimilative knowledge helps to maintain the knowledge base by improving on existing

knowledge (Hussain & Lucas, 2004).

Additionally, the knowledge will be classified as:

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Advantaged knowledge, which can be described as the knowledge that can provide

competitive advantage.

Base knowledge as knowledge that is integral to the organization, providing it with

short-term advantages (best practices).

Trivial knowledge as knowledge that has no major impact on the organization

Intellectual capital is the competence of an individual and the commitment of the

individual to contribute to the organization’s goals.

Tacit knowledge is the cumulative store of experiences, insights, expertise, know-how,

trade secrets, understanding and learning. It is also referred as embedded knowledge and

is unstructured and intangible and thus hard to codify.

Explicit knowledge is the policies, procedural guides, reports, strategies etc of the

enterprise that has been codified and can be distributed to others without interpersonal

interactions (Hussain & Lucas, 2004).

Knowledge Elicitation Methods

In general, knowledge elicitation methods and techniques are capable of providing rich

information regarding the concepts, relations, facts, rules, and strategies relevant to the domain

in question. The methods and techniques differ in terms of their procedures, as well as their

emphases on one type of knowledge or another. No method or technique is guaranteed to result

in a complete and accurate representation of an expert's knowledge, although the goal is to

model the expert's knowledge, not to extract or reproduce it in its entirety (Cooke, 2013).

While developing the knowledge elicitation strategy it’s important to keep in mind that

due to the wide ranging problems, domains, tasks, and knowledge types, multiple knowledge

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elicitation methods and techniques are warranted for nearly any problem. Also, it is crucial to

keep in mind those different elicitation methods and techniques may tap different types of

knowledge. Equally important is the fact that there is no single definitive procedure for applying

each of the methods or techniques. Although a method and an associated procedure are

specified for the hypothetical problem, there are most assuredly other methods and procedures

that would also be reasonable. Knowledge elicitation is a modeling enterprise and the methods

can be thought of as tools to facilitate the modeling process. These tools may need to be

modified to fit the specific situation (Cooke, 2013).

Many knowledge elicitation methods and techniques have been used to obtain the

information required to solve problems and they can be classified in many ways. One common

way is by how directly they obtain information from the domain expert. Direct methods and

techniques involve directly questioning a domain expert on how they do their job. In order for

these methods to be successful, the domain expert has to be reasonably articulate and willing to

share information. The information has to be easily expressed by the expert, which is often

difficult when tasks frequently performed often become “automatic.” Indirect methods and

techniques are used in order to obtain information that cannot be easily expressed directly.

Other factors that influence the choice of knowledge elicitation method and techniques are the

amount of domain knowledge required by the knowledge engineer and the effort required to

analyze the data (Burge, 2013).

Knowledge Elicitation Methods by Interaction Type

There are many ways of grouping knowledge elicitation methods. One is to group them

by the type of interaction with the domain expert (Burge, 2013). In this section, 10 categories of

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knowledge elicitation methods are identified and briefly described. Each of these categories or a

combination of them will be used as knowledge elicitation tools in order to support the overall

knowledge elicitation strategy.

Interviewing: Interviewing consists of asking the domain expert questions about the

domain of interest and how they perform their tasks. Interviews can be unstructured,

semi-structured, or structured. The success of an interview session is dependent on the

questions asked (it is difficult to know which questions should be asked, particularly if

the interviewer is not familiar with the domain) and the ability of the expert to articulate

their knowledge. The expert may not remember exactly how they perform a task,

especially if it is one that they perform “automatically." Some interview methods are

used to build a particular type of model of the task. The model is built by the knowledge

engineer based on information obtained during the interview and then reviewed with the

domain expert. In some cases, the models can be built interactively with the expert,

especially if there are software tools available for model creation.

Observation: Knowledge elicitation often begins with observations of task performance

within the domain of interest. Observations can provide a global impression of the

domain, can help to generate an initial conceptualization of the domain, and can identify

any constraints or issues to be dealt with during later phases of knowledge elicitation.

Observations can occur in the natural setting, thus providing initial glimpses of actual

behavior that can be used for later development of contrived tasks and other materials for

more structured knowledge elicitation methods (Cooke, 2013).

Case Study: In Case Study methods different examples of problems/tasks within a

domain are discussed. The problems consist of specific cases that can be typical,

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difficult, or memorable. These cases are used as a context within which directed

questions are asked (Burge, 2013).

Interviews: The most direct way to find out what someone knows is to ask them. This,

in a nutshell, is the approach of unstructured interviews; the most frequently employed of

all elicitation methods. Like observations, unstructured interviews are good for early

stages of elicitation when the elicitor is trying to learn about the domain and does not yet

know enough to set up indirect or highly structured tasks. Unstructured interviews are

free -flowing, whereas structured interviews have predetermined content or sequencing.

The form of structured interview questions can range from open-ended (e.g., how, what,

or why questions) which impose minimal constraints on the response to closed (e.g.,

who, where, or when questions), imposing somewhat greater constraints. In addition,

question content can vary greatly, each type targeting a slightly different type of

knowledge. Therefore, interviews can be used to elicit a wide range of knowledge types

depending on the specific interview task (Cooke, 2013).

Protocols: Protocol analysis involves asking the expert to perform a task while

"thinking aloud." The intent is to capture both the actions performed and the mental

process used to determine these actions. As with all the direct methods, the success of

the protocol analysis depends on the ability of the expert to describe why they are

making their decision. In some cases, the expert may not remember why they do things a

certain way (Burge, 2013).

Process Tracking: Process tracing involves the collection of sequential behavioral

events and the analysis of the resulting event protocols so that inferences can be made

about underlying cognitive processes. Therefore, these methods are most often used to

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elicit procedural information, such as conditional rules used in decision making, or the

order to which various cues are attended (Cooke, 2013).

Construct Elicitation: Construct Elicitation methods are used to obtain information

about how the expert discriminates between entities in the problem domain. The most

commonly used construct elimination method is Repertory Grid Analysis. For this

method, the domain expert is presented with a list of entities and is asked to describe the

similarities and differences between them. These similarities and differences are used to

determine the important attributes of the entities. After completing the initial list of

attributes, the knowledge engineer works with the domain expert to assign ratings to each

entity/attribute pair (Burge, 2013).

Conceptual Methods: Conceptual methods elicit and represent conceptual structure in

the form of domain-related concepts and their interrelations. Several steps, are generally

required, each associated with a variety of methods. The steps are: (1) elicitation of

concepts through interviews or analysis of documentation, (2) collection of relatedness

judgments from one or more experts, (3) reduction and representation of relatedness data,

and (4) interpretation of the resulting representation (Cooke, 2013).

20 Questions: This is a method used to determine how the expert gathers information

by having the expert as the knowledge engineer questions (Burge, 2013).

Document Analysis: Document analysis involves gathering information from existing

documentation. May or may not involve interaction with a human expert to confirm or

add to this information (Burge, 2013).

Concept Mapping: Concept maps will be used to assist in development of new

knowledge creation and transforming tacit knowledge from organizational experts,

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mapping team knowledge to stimulate the generation of ideas and to aid in creativity and

during brain-storming secessions between experts and novice within the organization

(Hoffman, & Moon, 2010).

Cognitive Decision Methods: The Cognitive Decision Method (CDM) will be used to

assist in guiding experts in the recall and elaboration of previously-experienced difficult

cases. The CDM will assist in leveraging the detailed fact the experts retain especially

those that were unusual, challenging, or involved critical decisions (Hoffman, & Moon,

2010).

Knowledge Retention & Preservation Strategy

To address problems of knowledge retention as part of the overall knowledge elicitation

strategy, a set of knowledge-sharing practices will need to be developed. The practices will

include knowledge-sharing practices such as mentoring programs, knowledge networks or

communities of practice, after-action reviews, lessons learned, etc. Additionally, IT resources

will be an important part of the knowledge retention strategy and incorporated as enablers to

assist in knowledge transfers (De Long, 2002). Technology applications that can be

incorporated to support knowledge retention objectives include:

Databases to track skills and competencies. This database could be utilized in building a

talent management database to help identify current and emerging gaps, as well as future

technical skill needs.

Lessons-learned repositories. The purpose of this repository is to foster knowledge

sharing across the organization to avoid repeating past mistakes and to utilize positive

experiences that improve a design or process. This repository will be populated with

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lessons from organizational projects and other sources and will be available to all

personnel.

Communication and knowledge-sharing systems. These applications support distributed

organizations or virtual communities of practice. Communities of practice are groups of

people who share a concern or a passion for something they do and learn how to do it

better as they interact regularly.

E-learning applications. This capability will be utilized to assist in shortening the

learning curve for new employees through the capture of expert knowledge being

injected into computer-based courses before a particular specialist retired (De Long,

2002).

Concept-mapping and mind-mapping software. This software will be used to create

diagrams of relationships between concepts, ideas or other pieces of information. This

software will be used to support the improvement of the organizations learning and study

efficiency.

Mentoring and apprenticeship programs. These programs transfer tacit knowledge from

experienced to newer employees, especially in technical and engineering domains where

talent is often in short supply.

Greater access to subject matter experts. Online directories, expertise locators, and other

tools make it easy for employees to find people with the expertise they need, regardless

of geographic and departmental boundaries. Experts help educate their colleagues and

support diverse areas of the business in developing innovative solutions.

Storytelling programs. People tend to conceptualize and learn better through stories.

Stories make abstract concepts concrete and demonstrate how employees can apply the

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skills they learn. They also help bridge generational gaps, communicate important

information about an organization’s culture, and foster an organizational identity.

Leveraging retirees. Retirees can provide needed skills and experience on specific

projects, mentor newer employees, and participate in storytelling and training activities

that allow them to share their experiences (APQC, 2013).

Knowledge Sharing and Implementation

Knowledge may be accessed at three stages: before, during, or after any knowledge

management related activities. Also there are varying ways of sharing captured knowledge and

making it findable and accessible across the enterprise. One approach to sharing the knowledge

elicited through the elicitation strategy involves actively managing the knowledge through a

push strategy. In the case the knowledge is explicitly encoded into a shared knowledge

repository, such as a database, as well as retrieving knowledge needed that other individuals

have provided to the repository. This is also commonly known as the codification approach to

knowledge management (APQC, 2013).

Another strategy for sharing the elicited knowledge involves individuals making

knowledge requests of experts associated with a particular subject on an ad hoc basis or a pull

strategy). In such an instance, expert individual(s) can provide their insights to the particular

person or people needing the knowledge, which can then be shared with others across the

enterprise. This process is commonly known as the personalization approach to knowledge

management (APQC, 2013).

Simply put, codification focuses on collecting and storing codified knowledge in

previously designed electronic databases to make it accessible to the organization. Codification

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can therefore refer to both tacit and explicit knowledge. However, in contrast, the

personalization strategy aims at encouraging individuals to share their knowledge directly.

Information technology plays a less important role, as it is only supposed to facilitate

communication and knowledge sharing among members of an organization (APQC, 2013).

Knowledge Elicitation Governance and Management

The overall knowledge management governance process will be the used as the

framework of authority that ensures the elicitation, capture, and preservation of the organizations

knowledge assets. The operationalization of that strategy will be lead and managed by the

organizations Knowledge Management Officer and therefore executed in an authorized and

regulated manner. Knowledge management governance mechanisms must be invoked to guide

both the initial implementation and the ongoing control and authority over the knowledge

elicitation and supporting knowledge management strategies. A governance framework will

provide management of risk, review mechanisms and fiscal accountability in leveraging tacit

knowledge and sharing explicit knowledge across the organization (Zyngier, 2005).

This knowledge elicitation and knowledge management governance will center of the

decision-making authority as an executive framework to deliver the expected benefits of the

strategy and for these benefits to be delivered in a controlled manner. This will be achieved by

establishing checks and balances in the implementation of the knowledge elicitation strategy. It

ensures that evaluation measures feedback that enables deliberate adjustment of the delivery of a

successful strategy and ensures that needs and expectations are being met. If for some reason the

needs and expectations of the organization are not being met the governance process should then

be able to establish and manage the strategy (Zyngier, 2005).

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This governance processes will provide the needed management of the risks of the

knowledge elicitation strategy to acknowledge and challenge the cultural issues, structural

obstacles and other relevant issues as they arise during the implementation and ongoing

operation of the strategy. The management of these risks assists in their resolution and

strengthens strategies to manage the knowledge elicitation and retention within the organization.

The need for risk management will facilitate the need to identify the needed assets, the risks and

controls associated with the implementation of strategy (Zyngier, 2005).

Governance in knowledge management implies and demands deliberate consideration of

the strategies in place in the long and in the medium term. Knowledge management governance

processes incorporate evaluation and measurement in order to prove the value, to progress and to

develop existing practices. Governance mechanisms must maintain a collective knowledge of

trends in industry, technology, and the corporate structural and social environment. Evaluation

looks at both successes of and obstacles to the implementation of all knowledge management

strategies. Evaluation of successes must take into account the contribution made to the aims and

objectives of the organization. Where the successes make a contribution then they should be

continued. Where they do not make a contribution then consideration should be given to their

continuance. Evaluation of obstacles to the overall knowledge management strategy implies the

capacity to question why the risk may not have been foreseen and therefore managed (Zyngier,

2005).

Measurement and Validation

There are a number of criterions currently used to establish the return on investment for

knowledge management strategies. Some strategies look at look at human capital growth, some

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use intangible assets, some use the Balanced Scorecard with a number of measures including

financial, growth, customers and internal business processes. Others look at look at the

normative, operational and strategic goals of the strategy to see if they are being met. Other

common techniques include simple measures of staff retention or in improvement of “product to

market” delivered on time, in quantity and quality. If these are evident and are the only variance

from usual practice, then the strategy is seen as successful (Zyngier, 2005).

Once the knowledge elicitation program is up and running, efforts must be taken to

validate that the knowledge elicitation tools and approaches actually do what was hypothesized.

This can be accomplished by measuring the knowledge elicitation investments and outcomes,

including hard and soft measures. Many different measures can be used to track knowledge

elicitation and knowledge management performance, and the ones chosen will depend on the

knowledge elicitation approaches and objectives (APQC, 2012).

For example If the business case centers on decreasing time-to-competency for new

employees, then tracking how often those employees are using the knowledge elicitation tools

and systems and whether they are learning and developing more quickly than before. By

developing and supplying hard data to validate the knowledge elicitation business case and

demonstrating its impact on performance assists in securing continued funding and/or arguing

for the expansion of the current program (APQC, 2012).

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References:

APQC. (2012). Developing a Knowledge Strategy That Senior Leaders Can Get Behind.

American Productivity and Quality Center. Retrieved from

http://www.apqc.org/knowledge-base/download/273789/K03959%20Developing%20a

%20KM%20Strategy-senior%20leaders.pdf

APQC. (2013). Knowledge Retention and Transfer. American Productivity and Quality Center.

Retrieved from http://www.apqc.org/knowledge-retention-and-transfer

Burge, J.E., (2013). Knowledge Elicitation Tool Classification. Artificial Intelligence Research

Group. Worcester Polytechnic Institute. Retrieved from

http://web.cs.wpi.edu/~jburge/thesis/kematrix.html

Cooke, N.J. (2013). Knowledge Elicitation. Department of Psychology. New Mexico State

University. Retrieved from http://www.cerici.org/documents/Publications/Durso

%20chapter%20on%20KE.pdf

De Long, D. W. (2002). Confronting The Chemical Industry Brain Drain. Institute for Strategic

Change. Retrieved from http://www.smartworkforcestrategies.com/Portals/0/Latest

%20Research/FINALREPORT--Confronting%20The%20Chemical%20Industry

%20Brain%20Drain.pdf

Frost, A. (2013). Knowledge Management. KMT. Retrieved from http://www.knowledge-

management-tools.net/

Hoffman, R. R. & Moon, B. (2010). Knowledge Capture for the Utilities. American Nuclear

Society. Retrieved from https://learn.kent.edu/webapps/portal/frameset.jsp?

tab_group=courses&url=%2Fwebapps%2Fblackboard%2Fexecute

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%2FdisplayLearningUnit%3Fcourse_id%3D_70876_1%26content_id

%3D_2432808_1%26framesetWrapped%3Dtrue

Hussain, F., & Lucas, C. (2004). Managing Knowledge Effectively. Journal of Knowledge

Management Practice. Retrieved from http://www.tlainc.com/articl66.htm

Zyngier, S. (2005). Knowledge Management Governance. Monash University Caulfield East

Victoria, Australia. Retrieved from

http://www.sims.monash.edu.au/subjects/ims3012/resources/Zyngier

%20GovernanceEKM.pdf