Running head: KNOWLEDGE 1
Knowledge Elicitation Strategy
Kent State University
Instructors: Brian Moon, MSc
December 5, 2013
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,
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).
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,
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 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:
• Advantaged knowledge, which can be described as the knowledge that can provide
• 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
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
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, 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
• 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 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,
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,
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
lessons from organizational projects and other sources and will be available to all
• 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
• 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
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
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).
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,
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
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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