Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
Development and Analysis of a Dynamic Model of Organizational
Knowledge Repository Utilization
Poornima Panduranga Kundapur * and Lewlyn L Raj Rodrigues **
The main research issue in this article is to study the relationship between the knowledge
storage in the organizational knowledge repository and its subsequent use using a model
developed as a part of the study. In the present day “knowledge based economy”, the
organizational knowledge repository utilizes the intellectual capital not machine power, as
its critical resource and thus enhances the competitive advantage of knowledge intensive
organizations like those in the IT sector. Such organizations aim at sustaining the storage
and optimum utilization of their accumulated “knowledge repository” in order to increase
and maintain this competitive advantage. The five-step Systems Dynamics research
methodology was followed to design and develop the “Organizational Knowledge
Repository Utility Model” (OKRUM) model in two stages. This model simulates and studies
the causal dependencies between the knowledge worker and the storage and use of
knowledge repository. Simulation experiments were conducted to study the knowledge
worker storage and usage patterns using stock and flow form and their respective
equations. The repository was observed by varying values of the variables defined in the
model to look at the internally generated dynamics of such a system. Different scenarios
were designed and the variables were adjusted to compare the actual practices and model
assumptions. The dynamic hypothesis was formulated to set the knowledge storage based
on current usage pattern. The results were significant. The study found that in the first
stage, there is not much of variability in the knowledge storage flow and workforce stock
whereas the organizational knowledge repository shows a smooth transition. These results
are described in the simulated resultant graphs. The results show a difference in the stage
two refinement of the model designed in this exercise. Based on the additional equations
that were included in stage two, the model showed the “net hire rate” directly affects the
“knowledge workers stock whereas the “repository deviation parameter” influences the
target storage volume, net hire rate, knowledge workers, storage as well as the
organizational knowledge base. Stage two exhibits different behavioral patterns. The
simulation model developed as a part of this study was built using the software
VensimPLE® and the outcome of the study should aid organizational IT administrators and
policy makers to evaluate the impact of the identified factors on building and sustaining the
organizational knowledge base.
Key words: System Dynamics, OKRUM, Knowledge Repositories, Knowledge workers
1. Introduction:
In the current day “knowledge based economy”, organizations are realizing that there is a
urgent need to improve the mechanism of capturing, distributing, sharing, preserving and
securing their most valuable asset which is „knowledge‟ in order to stay ahead of their market
competitors (Sanghani, 2009).
Accordingly, a study by Davenport and Prusak (1998) emphasizes that the capability of
companies exploiting their intangible assets has become much more decisive than their
capability to invest and manage their existing physical assets. These companies therefore now
rely on their organizational knowledge repository which utilizes this intellectual capital as their
*Dr. Poornima Panduranga Kundapur, Manipal Institute of Technology, Manipal University, Manipal, Karnataka
State, India. E-mail: poornima.girish@manipal.edu
**Dr. Lewlyn L Raj Rodrigues, Manipal Institute of Technology, Manipal University, Manipal, Karnataka State,
India. E-mail: rodrigusr@gmail.com
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Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
critical resource thus providing them an edge in the competition involving knowledge intensive
organizations such as those in the IT sector. It therefore is imperative for these organizations
to aim at sustaining the storage and optimum utilization of the accumulated “knowledge
repository”.
Further, managing knowledge has now replaced managing physical assets as the top strategic
agenda in most companies. This trend is evident in the fact that the majority of Fortune 500
companies have a knowledge management program in one or another form (Sanghani, 2009).
Moreover, three fourth of world‟s corporate market value resides today in assets such as
intellectual property, customer data, financial records, strategies and trade secrets.
Interestingly all these assets are knowledge based (Chaudhuri, 2011). Understandably,
knowledge management initiatives are not restricted to knowledge intensive firms but are
being explored and deployed across different types of organizations the world over.
2. Literature Review
The goal of knowledge management is to capture, store, maintain, and deliver useful
knowledge in a meaningful form to anyone who needs it anyplace and anytime within an
organization. Knowledge management is collaboration at the organization level. (Turban,
Aronson, and Liang, 2005)
Davenport and Prusak (1998) state knowledge is a fluid mix of framed experience, values,
contextual information, and expert insight that provides a framework for evaluating and
incorporating new experiences and information. According to these authors, knowledge
originates and is applied in the minds of knower.
Another view is that „knowledge which is an intellectual capital is also assumed as value
creator‟ and knowledge management is seen as a competitive tool for value creation and
addition. Knowledge is formalizing and also systematically organizing the experience and
expertise that create new capabilities, enable superior performance, encourage innovation,
and enhance customer value (Chaudhari, 2011).
A knowledge-based perspective of the firm has emerged in the strategic management
literature (Nonaka and Takeuchi 1995). This perspective suggests that the services rendered
by tangible resources depend on how they are combined and applied, which is subsequently is
a function of the organization‟s knowledge. Furthermore, advanced information technologies
used in organizations namely, the Internet, intranets, extranets, browsers, data warehouses,
data mining techniques, and software agents, are in force to systematize, enhance, and
expedite large-scale intra- and inter-firm knowledge management (Alavi & Leidner, 2001).
Building motivation and commitment by incorporating knowledge management system (KMS)
or in this case the knowledge repository usage into personnel evaluation processes,
implementing KMS use/satisfaction metrics, and identifying organizational culture concerns
could inhibit knowledge repository usage (Jennex and Olfman, 2000).
A study of the papers investigating knowledge repository and knowledge sourcing have
suggested that a socio-technical perspective should be employed (Kankanhalli et al., 2005)
combining organizational, task, and technological factors (Desouza, 2003; Schultze and
Boland, 2000). Accordingly, some studies (Gray and Durcikova, 2006, Gray and Meister, 2004)
have examined various antecedents to knowledge sourcing, including individual characteristics
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Proceedings of International Social Sciences and Business Research Conference
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(e.g. learning orientation, risk aversion), and contextual factors (e.g. intellectual demands of
the job, knowledge sharing norms, incentive and resource availability). In the current day
scenario it is seen that many organizations deploy knowledge repositories to support
knowledge workers (Gallupe, 2001; Markus, 2001) for different levels of users.
2.1. Knowledge Storage
According to Alavi and Leidner (2001), empirical studies reveal that organizations that create
knowledge and learn, they tend to also forget. The authors mention that organizational
memory is the storage, organization, and retrieval of organizational knowledge. Further,
organizational memory includes knowledge residing in various component forms, including
written documentation, structured information stored in electronic databases, codified human
knowledge stored in expert systems, documented organizational procedures and processes
and tacit knowledge acquired by individuals and networks of individuals (Turbon, Aronson and
Liang, 2005).
2.2. Knowledge Application
It is interesting to note that the knowledge-based theory of the firm also emphasizes that the
source of the competitive advantage resides in the application of the knowledge rather than in
the knowledge itself. ((Alavi & Leidner, 2001)
Therefore, it can be seen the application of various information technologies (mentioned
earlier) can create an infrastructure and environment that contribute to organizational
knowledge management by actualizing, supporting, augmenting, and reinforcing knowledge
processes at a deep level through enhancing their underlying dynamics, scope, timing, and
overall synergy (Alavi &Leidner, 2001)
Quite a few reasons exist for organizational members accessing and assimilating knowledge
but not applying it. That is, they do not act upon it. These reasons include distrust in the source
of knowledge, lack of time or the opportunity to apply knowledge, or risk aversion in order to
avoid action taking when errors occur (Davenport and Prusak,1998).
2.3. Knowledge Repository
A knowledge repository is neither a database nor a knowledge base in the strictest sense of
the terms (Turbon, Aronoson and Liang, 2005). In fact a knowledge repository is a place to
capture and store knowledge that is generally text-based with different characteristics. It is
imperative not to confuse a knowledge repository with the knowledge base of an expert
system. The objective of the knowledge repository is to capture knowledge. Consequently, the
structure of the knowledge repository is dependent upon the type of knowledge stored which
can range from simply a list of frequently asked questions and solutions, to a listing of
individuals with their expertise and contact information, to detailed best practices for a large
organization (Turbon, Aronson and Liang, 2005). However, any knowledge stored in a
knowledge repository must be re-evaluated on a regular basis or else it will turn into a
knowledge landfill.
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Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
Another explanation for Knowledge Repository may be information technology based system
designed to store codified knowledge for future reuse, including solutions to problems, best
practices, and product knowledge (Gray and Durcikova, 2006 and Kankanhalli et al., 2005).
Therefore, managing the knowledge repository typically requires dedicated staff or knowledge
workers as they are addressed to as in this article. They examine, structure, filter, catalog, and
store knowledge so that it is meaningful and can be accessed by the people who need it
(Turbon, Aronoson and Liang, 2005).
The knowledge repository staff also ensures contribution of knowledge to the knowledge
repository. These staff includes the CKO (chief knowledge officer), the CEO, the other officers
and managers of the organizations and the employees. This paper generalizes the role of
these knowledge workers for the sake of simplifying the model.
2.4. Organizational learning
The development of new knowledge and insights having the potential to influence an
organization‟s behavior is often referred to as “organizational learning”. It occurs when
associations, cognitive systems, and memories are shared by members of an organization
(Turbon, Aronoson and Liang, 2005).
3. Research Methodology
This research methodology followed in this paper is based on the principles of System
Dynamics method (SD), developed by J. W. Forrester in the 1960s at the Massachusetts
Institute of Technology (MIT), Boston (Forrester, 1994). The SD methodology involves five
steps namely, Problem identification, System conceptualization, Model formulation, Simulation
and validation and Policy analysis and improvement. The stock and flow modelling and
simulation are performed using VENSIM PLE® software. Simulation models generate
behaviour through simulation. The SD process is iterative and flexible (Sterman, 2000).
The concern of knowledge usage when disseminated in any knowledge based organization,
can display a wide variety of behavior depending on the variables involved. Hence, testing
such variables in real time scenarios would be difficult mainly due to the cost of conducting
such experiments in real time given the fact that sometimes knowledge workers would be
unwilling to share their view on these issues. Simulating the utilization process, thus, becomes
a practical option for decision makers to assess the factors affecting usage of knowledge
repository in any organization.
5. Objectives of research
The purpose of this research article is to design and analyse a simulation model that tests the
storage and utilization pattern of knowledge repository by knowledge workers. To achieve this
purpose, the following objectives have been formulated:
1. Designing the “Organizational Knowledge Repository Utility Model” (OKRUM).
2. Analyzing behavior of model parameters.
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Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
6. The Model construction
This section focuses on conceptual issues related to the development, analysis and use of the
OKRUM model designed and validated in VensimPLE®.
6.1. Background
An organization builds its knowledge repository through knowledge storage activities followed
by its subsequent use. Generally there is a balanced approach to storage and development,
however there will be periods of low capacity utilization followed by storage ramp up and
added shifts (inputs). While all this is normally blamed on workforce demand and the
knowledge requirement cycle of the organization, a study into the deployment-storage pattern
shows that deployment is more stable than storage. The top and middle management would
want to determine what could be the cause of this and how this situation could be handled.
Attacking the problem would warrant as much simplification as possible of the current situation.
It is more effective to start with a simple model and add detail than to build a complex model
and attempt to extract insights from it after it is complete. Using a simple model forces a
modeller to take an overview which is usually useful in the initial modeling phases.
6.2. Reference modes
A reference mode is a graphical statement about a problem (Sterman, 2000). Verbally, the
problem could be stated as “storage is less stable than usage”.
This reference model is a sketch of expected behaviour that the model may produce. The
reference mode in Figure 1 supplies us with two variables – knowledge storage and knowledge
usage.
Figure 1: Reference mode for knowledge storage and usage (Adapted from Ventana Systems, 2014)
6.3. Dynamic Hypothesis
A dynamic hypothesis is an idea about what the structure might be capable of generating
behavior like that in the reference models. In this article, the dynamic hypothesis would be
“The middle management is setting storage limits based on current usage, but is amplifying the
amount resulting in higher (or lower) storage than is necessary. The model has been designed
and developed based on the generic basic diffusion model incorporated into the KMS scenario
of an organization (Sterman, 2000; Ranganath and Rodrigues, 2008, Ventana Systems, 2014).
The stock and flow diagram of the proposed OKRUM is presented in Figure 5. There are two
stocks in the model and they are:
1. Organizational Knowledge Repository: Indicating the pool of documents stored in the
organizational knowledge base
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Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
2. Knowledge Workers: Indicating the pool of employees working on creating, storing,
accessing and applying the knowledge
6.4. Phases of Model Development:
The model has been designed in two phases:
6.4.1. Phase 1
When developing the model, establishing the relation between knowledge storage and use
was essential. Clearly there is a close association, since it is necessary to store knowledge,
before it can be used. Knowledge workers need the knowledge to be available in the
repository so that they can use the same when they need it (Figure 2).
Knowledge
Storage
Organizational
Knowledge
Repository
Knowledge Usage
Figure 2: Relationship between knowledge storage and usage
The model was initiated with the assumption that when knowledge storage occurs, the
knowledge would not be utilized immediately. The next step was to find out how knowledge
storage gets determined. The assumption used here was more workers would add more
knowledge, so storage would increase based on the knowledge workforce. Hence the stock
“Knowledge Workers” was included. It was initially simply named „Workforce‟. Additionally,
there was the fact that the workforce would be affected by hiring, lay-offs, firings and
retirements of workers. Therefore, all these factors were combined into a composite flow – net
hire rate, which can increase or decrease the workforce (See Figure 3) Therefore, the net hire
rate is actually the net number of employees hired.
Workforce
net hire rate
Figure 3: Workforce and net hire rate stock and flow
Further, a stock adjustment process is added to the model by adding variables such as „target
workforce‟ and „time to adjust workforce‟. Here „time to adjust workforce‟ represents the time
required for top or middle level management to agree upon a change to be initiated in the
existing workforce, which could involve screening potential candidates or notification of
workers to be laid off. Thus, „target workforce‟ is the number of knowledge workers required to
produce the amount of knowledge to be stored. The stock „Knowledge Workers‟ is initialized at
this value. Next, the variable „target storage volume‟ is connected to the „target workforce‟,
which is set on the basis of „knowledge usage‟. This completes phase one for the model
(Figure 4).
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Proceedings of International Social Sciences and Business Research Conference
4 - 5 December 2014, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-65-8
Organizational
Knowledge
Repository Knowledge Usage
Knowledge
Storage
target storage
volume
productivity
Knowledge
Workers
net hire rate
target workforce
time to adjust
workforce
Figure 4: Phase 1 of OKRUM
6.4.2. Phase 2:
In order to refine the model further, the following variables were included: „target knowledge
repository‟, „repository deviation parameter‟ and two additional constants. Here, „target
knowledge repository‟ is the amount of knowledge that should be stocked based on the
expected usage. The „repository deviation parameter‟ is the factor for correction in case there
is a deviation of „Organizational knowledge repository‟ from its target. An additional loop was
introduced and has been shown in Figure 5.
Knowledge
storage
Organizational
Knowledge
Repository
productivity
Knowledge
Workers
Repository
coverage
Knowledge usage
target knowlegde
repository
repository deviation
parameter
target storage
volume
net hire rate
target work force
time to correct
repository
time to adjust
work force
Figure 5: Phase 2 of OKRUM
6.5. Governing Equations
(01)
FINAL TIME = 100 (The final time for the simulation)
Units: Month
(02)
INITIAL TIME = 0 (The initial time for the simulation)
Units: Month
(03)
Knowledge Usage= 100+STEP(50, 20 )
Units: Docs/Month
(04)
Knowledge storage= Knowledge Workers*productivity
Units: Docs/Month
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Proceedings of International Social Sciences and Business Research Conference
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(05)
Knowledge Workers= INTEG (net hire rate, target work force)
(06)
net hire rate= (target work force-Knowledge Workers)/time to adjust work
force
Units: Person/Month
Organizational Knowledge Repository= INTEG (Knowledge storage –
Knowledge Usage, 300)
Units: Docs
repository deviation parameter=(target knowledge repository-Organizational
Knowledge Repository)/time to correct repository
Units: Docs/Month
target creation=Knowledge deployment + repository deviation parameter
Units: Docs/Month
target knowledge repository=Knowledge deployment*Repository coverage
Units: Docs
target work force=target creation/productivity
Units: Person
(07)
(08)
(09)
(10)
(11)
Units: Person
7. Analysis
7.1. Model Scope
The variables defined in the model are endogenous to the model and serve the purpose
specified as per the requirements of SD model boundary identification.
7.2. Time Horizon
System dynamics is generally used as a prediction tool, and helps better comprehend the
problem under study along with the probable decisions that may be considered at the end of
experiment. Hence the modeller must be able to design for a particular purpose outside a
narrow time zone. In this case the time span has been taken as 100 months which is roughly 8
years.
7.3. Modelling conditions and results
7.3.1. Simulation 1: Phase 1
The simulations in Figure 5a and 5b display an obvious variability in „Knowledge storage‟ and
„Knowledge workers‟ variables. It can be observed that there is a smooth adjustment from the
initial 100 Docs/Month to around 150 Docs/Month. This is different from the reference mode
which showed more variability in storage compared with usage.
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Proceedings of International Social Sciences and Business Research Conference
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Knowledge Workers
200
170
170
Person
Docs/Month
Knowledge Storage
200
140
110
140
110
80
80
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
Knowledge Storage : Phase 1 BaseRun
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
Knowledge Workers : Phase 1 BaseRun
Figure 5a: Knowledge Store Base run results
Figure 5b: Knowledge Workers Base run results
Organizational Knowledge Repository
400
Docs
300
200
100
0
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
Organizational Knowledge Repository : Phase 1 BaseRun
Figure 5c: Organizational Repository stock simulation
Figure 5: Simulation behavior after Phase 1
Further, the „organizational knowledge base‟ stock shows a smooth downward fall from its
initial value (Figure 5c). This merits some correction as this would imply that there is something
wrong in the knowledge base available to the knowledge workers. A correction would seem
appropriate at this point to meet this requirement.
7.3.2. Simulation 2: Phase 2 Refinement
As mentioned in section 6.4.2, new variables were added in the phase 2 refinement of the
model. The variable „repository deviation parameter‟ is a stock adjustment parameter and „time
to correct repository‟ was introduced to denote the time required to observe significant
deviations in the „organizational knowledge repository‟ and subsequently plan corrections in
the existing knowledge storage procedure.
Figure 6b shows a different behavior after simulation in phase 2 refinement. „Knowledge
workers‟ is seen to be slightly less stable and an oscillation is noticed. Again Figures 6a and 6c
show similar variations. While usage of knowledge increases, the storage parameter adjusts to
a new higher level (as desired), rather than falling below.
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Proceedings of International Social Sciences and Business Research Conference
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Knowledge storage
Knowledge Workers
200
200
Docs/Month
170
170
140
140
110
110
80
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
80
0
Knowledge storage : Phase 1 BaseRun
Figure 6a: Knowledge Store phase 2 results
10
20
30
70
80
90
100
Knowledge Workers : Phase 1 BaseRun
Figure 6b: Knowledge workers’ phase 2 results
Organizational Knowledge Repository
Knowledge storage
600
400
500
300
Docs/Month
Docs
40
50
60
Time (Month)
400
200
100
300
0
0
200
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
Organizational Knowledge Repository : Phase 1 BaseRun
Figure 6c: Knowledge repository phase 2 results
10
20
30
40
50
60
Time (Month)
70
80
90
100
Knowledge storage : Phase 2
Knowledge storage : Phase 1 BaseRun
Figure 6d: Knowledge storage comparison
Organizational Knowledge Repository
Knowledge Base and Knowledge Workers
600
80
600 Docs
Docs
450
50
300 Docs
300
150
20
0 Docs
0
0
10
20
30
40
50
60
Time (Month)
70
80
90
100
0
10
20
30
40
50
60
Time (Month)
70
Knowledge Workers : Phase 2
Organizational Knowledge Repository : Phase 2
Organizational Knowledge Repository : Phase 2
Organizational Knowledge Repository : Phase 1 BaseRun
Figure 6e: Knowledge repository comparison
80
90
100
Docs
Figure 6f: Knowledge workers and repository
Phase 2 Comparisons
200
80
600
600
Docs/Month
80
20
0
200
Docs/Month
Docs
Docs
Docs
Docs
0
10
20
30
40
50
60
Time (Month)
Knowledge s torage : Phas e 2
Knowledge Workers : Phas e 2
Organizational Knowledge Repos itory : Phas e 2
target knowlegde repos itory : Phas e 2
70
80
90
100
Docs /Month
Docs
Docs
Figure 6g: Phase 2 comparisons
Figure 6: Simulation behavior after Phase 2
The simulations were further customized to gain insight into these oscillatory patterns of
behavior. Figures 6d, 6e and 6f display the output generated when comparing phase 1 and
phase 2 runs. Figure 6g shows when „organizational knowledge repository‟ is increasing, so is
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Proceedings of International Social Sciences and Business Research Conference
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„knowledge storage‟. As long as „organizational knowledge repository‟ is below „target
knowledge repository‟, there is pressure to increase „knowledge storage‟. Additionally, it was
observed „knowledge storage‟ needs increase sufficiently over „knowledge usage‟ so that the
ongoing difference balances the pressure from the correction mechanism added before „net
hire rate‟ goes negative. It is important to note when „knowledge storage‟ is equal to „target
knowledge repository‟, „knowledge storage‟ is still higher than the „knowledge usage‟. This is
seen because the knowledge workers are in excess, causing an increase in the stored
knowledge. The simulations, thus, seen in Figure 6, reveal the same kind of behavior as seen
in the reference mode which was our basis for model development.
8. Conclusions and Implications
This research article resulted in the OKRUM model which was designed, developed and
simulated using Systems Dynamics. The model offers insight into understanding the
behavioural patterns in knowledge worker‟s usage in relation to the knowledge storage in the
organizational repository. The work started with a set of written hypotheses and worked on
building the OKRUM. The simulation results indicate oscillatory behavior in organization
knowledge repository. A steady rise in the knowledge storage pattern was observed when the
correction factor was included in phase 2 of development. System dynamics provides methods
for validation of the model. The model is validated using Face validity test and Dimensional
consistency test. However to establish more confidence, data and reality checks need to be
implemented which will be worked upon as the next phase of research. Further, organizational
business researches may also refer to this model and explore dynamic structures not identified
based on specific scenarios. If initiated, strategically designed and supported by the
organizations‟ top level decision makers, the knowledge repository is going to bring positive
change in the organization-its people, process and practices.
9. References
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