Characterizing Knowledge Maturing: A Conceptual Process Model for Integrating E-Learning and Knowledge Management

Abstract

Knowledge management and e-learning both attempt to support learning and knowledge transfer in organizations. However, they aim at knowledge of different degrees of maturity. Central hypothesis of this paper is that the approaches can be integrated on the basis of a process that explicitly aims at designing the transitions of knowledge along varying degrees of maturity. The knowledge maturing process is presented as a conceptual model for explaining and analyzing disruptions in the inter-individual flow of knowledge within organizations. These disruptions can be attributed to a fragmented systems landscape and separated organizational units that foster knowledge of different degrees of maturity. The paper presents criteria for a characterization of this process model and discusses its implications for the design of learning support systems.

Full text

Characterizing Knowledge Maturing
A Conceptual Process Model for Integrating E-Learning
and Knowledge Management
Ronald Maier
Martin-Luther-University
Halle-Wittenberg
Germany
Ronald.Maier@wiwi.uni-halle.de
Andreas Schmidt
FZI Research Center for
Information Technologies,
Karlsruhe, Germany
Andreas.Schmidt@fzi.de
Abstract. Knowledge management and e-learning both attempt to
support learning and knowledge transfer in organizations. However,
they aim at knowledge of different degrees of maturity. Central
hypothesis of this paper is that the approaches can be integrated on
the basis of a process that explicitly aims at designing the transitions
of knowledge along varying degrees of maturity. The knowledge
maturing process is presented as a conceptual model for explaining
and analyzing disruptions in the inter-individual flow of knowledge
within organizations. These disruptions can be attributed to a
fragmented systems landscape and separated organizational units
that foster knowledge of different degrees of maturity. The paper
presents criteria for a characterization of this process model and
discusses its implications for the design of learning support systems.
1. Introduction
Knowledge management (KM) and e-learning (EL) are both approaches
that intend to improve construction, preservation, integration, transfer and
(re-) use of knowledge and competencies. In addition to these approaches,
programs of personnel development as part of human resource (HR)
management support training into the job, on the job, near the job, off the
job and out of the job [Scho00]. But despite increased interest in bringing
together these disciplines, there are still huge conceptual differences
resulting in a separation of research communities, of technical systems and
of corporate responsibilities. Whereas e-learning and personnel develop-
ment have their foundations in (learning) psychology, (media) didactics and
(learning) pedagogy and emphasize the importance of structural (by
preparing learning material) or personal guidance, knowledge management
envisions an organizational memory or organizational knowledge base into
In: 4th Conference
Professional Knowledge
Management (WM 07),
Potsdam, Germany, 2007

which the individual’s knowledge is supposed to be made explicit and
which is the basis for (more or less unguided) “knowledge transfer”.
From the perspective of information and communication technologies
(ICT), numerous systems aim at improving knowledge and learning
processes as well as organizational competency development. Examples are
HR systems, typically embedded in enterprise systems, document-oriented
KM systems, collaboration platforms, content management systems which
are easy to use, such as Wikis and Weblogs, as well as learning
management systems. Employees thus use a fragmented systems landscape
in which each system supports a certain part of knowledge and learning
processes. Development of enterprise knowledge infrastructures is a
technical solution that aims at integrating these systems [Mai05]. However,
there are also conceptual challenges which cannot be solved simply by
introducing a technical solution. These are challenges of designing learning
and knowledge processes that bring together the separated organizational
support infrastructures fostered by organizational units such as HR, EL,
KM, innovation and quality management. These are often as separated as
the supporting ICT systems. These organizational units again typically
target knowledge of different degrees of maturity.
Key hypothesis of this paper is that a so-called knowledge maturing process
provides a conceptual framework for the design of the required integrating
processes in organizations. This paper presents a systematic characterization
of the knowledge maturing process the first version of which was
introduced as a semi-formal model in [Schm05] for explaining integration
barriers between the different disciplines concerned with learning in
organizations. Section 2 gives an overview of the knowledge maturing
process on an individual and organizational level. Section 3 presents criteria
for characterizing knowledge of different degrees of maturity and discusses
the individual phases of the process model. Section 4 discusses a selection
of implications from this model before section 5 concludes the paper.
2. Knowledge Maturing Process
The starting point for distilling the model from real-world experiences was
the idea of a “knowledge flow”, which is seen as a metaphor for
interconnected individual learning processes where knowledge is passed on
and reconstructed and enriched by the individuals involved. Everyday talk
about the “quality” of knowledge in such a knowledge flow speaks of
“consolidating”, or “putting into the context of a bigger whole”, or just that
it is “not mature enough”. If this is not about one’s own knowledge, it is
judged from information artefacts produced in the process of passing on.

2.1 Basic Model
In a first step of structuring this process, five phases have been identified
after analyzing practical cases, e.g,. projects with industry partners like SAP
[SB06] or the KnowCom consortium [BEM05] (see fig. 1):
Figure 1: The Knowledge Maturing Process
1. Emergence of Ideas. New ideas are developed by individuals in highly
informal discussions. The vocabulary used for communication is vague
and usually restricted to the originator.
2. Distribution in Communities. This phase accomplishes an important
maturing step, i.e. the development of common terminology shared
among community members, e.g., in discussion forum entries or Blog
postings.
3. Formalization. Artefacts created in the preceding two phases are
inherently unstructured. In this phase, purpose-driven structured
documents are created, e.g., project reports or design documents.
4. Ad-Hoc-Training. Documents produced in the preceding phase are not
well suited as learning materials because no didactical considerations
were taken into account. Now the topic is prepared in a pedagogically
sound way, enabling broader dissemination.
5. Formal Training. The ultimate maturity phase puts together individual
learning objects to cover a broader subject area. As a consequence, this
subject area becomes teachable to novices.

2.2 Integrating the Organizational Perspective
Learning in organizations requires extending the individual perspective as
described in section 2.1 by an organizational perspective. Metaphors of
organizational knowledge and learning need to be considered in the design
of a knowledge maturing process. However, it is important not to simply
equate the individual and organizational levels [Wil01]. The model of
organizational information processing provides a starting point for an
organizational perspective on the knowledge and learning process ([Mai04],
133-138) a portion of which has also been investigated in an empirical study
of the 500 largest organizations and 50 largest banks and insurance
companies in Germany ([Mai04], 454-462). The model aims at integrating
concepts and theories of diverse research fields surrounding EL and KM
and helps to explain and design those organizational processes that underlie
the knowledge maturing process described in section 2.1.
The fields of organizational psychology and sociology suggest that the
group as a collective of people is the single most important entity
processing information in organizations ([HSD82], [Weg86]). Transactive
memory systems (TMS, [Weg86]) explain the impact of inter-subjective
knowledge, its linking and embedding on information processing in a group.
Levitan’s [Lev82] life cycle of information production extended by [RK96]
extends the organizational learning cycle to start with the perception of
information in an organization's environment and to end with the dissemi-
nation of new information resources. The SECI-model [NT95] shows which
knowledge conversion tasks are focused in each of the quadrants. Nonaka’s
spiral model ([Non94], 20) reflects the circular movement of knowledge in
the organizational learning cycle. The concepts used in Argyris/Schön's
theory [AS78] are assigned to the two fields institutionalized knowledge
(espoused theories) and knowledge-in-use (theories-in-use).
Organizational knowledge processing (see fig. 2) starts with the
establishment of data in the organization, called knowledge acquisition (1),
or from within the organization, called knowledge identification (2). Via
individual learning (3) knowledge sources become part of the organizational
learning cycle. Individual knowledge is analyzed, verified and its value is
determined by the individual. Knowledge is shared (4) and inter-subjective
knowledge is created. In order to be fully accessible and independent of
individuals, knowledge has to be institutionalized (5). Institutionalized
knowledge (espoused theories) represents proclaimed, officially accredited
or agreed ways of reacting to certain situations as opposed to knowledge
(theories) in use (6) which denotes rules and hypotheses that are actually
applied ([AS78], 11). Knowledge in use may or may not be compatible with

institutionalized knowledge. The results of actions finally give feedback (7).
New individual knowledge is created. The knowledge created, shared,
institutionalized and applied within the organizational learning cycle can be
refined and repackaged (8) and thus used to create knowledge products and
services. These products and services can be communicated, sold and
disseminated to the environment (9) or they can be communicated internally
and knowledge services can be offered to employees (10).
knowledge
in use
institutionalized
knowledge
inter-subjectiv e
knowledge
individua l
knowledge
knowledge
sources
knowledge
products & services
7
6
38
19
2
2
5
4
sharing
applica tion
individ ual lear ning repackaging
ORGANIZATIONAL LEARNING CYCLE
ORGANI ZATION
knowledge acquisition
ext ernal o fferi ng
institutionalization
feedba ck
identi fica tion
identi fica tion
10
internal offering
Figure 2: Model of organizational information processing ([Mai04],134)
According to the authors’ consulting experiences with organizations,
employees typically can choose from numerous media and locations to
preserve as well as channels to transfer knowledge of varying degrees of
maturity. The choice is often difficult, leading to inadequate supply of
information and knowledge in organizations and thus can be improved.
When comparing the two models in section 2.1 and 2.2, all processes in the
basic model of knowledge maturing are also part of the model of
information processing. The emergence of ideas corresponds to the process
of individual learning, distribution in communities corresponds to sharing,
formalization is reflected in institutionalization, ad-hoc training in feedback
and formal training in the refining and repackaging processes. The basic
model in section 2.1 sets the focus on a pragmatic chain of knowledge
development tasks that can be designed so that formal, mature knowledge
products are the outcome of the respective knowledge maturing process.
3 Phases of Knowledge Maturing
This section presents concrete criteria which can be used to classify
knowledge according to its level of maturity. The class then suggests the
appropriate form of learning and technical support systems. The following
criteria have been identified as useful:

• Hardness. In analogy to mineralogy, this criterion describes the
(alleged) validity and reliability of information or knowledge.
According to [Wat05], a possible scale ranges from unidentified
sources for rumours up to stock exchange data (see fig. 3)
1Unidentified source
rumors, gossip, and hearsay 6Budgets, formal plans
2Identified non-expert source
opinions, feelings, ideas 7News reports, non-financial data,
industry statistics, survey data
3Identified expert source
predictions, speculations, forecasts, estimates 8Unaudited financial statements,
government statistics
4Unsworn testimony
explanations, justifications, assessments, interpretations 9
A
udited financial statements, government
statistics
5Sworn testimony
explanations, justifications, assessments, interpretations 10 Stock exchange and commodity market
data
• Interconnectedness/contextualization. “Learning is network
creation” [Sie05]. With the deepened understanding, connections to
other topics become visible. This must not be confused with contex-
tualization of knowledge which decreases in the knowledge maturing
process and refers to the degree of implicit linkage to the creation
context, so that it cannot be used outside the original context.
Contextualization and interconnectedness are inverse properties.
• Commitment/legitimation. Knowledge can be structured according to
the amount of support it gets. Support can be in the form of
commitment by members of groups, teams, communities or other
organizational units. Another form of support can be authorization to
use knowledge by supervisors, executives or committees as well as
legalisation and standardization, forms of legitimation (see Fig. 4).
lessons
learned
good/best
practices
redesigned
business
processes
personal
experience
management
level of commitment & legitimation
Figure 4: Commitment & Legitimation
• Teachability. As knowledge maturing is basically interconnection of
individual learning processes where knowledge is taught and learnt, an
important criterion is its teachability. Whereas immature knowledge is
hard to teach (even to experts), formal training allows by definition for
wide-range dissemination.
Table 1 gives an overview of the phases of the knowledge maturing process
with an example list of typical types of knowledge, values according to the
criteria discussed in this section as well as implications for technical imple-
mentation which are discussed in the following.
Figure 3: Hardness scale according to [Wat05]

Type of
knowledge Hard
ness Medium/Inter-
connectedness Commitment/
Legitimation Form of learning Technical
implementation
Rumours 1 Human, highly
contextualized n/a informal & direct
communication communication
technology
(phone, IM, mail)
Personal
experiences 2 Human, personal
notes highly
contextualized
Commitment of
individuals,
confirmation by
colleagues
direct
communication,
exchange of
personal artefacts,
emergence of
communities
computer-
mediated
communication,
collaboration
technology,
Weblogs
Ideas and
proposals 2Forum entry,
suggestion form
explicit connections
to application context
Commitment of
inidividuals,
legitimation by
colleagues
organizational
process for
improvement
capturing ideas,
community format.
Community work-
space, forum,
suggestion system
Questions &
answers 3FAQ
explicit connections
to problem context
legitimation by
experts self-steered, on-
demand inform.
seeking, beginning
formalization
FAQ database and
Wikis
Project
results 3 project/milestone
report with
structure, explicit
connections
legitimation by
project manager on-demand
information seeking project &
document
management
system
Lessons
learnt 4 LL-document
project context made
explicit
legitimation by
project team case-based, self-
steered learning LL-database Wikis,
Weblogs
Learning
objects 3 well-defined digital
resource,
formal metadata
legitimation by
experts ad-hoc training learning object
repository
Good/best
practices 5 best practice
document
explicit creation
context
commitment of
an organiza-
tional unit
case-based, self-
steered learning,
ad-hoc training
best practice
database
Patents 7 patent application,
explicit connections
to potential usage
context
legitimation by
patent office specialized
information seeking patent databases
Reorganized
busin. proc. 6 process models and
descriptions commitment of
process owner standardized
training, courses process
warehouse
Courses 6 interconnected
learning objects,
notion of curriculum
legitimation by
course vendor standardized
training WBT-authoring,
LMS
Formal
Training Ad-Hoc Training Emergence of Ideas
Distribution in
Communities
Formalization
Table 1: Types of knowledge in different maturing phases
4. Implications
This section shows for two examples (a) how the conceptual model helps to
understand disruptions in the knowledge maturing process and (b) how or-
ganizational and ICT learning support helps to overcome these disruptions.

4.1 Formalization vs. Ad-Hoc-Training
The classical barrier between KM and EL can be located between the
formalization (KM) and ad-hoc training phase (EL). On a technical level,
formalization is usually supported by document management systems,
whereas the ad-hoc training phase is supported by learning (content)
management systems (LMS). On the organizational level, ad-hoc training is
under the responsibility of HR development or training departments,
learning in the formalization phase (usually not called “learning”) is
managed by the operating departments themselves. The differences between
these two phases can be directly derived from our criteria legitimation
(project teams & manager vs. training experts) and the paradigms of
learning (information seeking vs. ad-hoc-training).
One example of how to foster knowledge maturing at this “point of rupture”
on an organizational level is the Ramp-Up Knowledge Transfer program of
SAP in which developer documentation is transformed into ad-hoc training
material before rolling out new products on a large scale. In a moderated
process, development and training experts collaborate on the maturing task.
4.2 Distribution in Communities vs. Formalization
A less prominent barrier in the knowledge maturing process can be
identified between the second and the third phase. In KM, this is usually
investigated as the problem of externalizing knowledge. Barriers between
these phases can be traced back to human and social issues: the detachment
from the originator of an idea. In communities, the originator of an idea is
usually still active and mostly identifies with community goals so that the
distribution is seen as contributing to reputation and social esteem, rather
than losing something. But with formalization, knowledge is supposed to
spread far beyond community boundaries so that the community loses
control. Again, this can be illustrated using the criteria from the previous
section: the maturing step requires transformation from commitment by
individuals to legitimation by a formal organizational unit, and the
transition from community-driven systems to enterprise-level systems.
A promising approach to overcoming this type of disruption lies in an
increased visibility of the individual. Weblogs and Wikis (especially in
combination) are good instruments. Weblogs are communicative
instruments of individuals to spread their ideas and opinions and for infor-
mal formation of communities among the Blog readers. Trackbacks can link
different steps in the maturation of an idea. Wikis have evolved into useful
instruments for discussing and working collaboratively on the presentation
of a topic while still retaining visibility of individual contributions.

Additional conventions within Wiki systems allow for adding legitimation
and indicating maturity so that these systems can provide an interesting
alternative to classic CMS, proving transition opportunities from the
community phase up to the ad-hoc training phase.
5. Conclusions
The knowledge maturing process is a model for structuring real-world
phenomena of dealing with knowledge in companies and for systematically
elaborating technical solutions. This model is not an attempt to explain how
learning takes place, but rather to point out that learning takes place diffe-
rently based on the maturity of knowledge to be constructed. The maturity
level yields an indication for the appropriate medium, form of learning and
learning support technology. This allows for a systematic design of the ICT
infrastructure of companies, incorporating processes, roles, and tools with a
special awareness of disruptions in the maturing process. Future research
will concentrate on the elaboration and validation of a methodological
framework for maturity-aware learning support.
References
[AS78] Argyris, C., Schön, D.: Organizational Learning: A Theory of
Action Perspective, Reading (MA) 1978.
[BEM05] Bayer, F.; Enparantza, R.; Maier, R.; Obermair, F.; Schmiedinger
B.: Know-CoM: Decentralized Knowledge Management Systems for
Cooperating Die and Mould Making SMEs, in: Jennex, M. E. (ed.), Case
Studies in Knowledge Management, Hershey (PA, USA) 2005, 186-209.
[HSD82] Hartwick, J., Sheppard, B. H., Davis, J. H.: Group Remembering:
Research and Implications, in: Guzzo, R. A. (ed.): Improving Group
Decision Making in Organizations. Approaches from Theory and
Research, New York et al. 1982, 41-72.
[Lev82] Levitan, K. B.: Information Resources as „Goods” in the Life
Cycle of Information Production, in: Journal of the American Society
for Information Science, Vol. 33, January 1982, 44-54.
[Mai04] Maier, R.: Knowledge Management Systems. Information and
Communication Technologies. 2. ed., Berlin 2004.
[MHP05] Maier, R., Hädrich, T., Peinl, R.: Enterprise Knowledge
Infrastructures. Berlin 2005.
[Non94] Nonaka, I.: A Dynamic Theory of Organizational Knowledge
Creation, in: Organization Science, Vol. 5, No. 1, 1994, 14-37.

[NT95] Nonaka, I., Takeuchi, H.: The Knowledge Creating Company, New
York, 1995.
[RK96] Rehäuser, J., Krcmar, H.: Wissensmanagement im Unternehmen,
in: Schreyögg, G., Conrad, P.: (eds.): Managementforschung, Vol. 6:
Wissensmanagement. Berlin, New York 1996, 1-40.
[Schm05] Schmidt, A.: Knowledge Maturing and the Continuity of Context
as a Unifying Concept for Integrating Knowledge Management and E-
Learning. In: Proceedings I-KNOW ’05, Graz, 2005.
[SB06] Schmidt, A., Braun, S.: Context-Aware Workplace Learning
Support: Concepts, Experiences, and Remaining Challenges. In:
Proceedings of ECTEL ’06, LNCS vol. vol. 4227, pp. 518-524
[Scho00] Scholz, C. (2000): Personalmanagement. Informationsorientierte
und verhaltenstheoretische Grundlagen. Munich 2000.
[Sie05] Siemens, G.: Connectivism: Learning as Network Creation,
elearnspace, 2005, http://www.elearnspace.org/Articles/networks.htm.
[Wat05] Watson, R. T.: Data Management. Databases and Organizations. 5.
ed., New York 2005.
[Weg86] Wegner, D. M.: Transactive Memory: A Contemporary Analysis
of the Group Mind, in: Mullen, B., Goethals, G. R. (eds.): Theories of
Group Behaviour, New York et al. 1986, 185-208.
[Wil01] Willke, H.: Systemisches Wissensmanagement, 2nd ed. Stuttgart
2001.