Download knowledge management systems support seci

KNOWLEDGE MANAGEMENT SYSTEMS SUPPORT
SECI MODEL OF KNOWLEDGE-CREATING PROCESS
Srečko Natek
International School for Social and Business Studies, Slovenia
[email protected]
Moti Zwilling
Ariel University, Israel
[email protected]
Abstract:
Knowledge management (KM) has evolved from different disciplines and can be observed from
various perspectives. The most important KM perspectives are personal communication and
information technology (IT). Most IT companies have renamed their groupware and business
intelligence products to fit the KM concept and to further develop them as knowledge management
systems (KMS). In fact, they develop IT that can be used to implement or support KMS. Different IT
can be used to develop successful KMS. The functionality and purpose of KMS are the most important
criteria to define a system as KMS. The paper uses the SECI model of knowledge-creating process
(Nonaka & Takeuchi, 1995) as a framework to propose suitable KMS and an important tool to manage
the implementation of KM in contemporary organisations.
Keywords: knowledge management, knowledge management system, knowledge-creating process,
SECI model
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1. INTRODUCTION
According to Jashapara’s tree of KM – disciplines, content and activity (Jashapara, 2011), KM has
evolved from different disciplines and can be explored from various perspectives. Authors usually
observe KM from at least two most important KM perspectives: personal communication and IT (e.g.
Alavi & Leidner, 2001, Dalkir, 2011, Gomez & Perera & Manning, 2012, Hislop, 2009, Leonard, 1998).
Alavi and Leidner define KMS as a type of information system to support the creation, exchange and
application of knowledge, and the use of IT to support Nonaka’s SECI model (Alavi, 2001). Wang has
expanded the concept of KMS with business intelligence and data mining to support KM processes:
knowledge creation, storage, search, exchange and application (Wang, 2008).
As the IT industry follows academic terminology trends, most IT companies have renamed their
groupware and business intelligence products to fit the KM concept and to further develop them as
KMS. In fact, they develop IT that can be used to develop or support KMS. Different IT can be used to
develop successful KMS. The functionality and purpose of KMS are the most important criteria to
define a system as KMS. The paper uses a model of knowledge-creating process, where knowledge is
transformed between its tacit and explicit forms through four processes: socialisation, externalisation,
combination and internalisation (Nonaka & Takeuchi, 1995), as a framework to propose suitable KMS
and an important tool to manage the implementation of KM in contemporary organisations. Nonaka
and other authors have recognised the described model as the SECI model and have moved from a
concept of knowledge as substance to knowledge as a process in subsequent literature (Nonaka &
Toyama & Hirata, 2008).
2. IT AND KM PROCESSES
KMS are supported by different IT. The relationships are very different. IT can be used for KMS
development. Sometimes specific IT is only used for one development phase or just to visualise
results. KMS can be supported by nearly all the known IT. For example, an organisation can build a
content management type of KMS with File Management System and Office tools. On the other hand,
data mining technology can be used for knowledge discovery in rare data patterns or customer
behaviour. Some IT is very useful only in a specific KM process (e.g. e-mail for knowledge sharing). A
majority of KM authors try to clarify the role of IT in KMS and classify the available IT in relation to KM
processes.
In the digital era, KM is considered as a very important and popular compartment in understanding
consumer's behaviour related to data that is gathered through computers. One demonstration could be
viewed in the filled of recommendation systems: by building an automatic metadata engine to filter TV
programs and recommend viewers on programs and related features. In this area, most of the IPTV
set up boxes collect data on the viewer's habits. In addition a data mining engine scans and classify
each of the parameters related to: programs, viewers and the preferences of viewers. The knowledge
base created is used to form a knowledge discovery metadata which is used later to suggest upon
video on demand and new programs to viewers.
Becerra, Gonzales and Sabherwall classify IT according to KM processes (knowledge discovery,
capture, sharing and application), KM systems, KM sub-processes and KM mechanisms (Becerra &
Gonzales & Sabherwall, 2004). The matrix is very systematic but lacks clarity as much of IT is written
several times (Picture 1).
Picture 1: KM Processes, Mechanisms and Technologies
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Source: Becerra & Gonzales & Sabherwal, 2004, p. 41.
Smith and Lyles have attempted to classify IT for KMS according to integrative or interactive nature of
the available IT (Smith & Lyles, 2011). Both categories of IT can be used to support several KM
processes and the distinction is not very clear. For example, groupware could be easily described as
interactive IT (Picture 2).
Picture 2: Key IT associated with KM
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Source: Smith & Lyles, 2011, p. 85.
As described related to the IPTV viewers, collaborative authoring, discussion forums and real time
interactions, are taken place in many systems, such as IPTV. In this smart TVs, the knowledge base
related to viewers. The ability to evaluate their thoughts and behaviour, which is strongly influenced
from social media viewers, could be analysed by data mining tools. The integration in expert systems
can form a real time interaction engine which is able to suggest and evaluate which programs and
video on demand could be offered to current, old and new TV viewers.
The same authors have classified IT according to KM processes (Smith & Lyles, 2011). The list of IT is
very short (Picture 3). The matrix is detailed enough to understand basic representatives of the
available IT but not to guide a KMS development team in choosing the appropriate IT.
Picture 3: IT support KM processes
Source: Smith & Lyles, 2011, p. 111.
An excellent example of an overview model of complex KMS is provided by Laudon in Picture 4
(Laudon & Laudon, 2016). KMS in the centre of the model classify the available IT according to KM
processes: knowledge acquisition, storage, dissemination and application, in parallel with
management and organisational activities oriented into KM mechanisms (personnel- and organisationoriented). The main benefit of the model is its active value chain orientation with a feedback loop to
stress that KM is process- not IT-oriented. The key shortcoming of the model is its short IT list, not
long enough for practical purposes.
Picture 4: The KM value chain
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Source: Laudon & Laudon, 2016, p. 464.
3. THE SECI MODEL AS A KMS FRAMEWORK
Knowledge creation is the most important KM process. Nonaka has developed a well-known and
comprehensive SECI model of the knowledge-creating process (Picture 5). The SECI model is a
wheel of tacit and explicit knowledge transformation, following four sub-processes: socialisation,
externalisation, combination and internalisation (Nonaka, 2008).
The model is precise enough to allow for a deep understanding of micro-processes and can be easily
interpreted from the IT perspective. The essence of socialisation is knowledge sharing. The essence
of externalisation is writing (codification) of knowledge. The essence of combination is storage,
systemisation and processing of data, information and knowledge. Finally, the essence of
internalisation is learning. Using these simple facts, the SECI model is precise enough for practical
classification of the available IT for KMS development or support
Picture 5: The knowledge – creating process: SECI model
Source: Nonaka & Toyama, 2008, p. 19.
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New knowledge is an added value for all business, and especially KM, processes. Most of new
knowledge is created through the externalisation of tacit knowledge to explicit knowledge in the form
of knowledge articulation, writing or codification (all synonyms). In the process of externalisation,
organisations and communities are learning. New knowledge is also created in the process of
internalisation, transformation of explicit knowledge to tacit knowledge of a learning individual (Picture
6). New knowledge is also created through combination and socialisation (Jashapara, 2011).
To classify the available IT to support KMS development, the SECI model matrix of the knowledgecreating process is used in Picture 7.
IT for socialisation is socially oriented to support individuals, teams and participants in organisations or
a society. The aim of socialisation KMS is to cross the borders of time and location, and to encourage
people to communicate, share knowledge and enhance group decisions (Liebowitz, 2006).
IT for externalisation is codifying-oriented to articulate and codify knowledge. Through externalisation
KMS, knowledge can be used by more people, organisations and societies, thus ensuring better
decision-making and problem-solving (Turban, 2011).
IT for combination is typically data-, information- and knowledge-processing-oriented. Combination
was traditionally the domain of IT, demonstrating its computer-processing power. The list of
combination KMS is the longest list of the available IT, supporting data, information and knowledge
processing, storage and retrieval. The most comprehensive data mining and artificial intelligence
systems enable deep data and knowledge discovery based on huge amounts of data or
ontology/taxonomy stored knowledge (Han & Kamber & Pei, 2012, Natek & Lesjak, 2013).
IT for internalisation is learning-oriented. There are a variety of dedicated e-learning IT solutions
available. Individuals are learning when using KMS for business problem-solving and decision-making.
Organisations benefit from using KMS as learning organisations (Natek & Zwilling, 2014, Negnevitsky,
2011).
Picture 6: SECI model of knowledge creating process
Source: Jashapara, 2011, p. 167.
To classify available IT to support KMS development, the SECI model matrix of knowledge creating
process is used in Picture 7.
IT for socialization is social oriented to support individuals, teams and participants in organizations or
society. The aim of socialization KMS is to cross the borders of time and location and encourage the
people to communicate, share knowledge and enhance group decisions (Liebowitz, 2006).
IT for externalization is codifying oriented to articulate and write the knowledge. Through the
externalization KMS, the knowledge can be used by more people, organizations and society thus
ensuring better decision making and solving the problems (Turban, 2011).
IT for combination is typically data, information and knowledge processing oriented. Combination was
traditionally the domain of IT, demonstrating its comuter processing power. The list of combination
KMS is the longest list of available IT, supporting data, information and knowledge processing, storing
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and retreiving. The most comprehensived data mining and artificial intelligence systems enable deep
data and knowledge discovery, based on huge amount of data or ontology / taxonomy stored
knowledge (Han & Kamber & Pei, 2012, Natek & Lesjak, 2013).
IT for internalization is learning oriented. There are variety of available dedicated e-learning IT
solutions. The individuals are learning when using KMS for solving business problems and decision
making. Organizations benefit from using KMS as learning organizations (Natek & Zwilling, 2014,
Negnevitsky, 2011).
Picture 7: IT and KMS support SECI model of knowledge creating process
Socialization:
• Social networks
• Forums
• Communities of practice
• e-Mail
• Groupware
• Group decision support systems
• Conference systems
• Chat groups
• IPTV
• Etc.
Externalization:
• Expert Systems
• Blogs
• Wikis
• Good / bad
• Examples of god and bad practice
• Questions and answers
• Decision support systems
• Business modelling
• Knowledge warehouses
• Cognitive mapping tools
• IPTV
• Etc.
Internalization:
• e-Learning
• Using wikis
• Using expert systems
• Web browsers
• Using decision support systems
• Using blogs
• Using analytical solutions
• Using statistical analysis
• Using data mining results
• Using neural networks
• Using social networks
• Using forums
• Using communities of practice
• Using case based reasoning
systems
• IPTV
• Using other KMS
Combination:
• Wikis
• Content management systems
• Data bases
• Data warehouses
• OLAP analytical solutions
• Business intelligence
• Data mining
• Statistical analysis
• Machine learning
• Neural networks
• Intelligent agents
• Artificial intelligence systems
• Case based reasoning systems
• Document systems
• Workflow systems
• Yellow pages
• Knowledge maps
• Electronic bulletin boards
• Intranets
• Web portals
• Genetic algorithms solutions
• Etc.
Explicit
Explicit
Explicit
Tacit
Tacit
Explicit
Tacit
Tacit
One useful demonstration of this matrix,could be related to IPTV viewers. The knowledge on
program's viewers could be gathered through set up boxes and three personalization compartments:
content based filtering, collaborative filtering and rule based engine. The data which is collected by
examining the user's behaviour either directly from evaluating the watched programs or indirectly
through the collaboration of other users in the TV social network (Socialization/Tacit) is cross checked
with content that was written or received by the viewers. This content may be evoked from past
(good/bad) experience (explicit externalization). The combination of these resources could be serve to
take part in the building of rule based engine (Tacit - Internalization) which is composed of machine
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learning and Data warehouses. The process is made up of three steps: First, basic recommendation
program lists are created based on the program metadata and user preference data. Each
recommended program has a user rating, an explicit feedback button and a recommendation
reasons. Second, recommendation reasons that explain why the programs have been recommended
are created, hence the viewer will have access to not only personalized program lists but also to
recommendations reasons. Finally, the rule based engine could be used to modify the basic
recommendation program lists and create the recommendation program lists, which are finally
provided to a user through the KMS.
The framework is open to add additional IT/KMS examples to support different users, experts or
managers, and is thus an important tool to manage the implementation of KM and KMS in
organisations.
4. CONCLUSION
Students, practitioners and managers involved in KMS development or implementation need a
practical but theoretically founded framework for classifying the available IT tools, suitable to support
or develop KMS. The paper reviews KM literature with special attention to the relationship between IT
and KMS. There are several frameworks or models for IT classification, mostly in relation to its support
for KM processes (knowledge creation, storage, retrieval, exchange and application). Unfortunately,
most models are not detailed enough or lack the precise criteria to classify IT/KMS. The SECI model
of the knowledge-creating process proves to be an excellent and unique theoretical framework for
IT/KMS classification. The resulting matrix in Picture 7 is precise and practical enough to guide
students, practitioners and managers in choosing the appropriate IT to support or develop the
necessary KMS, and thus proves to be an important managerial tool in the field of KMS.
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