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TheSECIModelinKnowledgeManagementPractices.pdf
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The SECI Model in Knowledge Management Practices
Article in Mousaion · March 2020
DOI: 10.25159/2663-659X/6557
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3. 2
Introduction and Background
Since the introduction of the concept of knowledge management (KM) in the early
1990s, different disciplines that have adopted it have defined it differently. Regarding
the debate about the meaning of KM, Chatti, Schroeder, and Jarke (2012) conclude that
the definitions of KM revolve around two principal views of knowledge: knowledge as
a thing and knowledge as a process. The definition of KM as a process has led to creating
the concept of the KM cycle, which is a continuous process of identifying, obtaining,
refining, sharing, using, storing, and disseminating information (Mohajan 2016).
Several models of the KM cycle exist, such as the Wiig KM cycle, Meyer and Zack KM
cycle, McElroy KM cycle, the socialisation, externalisation, combination and
internalisation (SECI) model, and the Bukowitz and Williams KM cycle (Shongwe
2016). According to Chatti, Schroeder, and Jarke (2012), the dynamism of knowledge
as a process is best represented by Nonaka and Takeuchi’s (1995) knowledge creation
process, which has had a profound impact on many involved in the field of KM. Based
on the assumption that knowledge is created through the collaboration between tacit and
explicit knowledge, Nonaka and Takeuchi (1995) have adopted a dynamic model of
KM, known as the SECI model, according to which knowledge is viewed not as an
inflexible object, but as a flowing entity that focuses on organisational knowledge
creation and transfer.
The SECI Model
Nonaka and Takeuchi’s (1995) epistemological dimension of tacit knowledge has
provided a leap forward in the way knowledge is viewed. Instead of regarding tacit
knowledge as simply an embodied/personal/individual kind of knowledge that cannot
be expressed, these researchers have paved the way for regarding tacit knowledge as a
type of knowledge that has immense potential and value in a corporate organisational
setting. According to Nonaka and Takeuchi, tacit knowledge can be externalised and
made explicit. This idea forms the basis of their knowledge conversion theory, which
finds application in the SECI model, according to which knowledge is socialised (tacit
to tacit), externalised (tacit to explicit), combined (explicit to explicit) and internalised
(explicit to tacit) in a continuous, spiralling process. This process is portrayed in
Figure 1.
4. 3
Figure 1: The SECI model
Source: Nonaka and Takeuchi (1995)
The SECI model, which is said to be linear (Chatti, Schroeder, and Jarke 2012) and
sequential (Sian Lee and Kelkar 2013), is viewed by Yeh, Yeh, and Chen (2012) to be
the most famous and comprehensive model of knowledge creation in the KM process.
Existing studies describe the model as representative of KM (Zheng and Yu 2010), as
probably the most widely cited and influential theory in KM (Zhang et al. 2014), and as
the most adopted by researchers studying the relationship between knowledge creation
and innovation (Esterhuizen, Schutte, and Du Toit 2012). The SECI model is also
reported to be simple to use and suitable for explaining the process of knowledge
conversion (Zhuang and Tongxin 2010).
While the studies mentioned above report the successful application of the model, other
studies argue that the model has flaws and that its use in the practice of KM should be
questioned. For example, it is argued that, from an academic point of view the SECI
model is too simple (Razmerita, Kirchner, and Nabeth 2014, 79), and it is almost
impossible to codify many forms of knowledge using this model (Sarayreh, Mardawi,
and Dmour 2012, 47). Sarayreh, Mardawi, and Dmour (2012) opine that the originators
of the model did not put in place the role of the context of use. These authors also argue
that Nonaka and Takeuchi have disregarded learning theory and Western philosophy in
their SECI model. Sian Lee and Kelkar (2013) emphasise that although the SECI model
is inclusive and widely used in KM research, it does not take into account the cultural
differences of organisational members across geographic areas. They further point out
that the SECI model indicates the transfer of knowledge as a sequential process, whereas
it may not be the case in real-life situations. Tuomi (1999) finds the basic structure of
the Nonaka-Takeuchi model to be individualistic, which makes it difficult to describe
interactions and interdependencies across levels of analysis. From the above it is clear
5. 4
that scholars from different disciplines criticise the SECI model based on their different
perspectives of knowledge creation.
In spite of all the criticism, the SECI model has been successfully applied in various
disciplines, for instance in engineering, and in other fields of research such as general
manufacturing (Li et al. 2018, 2898), automobile manufacturing (Aghdasi and Tehrani
2011) and software engineering (Astorga-Vargas et al. 2017). Furthermore, the model
has been applied in studies in geographical locations across the globe, from Japan
(Bratianu 2010) and the United Kingdom (Scully et al. 2013) to Africa (in the latter case
in particular in the area of indigenous knowledge) (Ngulube 2003).
Purpose of the Study
The aim of the current study was to review the literature on the use of the SECI model
in KM from 1995 to 2018 with a view to creating a thematic compendium of major
findings and to predict the future use of the model.
The study addressed the following research questions:
1. How can citation analysis be used to establish the correctness of the claim that
the SECI model is more widely accepted as a knowledge management tool
than other KM models?
2. What are the demographic characteristics of articles written on the application
in practice of the SECI model from 1995 to 2018?
3. How can a compendium of the use in practice of the SECI model from 1995 to
2018 be created?
4. What are the weaknesses of the SECI model’s use in practice?
5. Is it possible to predict the future applicability of the SECI model?
Methodology
This study adopted a systematic review of literature and the methodology known as the
preferred reporting items for systematic reviews and meta-analyses (PRISMA) to
conduct the research. PRISMA is divided into four steps, which are: identification,
screening, eligibility, and inclusion (Moher et al. 2009). A flow chart of the literature
selection process is presented in Figure 2, and the steps in the search process are
described below Figure 2.
6. 5
Figure 2: Flow chart of the literature selection process
Source: Adapted from Moher et al. (2009)
Full-text articles assessed for eligibility to review (N = 143)
Articles included in the review (N = 52)
(Out of the 52, 11 articles did not reflect the subject in the title, abstract
or keywords)
Number of articles after removal of duplicates (N = 249)
Articles excluded from full-text reading (106)
Reasons
1) Studies mentioned in passing (84)
2) Irrelevant studies (22)
Articles identified through database searching (N = 259)
Taylor and Francis = 158
IEEE Xplore = 78
Emerald =17
Scopus = 6
Identification
Inclusion
Eligibility
Screening
Excluded from review (N = 91)
Reasons
1) Literature-based criticism of SECI
2) Comparison of models
3) Not addressing research
question
7. 6
Search process
Step 1: Relevant articles were identified by searching online databases for the use of
the SECI model. Four databases that were updated daily, had interdisciplinary field
coverage and contained peer-reviewed journal articles were chosen for the literature
search. These databases also allowed for the use of delimiters to refine search items.
The databases of choice for the literature review were: IEEE Xplore Digital Library,
Emerald Insight, Scopus, and Taylor and Francis. The study, using the database search
engine, Google Scholar, determined the number of times that the SECI model had been
cited compared to other models.
Step 2: The study was restricted to articles that had been published between 1995 and
2018. Duplicate articles were eliminated.
Step 3: The study included only open access articles that had been published in the
English language between 1995 and 2018. The search strategy was performed on the
databases using the Boolean operators “AND” and “OR” to filter the search. The
keyword combinations used were: “SECI” OR “knowledge creation theory” AND
“KM”. In order for an article to be regarded as eligible for inclusion in the current
study’s literature review, either the acronym SECI or the words knowledge creation
theory had to feature in the title, abstract or keywords. However, in some relevant
articles, these featured only in the body of the article and not in the title, abstract or
keywords. Such articles were later regarded as eligible and included in the current study.
Eligible articles were further filtered to eliminate irrelevant ones, such as the ones that
featured the acronym SECI that did not stand for the name of the model relevant to the
current study. Also excluded were articles with a passing mention of the subject of the
current study. A summary of the inclusion and exclusion criteria is presented in Table
1.
Table 1: Inclusion and exclusion criteria
Inclusion criteria
Open access articles about the
SECI model
Articles about knowledge
creation theory
Articles that can answer the
research question
Articles published from 1995 to
2018
Exclusion criteria
Duplicate articles
Articles that are not full texts
Articles that are not written in
the English language
Articles that do not answer the
research questions
Unpublished articles
Step 4: The full texts of the articles included were analysed independently to determine
whether or not they met the pre-determined criteria. Articles that met the criteria were
included for review in the current study.
8. 7
Data Extraction
Table 2 presents the results of the data extraction.
Table 2: Number of articles in selected sources
Source Direct search result
Emerald 17
IEEE 78
Scopus 06
Taylor and Francis 158
Total 259
The extracted data were screened for eligibility as presented in Table 3.
Table 3: Screening of search results
Screening label Number of
articles
A Directly eligible articles 113
B Subject not mentioned in the title, abstract or keywords
but in the body of the research article
30
C Subject mentioned in passing 84
D Not relevant 22
E Duplicate 10
Total 259
Findings and Discussion
The discussion in this section is guided by the research questions.
How can citation analysis be used to establish the correctness of the claim that the
SECI model is more widely accepted as a knowledge management tool than other
KM models?
The three universally most popular citation analysis databases are Web of Science,
Scopus, and Google Scholar. Whereas Web of Science and Scopus mainly cover
international journals, Google Scholar indexes all article types, for example, from
journals, conference proceedings, books, and theses, to preprints. These three databases
are free tools for citation analysis. For the purpose of our study we used Google Scholar
(Onyancha and Ocholla 2009) to compare the number of articles that cited the SECI
model and other KM models that emphasised the explicit and tacit knowledge
taxonomy.
9. 8
Article Search Process
In order to retrieve articles that were close to the KM models of choice, the following
procedure was followed:
1. The term knowledge management (written in full) featured in all the search
terms. This was done to eliminate the inclusion of irrelevant articles.
2. The year each KM model came into use was included in the search terms.
3. The names of the originators of the models were included in the search terms,
except in the case of the SECI model as its originators are well known. As
regards the other models, scholars other than the originators had described the
models, therefore it was important to include the names of the originators in
the search terms.
The citation analysis was restricted to results obtained from Google Scholar searches
(see Table 4) as searches conducted on both Scopus and Web of Science, using either
the names of the models or the originators, yielded almost no articles. In cases where
the model names were searched without including the names of the originators, the
searches on Scopus and Web of Science returned many irrelevant articles. As the
concepts of model and framework were used interchangeably in articles (depending on
the author), these concepts were used separately in the search terms and reported as
variations on the same theme.
Considering the fact that the various models had different knowledge taxonomies, the
current study focused on KM models with explicit and tacit knowledge taxonomies that
were similar to those of the SECI model. Hence, the KM models indicated in Table 4
were considered eligible for comparison.
Table 4: KM models and frequency of citation on Google Scholar from 1995 to 2018
S/N KM model search terms Number of
articles
1 Wiig’s knowledge management model (1993) 11,100
2 Hedlund’s knowledge management model (1994) 17,100
3 Krogh and Roos’s knowledge management model (1995) 9,500
4 Nonaka and Takeuchi’s SECI model (1995)
SECI model of knowledge management 18,000
5 Snowden’s knowledge management model (1997) 15,900
6 Carayannis’s knowledge management model (1999) 6,480
7 Inkpen and Dinur’s knowledge management model (1999) 1,460
As indicated in Table 4, it was found that the SECI model had been cited more widely
than the other models.
10. 9
What are the demographic characteristics of articles written on the application in
practice of the SECI model from 1995 to 2018?
The current study highlighted characteristics pertaining to the geographical distribution
of articles written on the application in practice of the SECI model, as well as to the
distribution of articles according to the different types of disciplines they covered. These
characteristics are portrayed in figures 3 to 5.
Geographical Distribution of Articles
From the articles that were reviewed, 62 per cent that dealt with the application of the
SECI model for knowledge creation originated in Asia, and 19 per cent in Europe. Only
four per cent of the articles originated in Africa, mainly East and North Africa. As far
as geographical distribution was concerned, China was the leading Asian country,
followed by Japan. The findings on geographical distribution are illustrated in figures 3
and 5 below.
Figure 3: Geographical distribution of articles on the practical application of the SECI
model
11. 10
The results regarding geographical distribution were not surprising as some developed
Asian countries had embarked on closing the digital divide and using knowledge rather
than industrialisation as a base for economic development (Menkhoff et al. 2011).
Figure 4: Distribution of studies within the Asian area
According to Andreeva and Ikhilchik (2011, 4), the socialisation model of knowledge
conversion is inherently a Japanese process, and they cite Glisby and Holden who claim
that each of the four modes of the SECI model is deeply rooted in the Japanese way of
life (2011, 6). They also cite Weir and Hutchings, according to whom Chinese
organisations practise externalisation in almost the same way as Japanese companies do
(2011, 5). It is no wonder then that these two countries have contributed the most to the
application of the SECI model as a KM tool. However, Weir and Hutchings (in
Andreeva and Ikhilchik 2011, 5) acknowledge that the SECI model can be explored in
a non-Asian context. It appears that the SECI model prospers more in community-
oriented societies as opposed to others; therefore, it should be compatible with African
oral culture, particularly where tacit or indigenous knowledge predominates.
Distribution of Articles according to Discipline
The review revealed that the SECI model had been applied in a variety of disciplines
(see Figure 5). Most applications were in computer science and information technology
(29%), followed by education (23%), and management and commerce (16% each).
China
63%
Japan
18%
Indonesia
11%
India
4%
Pakistan
4%
12. 11
Figure 5: Distribution of articles according to discipline
This distribution can be explained by the fact that KM is a multidisciplinary field that
increasingly depends on information and communications technology as well as
business and commerce to thrive. In addition, the leading three disciplines are
considered to be knowledge-intensive fields that rely on knowledge manipulation and
creation for the facilitation of tasks. Little and Deokar (2015) report that an increase in
the awareness of knowledge-intensive processes has led to attention being given to
knowledge creation in such knowledge-intensive organisations.
How can a compendium of the use in practice of the SECI model from 1995 to 2018
be created?
Table 5 presents the key findings from the reviewed articles in the form of a
compendium.
Management
16%
Computer and IT
29%
Education
23%
Commerce
16%
Engineering
8%
Health
6%
Other
2%
13. 12
Table 5: A compendium of the use of the SECI model from 1995 to 2018
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
1 Feng and Yu 2018 Engineering Integration
innovation
process
SECI model
used in
explaining
integration
innovation
process of China
railway highway
2 Sasaki, Zelaya,
and Uchihira
2018 Computer
science and IT
Systems
intelligence
SECI model
used in relation
to systems
intelligence
3 Kalogeraki et al. 2018 Commerce Logistics and
supply
SECI model
used in
identifying
threats in
maritime
logistics and
supply chain
4 Yang, Liu
and, Liang
2018 Education Teachers’
personal KM
SECI model
used for
personal KM of
teachers
5 Brundrett and
Lungka
2018 Education Training and
development
SECI model
used in training
programme to
improve
teachers’
knowledge and
behaviour
6 Chatterjee
Pereira, and
Sarkar
2018 Management Learning
transfer
SECI model
validated as
useful in transfer
process
14. 13
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
7 Thongkoo,
Panjaburee, and
Daungcharone
2017 Computer
science and
education
Learning SECI model
used in
developing
online enquiry-
based learning
support system
implemented in
a web
programming
course
8 Halim, Halim,
and Hebrard
2017 Computer
science and
health
KMS SECI model
used in
managing
medical records
in medical
institutions
across Indonesia
9 Duarte Alonso
and Alexander
2017 Marketing and
commerce
Emerging
industries
SECI process
used in aiding
new or improved
products or
services
10 Bassano et al. 2017 Commerce Marketing
decision-
making
SECI model
used in
developing
M@SECI model
for operational
marketing
decision-making
11 Putri, Hudiarto,
and Argogalih
2017 Health Telemedicine KM model
derived from
SECI model for
telemedicine in
Indonesia
12 Uwasomba et al. 2016 Management Knowledge
flows
SECI used in
analysing the
management of
knowledge flows
in a Mauritian
multinational
organisation
15. 14
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
13 Tyagi 2016 Management
and commerce
Product
development
SECI used
effectively in
conceptual
design phase of
product
development,
proving its
important role in
creating new
knowledge and
updating mental
models
14 Hubers et al. 2016 Education Knowledge
creation in data
teams
SECI model
used in gaining
insight into
process of
knowledge
creation in data
teams
15 Hvorecký,
Šimúth, and
Lipovská
2015 Management e-Learning SECI model of
learning used in
designing and
developing a
learning method
16 Hashimoto et al. 2015 Management
and
communication
Knowledge
propagation
SECI model
used in building
a multi-agent
model for
encouraging the
propagation of
knowledge in
organisations
17 Tang 2015 Project
management
(France and
China|)
Knowledge
transfer
SECI model
used in studying
the obstacles to
knowledge
transfer between
organisations
based in
countries whose
cultures differed
16. 15
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
18 Zhang et al. 2014 Computer
science and IT
Remote sensing SECI model
used in
managing the
conversion of
qualitative
remote sensing
knowledge
19 Hilwa Wirda 2014 Computer
science and IT
KMS SECI model
applied as a
knowledge
creation tool in
designing a
prototype web-
based KMS and
android mobile
20 Garstenauer,
Blackburn, and
Olson
2014 Engineering
management
Quality
management
SECI model
used in
developing a
traditional
Western QM
framework
21 Chaabouni and
Yahia
2014 Management
and commerce
Enterprise
resource
planning
SECI model
used
successfully in
analysing
enterprise
resource
planning
22 Hashim 2014 Government Policy planning
and
implementation
SECI model
found to be
integral to
Jatropha Projects
development
assistance
planning and
methodology in
Tanzania
17. 16
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
23 Tian et al. 2013 Engineering
science and
education
Knowledge
innovation
system
SECI model
applied to
provide a
meaningful
reference for
knowledge
innovation
system
24 Grzybowska and
Gajdzik
2013 Management Change
management
SECI model
found to
correctly present
the conversion
of knowledge in
the process of
introducing
organisational
changes
25 Lindlöf,
Söderberg, and
Persson
2013 Commerce Lean product
development
SECI model
found to be
useful in relation
to development
phases of lean
products
26 Oztok 2013 Education Online learning In the context of
online
education, the
SECI model was
found to fall
short in that it
did not identify
and address the
resources
available in a
social network
and how they
could be made
available to
individuals.
18. 17
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
27 Scully et al. 2013 Management HRM SECI model
found to be
useful in
strategic human
resource
management
practice
28 Shah, Khan, and
Amjad
2013 Computer
science and IT
Social media SECI-based
framework
(“SECI-SM1”)
proposed for
social media
29 Trindade et al. 2012 Computer
science and IS
Communities of
practice
SECI model
found to be
paramount to the
success of
community of
practice
30 Tammets, Pata,
and Laanpere
2012 Education Learning and
knowledge
building
SECI model
used in
developing a
learning and
knowledge-
building model
to support cross-
organisational
teacher
development
31 Kim and Hergeth 2012 Computer
science and IT
Industry
technology
roadmap
Critical steps in
creating new
knowledge are
the socialisation
and
externalisation
phases in the
SECI model. At
industry level, it
was found that
these phases
were not
working as
smoothly as
implied in the
SECI model.
19. 18
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
32 Karim et al. 2011 Computer
science and IT
KM readiness All four
variables of
SECI processes
emerged as
significant and
reliable
measures for
KM readiness.
33 Chai, Liu, and
Luo
2011 Computer
science and IT
Virtual
organisation
It was found that
the SECI model
needed
improvement for
use in virtual
organisations.
However, the
model gave birth
to FASECI, an
extended model
for the creation
of cross-
organisation
knowledge
transfer
channels.
34 Ahmad, Abu
Bakar et al.
2011 Education Online learning
management
system and
demographic
factors
SECI model
found to be able
to explain the
process of
knowledge
creation in
education for the
development of
an online
learning
management
system
20. 19
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
35 Ahmad, Husain
et al.
2011 Education Online learning
management
system
SECI model
found to fit the
data for thinking
and decision-
making skills
(but not learning
skills) in an
online learning
management
system for
postgraduate
students
36 Sano et al. 2010 Health Emergency
medical service
SECI spiral used
in analysing an
emergency
medical service
organisation and
was able to
clarify the
problems
inherent in the
organisational
structure of the
helicopter teams
37 Lu, Lu, and Liu 2010 Education Teaching
process
SECI model
used in
analysing the
transformation
of knowledge
and summarising
the teaching
process in
colleges and
universities
38 Zheng and Yu 2010 Commerce e-Commerce
ecosystem
SECI model
applied to better
understand the
formation and
evolution of
entire e-
commerce
ecosystem
21. 20
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
39 Ying and
Chouyong
2010 Commerce Supply chain
knowledge flow
SECI model
applied in
evolution model
in supply chain
40 Matsudaira 2010 Engineering Production SECI model
used in
examining
knowledge
creation to
improve on the
production line
in the
manufacturing
department of
Nissan Motor
Company
41 Wan, Huang, and
Wan
2009 Computer
science and IT
Software REP SECI spiral
model was a
factor in the
knowledge
conversion
model developed
in a software
REP.
42 Wei, Zhixin, and
Shaozhong
2009 Computer
science and IT
Quality function
deployment
SECI model
applied to
quality function
deployment
43 Jiao, Liu, and
Liu
2008 Education Online
collaborative
lesson
preparation
SECI used as a
mechanism to
share and utilise
knowledge in
online
collaborative
lesson
preparation
platform for
teachers
22. 21
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
44 Chalkiti and
Sigala
2008 Computer
science and IS
Online
discussion
forums
SECI model
used in assessing
knowledge
creation
capabilities of
the online forum
DIALOGOI
45 Kantola and
Hautala
2008 Computer
science and IT
Internalisation
network
SECI model
operationalised
in knowledge
creation process
in empirical
social co-
operation
situations
46 Minocha and
Roberts
2008 Online learning SECI framework
applied in
designing
activities
involving 3D
virtual worlds
and 2D tools in
socialisation and
knowledge
construction
processes
47 Chatti et al. 2007 Computer
science and IT
Network
learning process
SECI model
found to be able
to explain
network learning
process
48 Zhi-hong, Pei-
xuan, and Ji-le
2007 Management
and commerce
Enterprise
standard
management
SECI model
used in
analysing and
designing a new
enterprise
standard
management
system
23. 22
S/N Author Year Discipline Subject area Summary of
main reported
outcome of
SECI model’s
use
49 Li and Liu 2006 Management Enterprise
intellectual
capital
Through using
SECI
conversions,
enterprise
intellectual
capital created at
human capital,
structural
capital, and
relational capital
levels
50 Xue and Zhang 2006 Computer
science and IT
Regional
development
and innovation
networks
SECI model
found to be
sufficiently
applicable to
knowledge
transfer in
regional
development and
innovation
networks
51 Suzuki and
Toyama
2004 Management Project
management
SECI model
found to be
effective in
application in
project
management
52 Lin and Lin 2001 Computer
science and e-
commerce
Virtual
organisational
learning
SECI model
found to assist in
understanding
the virtue of
virtual
organisational
learning (a
junction of
individuals,
groups, and
organisations)
Note: IT = information technology; KMS = knowledge management system; QM =
quality management; HRM = human resource management; IS = information system;
LMS = learning management system ; REP = requirement elicitation process
24. 23
The review indicated that 48 (92.3%) articles reported that the SECI model was suitable
whereas only four (7.7%) reported that the model had limitations in specific areas of
application (see Table 6). Some of the results of the application of the SECI model, as
shown in Table 6, corroborated the submission that the SECI model was too
individualistic and not relevant in knowledge transfer at industry level (i.e. between
organisations) (Tuomi 1999).
What are the weaknesses of the SECI model’s use in practice?
The literature review brought to the fore some challenges encountered in applying the
SECI model. Some of the limitations had to do with the model as a process (as shown
in Table 6), whereas others had to do with separate modes or phases of the process (see
Table 7).
Table 6: Process challenges in the application of the SECI model
S/N Areas of limitation/weakness Source
1 The model did not address social networking in online
learning.
Oztok (2013)
2 The socialisation and externalisation modes of the
model did not work smoothly because the free
exchange of ideas was limited at industry level.
Kim and Hergeth
(2012)
3 SECI needed to be improved in the area of virtual
inter-organisational transfers.
Chai, Liu, and Luo
(2011)
4 The applicability of the model was limited in time-
dependent operations such as emergency rescue.
Sano et al. (2010)
5 The internalisation mode could not be examined in the
study involving online interactions.
Chalkiti and Sigala
(2008)
6 Context was required for the effective application of
the model.
Sasaki, Zelaya, and
Uchihira (2018);
Brundrett and
Lungka (2018)
7 Ba (“space”) was required for complete applicability. Yang, Liu, and
Liang (2018);
Bassano et al.
(2017); Tian et al.
(2013); Sano et al.
(2010); Wan,
Huang, and Wan
(2009)
8 The sequential order of processes was challenged. Tyagi (2016)
Table 7 presents other areas of emphasis of the SECI modes.
25. 24
Table 7: SECI modes’ emphasis
S/N SECI mode Areas of emphasis Source
1 Socialisation Processes including
individuals of varying
cultural backgrounds
Online
learning/interactions
Knowledge construction
processes (where crucial
information is required)
Duarte, Alonso, and Alexander
(2017); Hubers et al. (2016);
Hvorecký, Simuth, and
Lipovska (2015); Tang (2015);
Cerchione, Esposito, and
Spadaro (2015); Kim and
Hergeth (2012); Ahmad et al.
(2011); Chalkiti and Sigala
(2008); Minocha and Roberts
(2008)
2 Externalisation Online interactions
Network environments
Kim and Hergeth (2012);
Chalkiti and Sigala (2008);
Kantola and Hautala (2008)
3 Combination Network environments
Online interactions
System/engineering
management
Garstenauer, Blackburn, and
Olson (2014); Chalkiti and
Sigala (2008); Kantola and
Hautala (2008)
4 Internalisation Team
interaction/collaboration
e-Learning
Concept design phase of
product development
Hubers et al. (2016);
Hvorecký, Simuth, and
Lipovska, (2015); Tyagi (2016)
The socialisation mode of the SECI model appears to have been germane to virtually all
the research articles, highlighting its importance as the take-off mode in the SECI
knowledge creation process. This suggests that organisations have to pay more attention
to this mode that facilitates knowledge sharing. Two recent research articles suggest
that, in applying the model, context and cultural background should be factored in
(Brundrett and Lungka 2018; Sasaki, Zelaya, and Uchichira 2018). These studies also
support the criticism that the originators of the model did not include the role of context
of use (Sarayreh, Mardawi, and Dmour 2012). In order to tackle the challenge of
context, five of the reviewed research articles have introduced the concept of Ba in their
studies (Bassano et al. 2017; Sano et al. 2010; Tian et al. 2014; Wan, Huang, and Wan
2009; Yang, Liu, and Liang 2018). Ba is a Japanese word that can be likened to the
English word “space.” Building on the theory of SECI and the concept of Ba (created
by Kitaro, Nishida, and Shimizu as cited in Nonaka and Konno 2005), and in response
to criticisms, Nonaka and Konno (2005) came up with their adapted concept of Ba,
which places each mode of operationalisation of the SECI model in a context. Ba can
be a physical, virtual, or mental space, or any combination of these three.
26. 25
Is it possible to predict the future applicability of the SECI model?
The reviewed articles were divided according to five-year distribution periods, and it
was found that the application of the SECI model had been gaining momentum. Figure
6 (columns 1 to 4) illustrates that the use of the model more than doubled over the period
indicated. This suggests that by the end of the year 2019 (the next five-year period),
there could be over 34 applications of the SECI model.
Figure 6: Trend in the application of the SECI model from 1995 to 2018
(The period 2015–2018 is a four-year distribution period, as opposed to the others that
are five-year periods.)
Conclusion
The SECI model has, since its introduction in 1995, become the model of choice for
knowledge creation activities. However, over time it began to face its fair share of
criticism. The current study highlighted that in spite of its weaknesses, the model
actually remained widely accepted and applicable. Weaknesses that were noted in this
study included the limitations of specific modes of the model in some areas of
application, and the non-linearity of the model in specific areas. This review also
highlighted that, judging from the use of the SECI model over five-year distribution
periods, it was likely that the usage of the model would have doubled by the end of the
year 2019. It was also revealed that re-modelled versions of the SECI model had been
used from time to time, as had been the case with the unified theory of acceptance and
use of technology (UTAUT) as well as the technology acceptance model (TAM). Other
versions of the SECI model, such as M@SECI and FASECI, were noted in this study.
0
5
10
15
20
25
1995-1999
2000-2004
2005-2009
2010-2014
No. of articles
27. 26
These versions represented a reworking of the existing model to be more applicable to
the context of use and to counteract the observed weaknesses. In addition, the study
revealed the dearth of research on the application of the SECI model to knowledge
creation in Africa. According to Andreeva and Ikhilchik (2011), the SECI model
remains at the core of the knowledge conversion theory within the area of KM, and is
likely to apply to virtually all cultures.
The study revealed there had been growing interest in the SECI model in spite of its
weaknesses. Bandera et al. (2017, 164–6) have acknowledged the appropriateness of
this model in the modern era, and, at the International Conference on KM held at
Tsinghua University, Beijing on March 16–17, 2017, they alluded to the celebration of
the impact of the SECI model on both Western and Eastern scholarly research. The
current study agrees with existing literature about the major role played by the
socialisation mode of the SECI model for the creation of tacit knowledge, and calls on
the management teams of organisations to effectively apply this model in their KM
programmes. However, for deep socialisation to occur there must be willingness and
readiness to share, which is not automatic. It appears that Nonaka and Takeuchi’s (1995)
SECI model assumes that tacit knowledge will be readily shared by the owner, but this
is not necessarily the case. This assumption probably accounts for some critics’
comments about the model being too simple for practical applications (Sarayreh,
Mardawi, and Dmour 2012). Unless the owner of the knowledge willingly shares
knowledge, there cannot be deep socialisation, subsequent externalisation and,
ultimately, effective KM.
This study, therefore, suggests that KM enabling factors should be considered to
mediate the application of the SECI model. As most theories and models are not static,
consideration could be given to incorporating the Ba concept when applying the SECI
model, and to making other possible modifications to the SECI model. Such changes
could give more life to the model.
A limitation of the current study that was duly noted was that the search was limited to
only four databases. As regards future research, the authors of this article identified a
need for more empirical studies on the roles of each mode of the SECI model and on
the sequential order of the SECI model in organisational knowledge creation. This
article interrogated a popular KM model/theory and brought to the fore the dimensions
that might not have been sufficiently known or understood for wider theoretical
discussion.
Acknowledgements
A version of this article was presented as a paper at the First Biennial University of
South Africa International Conference on Library and Information Science Research in
Africa (UNILISA), Unisa, Pretoria, March 13–15, 2019. The University of Zululand,
28. 27
Richards Bay, South Africa is acknowledged for supporting the authors of this article
in attending this conference.
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