Innovations in Organisational Knowledge Management
Typology, Methodology and Recommendations
Tatiana Gavrilova, Dmitry Kudryavtsev and Anna Menshikova
Graduate School of Management, St. Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, Russia
Keywords: Knowledge Management, Innovation, Knowledge Typology, Customer Orientation, Knowledge
Representation.
Abstract: The INNOVARRA project is focused on the research and development of new models and methods of
knowledge management in the enterprises. The project aims to identify and develop knowledge
management methods and tools, which are the most appropriate for particular knowledge type and domain
of any company, as well as have the greatest impact on the final results of Russian companies. Special
attention is paid to the knowledge typology development, which helps to differentiate and select knowledge
management tools and methods. Research methodology is interdisciplinary and includes both the
behaviourist methods of empirical studies (surveys, statistical analysis) and design-oriented methods such as
ontology engineering, system analysis and enterprise architecture management.
1 INTRODUCTION
Knowledge is a key resource for creating and
maintaining a competitive advantage in modern
post-industrial economy. Knowledge management
(KM) is an interdisciplinary approach to achieving
organizational goals through the most effective
usage of knowledge.
Despite the fact that KM is actively discussed for
more than 20 years among academics and
practitioners of management, the effect of business
investment in KM is insufficient. One of the main
practical problems is the issue of choosing methods
and tools for KM. It is difficult for business to
understand which methods and tools of KM have the
greatest effect on the final results. Besides it is not
obvious which methods and tools are suitable for the
use in the particular knowledge domain. From
theoretical point of view there are discrepancies in
the findings of the empirical studies explaining
knowledge processes. For example, the existing
empirical evidence regarding the impact of the
rewards of knowledge sharing behaviour to be
contradictory – some found a negative relationship,
some found a positive relationship, and some found
no relationship at all.
Several prominent contributions suggested that
such discrepancies can be resolved by uncovering
and explicitly incorporating contextual conditions in
which the behaviour is taking place into the analysis
(e.g. Bamberger, 2008; Johns, 2006).
In response to the calls for more context aware
theorizing, A. Sergeeva and T. Andreeva (Sergeeva
and Andreeva, 2015) suggested a “Who? / Where? /
Why? / What?” framework of context dimensions
for knowledge sharing research. The current paper
describes ongoing INNOVARRA project, which
analyses and structures KM methods and tools
focusing on “What?” element of the context
framework. The project INNOVARRA (Innovations
in Organizational Knowledge Management:
Typology, Methodology and Recommendations)
aims to identify and develop KM methods and tools,
which are the most appropriate for particular
knowledge type and domain of the company.
Additionally the project study, what KM methods
and tools have the greatest impact on the final results
of Russian companies.
2 BACKGROUND AND
METHODS
The solution to the aim of INNOVARRA project is
based on the international best practices and has an
interdisciplinary approach, involving five tracks of
the research. Figure 1 illustrates these tracks. The
Gavrilova, T., Kudryavtsev, D. and Menshikova, A..
Innovations in Organisational Knowledge Management - Typology, Methodology and Recommendations.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 447-452
ISBN: 978-989-758-158-8
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
447

first track analyses the effect of existing KM
methods and tools on business results of Russian
companies (“as is” country-specific analysis). The
second “integrating” track describes knowledge
types and domains and creates the foundations for
linking them with the corresponding KM methods
and tools. Tracks 3.1-3.3 provide the examples of
methods and tools for several organizational
knowledge domains, particularly: for product/service
and customer knowledge, for operations
management knowledge, for strategic management
and organizational development knowledge.
Figure 1: Main tracks of INNOVARRA project.
Track 1
A number of studies have addressed the relationship
between intellectual capital, KM and performance of
companies (e.g., Andreeva, Kianto, 2012; Kimura et
al, 2010; Starowiz and Marr, 2005; Youndt, 2004).
Nevertheless, the empirical data on how intellectual
capital and KM work in Russian context is limited,
and the existing findings are controversial. For
example, one study suggests that intangible assets
have a less explanatory power in Russian
companies’ value in comparison to tangible assets
(Garanina, 2011). Another study demonstrated that
KM practices have a positive impact on
organizational performance of Russian companies
(Andreeva and Kianto, 2012). Therefore, further
empirical examination of these issues is needed.
Besides, a number of KM scholars highlight the
Western–Eastern division in the conceptual tradition
and management practice and argue that KM
practices are not so easily transferable across
countries (Nonaka and Takeuchi, 1995; Glisby and
Holden, 2003). Some authors argue that indeed
Russia represents a different context where
knowledge-based processes work differently
(Andreeva and Ikhilchik, 2009; May and Stewart,
2013). While some research has been done on the
applicability of foreign management theories in
Russia in general (Andreeva and Ikhilchik, 2011),
the applicability of intellectual capital and KM
concepts in the Russian context has not been studied
yet empirically.
Track 2
The basis for the differentiation of KM methods and
tools in INNOVARRA project is the developed
knowledge typology and a description of the typical
enterprise knowledge domains. Research in the field
of knowledge types is presented in (De Jong and
Ferguson-Hessler, 1996; Alavi and Leidner, 2001).
Different knowledge types require different
knowledge strategies, methods and tools. At the
most general level focus on explicit knowledge will
trigger codification strategy, while tacit knowledge –
personalization strategy (Hansen et al., 1999). Grant
differentiated knowledge based on the following
characteristics: transferability, capacity for
aggregation, appropriability, specialization. These
characteristics let him to suggest four mechanisms
for integrating knowledge: rules and directives;
sequencing; routines; group problem solving and
decision making (Grant, 1996). Some studies
included knowledge tacitness, explicitness or
codifiability in the empirical model and
demonstrated that the determinants of the knowledge
sharing behaviour differed depending on the type of
knowledge shared (Levin and Cross, 2004; Reagans
and McEvily, 2003). Ideas about the differentiation
of KM methods and tools depending on the types
and domains of knowledge are supported by
research by Jobe and Schulz (Schulz, Jobe, 2001). In
their work on the basis of empirical research, they
have shown a positive relationship between the
"focused" strategy, KM and performance of the
company. "Focused" strategy involves the use of
different methods of codification of knowledge
depending on the type of knowledge. Research in
knowledge/information representation emphasizes
the importance of cognitive fit theory, which
explains what problem representations (visual,
tabular etc.) are best used to support certain types of
tasks (Vessey, 1991; Gavrilova et al., 2014).
Different enterprise knowledge domains (e.g.
product knowledge, customer knowledge, operations
management or strategic management knowledge
etc.) have different knowledge characteristics and
knowledge types. As a result different knowledge
areas require corresponding methods and tools. As
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
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part of the INNOVARRA project the description of
typical enterprise knowledge areas will be presented
in the form of generalized (reference) enterprise
knowledge map. Research in the field of knowledge
maps ispresented in the (Vail, 1999; Eppler, 2008).
The results from enterprise functional decomposition
(Kudryavtsev, Grigoriev, 2011) and reference
classifications of business processes (e.g. APQC's
Process Classification Framework,
https://www.apqc.org/pcf) will be used in the
development of the generalized (reference)
enterprise knowledge map. An example of
associating knowledge areas (areas of expertise)
with knowledge types is provided in
(Chandrasegaran et al, 2013), where for each phase
of product design the prevalent form of knowledge
representation is given.
Track 3
Track 3.1
Currently, research on the role of market and
customer orientation in the context of innovation and
knowledge creation activity is in its active stage of
development considering under both narrow and
broad approach. Within the narrow research scope
approaches to the involvement of consumers in the
innovation process are studied.
Analysis of publications in Scopus and Web of
Science databases shows a significant increase in the
number of publications on the relationship of
innovation and market and customer orientation, as
well as related areas of research in several waves
since 2005 (including research on user -driven
innovation, lead user innovation, customer-focused
innovation). Despite the fact that this research
subject had been proposed much earlier (e.g., see E.
von Hippel (von Hippel & Euchner, 2013)), this
research development began only after the
technological progress that allowed to actively
involve users and customers in the process of
interaction, communication and thus increased the
role of customer innovations acceptance (Lusch &
Nambisan, 2015; Slater et al, 2009). The methods to
support knowledge creation in new product
development are studied in (Hoegl, Schulze, 2005).
Track 3.2
Context-aware computing, which can be applied to
operations management knowledge, plays an
important role in the modern information systems
(Preuveneers, Berbers, 2008; Zhang et al., 2011).
Schilit and Theimer firstly proposed such computing
in 1994 (Schilit, Theimer, 1994). They considered
context as some information characterizing locations
and the time of an object. Dey (Dey, 2001) defined
context as "any information that can be used to
characterize the situation of an entity." Because of
the increased user mobility, computing power and
functionality of mobile devices and sensors, and
amount of available information, ways to adapt
computing devices and information systems to the
needs of users, based on the use of the user profiles
and preferences, are no longer sufficient. According
to the scientific community and the expectations of
the end-users, services that are part of ubiquitous
computing should be adapted to the specific
circumstances or situation, and perhaps for this
purpose to determine all the relevant parameters.
Such circumstances and situations, including
personal preferences and tasks, often referred to as
context. A large set of approaches exists to represent
context using formal languages, e.g. UML, OWL,
and some others. Approaches to context
representation using informal languages are known
as well, e.g. using a graphical user interface in the
tool Context Toolkit (Dey, Salber, Abowd, 2001).
Track 3.3
Research in methods of structuring and
representation of knowledge in the field of strategic
management and organizational development are
carried out in different areas. Some works are being
conducted by experts in the field of economics and
management (Tikkanen, Lamberg, 2005), some – by
experts on visualization and knowledge
representation (Lengler, Eppler, 2007), some – by
experts in enterprise modeling and enterprise
architecture (Frank, 2002; Iacob et al, 2012). Visual
(Eppler, Platts, 2009) and tabular methods have high
potential for these knowledge areas. Visual
knowledge representation will include different
methods for different type of content (Kudryavtsev,
Gavrilova, Leshcheva, 2013). The use of the tabular
(or matrix) methods in management is considered in
the work (Phaal et al, 2006). However, not many
works explore the joint use of visual and tabular
(matrix) methods (Grigoriev, Kudryavtsev, 2013).
3 RESULTS AND APPLICATIONS
Research results include:
1. Identification of KM practices that affect the
key elements of the intellectual capital of Russian
companies and, accordingly, have the greatest effect
on the performance of Russian companies;
2. Development / updating the typology and the
generalized knowledge map (knowledge domains) of
the enterprise, which helps to differentiate KM
methods and tools;
Innovations in Organisational Knowledge Management - Typology, Methodology and Recommendations
449

3. Development of KM methods and tools for
specific knowledge types and domains (for
product/service and customer knowledge, operations
management knowledge, strategic management and
organizational development knowledge).
These results and their novelty are described
hereafter.
Project findings allow to clarify what elements
of intellectual capital are most frequently used in
Russian companies, and which of them contribute
most to value creation. They also demonstrate which
KM practices contribute most to development of
intellectual capital elements. Analysis of the data
expands the existing theoretical concepts in the areas
of KM and intellectual capital. The novelty of the
project in terms of the impact studies on the results
of KM practices of the company is as follows:
- Synergetic combination of control theory of
intellectual capital and the theory of KM.
- The collection of empirical data on the
elements of intellectual capital and KM practices of
Russian companies will provide an opportunity to
examine the relationship between the elements of
intellectual capital, company KM, its
competitiveness and performance. These issues were
not previously subject to systematic empirical
research, either globally or based on Russian data.
- Combining the two different approaches to the
evaluation of the company – through subjective
assessment of the organization and through open
financial performance.
This study is the first to investigate the
hypothesis on the influence of various elements of
intellectual capital and KM. It is planned to be
checked not only with the help of open source, but
with the help of information provided by the
managers of Russian companies. First, the
hypothesis is tested on the basis of the initial
information, collected through questionnaires (where
senior managers shared their opinion about various
elements of intellectual capital and KM in the
company and their impact on the results of its
operations), then this relationship is tested, taking
into account the information provided in the
company's financial statements, which reflect data
on various indicators of financial performance of
companies.
The suggested knowledge typology includes
many popular dimensions (e.g. generality, content
type, form, representation/modality, owner, etc.) and
pay more attention to representation of knowledge
(text, graphics, charts, numbers and formulas, etc.),
content types (what knowledge, how knowledge
etc.), as well as the knowledge owner (employees,
customers, partners). The novelty of the project in
terms of knowledge typology refers to the detailed
specification of each type of knowledge based on the
ontological approach (Kudryavtsev et al, 2013) and
in analysis of knowledge modality. Further, we
explore and describe the link between areas of
enterprise knowledge (generalized knowledge map
of the enterprise) and types of knowledge, as well as
propose KM methods and tools for various types of
enterprise knowledge. We plan to provide such a
link through the analysis of typical enterprise
activities (the basic processes, management,
providing; types of administrative activities, etc.)
and typology of problems (Jonassen, 2000).
Development of KM methods and tools for
specific knowledge types and areas provide the
following contributions.
With respect to knowledge on products/services
and customers the novelty of the objective is to
create an integrated approach to defining the
principles of a successful balance between the
factors of organization’s success in external
interaction with customers and in establishment of
cross-functional relationships within the
organization to facilitate the exchange of knowledge
and solve the problem of “intra-organizational
information stickiness” and the gap created in the
organizational abilities (Atuahene-Gima, 2005).
Special importance of the approach is proved by the
previous work in the field of features of customer-
orientation in Russian companies (Rozhkov, 2014;
Smirnova et al, 2015), showing a gap in
understanding of the opportunities and putting into
practice the integration of clients into in-house
processes. Finally, reliance on model testing on the
sample of the Russian companies makes it possible
to produce a significant contribution to
understanding not only the possibility of building a
successful customer focus in the context of an
emerging economy, but also the role of customer
orientation in supporting and stimulating the
innovation activity success of the company through
the exchange of knowledge and creation of
sustainable capabilities.
The novelty of working with knowledge in
operation management track consists in the usage of
the context-aware technology applying to production
networks. In particular, an approach to knowledge
logistics, a methodology of context management, a
context-aware methodology and a hybrid technology
for intelligent decision support in an open
information environment, and integrated models of
adaptive control of dynamic supply chains based on
Web services have been developed. The supposed
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450

research is oriented to semantic interoperability of
resources (components) of the production networks
based on context-oriented KM for decision support
by the participants of these networks.
With respect to knowledge in strategic
management and organizational development the
review and comparative analysis of structuring and
representation methods is provided. Visual
knowledge structuring methods for strategic
management and organizational development will by
classified using ontological (semantic) analysis.
Finally the method combining visual and table
representations is suggested in order to link strategy
with enterprise operations.
4 CONCLUSIONS
An effective use of knowledge in the company helps
to find a better way in achieving organizational
goals. INNOVARRA project aims to strengthen the
context awareness of KM efforts by linking KM
methods and tools with organizational knowledge
types and domains, which are the most suitable for
them. Additionally the project studies specialties of
KM adoption in Russia – the practices, which have
the greatest impact on the final results of Russian
companies.
ACKNOWLEDGEMENTS
Research has been conducted with financial support
from Russian Science Foundation grant (project No.
15-18-30048).
REFERENCES
Alavi, M., Leidner, D. E. (2001) Review: Knowledge
management and knowledge management systems:
Conceptual foundations and research issues. In MIS
quarterly, pp. 107-136.
Andreeva, T., Ikhilchik, I. (2011) Applicability of the
SECI Model of Knowledge Creation in Russian
Cultural Context: Theoretical Analysis. In Knowledge
and Process Management, vol 18, no. 1, pp. 56-66.
Andreeva, T., Kianto, A. (2012) Does knowledge
management really matter? Linking knowledge
management practices, competitiveness and economic
performance. In Journal of Knowledge Management,
vol.16, no.4, pp. 617-636.
Atuahene-Gima, (2005) Resolving the Capability–Rigidity
Paradox in New Product Innovation. In Journal of
Marketing, no. 69, pp. 61–83.
Bamberger, P. (2008). From the editors beyond
contextualization: Using context theories to narrow the
micro-macro gap in management research. Academy
of Management Journal, 51(5), 839-846.
Baumeister, J., Reutelshoefer, J., & Puppe, F. (2011)
Engineering intelligent systems on the knowledge
formalization continuum. In International Journal of
Applied Mathematics and Computer Science, 21(1),
pp. 27-39.
Chandrasegaran, S. K., Ramani, K., Sriram, R. D.,
Horváth, I., Bernard, A., Harik, R. F., & Gao, W.
(2013) The evolution, challenges, and future of
knowledge representation in product design systems.
In Computer-aided design, 45(2), pp. 204-228.
De Jong, T., Ferguson-Hessler, M. G. (1996) Types and
qualities of knowledge. In Educational psychologist,
31(2), pp. 105-113.
Dey A.K. (2001) Understanding and using context. In
Personal Ubiquitous Computing, vol. 5, no. 1, pp. 4–7.
Dey A.K., Salber D., Abowd G.D. (2001). A conceptual
framework and a toolkit for supporting the rapid
prototyping of context-aware applications. In Context-
Aware Computing. A Special Triple Issue of Human-
Computer Interaction, vol. 16, pp. 229–241.
Eppler, M. J. (2008) A process-based classification of
knowledge maps and application examples. In
Knowledge and Process Management, 15(1), pp. 59-
71.
Eppler, M. J., Platts, K. W. (2009) Visual strategizing: the
systematic use of visualization in the strategic-
planning process. In Long Range Planning, 42(1), pp.
42-74.
Frank, U. (2002) Multi-perspective enterprise modeling
(memo) conceptual framework and modeling
languages. In System Sciences, 2002. HICSS.
Proceedings of the 35th Annual Hawaii International
Conference on (pp. 1258-1267). IEEE.
Garanina, T. (2011) Intellectual capital structure and value
creation of a company: evidence from Russian
companies. In Open Journal of Economic Research,
vol. 1, issue 2, pp. 22–34.
Gavrilova T., Leshcheva I., Strakhovich E. Gestalt
Principles of Creating Learning Business Ontologies
for Knowledge Codification. In Knowledge
management research and practice journal, advance
online publication, January 20, 2014; doi: 10.1057/
kmrp.2013.60
Glisby, M., Holden, N. (2003) Contextual constraints in
knowledge management theory: the cultural
embeddedness of Nonaka’s knowledge-creating
company. In Knowledge and Process Management,
vol. 10, no. 1, pp. 29-36.
Grant, R. M. (1996). Toward a knowledge-based theory of
the firm. In Strategic management journal, 17(S2), pp.
109-122.
Grigoriev L., Kudryavtsev D. (2013) Non-diagrammatic
method and multi-representation tool for integrated
enterprise architecture and business process
engineering // Proceedings of 15th IEEE Conference
Innovations in Organisational Knowledge Management - Typology, Methodology and Recommendations
451

on Business Informatics (CBI 2013), 15–18 July.
Vienna, Austria. P. 258–263.
Hansen, M. T., Nohria, N., Tierney, T. (1999) What's your
strategy for managing knowledge? In Harvard
business review, 77(2), p. 106.
Henricksen, K., Indulska, J., Rakotonirainy, A. (2002)
Modeling context information in pervasive computing
systems. In Pervasive ’02: Proceedings of the First
International Conference on Pervasive Computing
UK, London. Springer-Verlag, pp. 167–180.
Hoegl, M., & Schulze, A. (2005). How to Support
Knowledge Creation in New Product Development:
An Investigation of Knowledge Management
Methods. European Management Journal, 23(3), 263-
273.
Iacob, M., Jonkers, H., Lankhorst, M., Proper, E., Quartel,
D. A. C. (2012) ArchiMate 2.0 Specification.
Jonassen, D. H. (2000). Towards a design theory of
problem solving. Educational Technology, Research
and Development, 48(4), 63–85.
Johns, G. (2006). The essential impact of context on
organizational behavior. Academy of management
review, 31(2), 386-408.
Kimura, H., Cruz Basso, L.F., Nogueira, S.G., de Barros
L.J. (2010) The impact of intellectual Capital on value
added of Brazilian companies traded at the BMF-
Bovespa. In Journal of International Finance and
Economics, vol. 10, no. 2, pp. 1-11.
Kudryavtsev, D., Gavrilova, T., Leshcheva, I. (2013) One
approach to the classification of business knowledge
diagrams: practical view. In IEEE 2013 Federated
Conference on Computer Science and Information
Systems (FedCSIS), Krakow,, pp. 1259-1265.
Kudryavtsev, D., Grigoriev, L. (2011) Systemic approach
towards enterprise functional decomposition. In The
workshop “Convergence of Business Architecture,
Business Process Architecture, Enterprise
Architecture and Service Oriented Architecture”
within the 13th IEEE Conference on Commerce and
Enterprise Computing, Luxembourg, September 5-7,
pp. 310-317.
Lengler, R., Eppler, M. (2007). Towards a Periodic Table
of Visualization Methods for Management. Proc. of
the Conference on Graphics and Visualization in
Engineering, 2007 (pp. 1-6).
Levin, D. Z., Cross, R. (2004). The strength of weak ties
you can trust: The mediating role of trust in effective
knowledge transfer. Management Science, 50(11),
1477-1490.
Lusch, R. F., Nambisan S. (2015) Service Innovation: A
Service-Dominant Logic Perspective. In MIS
Quarterly, vol. 39, issue 1, pp.155-175.
May, R. C., Stewart, W.H., (2013) Building theory with
BRICs: Russia’s contribution to knowledge sharing
theory. In Critical Perspectives on International
Business, vol. 9, no. 1/2, pp. 147-172.
Nonaka, I., Takeuchi, H. (1995) The Knowledge-Creating
Company: How Japanese Companies Create the
Dynamics of Innovation. Oxford University Press: N.
Y.,
Owen, R., & Horváth, I, (2002) Towards product-related
knowledge asset warehousing in enterprises. In
Proceedings of the 4th international symposium on
tools and methods of competitive engineering (TMCE
2002), Hubei, China, pp. 155-170.
Phaal, R., Farrukh, C. J., & Probert, D. R. (2006).
Technology management tools: concept, development
and application. Technovation, 26(3), pp. 336-344.
Preuveneers, D., Berbers, Y. (2008) The Internet of
Things: From RFID to the Next-Generation Pervasive
Networked Systems. USA, New York: Auerbach
Publications, Taylor and Francis Group, pp. 287–307.
Reagans, R., McEvily, B. (2003). Network structure and
knowledge transfer: The effects of cohesion and range.
Administrative Science Quarterly, 48(2), 240-267.
Rozhkov A.G. (2014) Personal Level Customer
Orientation in Russian Direct Selling Market. In
TRŽIŠTE / MARKET, vol. 26, issue 1, pp. 7-22.
Schilit, B.N., Theimer, M.M. (1994) Disseminating active
map information to mobile hosts. In Network. IEEE,
vol. 8, no. 5, pp. 22–32.
Schulz, M., Jobe, L. A. (2001) Codification and tacitness
as knowledge management strategies: an empirical
exploration. In The Journal of High Technology
Management Research, 12(1), pp. 139-165.
Sergeeva A., Andreeva T. (2015) Knowledge sharing
research: Bringing context back in. Forthcoming in
Journal of Management Inquiry.
Slater, S., Mohr, J., Sengupta, S. (2009) Know your
customers. In Marketing management, 18, 36-44.
Tikkanen, H., Lamberg, J. A., Parvinen, P., &Kallunki, J.
P. (2005) Managerial cognition, action and the
business model of the firm. In Management Decision,
43(6), pp. 789-809.
Vail, E.F. (1999) Mapping organizational knowledge. In
Knowledge Management Review, 2(8), 10–15.
Vessey, I. (1991) Cognitive fit: A theory-based analysis of
the graphs versus tables literature. In Decision
Sciences. 22(2), pp. 219-240.
Von Hippel, E., Euchner, J. (2013) User Innovation. In
Research Technology Management, 56 (3), pp. 15-20.
Zhang D. et al. (2011) Survey on context-awareness in
ubiquitous media. In Multimedia Tools and
Applications, pp. 1–33.
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
452