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Showing asymmetries in knowledgecreation and learning throughproactive visionJussi Kantola a , Hannu Vanharanta b , Petri Paajanen b & AnttiPiirto ca Department of Knowledge Service Engineering , KAIST, KoreaAdvanced Institute of Science and Technology , 291 Daehak-ro,Yuseong-gu, Daejeon , Koreab Department of Industrial Management and Engineering ,Tampere University of Technology , Pori , Finlandc Teollisuuden Voima Ltd. , Olkiluoto , FinlandPublished online: 21 Mar 2011.
To cite this article: Jussi Kantola , Hannu Vanharanta , Petri Paajanen & Antti Piirto (2012) Showingasymmetries in knowledge creation and learning through proactive vision, Theoretical Issues inErgonomics Science, 13:5, 570-585, DOI: 10.1080/1463922X.2011.552130
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Theoretical Issues in Ergonomics ScienceVol. 13, No. 5, September–October 2012, 570–585
Showing asymmetries in knowledge creation and
learning through proactive vision
Jussi Kantolaa*, Hannu Vanharantab, Petri Paajanenb and Antti Piirtoc
aDepartment of Knowledge Service Engineering, KAIST, Korea Advanced Institute of Scienceand Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea; bDepartment of Industrial
Management and Engineering, Tampere University of Technology, Pori, Finland;cTeollisuuden Voima Ltd., Olkiluoto, Finland
(Received 12 July 2010; final version received 3 January 2011)
Communicating with personnel is difficult if the concept in hand is complex, hardto perceive, has characteristics that are fuzzy in nature and that need a long-termperspective to show the results of them as a real benefit and advantage.Knowledge creation and learning concepts both belong to these kinds ofmanagement objects. Both have characteristics that are difficult to manage andlead, and that are difficult to articulate in detail to the organisation. Knowledgecreation has long been one of the key concepts in modern management scienceand research. Learning, however, has not achieved that high a status. Manycontemporary scientists like to bring in new constructs to better understand themechanisms behind knowledge creation and learning; however, measuring thesekinds of abstract concepts needs support from theory as well as methodology, sothat communication to personnel can be objective and, from a management pointview, effective. In this research, we have used Internet-based computer applica-tions to measure current knowledge creation and learning levels, and to gaininsight into how members of organisations are willing to show their proactivevision, as well as priorities in knowledge creation and learning concepts, insidetheir organisation. Practical asymmetries can be shown with test subjects, whichare important to understand from a leadership and management point of view.The dataset used for this article contains academic and private organisations.
Keywords: knowledge creation; organisational learning; ontology; fuzzy logic;neural network; SOM
1. Introduction
Organisational success is one of the main goals in leadership and management. In otherwords, it is assumed that with good leadership and management, success can be achieved.An organisation’s success highly depends on how well the leaders of the organisation canmanage that organisation’s intellectual capital (Edvinsson and Malone 1997, p. 52).Intellectual capital can be separated into human capital and structural capital, wherecustomer capital and organisational capital are the most important outside human-dependent capital components, affecting the development and growth of an organisation(ibid). The leaders and managers are responsible for the development and growth of theseassets and must realise how these components can be affected. Development and growth
*Corresponding author. Email: [email protected]
ISSN 1463–922X print/ISSN 1464–536X online
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are important to everyone in the organisation. However, how people behave in theirdifferent work roles is the core factor behind every organisational change and success.From the leadership and management point of view, we therefore have to concentrate onincreasing the power of intellectual capital to affect the success of the organisation. Manycontemporary scientists suggest that the best results from this can be achieved byincreasing the effectiveness of knowledge creation and learning (cf. Knowledge Spiral byNonaka and Takeuchi 1995, p. 71; cf. Double-Loop Learning by Argyris and Schon 1996,p. 21; and cf. The Learning Disciplines by Senge 1990, p. 376).
There is also a strong and constant need to create new markets, products and services,while at the same time keeping both customers and stakeholders satisfied. It is thereforeimportant for leaders to understand how the future is created. Management, in turn, needto understand the past and the current states of their organisation very well in order tomake the change happen. Nevertheless, organisational success starts from the individuallevel. Success requires a workforce which follows visions, challenges and objectives;however, this progressive way also calls for discontinuities, lateral thinking, conceptualthinking, analytical thinking, integrative thinking, etc., so that a new future can be created.If the opportunity exists for individuals to impact organisational goals and objectives, thenthey are more likely to put in the effort and hard work to reach these objectives. This mayalso be the case with knowledge creation and learning goals. Crafting the future istherefore a better way than simply coping with the past, present and current in the future.The future is supported by new ideas, new ways of producing, new products and servicesfor current and future customers. Crafting the future will lead to the continuous success ofan organisation. This, in turn, requires a strong understanding of how to make a changehappen, how to manage the ever increasing demand from the markets especially if theworkforce, i.e. the most important part of the intellectual capital, does not fullyunderstand the constructs and concepts that their managers and leaders are using.
What possibilities do managers and leaders have to improve communication with theworkforce and support and increase the intellectual capacity of individuals to betterunderstand the complex world? What can the new knowledge society offer to its members?
This research focuses on knowledge creation and learning concepts that are difficult toarticulate and manage in organisations. It first shows the asymmetries between how peoplein business and the academic world view their current situation, as well as how they wouldlike to see the future. Subsequently, we present the possibilities to use the establisheddatabase to group the whole dataset to show the asymmetry between the proactive visionand the current and future desires to improve knowledge creation and learning in theorganisation. We also present the evidence supporting the use of this methodology toreveal the asymmetries and why it is so important to understand these in terms ofmanagement and leadership.
In the following sections, we present the theoretical background and methodology.This will be followed by mathematical and visual representations of the results and a shortdiscussion on the findings.
2. Theory
2.1. Organisational knowledge creation
In order to succeed in competition, organisations must have the ability to create newknowledge continuously. Knowledge is an important factor that adds value to an
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organisation’s products and services. According to the theory of organisational knowledge
creation introduced by Nonaka and Takeuchi (Nonaka and Takeuchi 1995), knowledge is
created in a spiral process where tacit and explicit knowledge interact. This knowledge
creation process is based on four different modes of knowledge conversion (the SECI
process): socialisation, externalisation, combination and internalisation. Socialisation
(from tacit knowledge to tacit knowledge) is a process of sharing experiences; external-
isation (from tacit knowledge to explicit knowledge) is a process where tacit knowledge is
articulated to explicit concepts; combination (from explicit knowledge to explicit
knowledge) is a process where concepts are systematised into a knowledge system;
internalisation is a process where explicit knowledge is embodied in tacit knowledge and is
closely related to learning by doing (Nonaka and Takeuchi 1995). Organisational
knowledge creation starts at the individual level and then moves up through communities
of interaction crossing sectional, departmental, divisional and organisational boundaries
(Nonaka and Takeuchi 1995). This spiral process of organisational knowledge creation
presents a systemic view on how organisations create new knowledge. It is vital that the
organisation offers an environment that supports and motivates creative individuals and
facilitates interaction between them. Nonaka and Takeuchi describe five conditions that
are required in order to promote the knowledge spiral: intention, autonomy, fluctuation
and creative chaos, redundancy and requisite variety (Nonaka and Takeuchi 1995).
If these conditions are not put into practice, it is impossible to continuously create
new knowledge in a spiral process. The knowledge spiral is the only way to expand
individuals’ knowledge assets and create new knowledge at an organisational level.
Traditional knowledge management only considers some parts of the SECI process
(cf. Malone 2002). Thus, something more is needed to support and develop knowledge
creation activities within the organisation. The following section describes a responsive
environment that comprises those factors essential in developing a positive learning
environment and in supporting knowledge creation activities.
2.2. Organisational learning
According to Senge, individual learning does not guarantee organisational learning; but
without individual learning, no organisational learning occurs (Senge 1990). Therefore, it
is essential that an organisation supports and facilitates individual learning and knowledge
creation. This creates a chain of positive events: learning, applying new skills/knowledge
and recognition can increase self-confidence in learning new skills and performing them
efficiently (Tannenbaum 1997). The starting point of the organisation’s learning cycle is its
present actions (Sydanmaanlakka 2003). With the help of feedback systems, diverse
feedback is systematically gathered. This feedback is then interpreted in order to gain new
knowledge and to clarify the vision, strategy and goals. It is then possible to develop the
organisation’s mental models, actions and know-how. Sydanmaanlakka (2003) also argues
that strategic learning, an organisation’s ability to detect weak signals and its ability to
regenerate itself, is emphasised in this process. The organisation’s learning cycle is closely
related to single-loop and double-loop learning (Argyris and Schon 1996). Organisational
learning and organisational knowledge creation are complementary, supporting theories.
Yet how can these theories be grasped in real organisations? Ontologies and fuzzy logic
both seem well-suited to this task.
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2.3. Ontologies
Ontology is an explicit specification of the conceptualisation of a domain (Gruber 1993).Conceptualisation is an idea of (part of) the world that a person or a group of people mayhold (Gomez-Perez 2004). Ontologies define the common words and concepts (meanings)that describe and represent an area of knowledge (Orbst 2003). They thus represent amethod of formally expressing a shared understanding of information (Parry 2004). Themain components of an ontology are classes (concepts), relations (associations between theconcepts in the domain) and instances (elements or individuals in the ontology) (Gomez-Perez 2004). Using ontologies can have several benefits, such as interoperability, browsingand searching, reuse and structuring of knowledge (Menzies 1999). Ontologies also enablethe computational processing of information. Ontologies are becoming increasinglyimportant in fields, such as knowledge management, information integration, co-operativeinformation systems, information retrieval and e-commerce (Baader et al. 2004).Ontologies serve the need for storage, data exchange corresponding to the ontology,ontology-based reasoning and ontology-based navigation (Crubezy and Musen 2004).By its definition, ontologies are well-suited to explicitly describing those concepts thatbelong to organisational knowledge creation and learning domains. To discover how theseconcepts are actually perceived in real organisations and how to handle the imprecisehuman perception of knowledge creation and learning concepts, fuzzy logic is required.This concept is explained in the following section.
2.4. Fuzzy logic
Our ability to make precise yet significant statements about a system’s behaviourdiminishes as the complexity of the system increases (Zadeh 1973). This would mean thataccurate observations cannot be made about the most complicated system in the world –the human being. Organisational knowledge creation and learning take place in complexsocial systems involving many humans and other system parts.
Vagueness in linguistics can be captured mathematically by applying fuzzy sets(Zadeh 1965; Lin and Lee 1996). This is done by creating linguistic variables that ‘contain’fuzzy sets. Fuzzy sets represent systems better than crisp sets for two reasons. First,the predicates in propositions representing a system do not have crisp denotations. Second,explicit and implicit quantifiers are fuzzy (Zadeh 1983). ‘Conventional’ mathematicalmethods require that several preconditions are met before they can be utilised, especiallywhen there is a concern about the independence of the factors used. Fuzzy logic allows usto ignore these preconditions due to the use of linguistic variables (Wilhelm and Parsaei1991). Therefore, conventional mathematical methods encounter difficulties when appliedto human beings or human systems. A fuzzy set can be defined mathematically byassigning to each possible individual in the universe of discourse a value representing itsgrade of membership in the fuzzy set. This grade corresponds to the degree to which thatindividual is similar to or compatible with the concept represented by the fuzzy set(Klir and Yuan 1995). In this study, the perception of different aspects of knowledgecreation and organisational learning becomes a degree of membership in fuzzy sets. Justlike in real life, everything is a matter of degree. Linguistic variables bridge the gapbetween the mathematical base variable in the universe of discourse and the meaning in thehuman mind.
Fuzzy logic is the precise logic of imprecise things (cf. Zadeh 1973). Fuzzy logic allowsreasoning using fuzzy sets and fuzzy rules. It has two principle components. The first
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is a translation system for representing the meaning of propositions and other semanticentities; the second is an inferential system for arriving at an answer to a question thatrelates to the information resident in a knowledge base (Zadeh 1983). Here, propositionsrefer to the semantics (statements) of the concepts of organisational knowledge creationand learning. The knowledge base refers to the concepts (ontology) of organisationalknowledge creation and learning. In general, a fuzzy logic application resembles anexpert’s task to evaluate and reason based on linguistic information. A general fuzzy logicapplication consists of four modules: (1) a fuzzy rule base, (2) a fuzzy inference engine,(3) a fuzzification module and (4) a defuzzification module (Klir and Yuan 1995).
3. Methodology
3.1. Folium and Talbot ontologies
Folium and Talbot are ontologies that can be used to help the organisation’s managementin the decision-making process when target development plans are made to improve andsupport organisational knowledge creation and organisational learning on an objectivelevel. On a practical level, Folium is used within the organisation to evaluate features thatdescribe activities, functions and practices concerning organisational knowledge creation.In the same way, Talbot is used to evaluate features that describe activities, functions andpractices concerning organisational learning. Folium and Talbot contain linguisticindicative statements which describe the features of knowledge creation and learningorganisation in practice, and respondents are asked to evaluate their current reality andfuture vision as they perceive it according to these statements. As a result of the evaluation,the proactive vision (cf. Paajanen 2006) is visualised, i.e. the gap between the currentreality and future vision. The reasoning from the indicative statement evaluation to thevisualised proactive vision is made with fuzzy logic; the statements are semantic entitiesand the ontology is the information resident in a knowledge base (Zadeh 1973).The content of the Folium (knowledge creation) ontology (Paajanen 2006) is given inTable 1 and the content of the Talbot (organisational learning) ontology (Paajanen 2006)in Table 2.
4. The Evolute system
Evolute is an online system that supports specific-purpose fuzzy logic applications to beused over the Internet (Kantola et al. 2006, Kantola 2009). The Evolute system allowsresearchers to develop specific domain ontology and present it online to the target group
Table 1. Classes in the Folium ontology.
Features (sub-classes) Main classes
Sharing of experiences, observation of others’ work and spending timeand doing things together
Socialisation
Articulation of tacit knowledge and translation of tacit knowledge intoan understandable format
Externalisation
Adoption of new knowledge and combination with existing knowledge,spreading new knowledge in the organisation and evaluation of newknowledge
Combination
Making knowledge visible in operations and practices and exploitationof training and simulation
Internalisation
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through semantic entities, such as statements. Evolute provides ontology-based ‘answers’
to perceived propositions. The integral perception of a single person over all the presented
propositions will produce an answer, called an instance (Kantola 2009).The collection of instances reflects a specific management object (assets) portfolio
under scrutiny. The collection of instances forms the instance matrix (Kantola 2009):
ONTOLOGY identifiers 1�m ðindividuals 1� n, instanceÞ:
The instance matrix, as a function of time, describes the Management Object
development in the organisation. In other words, it charts the organisation’s assets over
time. The instance matrix, as a function of time, can be stated as:
ONTOLOGY identifiers 1�m ðindividuals 1� n, instance 1� kÞ:
The instance matrix is of great use to managers since it represents the collective mind of
the target group.The Evolute system utilises fuzzy logic to capture the subjective, abstract and vague
nature of the learning and knowledge creation environment without the individual having
to convert any of this on a numerical scale. The goal is to capture a true bottom-up view of
the current reality and envisioned future of the features and practices of knowledge
creation and the learning environment of an organisation. The Evolute system works as a
generic fuzzy rule base system in the following way (Kantola 1998):
(1) Evaluation of linguistic statements describing the features of the ontology. Inputs
from the evaluation are converted into fuzzy sets (fuzzification).(2) Fuzzified inputs are used by an inference engine to evaluate fuzzy decision rules in
the fuzzy rulebase. This results in one fuzzy set per each class in the ontology
(inferencing).(3) Fuzzy sets are converted into crisp values that represent the meaning of the
perception of the domain by the individual (defuzzification).(4) Defuzzified results (the instance) are presented visually and numerically for
decision making.
Each ontology and its propositions in the Evolute knowledge base can (and should) be
fine-tuned as researchers learn more about the domain they are investigating. This is done
by adjusting the fuzzy set design and fuzzy rule design. Furthermore, the content of the
Table 2. Classes in the Talbot ontology.
Features (sub-classes) Main classes
Opportunity for learning, tolerating mistakes as part oflearning and striving to avoid mistakes
Learning and tolerationof errors
Policies and practice support training and managerial supportof training
Support of training
Openness to new ideas and changes, support from co-workersof new ideas and demand made by the situation
Requirements for newideas and learning
Awareness of the big picture, expectations of and commitmentto a high standard, own abilities, satisfaction withdevelopment and training presented as something positive
Individual awarenessand development
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ontology will develop over time as the domain evolves and as researchers learn more aboutit – this is the ontology life cycle in ontology engineering (Gomez-Perez 2004). The datasetof Folium and Talbot instances used for this article are described next.
5. Dataset
With the Evolute system, we collected 264 Talbot instances (216 academic and 48 companyinstances) and 300 Folium instances (247 academic and 53 company instances) thatdemonstrate knowledge asymmetries in these concepts. The academic dataset was collectedduring the period of June 2005–June 2010 in universities in Finland, Spain, Poland andSouth Korea. The company dataset was collected during the period of December 2005–August 2007 in Finland in organisations that represent different areas of work andbusiness. Large parts of these two datasets do not have detailed demographic labelsassociated with the instances and therefore we cannot present that information. Addingone’s demographic data to instances during the self-evaluation was always voluntary.
6. Results
The initial results of our analyses of the Folium and Talbot runs are presented here.Though the results are general in nature due to the many test runs required, the trend isvery clear and offers new ideas and possibilities to develop knowledge creation andorganisational learning within the organisation.
6.1. Folium and Talbot classes
Figures 1 and 2 show the Folium results for the academic and business samples,respectively. The classes in the ontology are listed on the left side of the figures and theresults are represented by bars. The longer the bar, the higher is the perceived level ofthe class. The difference between the target and current bars shows the participants’proactive vision.
Figure 1. Folium academic case – 247 instances.
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6.1.1. Folium results
Based on the numerical data for both groups, the proactive vision for the main classes canbe ranked from the largest to smallest as follows:
(1) Internalisation (highest priority)(2) Combination(3) Externalisation(4) Socialisation.
The academic and company results for current and target levels are very close to each
other. On the numerical scale, the fuzzy bars are in the range 0.5–0.88. Detailed analysisshows the different aspirations of each group.
The high target levels show major support for the improvement of each concept, 110–145% more than current levels. Pressure to improve some of the main knowledge-creationitems therefore exists.
Among academic participants the main priorities are the use of simulation and training,
merging new knowledge with the old one (knowledge) and the evaluation of new knowledge.Company participants hold the same main priority, while the second and the thirdpriorities change places. The fourth priority is the same for both groups, abstract newknowledge in practice. After this the concepts follow a different order, but both groups end
on the same, following other peoples’ work.In brief, we can conclude that standard deviations in the current and future stages are
at the same level and very clearly reveal the asymmetry in the results.Each concept of the ontology could be perceived and evaluated by respondents, and
with the collective data gathered we can say that there is a general willingness to improveknowledge creation within these organisations. However, the asymmetry reflects the
different views held by participants.From the above results it is possible to show priorities in the development of items in
order to help the design of knowledge creation processes as well as to later follow thedevelopment of results.
Figure 2. Folium company case – 53 instances.
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Detailed analysis shows that those concepts belonging to socialisation, externalisationand combination have higher (average) perceived current and target levels in the companydataset. The situation is the reverse for concepts belonging to the internalisation class.In that category, the current level and perceived need are higher in the academic group.We can also see that the proactive vision was bigger in the academic group in all categoriesexcept internalisation. In short, according to the dataset, companies need the most supportin the internalisation of new content, while universities need the most support insocialising, externalising and combining aspects of new knowledge.
This material can be used for management and leadership purposes. The asymmetryreveals that those in both academic and business environments view knowledge creation asa very important development area as a whole, but with different focus. The clear messagefrom both groups to management is that most help is needed in internalising new content,then in combination with existing resources, third, in externalising new content andfinally in socialising it. Perhaps the SECI process should be ‘rotated’ in name to theInternalisation, Combination, Externalisation and Socialisation (ICES) process accordingto the perceived need.
6.1.2. Talbot results
Based on the numerical data for both groups, the proactive vision for the main classes canbe ranked from the largest to smallest as follows:
(1) Support for training (highest priority)(2) Requirements for new ideas and training(3) Individual awareness and development(4) Learning and toleration of errors.
The academic and company results for current and target levels with Talbot (Figures 3and 4) show bigger differences than in the case of Folium. On a numerical scale the fuzzybars for the academic sample are in the range 0.52–0.78 and for the business sample 0.5–0.83. Further analysis reveals different aspirations for each group.
The target level for improvement of each learning concept is 106–156% more than thecurrent level for the academic group. This reflects a collective will to improve the learningenvironment. Some individuals have a stronger desire than others in this regard as thedeviations in class results show, Figure 4. This indicates many possibilities to improvelearning and the learning environment in the academic institutions involved in thisresearch.
The main priorities of the academic sample are managers’ support of training,satisfaction with development and policies and practices that support training. For thebusiness group, the highest proactive vision is found in demand created by the situation,followed by managers’ support of training and finally, as with the academic sample, policiesand practices that support training. Similar proactive vision is shown from both groups forsupport from co-workers of new ideas and training that is presented as something positive.Both groups also view tolerating mistakes as part of learning similarly. Awareness of the bigpicture is not as clear for the academics; however, both parties desire to make an effort onthis front. Both groups rely on their own abilities, but are willing to improve to developbetter competences in the future. Overall, the academic group strives to improveopportunities a little more than in the business world. The academic group shows a lighttendency to avoid errors, whereas company participants did not indicate a wish to improvethis area – it may be that this item was already so good in the companies that participants
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Figure 4. Talbot company case – 48 instances.
Figure 3. Talbot academic case – 216 instances.
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didn’t think it necessitated further development. However, we must note that the resultsfor striving to avoid errors are not very reliable since a scale-reverse error was found in oneof the statements indicating this class in the middle of test runs.
The standard deviations for the Talbot results in the current and future stages are atthe same level in both groups and very clearly reveal the asymmetry in the results.
Again, it is evident that the content of each concept of the ontology could be well-perceived and it appears that collectively participants are willing to improve learning insidetheir organisations. However, the asymmetry reflects the different views of the individuals.From the above results it is possible to show the priorities of the various items in order tohelp design the learning processes as well as to later follow the development of the results.
Detailed analysis shows that the concepts belonging to managers’ support of training,requirements for new ideas and learning and toleration of errors have higher (average)perceived levels in the company group than in the academic group. The situation is thereverse for the concepts belonging to individual awareness and development. In thatcategory the current level and perceived need is higher in the academic group. We can alsosee that the proactive vision was bigger in the academic group in all classes of the Talbotontology.
These results can be used for management and leadership purposes. The asymmetryfrom the individual evaluations clearly shows that the academic and business groups seelearning as a very important development area.
6.2. Academic versus company
This section examines patterns found in the dataset using self-organising maps (Kohonen2001), especially in differences between academic and company groups.
6.2.1. Folium SOM results
Figure 5 presents a self-organising feature map (SOM) of the Folium dataset. The datasetforms six clusters on the SOM, representing the six different categories with uniquecharacteristics that responses to Folium roughly fall into. Table 3 lists how the instances ineach cluster can be described in words. Short labels in the table describe the perceivedcurrent (C) and target (T) levels of the concepts in each cluster of the Folium ontology.The highlighted cells in Table 3 show which knowledge creation features were perceived ashigh (H) or very high (VH). We can see that the red (R) cluster contains the highestperceived levels and the blue (B) cluster contains the second highest levels. Tables 4 and 6show how much academic and company presence are in each cluster in relative terms (%).
When we compare Tables 3 and 4, we can see that the ‘best’ (highest values) cluster Rcontains relatively twice as many individuals in companies than in universities. This orderis true also for the second best cluster B. Green (Gn) and yellow (Y) clusters have mixedmiddle area results. But grey (Gr) and orange (O) clusters contain relatively moreacademic respondents than company respondents. We can say that the companyemployees seem to have a tendency towards the highest levels of the knowledge creationspiral (SECI). In other words, university students usually work alone and compete againsteach other, and therefore maybe do not have such a need to pursue the highest levelsin knowledge creation. This is interesting since the main vehicle and the core ‘component’in organisational knowledge creation is human. This means that when new knowledge iscreated in an organisation this must happen through individuals – there is no other way.As a result of this, individuals are the ones who learn and retain the new knowledge.
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Table 3. Description of the nature of the six clusters in the SOM (Folium).
Note: VH, very high; H, high; LtH, lower than high; HtM, higher than medium; M, medium; LtM,lower than medium; HtL, higher than low.
Figure 5. SOM on current and target levels of the Folium dataset: A, academic; B, business;six clusters.
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These results show that universities probably should consider organisational knowledgecreation theory when designing their education system and methods.
6.2.2. Talbot SOM results
The Talbot dataset also contains six clusters, as shown in Figure 6. Please note that thecluster colour does not indicate the level of instances – the colours are in a different orderto the Folium SOM. In this case, green (Gn) and yellow (Y) represent those individualswho seek the highest levels of the different classes of the Talbot ontology in theirorganisation. Table 5 shows the description of the clusters in words and Table 6 theacademic and company presence in each cluster of the Talbot dataset.
Figure 6. SOM on current and target levels of the Talbot dataset: A, academic; B, business;six clusters.
Table 4. Relative academic and company presence in the SOM clusters(Folium).
Reference colourof cluster
Academicparticipants (%)
Companyparticipants (%)
Sum(%)
Red 17 33 20Blue 28 27 28Green 20 10 18Yellow 12 15 13Grey 14 10 14Orange 9 4 8
Total 100 100 100
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When we compare Tables 5 and 6, we can see that the yellow cluster (roughly fromhigh to very high) contains relatively more company employees than academic individuals.The green cluster (roughly from higher than medium to very high) has relatively twice asmany academic participants than company participants. This indicates that 20% of theacademics see a great need for support according to the categories specified by Talbot.If universities could provide support for these perceived needs, the results from theuniversity sample could rise to a new level. Individuals in the red (R) cluster roughly aimfrom lower than high to high levels. This red cluster contains relatively twice as manycompany participants than academic participants. It may be that universities did not takethese concepts into consideration when designing their education system and methods.These SOM results for Talbot show that there is great potential for the improvement ofindividual and organisational outcomes by considering the concepts that arise in theresults of this dataset.
Table 5. Description of the nature of the six clusters in the SOM (Talbot).
Table 6. Relative academic and company presence in the SOMclusters (Talbot).
Reference colourof cluster
Academicparticipants (%)
Companyparticipants (%)
Sum(%)
Red 9 17 20Blue 16 17 28Green 15 11 18Yellow 14 17 13Grey 23 19 14Orange 22 19 8
Total 100 100 100
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7. Discussion
Knowledge creation and learning are important activities in the academic and businessworlds. The applications developed follow modern new methods where knowledge iscreated in groups and teams and where learning is encouraged to support growth anddevelopment. The importance of both contents, i.e. knowledge creation and learning, canbe extremely difficult to quantify. With the method presented here, we have tried to usefuzzy measures to show how internal tacit knowledge of specific situationality can bequantified. The methodology as a whole offers new ways of understanding knowledgeasymmetry, which in turn is also strongly linked with learning asymmetry. This kind ofconceptual semantic will be important in the future as more and more research onknowledge enter into companies from the academic world. The initial results presented inthis article give a solid indication of which areas could be improved in universities andcompanies in order to really enable knowledge creation and organisational learning inthese organisations. These areas, represented in this article by hundreds of individuals, arenon-traditional ways to improve how things are usually done in organisations in practice,and they may lead to a real ‘performance boost’ in the future. The question then arises,how can meta-knowledge be used to lead and manages these dynamic ontologies ofknowledge creation and learning? We believe that these initial results show how multi-dimensional and difficult a concept asymmetry really is in real working environments.
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About the authors
Jussi Kantola is an associate professor in Department of Knowledge Service Engineering at KoreaAdvanced Institute of Science and Technology (KAIST). During 2003–2008 he worked in TampereUniversity of Technology and University of Turku in various research roles, including researchdirector in the IE department and IT department. He received his first PhD degree in IndustrialEngineering at University of Louisville, KY, USA in 1998 and his second PhD degree in IndustrialManagement and Engineering department at Tampere University of Technology, Finland in 2006.During 1999–2002 he worked as an IT and business and process consultant in USA and in Finland.
Professor Hannu Vanharanta, 1949, began his professional career in 1973 as Technical Assistant atthe Turku office of the Finnish Ministry of Trade and Industry. 1975–1992 he worked for Finnishinternational engineering companies, i.e. Jaakko Poyry, Rintekno and Ekono as process engineer,section manager and leading consultant. His doctoral thesis was approved 1995. In 1995–1996 hewas professor in Business Economics in the University of Joensuu. In 1996–1998 he served asPurchasing and Supply Management professor in the Lappeenranta University of Technology. Since1998 he has been professor in Industrial Management and Engineering in Tampere University ofTechnology at Pori. The research interests are: Human Resource Management, KnowledgeManagement, Strategic Management, Financial Analysis, and Decision Support Systems.
Petri Paajanen has worked for TVO Nuclear Services Ltd during years 2004–2009. Mr Paajanen’swork was related to international consultancy, particularly third party funded projects of EU’sformer Tacis and PHARE Nuclear Safety Programmes. Since 2009 Mr Paajanen has worked for alocal energy company Vatajankosken Sahko Oy as a head of energy business. He is also PhDresearcher in Industrial Management and Engineering Department of Tampere University ofTechnology at Pori, Finland. Mr Paajanen’s research interest is in the area of knowledge creationand learning.
Antti Piirto works in TVO nuclear power company. He has large experience in internationalevaluation of nuclear plants. He is especially interested in nuclear power plant safety issues. AnttiPiirto is also PhD student at Tampere University of Technology, Finland.
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