Special Issue Article
Modelling knowledge transfer: A knowledge dynamics perspective
Concurrent Engineering: Research and Applications 1–12 Ó The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1063293X15592185 cer.sagepub.com
Jonathan Mougin1,2, Jean-Francois Boujut1, Franck Pourroy1 and Gre´gory Poussier2
Abstract The increasing complexity in design activities leads designers to collaborate and share knowledge within distributed teams. This makes designers use systems such as knowledge management systems to reach their goal. In this article, our aim is to investigate on improving the use of knowledge management systems by defining a framework for modelling knowledge transfer in such context. The proposed framework is partly based on reuse of existing models found in the literature and on a participant observation methodology. Then, we tested this framework through several case studies presented in this article. These investigations enable us to observe, define and model more finely the knowledge dynamics that occur between knowledge workers and knowledge management systems. Keywords knowledge transfer, knowledge dynamics, knowledge management, descriptive model, participant observation
Introduction As products are becoming more and more complex, design activities require the participation of an increasing number of expertise, and designers have to collaborate within distributed environments. Furthermore, design being a cognitive process (Ma et al., 2013), it also involves logical reasoning and complex cognitive operations in which knowledge plays a key role. This knowledge can be of different nature, including technical or organisational, declarative or procedural, theoretical or resulting from practice. While some part of this knowledge preexists in the design and is used as a support for the related activities, some new knowledge is also created during design. Knowledge is therefore in constant evolution in any technical activity and is particularly an important factor in design and engineering. Manufactured products tend to become more and more complex; this is constantly challenging the skills and experience of the designers. Combined with a high competitive market pressure and globalisation of the economy, this often leads the companies to put an emphasis on their knowledge assets, making knowledge flows between the design stakeholders a key element of the management strategies. Knowledge management (KM) is hence considered ‘as one of the key enabling
technologies for distributed engineering enterprises in the 21st century’ (Mcmahon et al., 2004), and companies have started to adopt different technical and managerial initiatives in order to foster the knowledge sharing within their design teams. Among these initiatives, the adoption of a knowledge management systems (KMS) is one of the most popular ones. A KMS can be defined as an information system developed to support and enhance the three processes of knowledge creation, knowledge codification and knowledge application (Alavi and Tiwana, 2002). Designing this kind of system has been the centre of many research works in engineering design, management science and information science over more than a decade. Examples include the engineering design reuse methodology by Baxter et al. (2007) and the collaborative product design (CPD)–based engineering-KM methodology by Chen et al. (2008). Nevertheless, beyond the promising 1
Univ. Grenoble Alpes, G-SCOP, Grenoble, France BASSETTI, Rue du Ge´ne´ral Mangin, Bassetti, Genoble, France
2
Corresponding author: Jonathan Mougin, Univ. Grenoble Alpes, G-SCOP, 46, avenue Fe´lix Viallet, F-38000 Grenoble, France. Email:
[email protected]
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idea of supporting knowledge flows through those KMS, only few organisations have implemented successful KMS (Atwood, 2002) and some studies even depict major failures (i.e. Akhavan et al. (2005) or Malhotra (2004)). Much attention has been given to the possible reasons of these failures which are often discussed in terms of psychological, social, technical and organisational factors that influence the use and acceptance of KMS (Bernard, 2006; Dargahi et al., 2010). Beyond these factors, Bernard (2006) suggests that ‘what is important is not to focus on what is captured by the KMS, but on what is not captured by the KMS: the ‘‘near-misses’’ that are hard to identify and disclose’. Moreover, Grant (2007) argues that significant failures in KMS could result from the overly simplistic view of the tacit/explicit dimension of knowledge adopted by some knowledge transformation theories. The aim of our research is to investigate on improving the use of KMS by engineering teams. Paying attention to the above issues, in this article, we focus on defining a framework for modelling knowledge flows along the tacit/implicit axis. This framework will help us to model knowledge transfer situations and to get a better understanding on how knowledge can be elicited, formalised and shared among a group of people. Starting from essential considerations about knowledge and information, the following section briefly reviews some key knowledge transformation models from the literature. Then, section ‘Research methodology’ describes both our research methodology and the context of this research. The proposed framework for modelling knowledge transfer is then presented in section ‘A framework for knowledge transfer modelling’. This framework is based on several tangible objects corresponding to different formalisation levels in the knowledge transfer process. Some case studies are then presented and analysed in section ‘Application to knowledge transfer situations’. They show the framework’s aptitude at modelling different real-life knowledge transfer situations. Some benefits of using this framework in some engineering-KM activities are finally discussed.
State-of-the-art review Although there is a general intuitive notion of what knowledge is, this concept still lacks a shared definition. This literature review starts with a quick overall look at the concept in order to clarify what will be named as knowledge all along the article. Then, the focus is made on knowledge transfer. The objective is twofold: to identify the main dimensions of knowledge transfer and then to characterise the main actors of this process. Finally, some existing knowledge transfer models are analysed, highlighting their potential benefits and drawbacks.
Knowledge and information: a useful clarification The initial point of this study aims to highlight the different definitions and uses of the concepts of knowledge and information. Serenko et al. (2010) observed that ‘over the past 15 years, there has been a remarkable increase in papers, books, conferences and job titles all related to the primary issue of harvesting intellectual capital through knowledge management’; however, there is not a real consensus on the definition of the concepts of ‘knowledge’ and ‘information’ (Wilson, 2002). Exploring the literature reveals various definitions that are sometimes contradictory. Polanyi is often considered as the first author who introduced the tacit dimension of knowledge. He argued that not all knowledge can be codified, which is brilliantly summarised in his famous quote ‘we can know more than we can tell’ (Polanyi, 1966). His definition shows that a part of knowledge is created through experience accumulated over the years of practice which makes knowledge difficult to codify. Building on Polanyi’s definition, Nonaka (Nonaka and Takeuchi, 1995; Nonaka and Von Krogh, 2009), whose work inspired many other researchers, makes the distinction between tacit and explicit knowledge. With his colleagues, he defines tacit knowledge as subjective and experience-based which makes it hard to transfer from one person to another and which is consequently difficult to express in words, sentences or formulas. On the other hand, explicit knowledge is considered as objective and rational which is easier to formalise, transfer and store. Since then, many researchers used these definitions in their research and considered that explicit knowledge can be easily stored in documents and databases. As a consequence, many research works asserted that computers can store knowledge. Considering these definitions, Nonaka assumed that even the tacit dimension of knowledge can be made explicit through an externalisation mechanism. During this conversion, knowledge becomes ‘crystallised’. However, with this point of view, the processes of interpretation and performance can be overlooked (Brohm, 2007). Wilson (2002) also argued that ‘problems in the distinction between ‘‘knowledge’’ and ‘‘information’’’ are crucial if we consider the dynamic aspects of knowledge (i.e. creation, elicitation, acquisition, transfer, etc.). This lack of distinction explains many failures and disappointments in KM projects. For many authors, however, knowledge is not a tangible object that can be transmitted from one person to another. We previously quoted Wilson (2002) who defines the concept of knowledge as follows: ‘knowledge involves the mental processes of comprehension, understanding and learning that go on in the mind and only in the mind’. Considering this definition,
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knowledge cannot be found outside of people’s mind. That means ‘whenever we wish to express what we know, we can only do so by uttering messages of one kind or another – oral, written, graphic or gestural’ (Wilson, 2002). These messages are information externalised by a person that might be interpreted and assimilated (internalised) by another person, the result being a new piece of knowledge for the person. Another close definition comes from Prudhomme et al. (2007) where ‘knowledge is seen as personal, as belonging to an individual’. To conclude this section, it appears illusory to look for a unified definition of knowledge. Nevertheless, either it is more on the implicit or on the explicit side; it is clear that knowledge remains something intangible and closely related to individuals (Brohm, 2007; Nonaka and Von Krogh, 2009; Wilson, 2002). On the other hand, relying on their knowledge, people produce different kinds of tangible things such as talks, texts and acts. In this article, all these tangible, and therefore observable things, will be considered as information. The next section will review the idea of knowledge transfer in the light of those definitions.
The winding path of knowledge transfer Knowledge transfer occurs when knowledge is diffused from one entity to another (Joshi et al., 2004), or when one entity is affected by the experience of another (Argote and Ingram, 2000). The entity might be either an individual or a group of people belonging to a specific organisation, for example, a project team, a service or a company. Knowledge transfer is often claimed as a basis for competitive advantage in firms (Argote and Ingram, 2000; Bou-Llusar and Segarra-Cipre´s, 2006) and has been the focus of many research works. Indeed, although at first glance this definition of knowledge transfer seems very simple, it raises the nontrivial question of how to diffuse something so intangible, and so intimately linked to individuals. This is why interaction between people is considered as a key element in knowledge transfer. Joshi et al. (2004) have studied knowledge transfer within software engineering face-to-face teams. Their findings are that an individual is perceived to transfer a significant amount of knowledge to his or her team members if the individual extensively interacts with other team members and is perceived as reasonably credible. They consider knowledge transfer to unfold through processes of socialisation, education and learning and emphasis that knowledge may be purposefully transferred or may occur as an outcome of other activity. In line with this idea, Smith and McKeen (2003) explain that ‘just as knowledge is fundamentally an attribute of human beings, so knowledge transfer is a function of the
interaction between people’. Citing Denning (2011), they also argue that instead of a situation where ‘one person has a huge mental bin of information in his or her mind that is transmitted to the minds of the listeners’, knowledge transfer should be considered as ‘an interaction between people who together create new perspectives and understanding’. Hence, knowledge transfer should be considered as a somewhat complex process by which, whether intentionally or not, people co-construct some shared understanding and shared perspectives through their interaction. Hence, knowledge transfer initiatives often combine technical, social and organisational aspects (Strohmaier et al., 2007), and many authors, especially in management science, have studied the influence of different factors related to those dimensions on the efficiency of the transfer (Frank et al., 2014). Rather than these factors, here we focus more on the knowledge transfer process itself, and the following examines the stakeholders of those interactions.
Roles involved in knowledge transfer During the 1990s, KM studies were mainly related to information system (Scarbrough et al., 2005). However, since 2000, research studies started moving towards a more human-focused KM (Jakubik, 2011). Following this orientation, we will point out the role of the different stakeholders who are involved in knowledge transfer. Markus (2001) identifies three major roles involved in what she called the ‘knowledge reuse process’. In her article, she first describes the ‘knowledge producers’. These people own knowledge and externalise in the form of information. Then, the ‘knowledge intermediaries’ assist the knowledge producers in the externalisation process by helping them to elicit their knowledge. They also have to transform and format the information collected with regard to knowledge reuse situations. Subsequently, they distribute the formalised content to the knowledge ‘reusers’. This role is also called ‘knowledge brokers’ in the literature (Meyer, 2010). The last role described by Markus is the one of ‘knowledge reusers’. They retrieve the formalised content and interpret it and put it into practice. This perspective is consistent with Maruta’s point of view (2012) who classifies workers in companies into two different categories. Initially, he categorised people as knowledge workers and non-knowledge workers. He defines knowledge workers by modifying Davenport’s definition: ‘knowledge workers have high degrees of expertise, education, or experience, and the primary purpose of their jobs involves the creation, distribution and application of knowledge’. In a second article, Maruta (2014) subdivides the knowledge workers into
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two categories (Type-1 and Type-2 knowledge workers). Type-1 knowledge workers acquire their knowledge through ‘learning opportunities’ while Type-2 knowledge workers self-create their knowledge. This distinction puts a specific light on reflexive skills required for certain knowledge-intensive jobs. In our study, we will consider that the actors involved in knowledge transfer process will be identified and classified using these roles. We will now emphasise on the knowledge transfer process itself.
Knowledge transfer models Modelling knowledge transfer in order to manage this process more efficiently has been the focus of some scholars over the past 10 years. In a recent article, Frank and Ribeiro (2012) draw a comparison of 14 knowledge transfer models from the literature. They classify these models into two main categories: the emergent approach (social interaction perspective) and the engineering approach (technology and information management perspective). All these models are analysed and arranged following a process view of knowledge transfer in five main phases: knowledge generation in the source, knowledge identification, knowledge processing, knowledge dissemination and knowledge applying in the recipient. From this analysis, they propose a unified model of knowledge transfer between new product development project teams. An interesting feature of this approach is that the whole process of knowledge transfer is under consideration, including the knowledge creation phase. Nevertheless, despite a breaking down of the phases in several stages, this global model of knowledge transfer as a process is not convenient for closely representing interaction between stakeholders Rather than a process approach, some other models in the literature are more focused on the way knowledge is transformed during the knowledge transfer process. The Socialization, Externalization, Combination, Internalization (SECI) model from Nonaka et al. (2000) is probably one of the most popular ones. It focuses on how knowledge is created and transformed over successive ‘conversions’ between mainly tacit and mainly explicit knowledge (Nonaka and Von Krogh, 2009). There are four different conversions articulated as a spiral. The spiral is passing successively from tacit to explicit (externalisation), from explicit to explicit (combination), from explicit to tacit (internalisation) and from tacit to tacit (socialisation). The model is of great interest for explaining knowledge transformation inside people’s mind along a continuum between the tacit and explicit dimensions of knowledge. It is, however, restricted to the knowledge space and cannot be used for representing how tangible things are generated
from knowledge, and how they are used to transform or create new knowledge. Another model of interest is the I-Space model from Max Boisot and Canals (2004). This conceptual framework uses the metaphor of a cube to describe information flows among a population. For the authors, the population can be either human of silicon based (computers), as long as it can be considered as data processing agents. The information is supposed to flow inside the cube according to three main dimensions (the three axes of the cube). These dimensions are as follows: ‘abstraction’ that indicates the level of generality of the information, ‘codification’ that indicates the level of formalisation of the information and ‘diffusion’ that indicates the degree to which the information is shared among the organisation. Based on these three dimensions, Boisot and Canals draw the social learning cycle that is composed of six phases: scanning, codification, abstraction, diffusion, absorption and impacting. These six phases describe the flow of information within a knowledge transformation cycle. Conversely to the previous model, the Boisot and Canals’ model is restricted to the information space (namely, the I-Space). It allows representation some transformation mechanisms but does not pay attention to objects generated by the social learning cycle. This model is however very useful to get a close view of this information space. The knowledge-based theory of the firm is another model which was proposed by Sveiby (2001). This model focuses on intangible resources owned by three structures. The first structure is the individual competence which represents the competences of people who are working in an organisation. The second one, is the internal structure, represents the resources that belong to the organisation (such as all the internal processes). So, this structure is partly dependent on the first structure as people are working for the organisation. Finally, the external structure stands for the relationship between a given organisation and its customers/ suppliers (for instance, the reputation of this organisation). From these structures, Sveiby points out conversions that occur from one structure to another, and within a structure itself. He also argues that the notions of ‘conversion’ and ‘transfer’ related to knowledge lead to a directional movement. This implies that the direction plays a role in the knowledge transformation, which is close to our point of view. Sveiby finally obtains 10 different conversions which show how complex the knowledge transfer is within and across organisations. Focused on interactions within and inside structures, this model appears to be useful for describing knowledge transfer at the macro-level of an organisation. It is, however, unsuitable for representing knowledge transfer at the individual level.
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The last model that we consider in this article comes from Maruta (2014). It illustrates how people create knowledge from information input. These information inputs can come from education, experience, news or reports. These inputs are analysed and interpreted through individual’s mental processes. Here, he introduces the concept of insight. It is defined as ‘the ability to conduct mental activity’ (Maruta, 2012). Two types of insight exist. The first one is insight for comprehension. It allows people to interpret the input. The second one is insight for creation. It provides mechanisms to create new knowledge and allows to account for situations where existing knowledge is not sufficient. This mental model considers explicit knowledge as an output and takes into account the understanding in the knowledge creation process. Nevertheless, it remains focused on the knowledge space and do not stress sufficiently on the object related to the knowledge transformation. In summary, some important outcomes emerge from this literature review. Knowledge, which is so important to engineering activities, is an intangible thing, more or less deeply anchored in people’s mind and behaviours. Hence, interaction between those people is the heart of any knowledge transfer process. Nevertheless, more tangible and visible elements (i.e. written documents, acts, objects or discussions) can be considered, to some extent, as traces (observables) of this knowledge. Although some interesting key points have been identified during the analysis of the different models of the literature, we still need a model that precisely describes knowledge transfer through the interactions between stakeholders involved in the process, highlighting the objects that support these interactions, and addressing both the knowledge and the information spaces.
Research methodology The aim of the research presented in this article is to describe knowledge transfer and identify the object raised in this process. In order to address this research, a literature review has been conducted simultaneously for observations in an industrial context. The literature review is presented in section ‘State-of-the-art review’. Regarding observation in an industrial context, a participant observation method has been adopted within a company. This method allows the participant-observer to be immersed for a long period. During this period, the participant is able to perform interviews, take notes and make audio records. An information and technology (IT) company specialised in KM services welcomed us. We will name this company BAS. BAS has two main activities. First, it implements KM methods such as setting up communities of practice and supporting engineering expert’s departures through its consulting
services department. Second, it also develops and implements its own KM system, both activities being of course highly connected. Within this company, we participated in all the activities performed by BAS’ consultants, for example, assisting an expert’s departure, implementing the KMS and training customers to use it. These activities allowed us to observe how knowledge is transferred in the various situations. In total, 18 semi-structured interviews have been performed with two senior consultants who are considered as experts in the company. During these interviews, senior consultants described some cases they experienced. Each interview was about 2 h long. The authors also participated in setting up a community of practice within the company for the KM consultants. These different sources provided us with many inputs and contributed to establish a first version of our framework. Therefore, this framework is partly based on reuse of existing models and concepts found in the literature (presented in the previous section). The rest of the model comes from the observation made in the company. Elements that compose our framework are defined in detail and illustrated through an example in section ‘A framework for knowledge transfer modelling’. The framework needed to be tested in different applications. Section ‘A framework for knowledge transfer modelling’ describes three types of actual knowledge transfer situation which are modelled with our framework: Knowledge transfer within discussions. Two types of discussions are considered: face-to-face discussions (one community of practice was observed) and distant conversation by using a technical forum. Knowledge transfer through an on-the-job training situation. Knowledge transfer through formalised documents between experts and novice engineers.
A framework for knowledge transfer modelling Through the last sections, we pointed out the strengths and weaknesses of existing models. We will now classify knowledge and the different states of transformation within the knowledge transfer process. Considering knowledge as embedded in people, it cannot be formalised as is. Knowledge is therefore irreducibly partly tacit. The other part is considered as explicit. It means people are conscious of it and it can be more easily externalised into information. Jakubik (2011) also argued that ‘knowledge cannot be managed as an object separate from human actions because it is continuously
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shaped and being shaped by social practices of individuals’. This statement confirmed our opinion that we have to focus on observable and measurable objects located in the information space but in relation to the actors that originated them. However, talking about information is too broad. This research study aims to be more precise in the characterisation of these objects. In that way, contributions already exist in the literature. For instance, Ancori et al. (2000) introduce the concept of piece of information that is defined as follows: ‘each piece of information brings with it a ‘‘quantum of novelty’’ that contributes to increasing the stock of knowledge’. They based their definition according to the information theory of Shannon and Weaver (1949). More recently, Bolisani (2010) reused this concept and proposed the following definitions: ‘communications carry pieces of knowledge’ and ‘each piece of knowledge has a value for both the ‘‘sender’’ and the ‘‘receiver’’’. These definitions give first insights to define the nature of externalised knowledge into information. However, they do not integrate the ephemeral nature of these messages. So, we reuse the essence of the concept ‘piece of knowledge’ and transform it in order to introduce the concept of knowledge footprint. A knowledge footprint is defined as an oral or written element that is externalised by a person. An utterance is considered as a footprint if it brings ‘a new quantum of novelty’ to the discussion and has a given value for the sender and the receiver. For instance, an engineer named Paul explains to his colleague Peter how to calibrate the measuring device for temperature control. Paul’s explanations are knowledge footprints. They show Paul’s knowledge about calibrating the device. However, when Peter asks questions or rephrases Paul’s sentences, it does not bring novelty in the conversation. So, it is not considered as knowledge footprint. In the context of software design teams, Petre (2004) has studied the relationship between mental imagery and external representations and the effect of the latter on the rest of the group. The author studied the mechanisms of building a unified version of the object of the design and the effect on coordination. From a cognitive point of view, what we call knowledge transfer relies on the ability of the sender to externalise in an artefact a mental imagery and for the receiver to build a compatible mental imagery that transforms his internal model (also called mental model). Through a conversation, words or attitudes such as ‘OK, if I understand ...’ or more generally, the rephrasing processes, show the moment where the sender and the receiver have a shared representation. The exchange which sums up the knowledge footprints is called a shared knowledge footprint. For example, Paul just explained how to calibrate the measuring device for temperature control. Then he
asked Peter to rephrase his sentences. When Peter rephrases them, he expresses a shared knowledge footprint to show Paul that he understood how to calibrate the device. In this step, Peter interpreted Paul’s knowledge footprints and created knowledge. However, neither the knowledge footprints nor the shared knowledge footprints explain how people can share their tacit knowledge. Many authors in the literature presented the way that people can exchange this kind of knowledge. For instance, socialisation conversion from the well-known Nonaka theory pointed out this process. Maruta (2014) also uses ‘on the job training’ and experience as inputs of his mental model. Joshi et al. (2004) explain that knowledge may be transferred ‘as an outcome of another activity’. In order to cover these situations, we introduce the concept of knowledge facts as an observable element that can be captured with video-recording or direct observation of professional situations. What differentiates a knowledge fact from a knowledge footprint is mainly related to the intention of the originator and the context of the observed situation. A knowledge fact can be observed in any professional situation when an expert puts his knowledge into practice. A knowledge footprint is an intentional movement of an expert to create an observable of his knowledge with the intention to communicate this piece of knowledge. Knowledge facts are important in our model because they represent the proof that knowledge transfer has been effective. By comparing knowledge facts in a given timeframe, we can evaluate the knowledge transfer that actually occurred. For instance, Peter now understood the calibration process. He now can use this newly created knowledge, and somebody observing Peter calibrating some device will see him pushing and turning buttons in a certain way. These observables are what we call knowledge facts. If we go a little bit further in the knowledge transfer process, we can imagine that during an expert meeting a great number of knowledge footprints are produced. These knowledge footprints are discussed, merged transformed and so on in order to get a reasonably shared consensus on the considered topic. The outcome of these discussions is well formalised in a document. We name this element knowledge object. However, even if many researchers worked on this concept, there is no clear definition in the literature. In 2007, Prudhomme et al. used the concept of object of knowledge quoting Chevallard (1991). For them, ‘when an individual interacts with an object of knowledge in a given context, he or she will create a personal relation with the object’. In that way, any externalised object is potentially an object of knowledge. Markus (2001) also uses this concept and mentions that ‘not surprisingly, the assistance of intermediaries is required
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to produce these knowledge objects’. In this quotation, knowledge object refers to formalised documents. This definition is interesting but it does not precise what are the inputs of these documents, nor it gives concrete characteristics. Di Maio (2012) went a bit further and defined it as an object which ‘is a highly structured and inter-related chunk of contextualised content’. In order to get one step forward, we propose to add a characteristic: a knowledge object should be interpretable by a third-party that did not participate in its construction. Therefore, the knowledge object should be structured as a combination of shared knowledge footprints and context elements that makes it interpretable in another context, that is, the creation context. It allows to ‘distil the raw material (our knowledge footprint) into a coherent set of themes’ (Roth and Kleiner, 1998). As an example, when Peter interpreted the calibration process, Paul asked him to formalise it within a document in order to disseminate this practice in the organisation. This document is called knowledge object. However, this object is still not tidy in the information system. Finally, our last element is a consequence of the knowledge object. Even so, it is subjected to another transformation. From the interviews made with BAS senior consultant, we observed that knowledge object can be insufficiently documented. A knowledge object can potentially be useful in another context of use, for example, in another department or unit. Then, the knowledge object becomes an intermediary object or a boundary object (Star and Griesemer, 1989). As Vinck (2011) observed, the action of equipping an intermediary object with metadata, for example, makes it interpretable in other temporalities. In line with Vinck, we propose the concept of packaged knowledge object for describing the transformation of a knowledge object into a format compatible with an information system standard. For instance, Peter’s knowledge object cannot be found easily. So, Paul asks him to tidy it in KMS. The chart at the bottom of Figure 1 shows the graphical representations of our main concepts. They are sorted from the less formalised to the more structured and considering the ‘brain boundary’, that is to say, the limit between internal and external world (i.e. knowledge space or information space). We will use this model to describe actual knowledge transfer situations and propose more desirable processes.
Application to knowledge transfer situations After having introduced and justified our basic concepts and framework, this section aims at showing the
potentiality of our approach. Three case studies are presented here to illustrate three different types of knowledge transfer process. In each case, the knowledge transfer processes are characterised and reveal their underlying models. The literature is used to analyse the cases and observations are discussed.
Knowledge transfer through discussion Face-to-face situations. Illustrating knowledge transfer models obtained in the literature was a promising start. However, it does not provide a real test. A first opportunity came up to confront our framework in a real situation. This company is named Global Alloy Corp. It is an international company which produces alloys through nine different plants. These plants are distributed over the world. For 2 days, a metallurgist community of practice gathers in one of the plant to observe the local practices and to talk about issues defined by the top management. This community of practice is composed of 17 metallurgist experts from five plants. This meeting was held in one of the plants of the company. The first day was dedicated to the visit of the plant and will be the focus of section ‘Knowledge transfer within on the job training’. The second day was centred on the workshop. On the second day, they split into groups which were composed of at least one person from each plant. The group that we observed worked for 1 h on the topic ‘How is the maintenance performed?’ Members produced knowledge footprints by explaining the practices of their own plant, talking about who performs the maintenance, which equipment is checked and at which period. The other participants asked questions and reformulated the knowledge footprints, so they created shared knowledge footprints. The knowledge leader wrote down the shared knowledge footprints. After the meeting, he spread the different shared knowledge footprints in the KMS and asked the group members to validate the content of the packaged knowledge object that he created. The conversation was recorded, transcribed and analysed with our framework. The analysis was then modelled through a chart. A simplified view of this chart is presented in Figure 1 on the top-left part ((a) Global Alloy Corp – CoP). In this context, the participants exchanged their knowledge footprints. Then, they asked questions in order to get details and get a better understanding. Finally, one of them reformulates the knowledge footprints. Here, the knowledge leader, who plays the role of knowledge intermediary, took notes directly. Then, he translated directly from the shared knowledge footprint to the packaged knowledge objects. The formalisation is directly made in the KMS. An interview afterwards has been performed with the knowledge
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Figure 1. Knowledge transfer modelling cases and elements of representation: (a) Global Alloy Corp – CoP, (b) Forum, (c) Global Alloy Corp – on-the-job training and (d) Energy Tech.
leader to ask him why he was formalising the shared knowledge footprints in that way. In fact, he also has the responsibility to manage the KMS. So, he knows how the KMS is structured. This allows him to make the formalisation and the packaging steps at the same time.
Distant conversation (forum). From the Oxford Dictionary definition, a forum is ‘an Internet site where users can post comments about a particular issue or topic and reply to other users’ postings; a message board’. There are plenty of forums on Internet, but only the ones which concern technical issues interested us. A forum
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about engineering simulation software was finally selected. Forums are often used by groups of experts to share knowledge. However, this kind of tools contains many overlaps. So, it would be interesting to use our framework to get a better understanding of what happens in this kind of environment. After the analysis of several threads, similar ‘patterns’ have been identified and are represented in Figure 1, top-right model ((b) Forum). This model represents the usual knowledge transfer process which can be found in the threads analysed in this forum. Participants expressed their knowledge on knowledge footprints. Participant A who asks, for instance, to solve a problem, usually reformulates the footprints into a shared knowledge footprint. However, there is no work done afterwards to formalise the shared knowledge footprints contained in the threads into a knowledge object. It could be argued that for a better efficiency, a knowledge object could be presented and packaged as a tutorial, for example, supplied by the threads. This lack of structure and synthesis can explain the redundancy of topics. Many threads dealing with the same topic or open discussion are never closed. So, the framework can also be relevant to observe anomalies that occur in IT tools.
Knowledge transfer within on-the-job training Knowledge transfer illustrated in discussion situations shows some knowledge transformation described by Nonaka. Nevertheless, the phenomena that occur during the transfer of tacit knowledge were still not observed. On the first day of the Global Alloy Corp meeting, engineers moved around the plant and observed how the local engineers were working. Notes about their attitudes, their questions and their comments were taken. The engineers observed the methods used in the different process steps for the alloy design and asked how these methods were implemented. For instance, just before entering the plant, one of the participants ((b) in Figure 1 bottom-left; (c) Global Alloy Corp – on-the-job training) observed how the paste blocks used as inputs to the process were transported from the truck to the plant. This was the opportunity to ask questions and get new insight on the process from ‘A’. After that ‘B’ reformulates the explanations to be sure that he understood. Figure 1(c)‘Global Alloy Corp – on-the-job training’ shows the knowledge transfer model based on practice observation. It starts when an engineer A is practising. This practice is observed and identified by another engineer B. This second engineer B asks questions about the practice. The question is not a knowledge footprint but it makes A externalise his or her knowledge into knowledge footprints. In this context, knowledge footprints
are drawings and explanations. Finally, B reformulates the knowledge footprints into a shared knowledge footprint in order to show his or her understanding. As a result of these observations, the model made with the framework highlights a failure in the knowledge transfer. During the practice observations, metallurgist engineers shared knowledge as knowledge footprints. They interacted with them to get shared knowledge footprints. However, they did not track this latter. So, the knowledge transfer process occurred only among the group of visiting engineers. To complete the knowledge transfer process, engineers should have formalised the shared knowledge footprint into a knowledge object and then packaged it in the KMS. This would allow sharing knowledge with other engineers who could not come to the meeting.
Knowledge transfer using formalised documents To illustrate this third knowledge transfer situation, one of BAS customers named ‘Energy Tech’ was identified. Energy Tech is a large company in the energy sector. Its managers contacted BAS for consulting services concerning problems that the company encountered about knowledge transfer between its experts and its engineers. Based on the positive feedback of our framework, the analysis of this problem was realised using our framework in order to detect dysfunctions. In this context, five interviews of 1 h with the manager in charge of their KMS have been performed. From these interviews, a model from the situation has been realised using our framework. In fact, experts are supposed to write reports that contain feedbacks on their experiments (which constitute package knowledge objects). However, these reports are not understandable by the less experienced engineers. The rewriting process is based on a meeting between the engineer and the expert. In order to give clarifications to the engineer, the expert expresses knowledge footprints. The knowledge engineer reformulates them to make sure he understood. After this verification, he formalises the shared knowledge footprint and upgrades it with the existing packaged knowledge object. Figure 1 (bottom-right (d) Energy Tech) shows a different structure from model Figure 1(a). Here, the process starts with the creation of a knowledge object. This object results from the formalisation of one person only. Then, the knowledge object is stored in the KMS. The confrontation with the users or the other experts occurs later. The expert has to contribute again to explicit knowledge footprints for sake of clarification. This structure may appear when there is no real collaborative culture and particularly no communities of practice. The experts are isolated and work on their own; knowledge dissemination requires then some
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reformulation and rework afterwards. So, this analysis points out the importance of co-construction of the knowledge object. Through the use of our framework, we have been able to model this situation and we clearly observe the differences between this model (Figure 1(d)) and the models obtained in Figure 1(a).
Conclusion With our modelling framework, we intend to provide a new input to the state-of-the-art of knowledge transfer studies in organisations. Although in line with the recent publications of Nonaka, in particular Nonaka and Von Krogh (2009), where they introduce the concept of knowledge conversion, we provide here a modelling framework that allows to represent some aspects of the complexity of knowledge transfer in particular between experts (also called knowledge workers) in communities of practice. Additionally, as we focused on knowledge dynamics, we adopted a point of view that fits to the latest developments of the cognitive psychology by considering knowledge as a process. In this article, we demonstrate that it is possible to model various dimensions of knowledge transfer, from the elicitation process where knowledge is externalised to the processes where shared knowledge footprints are transformed into an object of knowledge and then packaged in a KMS. Basically, our model can be considered as an instrument to explore the knowledge continuum between tacit and explicit knowledge defended by Nonaka. To go further, future works will look into the individual dynamics of knowledge elicitation by various means of formulation and how to model and represent various forms of knowledge elicitation, being the individual or collaborative. Up to now, we already have interesting and fruitful results. So far, the framework helped BAS consultants to model and analyse, over a period of 1 year, several knowledge transfer situations into 11 companies (including A2D, Global Alloy Corp and Energy Tech) ranging from small- and medium-sized enterprises (SMEs) to large companies and targeting different contexts (located or distributed teams). Furthermore, two unexpected cases appeared when BAS consultants started to systematically review their practice and model BAS KM elicitation methodologies. This interesting process of self-introspection led to highlight two new potential application cases. First, they now use these models to train new comers and customers. Second, during sale phases, sale consultants extract part of these models for illustrating BAS strategies for managing knowledge transfer as exemplars. This model is therefore used to categorise and to present BAS strategies. Largely positive feedback from the different
types of recipients (new comers, current customers or business prospects) gives us good indication on the practical (or operational) usefulness of the method. Furthermore, our approach clarifies knowledge transfer situations and allows to recognise them in the everyday activity of the engineers. Additional developments are now being carried out in order to cover the entire process including reuse of objects of knowledge. Our project will then provide an entire model for describing knowledge transformation in a context of corporate knowledge creation and management. In further work, we will focus on modelling specific knowledge transformations with the framework, for example, individual learning process. The next step will be to measure and evaluate knowledge transfer situations modelled with our framework. These metrics will help to improve the diagnosis of existing situations and measure the benefit of the new situation. We intend here to go one step forward in the definition of influencing factors related to knowledge transfer. Our work intends to provide a better understanding of the complex processes and hopefully gives some opportunities to the research community to discuss and refine these modelling tools, particularly the cognitive or social implications of this approach. Declaration of conflicting interests The authors declare that there is no conflict of interest.
Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
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Author biographies Jonathan Mougin is a PhD student at the University of Grenoble Alps, France. He has an engineering degree in information system management and a master degree in information technologies. His research is focused on knowledge creation and knowledge transfer in organizations. He also works as consultant and project manager at BASSETTI.
Jean-Franc xois Boujut is professor of engineering design at Grenoble Institute of Technology in the Industrial Engineering school. He earned his PhD in 1993 and his Habilitation in 2001. His research interest is on design communication and collaborative aspects of innovative design including tools for managing informal information and knowledge sharing. He teaches creativity and innovation methods and collaborative engineering aspects.
Franck Pourroy received the PhD degree from the Grenoble Institute of Technology. France, in 1992. He works as an associate professor at the University of Grenoble Alps, France. Carried out in the G-SCOP Laboratory, his research activities are related to engineering design, and more particularly to knowledge creation and knowledge transfer in collaborative design.
Gre´gory Poussier is technical director at BASSETTI. Over the past ten years, he developed several methodologies for transferring expert’s knowledge, setting up communities of practice and implementing knowledge management systems (KMS) in company.
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