A study of organisational knowledge management in Philips UK

A study of organisational knowledge management in Philips UK


The aim of this research is to comprehensively explore some key issues surrounding knowledge management and provide some additional dialogue to the discussion surrounding organisational knowledge creation.

Nonaka and Takeuchi’s (1994) accepted model of Dynamic Organisational Knowledge Creation only considers the creation of organisational knowledge by looking at case-studies of exclusively Japanese organisations. The Japanese cultural convention is to adopt a middle management out culture of decision making which differs from the top down decision making culture which is typical of a western organisation. By using a case-study of a western organisation this research considers how the theoretical model can be interpreted from a different perspective. It looks at how the notion of the Dynamic Organisational Knowledge Creation model is interpreted by Philips UK and applies the Philips interpretation of the model to Nonaka’s original theory looking for similarities and differences which will add to our understanding of knowledge creation and management in an organisational setting.

This research will also explore the hierarchical relationship between ‘data’, ‘information’ and ‘knowledge’ and how this relationship impacts on how Philips UK view knowledge creation by considering the Philips UK notion of these terms, how they are defined within the organisation and how they relate to the definitions available in the knowledge management literature. It also identifies the part that human interaction plays in the transition of ‘data’ to ‘information’ and ‘information’ to ‘knowledge’ within the context of Philips UK. Some conclusions can be drawn as to whether ‘data’, ‘information’ and ‘knowledge’ exist along a linear relationship within Philips UK with ‘data’ at one end, ‘information’ in the middle and ‘knowledge’ at the other end, whether they exist in a circular relationship or in fact whether there is a case for suggesting that there is no particular hierarchical relationship between ‘data’, ‘information’ and ‘knowledge’ within Philips UK.

It can sometimes be difficult to provide clear definitions for the terms ‘data’, ‘information’ and ‘knowledge’ due to the way in which these classifications are used interchangeably within modern knowledge management practice and across different disciplines. For instance it can be argued that the term ‘knowledge’ often seems to refer more to ‘information’. Therefore it would be useful to establish a clear definition for each of these classifications within Philips UK as although they may exist on a continuum to some extent, defining them as separate entities may bring distinct problems to our attention which may in turn suggest different types of remedy (Mutch 2008). This research will enable Philips UK to fundamentally understand how ‘data’, ‘information’ and ‘knowledge’ are classified within the organisation and use these distinct classifications to inform further development of internal systems and processes.

It is fairly easy to distinguish between ‘information’ and ‘knowledge’ and although software developers have used these terms interchangeably when marketing their products in the past, as presumably the idea that a system which manages knowledge has vastly superior connotations to a system which merely manages information even when the terms information and knowledge are being used ambiguously (Wilson 2002). This ambiguity has to some extent shaped our perceptions of what knowledge actually is, according to Mutch “reducing it to those things capable of storage in technology” (2008:43) which would underplay the case that focussing on ‘knowledge’ may be a response to some of the fundamental challenges that face contemporary organisations.

We must also consider the term ‘data’ within the context of this research as according to the conventional hierarchy in the knowledge management literature ‘data’ is related to and a prerequisite for ‘information’ and ‘information’ is a prerequisite for ‘knowledge’ and in fact according to Davenport and Prusak (1998) the differentiation between them is merely a matter of degree. Although this convention is not universally accepted and authors such as Tuomi argue that in fact data only emerges after we have information and information only emerges after we already have knowledge (Tuomi 1999b).

Considering how organisational ‘knowledge’ is created within Philips UK must start with identifying the differing typologies of ‘knowledge’. These include tacit and explicit knowledge. Tacit knowledge is defined as knowledge which is personal in nature and generally is stored in the heads of the individual; it is difficult to formalise into a document or other means that can be easily shared. Explicit ‘knowledge’ is knowledge that can be explained by individuals and is transferable either in verbal or written form. Experience is often identified as the path which separates Tacit and Explicit ‘knowledge’ (Rooney, Hearn and Ninan 2005). Identifying the typologies of ‘knowledge’ which are present within Philips UK will help to evaluate the process of organisational ‘knowledge’ creation within the organisation.

Context – Why Philips UK?

Philips UK was chosen as a suitable candidate for case-study as it bears similarities to the Matsushisha Company which was a case-study used by Nonaka and Takeuchi (1994) for their original research. Like the Matsushisha Company, Philips UK is involved in the consumer electronics industry and has a strong focus on product innovation. This alignment allows for valid comparisons to be drawn between the two case-studies without criticism that any differences which are identified in the organisational knowledge creation model are due to differences in the industry that the case-study organisation is involved in rather than the decision making culture of the organisation.

Philips UK provides products, systems and services to the healthcare, wellbeing, lifestyle and innovation markets. The UK head office is based in Guilford just south of London and employs many people in the UK operations over a wide spectrum of business activity.

Philips was established in Eindhoven in the Netherlands in 1891 by Anton and Gerard Philips, they began by manufacturing carbon-filament lamps and by 1910 they became the largest single employer in the Netherlands. Fuelled by the Industrial Revolution in Europe Philips opened their first research laboratory in 1914 and started to introduce their first innovations in Radio and X-Ray technology. Over the years there has been a long history of innovation and breakthrough within Philips which continues even today. (Anon. )

Literature Review

In reviewing the current knowledge management literature it is clear that there is an insufficient emphasis in the grass roots business of defining ‘data’, ‘information’ and ‘knowledge’ and understanding the relationship and interaction of these concepts. Sveiby maintains that:

“Some of the present confusion concerning how to do business in the knowledge era would probably be eliminated if we had a better understanding of the ways in which information and knowledge are both similar and different. The widespread but largely unconscious assumption that information is equal to knowledge and that the relationship between a computer and information is equivalent to the relationship between a human brain and human knowledge can lead to dangerous and costly mistakes” (Sveiby 1997).page number

In order to consolidate our understanding of ‘data’, ‘information’ and ‘knowledge’ a comprehensive review of the literature will lead us to a greater understanding of the definition of each of these concepts and a description of how they interact with each other for the purpose of this research. It seems to be that because the hierarchy of ‘data’, ‘information’ and ‘knowledge’ is so apparently obvious, authors tend to prescribe a definition to these concepts which although conforms to the accepted structure, ‘best fits’ the research which they are undertaking. An example of this is can be seen in a study by Braganza which considers applying the Knowledge-Information-Data (KID) model to the case study of the design and implementation of a knowledge management system in a utility company. Braganza notes that there are contrasting definitions of ‘data’, ‘information’ and ‘knowledge’ which makes it difficult to distinguish between them.

“The hierarchy is problematic because it is difficult to distinguish between data and information and between information and knowledge (Tuomi 1999b). Zack argues that data can be considered as facts or observations whereas information is data in a context; knowledge is information that is accumulated and organised in a meaningful way (Zack 1999). In contrast, El Sawy and his colleagues propose that data is defined as a carrier of information and knowledge, where information is thought of as facts, and knowledge as a new or changed understanding (El Sawy, et al. 2001). According to (Gunnlaugsdottir 2003), data are facts without context; whereas data transforms to information once it has the attributes of being organised, analysed and interpreted to acquire a meaning. Information becomes knowledge when it is used to resolve a problem or finish a task. Knowledge itself is deemed to be context sensitive and experiential in nature.(Braganza 2004)” page 352-353

Although these definitions are all similar in context they are however different in nuance which leads to a constant source of confusion and the temptation to simply adhere to a definition which supports the preferred outcome of the research in question. Some other examples of the definitions of ‘data’, ‘information’ and ‘knowledge’ available in the knowledge management literature are as follows:

“Data are understood to be symbols that have not yet been interpreted, information is data with meaning, and knowledge is what enables people to assign meaning and thereby generate information (Spek 1997)”.page number


“Data are simple observations of states of the world, information is data endowed with relevance and purpose, and knowledge is valuable information (Davenport 1997)”.page number


“Information consists of facts and data that are organized to describe a particular situation or condition, whereas knowledge consists of truths and beliefs, perspectives and concepts, judgments and expectations, methodologies and knowhow (Wiig 1993)”.page number

These definitions all conform to the conventional hierarchy that ‘data’ is a prerequisite of ‘information’ and ‘information’ is a prerequisite of ‘knowledge’ however although problems with this convention are recognised, they are not discussed within the literature (Meindl, Stubbart and Porac 1994). The conventional model has been heavily criticized during the last century by several prominent philosophers of knowledge, Polanyi (1958) for example (Tuomi 1999a).

An alternative approach introduces the reversed hierarchy theory which is put forward by Tuomi, this approach argues that

“Data emerge last—only after knowledge and information are available. There are no “isolated pieces of simple facts” unless someone has created them using his or her knowledge” (Tuomi 1999b).page number

As an example of this theory in practice Tuomi (1999b) suggests that defining a conceptual database model begins with knowledge i.e. selection of fields should be included in the database design (name, address, date of birth etc.). Information is then fed into these database fields, and the database can then be used to output statistical data.

Rather than thinking of ‘data’, ‘information’ and ‘knowledge’ as existing along a linear model with ‘data’ at one end ‘knowledge’ at the other end and ‘information’ in the middle, it is useful to consider the age old question ‘What came first, the chicken or the egg?’ and consider a dynamic and circular relationship between ‘data’, ‘information’, ‘knowledge’ with ‘human activity’ in the centre, this view is suggested by Knox (2007):

“Data, information and knowledge are not separate entities there is a dynamic and circular interaction between them which places the human element at the centre. Knowledge can generate new data and this is a recurring process.” (figure.1) (Knox 2007) page number

The imposition of human interaction into the data-information-knowledge transformation process brings with it issues of consistency and unpredictability, the human element brings differing individual attributes which are influenced by experience and understanding so are therefore ‘tacit’ by their very nature and are hard to measure or validate.

Figure 1: The Dynamic (and circular) Relationship between data, information, knowledge

and humans (Knox, 2007)page number

A critique of Knox’s (2007) model is as with many theoretical models is that it considers the data-information-knowledge transformation process to be clean and efficient, however in practice this process is undoubtedly more complicated due to external factors and human interaction.

Although we have highlighted some flaws with the conventional perception of ‘data’, ‘information’ and ‘knowledge’ it is not within the scope of this research to allow for new definitions to be created, we are simply looking to identify how the Philips UK notion of these terms can be applied to the available literature. This research considers the Philips UK interpretation of the traditional model of organisational knowledge creation and therefore it would be sensible to use the definitions provided by Nonaka and Takeuchi in the ‘Dynamic Theory of Organizational Knowledge Creation’ (1994) and ‘The knowledge-creating company: how Japanese companies create the dynamics of innovation’ (Nonaka and Takeuchi 1995) which are seminal works in the subject of organisational knowledge management. Nonaka and Takeuchi define knowledge as “justified true belief” (1994:15), and adopt Machlup’s (1983) definition of Information as “a flow of messages which might add to restructure or change knowledge” (Matchlup 1983:) page number.

Streatfield and Wilson (1999) argue that the concept of knowledge is over-simplified in the knowledge management literature, and they seriously question the attempt to manage what people have in their minds.

Organisational knowledge can be categorised into typologies. For example, Nonaka and Takeuchi (1994) identify tacit and explicit knowledge; Choo (2006) sees three different types of knowledge (tacit, explicit, and cultural); and Boisot (1998) describes four types (personal, proprietary, public knowledge and common sense). For the purpose of this research we will concentrate on the ‘tacit’ and ‘explicit’ knowledge typologies which were utilised by Nonaka (1994). Polanyi (1967) first drew a distinction between these typologies in his seminal work ‘The Tacit Dimension’ (Polanyi 1967) with ‘tacit’ knowledge being impossible to express because “we know more than we can tell”, this means that we cannot express what we know in words because we are not fully conscious of all the knowledge which we possess (Bouthillier and Shearer 2002).

‘Explicit’ knowledge however can be formalized, codified and communicated, it is often transferred through formal education and training programs (Kogut and Zander 2008) and therefore can be easily stored with information technology (Martensson 2000). ‘Tacit’ knowledge is perceived as an opposition to ‘explicit’ knowledge in the knowledge management literature when in fact it is merely its other side (Tsoukas 2003).

Nonaka’s (1994) ‘Dynamic theory of organisational knowledge creation’ (Nonaka 1994) presents the theory that organisational knowledge is created through four knowledge conversion processes: socialisation, externalisation, combination and internalisation with each process converting ‘tacit’ knowledge to ‘explicit’ knowledge or ‘explicit’ knowledge to ‘tacit’ knowledge (figure 2)

Nonaka (1994) SECI model of organisational knowledge creation (figure 2)

The first step is socialization this transfers tacit knowledge between individuals through observation, interaction and practice. The next step is externalization this is triggered by dialogue or collective reflection and relies on analogy or metaphor to translate tacit knowledge into documents and procedures. Combination then reconfigures bodies of explicit knowledge through sorting, adding, combining and categorizing processes and spreads it throughout an organization. Lastly, internalization translates explicit knowledge into individual tacit knowledge. Eventually, through a phenomenon that Nonaka (1994) calls the “knowledge spiral”, knowledge creation and sharing become part of the culture of an organization. If we take for instance the example of training an employee to use software used by Philips called ‘Helios’ which is used by account managers to produce sales figures. When being trained, the employee first shadows someone who is experienced in using the system (socialization), they make their own notes whilst they are shadowing (externalization), they attend training and are given a manual on how to use the software (combination) and finally they are able to use the system in their own ‘tacit’ way to enable them to do their job (internalization). The trainee will then be in a position to transfer this knowledge by being shadowed themselves by a new trainee and the cycle begins again. It is important to note that the quality of the knowledge creation relies heavily on the personality and innate ability of both the mentor and the mentee. It can be argued that this is the case with all ‘tacit’ knowledge creation or transfer processes.

The quality of knowledge created or transferred within an organisation is dependent on the individual attributes of the employees who are involved in the particular knowledge creating or transferring process (Cho, Guo and Che-Jen Su 2007). Some of the individual factors

which determine the quality of knowledge transfer or creation include individual ability (Wasko and Faraj 2008), greed (Lu, Leung and Koch 2006) fear of punishment (Burgess 2005), expected rewards (Gee and Young-Gul Kim 2002), extrinsic and intrinsic benefits (Kankanhalli, Tan and Kwok-Kee Wei 2005) and sense of self-worth (Gee-Woo Bock, et al. 2005).

The role that the individual’s personality and ability contributes to the success of knowledge creation or transfer within an organisation is critical, it may be that the quality of organisational knowledge is destroyed or in the very least corrupted by the innate inability or personal agenda of employees. As these factors are ‘tacit’ it is very difficult for an organisation to validate the quality of the knowledge transfer which it is managing as ‘tacit’ knowledge cannot be codified.

“Without a doubt, one’s personal direct or indirect connections with others play a critical role in the transfer of knowledge. However, little research has been conducted on how individual characteristics relate to knowledge sharing despite the rising interest on this issue. Considering that it is the individuals who are ultimately responsible for sharing knowledge, it is essential to understand the roles of individual variables to get a better picture on how to promote knowledge sharing and better manage employees’ knowledge” (Cho, Guo and Che-Jen Su 2007) . page 3

Nonaka and Takeuchi (1995) use the key example of the development of the ‘Home Bakery’ by the Matsushita Company as an example of organisational knowledge creation.

“The company were trying to develop an automated bread making machine, however they could not replicate the quality and taste of traditionally made bread. A software developer spent time with the head baker at the Osaka International Hotel, where she learned the secret of kneading dough. She realised from her experiences that there was a simultaneous twisting and stretching movement to produce the perfect bread. This needed to be replicated in the machine which went on to become a huge success on its introduction in 1987. The software developer could not learn how to make the perfect bread through simply observing the baker or asking the baker to explain the process. This is because the act of kneading is ‘tacit’ and could not be codified by the baker. This knowledge could only be passed on through socialisation, in the same way as knowledge is passed on to an apprentice. When acquired by someone with an external purpose, it can be externalised and used by others. This ‘explicit’ knowledge can then be combined with other forms of ‘explicit’ knowledge, in this case the concept of the twisting motion was combined with engineering knowledge to create new knowledge and the cycle is completed through internalisation to produce the ‘taken for granted’ methods of operating” (Mutch 2008).

There are however some potential flaws with this theory, firstly it has been noted that although Nonaka considers four processes of knowledge conversion, in fact only the processes of externalisation and internalisation actually transform knowledge from one type to another, socialisation and combination only exchange knowledge from one person to another and do not transform it (Bratianu 2010). Harsh (2009) considers that much of the initial knowledge runs through the cycle many times; this means that there is a kind of ‘reusable’ knowledge that Nonaka does not recognise. Nonaka only looked at case-studies from Japanese organisations and he states that the spiral of organisational knowledge originates in middle management and evolves upwards and downwards. This may be true within Japanese culture; however in western organisations where decision making is often a top-down process this may not be the case (Bratianu 2010). This research looks at the cultural issue which has been exposed in Nonaka’s work by applying the Philips UK interpretation of the knowledge creation spiral to Nonaka’s original model and considering how it differs. It also considers whether in fact there is any correlation between the theoretical knowledge creation model and its interpretation within a ‘real life’ organisation or whether the theoretical model is just a convenience for management theorists.

As this research considers the effects that a top-down organisational decision making culture has on an interpretation of organisational knowledge creation, it is prudent to discuss how top-down management differs from middle-up-down management. Nonaka (1988) describes top-down management as the implementation and refinement of senior management decisions as they work their way down the organisational hierarchy. Nonaka also describes bottom-up management, which highlights the effect of information coming up from lower levels of management on organisational decision making. Middle-up-down management is the synthesis of these concepts.

“It is a process that resolves the contradiction between the visionary but abstract concepts of the top management and the experience-grounded concepts originating on the shopfloor by assigning a more central role to middle managers “ (Nonaka 1988) page 9

In essence this style of management empowers the middle management to assimilate the company vision coming down from senior management with the experience driven concepts coming from lower management and translate this into effective operational decision making which has the benefit of being agile and responsive to changing market conditions such as increasing competitiveness and technological advancement. Nonaka’s example of middle-up-down management is the development of Honda City in Japan in 1978.

Research Questions

1. How does Philips UK interpret the notion of data, information and knowledge?

What is the widely held definition of ‘data’, ‘Information’ and ‘knowledge’ within Philips UKHow does this compare with the definitions available in the knowledge management literatureWhen applied to a ‘real life’ organisation is ‘data’ indeed a prerequisite of ‘information’ and is ‘information’ a prerequisite of ‘knowledge’ or is this convention merely a convenience for management theorists?

2. Why is knowledge transfer an issue for Philips UK?

How does Philips UK manage the transfer of its ‘tacit’ and ‘explicit’ knowledgeWhat strengths and limitations are identified in the current knowledge transfer systems How does Philips UK deal with issues such as validation of knowledge and loss of knowledge when an individual becomes incapacitated or leaves the organisationWithin Philips UK is knowledge transfer a voluntary activity, does this cause issues for Philips UK?

3. Has the theoretical model of knowledge creation been adapted to suit Philips UK?

Considering Nonaka’s model of organisational knowledge creation, can aspects of this theory be identified in knowledge management systems within Philips UKHas the model been adapted by Philips to suit their particular needsIs the Philips UK interpretation of the model unique or are there issues identified in the model itself when it is applied in practice

Research Method

Research Strategy

This research is an exploratory study (Saunders 2009) and is designed to be a loose reflection of an existing study; therefore it is important that cues are taken from that existing study when designing the research method. This ensures consistency in the primary research method used in both studies and provides the greatest degree of validity to any comparative analysis which is undertaken.

The study which is to some extent being mirrored during this research is Nonaka’s (1994) Study of ‘Dynamic Organisational Knowledge Creation’. Nonaka adopted a strategy of building comprehensive case-studies of his chosen organisations by conducting semi-structured interviews which targeted key personnel within the organisation. A qualitative approach to primary data collection using semi-structured interviews has been adopted for the purpose of this research as it is considering the Philips UK interpretation of a theoretical model and therefore it is the most effective method of capturing opinion and nuance in the participant responses. Mirroring the data collection methods that were used by Nonaka in his original study also adds validity to any comparisons which are made between the studies’ due to a consistency in the data collection process.

Quantitative primary data collection methods such as surveys and questionnaires tend to produce the most valid findings when they are distributed to a large number of participants i.e. the entire organisation, as they highlight trends in participant responses. They are not an appropriate data collection method for the purpose of this research as the specific knowledge which is sought is only held by a few key personnel, there is not a large enough sample size to produce meaningful results and also it is contextual opinion and anecdotal evidence which is valuable for this study which is not provided by quantitative research methods.

There is some argument for distributing a questionnaire or conducting a survey which targets only the employees who have been identified either by virtue of their job role or their past project experience as having the desired knowledge base which is required by this research. Although when researching an extremely specific and complex area such as organisational knowledge creation there is the possibility that a question could be misinterpreted or misunderstood completely, therefore the responses may not be reflective of the participant’s actual viewpoint and therefore valuable information could be missed.

A semi-structured interview comprises of a list of themes which the interviewer covers with each interviewee although the particular questions asked may vary from interview to interview (Saunders 2009). This method of interviewing provides the ability to tailor each interview to the specific knowledge of each participant whilst still keeping a structure which is useful for comparative analysis. By contrast structured interviews ask a predefined set of questions to each participant; this interview style was considered incompatible with this study as key employees have been selected with different knowledge bases and at different management levels, therefore asking an identical set of questions to each of these participants would be inappropriate. Unstructured interviews take the form of a relaxed discussion covering a general topic area, there are no predefined questions and the interview has no structure apart from the general topic, they rely on the respondent talking about their feelings about a particular subject (Saunders 2009). Again this interview style was seen as inappropriate for this study due to the fact that for consistency, specific areas need to be covered by the interview, also unstructured interviews are difficult to analyse comparatively.

When considering the design of this research method and the comparative appropriateness of the primary data collection methods which are available, selecting to carry out exclusively semi-structured interviews results in purely qualitative data being available for analysis. Qualitative data can be contrasted with quantitative data in a number of ways some of the main contrasts are that quantitative research tends to be to do with measurement, it’s highly structured and focuses on the point of view of the researcher, the researcher tends to be distant and uninvolved with their subjects, quantitative data tends to be generalizable to the relevant population and the research is conducted in a contrived context. By contrast qualitative research is to do with words in the presentation of analysis, it looks at the perspective of those being studied and is relatively unstructured, the qualitative researcher seeks close involvement with the people being investigated, qualitative research provides a contextual understanding and the research is conducted in natural environments (Bryman 2007). To summarize, quantitative research provides hard, reliable data but it misses a deep contextual element and qualitative research provides rich, deep meaningful data but is harder to measure.

A total of four interviews were conducted with each interview lasting for approximately an hour; each consisted of a series of open and closed questions. The first section of questions were standard across all interviews; this set the context for the interview, they ascertained what that particular employee’s job title is, what their key responsibilities are, what level of management they feel applies to them and why they feel that they have been chosen to participate in this research. Although for ethical reasons some of this data has been made anonymous in the final study, it is still important to consider during analysis. There was then a set of semi-structured questions which are specifically tailored for each interviewee, these follow the main themes of the research and provide the information required to build a robust case-study.

The interviews which are required and have been agreed by the organisation are as follows:

Senior Management: An employee who has experience of high level knowledge management, this interview provides an insight as to how the formal knowledge management strategy operates within the organisation. This employee is concerned with knowledge creation and knowledge transfer within Philips UK.
Middle Management: An employee who is not directly involved with knowledge management, this interview clarifies the role of the middle manager in Philips UK and provides information relating to the Philips decision making culture and the role that middle management plays in this process.
Lower Management: This employee is responsible for maintaining Microsoft SharePoint within the organisation which is a key knowledge management system used by Philips. This interview will collect information regarding the knowledge management systems within Philips UK from an operational level; it will also uncover the strengths and weakness within the current knowledge management strategy when applied in practice.
Non-Management: This interview looks at an employee’s view of how the concepts which are derived from shop floor experience are translated up the organisational hierarchy.

The qualitative data which was collected from these interviews is transcribed using a date-sampling method; this technique means that only the areas of the interview which are relevant to the research are transcribed (Saunders 2009) and allows the researcher to concentrate on the most relevant parts of the interview. Date-sampling has potential problems such as the researcher having to listen to the recording several times to determine which sections should be selected for transcribing and also the risk of disregarding sections of the interview which may be prove important in the future and having to go back to find them at a later date.

To analyse the data collected, firstly a process of categorisation is undertaken which splits the transcribed data into several main areas, this provides a loose structure and a basis for further analysis. There will follow a process of unitising the data which achieved by splitting it up into chunks or related words (units). These could be anything from a sentence to an entire paragraph, sorting these units into each category reduces and rearranges the data into a more manageable form. In completing this categorisation process relationships will be identified between the categories and the data within them. It is important to test the validity of these relationships by developing a hypothesis for each relationship and then looking at alternative answers within the data or the literature in order to prove the initial hypothesis. This is a valuable step in the analysis process as a relationship between the data is only valid if it can be proven. The relationships which are identified in the data are then used to formulate sound conclusions (Saunders 2009). The qualitative primary data collection and data analysis process which has been undertaken in this study is graphically represented as a flow chart (figure 3) to allow for ease of understanding by the reader.

In conducting the analysis of qualitative research Burgess and Bryman (1994) recognise that in unitising and categorising the findings what is actually occurring is a ‘quasi-quantification’ of the data, it is in effect noting the frequency that certain words, phrases or themes occur in those findings. This may afford the researcher the ability apply a more quantitative approach to the data analysis and produce a measurable output, this method of analysis is called ‘thematic content analysis’(Bryman 2007). A qualitative research method was deemed the most appropriate in conducting this study as it is nuance, opinion and anecdotal evidence within a contextual setting which is necessary to enable valid conclusions to be drawn. By quantifying the findings, the context, depth and richness of data is to some extent lost and this could be detrimental to the overall study.

A set of pilot interview questions were tested on a group of the researchers’ peers before the actual interviews took place. There was some discussion regarding initial absence of context setting questions which was identified during this piloting process. This feedback resulted in the inclusion of a short set of questions which clearly establishes the initial context of the particular participant interview within the research structure.

The secondary research method takes the form of a search of academic journals and academic textbooks. A comprehensive review of the current literature available on the subject of knowledge management with particular focus on organisational knowledge creation theories has been conducted.

Figure 3: Process flowchart for qualitative primary data collection (semi-structured interview) and data analysis.

Access and Limitations

Access to Philips UK was granted as a result of a personal contact within the organisation. Contact was made with the HR department via Peter Maskell who is Philips UK Chairman and Managing Director. This research has been carried out in strict accordance with the NBS (Nottingham Business School) Ethical Guidelines (Appendix 2)

In conducting this study, specific ethical considerations have been made in order prevent participants coming to any harm as a result of the research. This harm may take the form of an effect on career prospects, stress and anxiety or reprimand by the organisation as a result of comments made in an interview. In order to ensure protection, all participant contributions have been made anonymous. Where direct quotes are used the participant will be identified only by their managerial level (Senior Manager, Middle Manager, Junior Manager and Non-Manager). A profile of each interviewee is available to the reader to add context (Appendix 4). This gives general background information such as how long the employee has worked for the company but omits identifiable details such as name or job title (Bryman 2007).

There are several limitations of the chosen research method which have been identified during this process. Although every effort made has been made to overcome the impact that these limitations have had on the quality of the research this has not always been possible.

The first limitation which has been identified in the research is that the interview participants will be selected by the Philips HR department based on a set of requirements provided by the researcher and the interviews will be conducted at the Philips UK Head Office in Guilford. This selection method does ensure that the interviewees meet all of the required criteria for the research; however a limitation of this method of interviewee selection is the potential for interview bias, although due to the nature of the research subject this is likely to be limited. There is the potential that only employees’ who are likely to give positive feedback about the organisation will be selected to participate, therefore it is important to discover why the participants feel that they have been selected to participate in the study. There is also the possibility of employees being briefed in advance by the organisation on how they are required to answer the interview questions which may result in invalid or manipulated data being collected.

This limitation is necessary however and cannot be overcome as the research is highly specialised and only certain employees’ are able to provide the required information based on job role and past project experience and also their availability to be interviewed within the research timeframe.

Another limitation is the restricted number of interviews which are being conducted; this limitation is due to Philips stipulation of a maximum time allowance so that this research does not interfere with the day to day operation of the organisation. As the aim of the interview process is to seek specific knowledge and information from subject matter experts within the organisation and not to identify employee trends, this limitation did not affect the research process to a great extent, however a greater number of interviews would help to validate the data which has been collected. It is important to consider that as the research sample is small it would be unwise to generalise the results, as this is a study of a specific process in a specific organisation and is therefore unlikely to be generally applicable. There is however the potential for transferability of the research due to the detailed description of the research situation and processes provided. Lincoln and Guba (1985) refer to this detail as a ‘thick description’ of the research context. Transferability occurs when the reader of a study notices enough similarities to their own situation to infer that the findings of that study will be the same or similar in another context (Lincoln 1985). A word of warning to readers is that no two situations are identical and therefore appropriate amendments need to be made to the research process if this study is to be used in a different context.

A limitation which has been identified is that some participants in the study are not based in Guildford and were unable to attend interviews at Philips UK Head Office, this limitation was overcome by scheduling the interviews to be held in a different location which is near to where the participant will be located on that particular day, for example a local hotel.

The final limitation which was identified during the research process is difficulties in coordinating interviews so that they fit within the research timeframe due to employees being out of the UK on business trips to the Philips Global Head Office in Eindhoven in the Netherlands or on annual leave. Although video conferencing facilities were made available by Philips to overcome this limitation they did not need to be utilised as all interviews were eventually able to be conducted on a face-to-face basis within the research timeframe.

The particular resources required for conducting and transcribing interviews include a Dictaphone for all interviews. For any long distance interview a video-conference suite is required.

Findings and Discussion

Research Question 1 – How does Philips UK interpret the notion of ‘data’, ‘information’ and ‘knowledge’?

The individual lay definitions of ‘data’, ‘information’ and ‘knowledge’ within Philips UK are not standard across the organisation, although each participant did feel that their own definition of each of these concepts was the most widely held view and could be considered as Philips official interpretation of the terms ‘data’, ‘ information’ and ‘knowledge’. At senior management level they are considered to be three distinct concepts whereas at lower management levels the distinction between ‘data’ and ‘information’ is blurred somewhat, to such an extent that the middle manager defines them as exactly the same thing.

“Data and information are exactly the same, if I look at sales figures for instance, I could call them data or I could call them information, its exactly the same thing” (Middle Manager).

This is interesting considering the emphasis in the literature regarding the highlighting of the ambiguity between ‘information’ and ‘knowledge’ (Mutch 2008). This contrast could be explained by considering the context of previous studies and the management level which was involved in those studies. One would assume that a manager who had the authority to consider implementing a Knowledge Management System (Wilson 2002) would be of a relatively senior level within an organisation and therefore have to make considerations at the strategic level (Nonaka 1988). It is arguably the difference between the strategic level (senior management level) and the operational level (lower management levels) which accounts for the different areas of ambiguity. The middle, lower and non-management levels tend to be more operationally focussed and therefore tend to see ‘data’, ‘information’, and ‘knowledge’ within the organisation from their own operational perspective. This would account for the ambiguity between ‘data’ and ‘information’ as it is generally accepted that “information is data which has meaning” (Spek 1997) page number. As lower management levels are likely to only ever be exposed to ‘data’ which has meaning for themselves and their peers, this ‘data’ is automatically ‘information’ for that manager and therefore no distinction is made between them, leading to a sense of ambiguity.

When asked how Philips interprets the relationship between ‘data’, ‘information’ and ‘knowledge’ again the differences in the interpretation of this relationship are significantly different depending on the management level of the participant. The senior management interpretation was along the lines of the reversed hierarchy theory (Tuomi 1999b)

“I’d see the data as being very much the output of some of that knowledge and information and as such the data is often easier to get your hands on then what lies behind it.” (Senior Manager).

Whereas the lower management levels tend to interpret the relationship in the conventional way,

“The data that is sent to me becomes information in my head and then the way that I use the information is then knowledge for me” (Non-Manager).

This distinct difference in interpretation seems to again fall along the lines of strategic level thinking (Senior Manager) and operational level thinking (Lower management levels) where the senior manager is in a position to appreciate the ‘knowledge’ and ‘information’ that was involved in designing and implementing a new process or system and they see the output of this as being the ‘data’ which is produced by that system. The lower management levels however again only see the relationship between ‘data’, ’information’ and ‘knowledge’ as a snapshot which focuses on their own operational role. Their first contact is with the ‘data’ which is produced by a system, they appreciate how the data is transformed into ‘information’ and then into ‘knowledge’ but as they are focussed on how this is used in their own role they do not appreciate the wider implications of how that ‘data’ was created or what happens to the ‘knowledge’. In considering whether the relationship between ‘data’, ‘information’ and ‘knowledge’ within Philips UK takes the conventional form, whether (Tuomi 1999b)’s reversed hierarchy theory is evident or indeed whether (Knox 2007)’s model of the dynamic and circular relationship between ‘data’, ‘information’, ‘knowledge’ and ‘humans’ can be identified. A surprising conclusion can be drawn, in fact it is true to say that there is evidence of all three within Philips UK, the senior management highlight the reversed hierarchy theory (Tuomi 1999b) and the lower management levels highlight the conventional view, but as a whole organisation the dynamic and circular relationship (Knox 2007) can be identified. It is just that the strategic level (senior) management don’t appreciate how the relationship is interpreted in an operational sense and the operational (middle, lower, non) management don’t appreciate how the relationship is interpreted in a strategic sense and it therefore takes someone from outside of the organisation who takes an overview of the process to recognise that it is a cyclical relationship that is actually occurring.

In looking at whether Philips UK considers that an aspect of the ‘data’, ‘information’, and ‘knowledge’ transformation process could be omitted, i.e. ‘data’ could become ‘knowledge’ without ever being ‘information’. There was a consensus across all interviews that participants felt that they could identify each stage of the transformation process in any example that they thought about. An example of psychometric testing was given, a manager has psychometric test results which are sent as a set of numbers (‘data’), the manager then interprets the results and converts them to ‘information’ for that manager, and sends them to the candidate, they are ‘knowledge’ for that candidate. If the ‘information’ stage is removed and the candidate is just sent the un-interpreted ‘data’ they would not be able to interpret it themselves and it would stay as ‘data’.

“You would have no chance of interpreting it whatsoever” (Senior Manager)

Therefore the ‘information’ is very important in this example as in all other examples which were considered.

Research Question 2 – Why is ‘tacit’ knowledge transfer an issue for Philips UK?

Philips UK attempts to manage ‘tacit’ knowledge as an asset, they have identified that ‘tacit’ knowledge creation and transfer is an important part of their on-going employee training, and a key part of overcoming the issue of an employee becoming incapacitated or leaving the organisation, which may lead to the loss of valuable ‘tacit’ knowledge. Some employees have become indispensable to the organisation due to the ‘tacit’ knowledge that they possess. Measures have had to taken within the organisation to maintain and grow the ‘tacit’ knowledge base.

“I’ve got a really good example of something we did a couple of years ago, we were confidentially doing a massive restructure in our lighting business and a result of that we retired six people early. Now we needed to retire them for headcount issues and all sorts of other good reasons but we were right in the middle of our bids for the Olympics and we desperately needed these guys for their knowledge, so what we have done is retain them, and some of them two and a half and three years later are still working for us but on a consultancy basis because that knowledge can’t just be written down and kept in a nice file somewhere for the next person to pick up, it’s their experience and the wealth of knowledge that they’ve got, we just had to tap it, so we tap it by physically employing them still in some way, shape or form” (Senior Manager).

Although the ‘tacit’ knowledge that those employees hold has been protected in the short term, eventually those employees will leave the organisation. Unless measures are taken to transfer the ‘tacit’ knowledge to other employees, that knowledge which is understandably important to the organisation will be lost.

In response to the issue of an employee holding ‘tacit’ knowledge which makes them indispensable to the organisation, a number of measures have been taken, such as introducing implicit training methods {{101 Benjamin Martz, Wm. 2003}} and IT systems which attempt to pass on or retain ‘tacit’ knowledge. Training is to some extent delivered through experiential activities.

“A lot of the core skills training which I would have people go through for instance coaching, you can’t read how to coach, you need to experience to do it and you need to watch a role model doing it” (Senior Manager).

This takes the form of shadowing and mentoring activities in conjunction with the Philips UK traditional ‘explicit’ forms of training.

“There are a lot of those types of things that go on and if I think even from a sales point of view. Any of our sales guys who go out there, they can read about the products but only by experiencing and watching from someone that knows and using that to transform that into the customer proposition” (Senior Manager).

‘Tacit’ knowledge is also created, transferred and to some extent catalogued by using IT solutions which in this case is a number of knowledge management systems. These systems, although they can document projects worked on and the tangible skillset of a particular employee, as Polanyi said “we know more than we can tell” page {{67 Polanyi,Michael 1967}} and therefore it is impossible for an individual to recognise all of the skills and knowledge that they possess.

‘People Finder’ is the Philips UK internal telephone directory, it can be utilised for searching for the skills and experience required for a particular project. People Finder replaces a system called ‘Yellow Pages’ which listed experts with different types of knowledge within Philips and their contact details which could be searched via the intranet (Sanchez 2005).

“Similar to what we did with yellow pages, is our internal telephone directory which is called People Finder. We have got fields in there that talk about key projects that you have worked on, knowledge and skills, experience and we are also using that as an internal recruitment tool” (Senior Manager).

Although the premise of this system is understandable to catalogue and search for ‘tacit’ skills and experience, it is arguable how widely this system is used by lower management levels for this purpose, as none of the lower management level participants used the system in this way.

“We just use People Finder to look for telephone numbers, and it also shows us our sales figures and targets, I didn’t know that you could use it to search for people with specific skills in the business”.

‘Social Cast’ is another tool which facilitates knowledge transfer and creation within Philips UK; it is a knowledge repository and collaborative working portal in which employees can upload bits of data, this data can then be processed, sorted into groups, selected or filtered. It affords an employee the ability to advertise the project that they are working on and request feedback and help from someone with experience in that field, it also facilitates networking which leads to knowledge sharing.

“Socialcast is one way of doing it. So for instance it’s particularly heavily used amongst the research community, so they say “I’m working on such and such a project based in England is anyone doing anything similar to me?” and then they will share, then it’s a way of networking getting together and sharing” (Senior Manager).

Although these systems can facilitate the transfer of ‘tacit’ knowledge, or at least act as a first point of contact for an employee who is looking for a particular skillset, it is arguable as to whether these systems are fully utilised or indeed if they could ever be fully utilised within an organisation. Even in a knowledge friendly culture such as is the case in Philips UK, an employee may think twice before they list their skills, knowledge and experience (or at least the skills and knowledge that they know that they possess), and then pass that knowledge on to others which may lead to them becoming dispensable to the organisation and impact on their job security {{102 Davenport,Thomas H. 1998}}.

Screenshots of the knowledge management systems utilised by Philips UK are available in the appendices (Appendix 5).

Research Question 3 – Has the theoretical model of knowledge creation been adapted to suit Philips UK?

Nonaka’s (1994) SECI model is evident within Philips UK; an example is given of the Philips UK lighting division. Philips have realised that as their market and the requirements of their customers have changed, from selling light bulbs and lamps etc. to creating high value bespoke lighting installations for high-end clients such as the Hotel Raphael in London. It is necessary to facilitate organisational knowledge creation in a structured way to enable an account manager to produce a market leading customer proposition. As we move into an experience economy {{103 Pine, II,B.J. 1998}} it is more and more about giving the customer an experience right from the beginning of the process, through to installation and maintenance, it’s about creating a whole package. It is only by creating new organisational knowledge and successfully transferring and growing that knowledge base that a consistent and improving customer proposition can be made.

“Well you used to be able to get people in, we have a showroom downstairs and a training room and you get people in when their new and show them the products, thank you very much, go off and sell the products, this is what they do, this is what their luminescence is” (Senior Manager).

However selling a product is different to selling an experience and much like the ‘Home Bakery’ case study used by Nonaka (1994) it is impossible for an experienced account manager to write down how they go about selling that experience. The first stage in a brand new account managers training process is socialisation.

“What they have to do is they will go out with people who have already specified on big jobs and go and experience that for themselves. So for instance we have got a big hotel, Hotel Rafael in London, very high end hotel and we have created the lighting experience for them. So you go along, you learn it you hear the stories about how it was created and the same with another lot of big projects” (Senior Manager).

The new account manager takes notes during the shadowing; this is the externalisation process where experience is written down or documented using analogy or metaphor. An example of this could be, ‘Greet the customer like he is your friend’. The combination process is where that ‘tacit’ experience is combined with ‘explicit’ knowledge such as training in the specific products, pricing etc. The combination process then creates a unique knowledge base for that account manager; eventually the internalisation process means that the account manager can specify and quote without having to refer to the handbooks or processes as this knowledge has been internalised by them and is unique to the individual, but also adds to the organisational knowledge base.

The SECI model {{14 Nonaka,Ikujiro 1994}} can be identified within Philips UK, however when considering how organisational knowledge creation is interpreted by account managers within the lighting division of Philips UK, there has to be some considerations made which may lead to adaptations to the theory in this situation. The first point is that Philips relies on the account manager who is being shadowed to make value judgements and only pass on knowledge which is valuable to the organisation {{104 O’Dell, Carla 1998}}. There is the argument that if we “know more than we can tell” {{67 Polanyi,Michael 1967}} it makes it difficult to tell if what we know is correct. This could lead to a corruption of organisational knowledge within the organisation, as the mentor cannot codify their ‘tacit’ knowledge, it cannot be checked or approved by the training department. This leads to an adaptation being made to the model as interpreted by Philips UK. Organisational knowledge creation for Philips UK has to be split into constructive (positive) knowledge creation and destructive (negative) knowledge creation.

It is also the case with the example of the Philips UK lighting division that in recruiting an account manager there has to be a process that occurs before socialisation, where the individual is selected based on their past experience, their personality, and their innate ability to sell {{90 Cho,Namjae 2007}}, as some individuals have the innate ability to be a sales person and some do not.

“Some people are born to be salesmen and some just can’t do it, it’s like an X-factor that some people have” (Middle Manager)

The issue is that personality, experience and innate ability are all ‘tacit’ qualities and therefore it is difficult to recruit for these qualities.

“This is where people become really valuable, well we struggle now recruiting to specify what kind of people we want is because we are not really interested in the knowledge that they have got, it’s what they can do with the knowledge that we can give them”(Senior Manager).

Considering the Philips interpretation of the SECI model another process occurs prior to socialisation which is recruitment, this recruitment could be external (new employees joining the company) or indeed internal (looking at existing employees). Philips UK invests in psychometric testing to aid in the selection of both internal and external talent in their endeavour to protect the quality of their organisational knowledge base.

The adaptations made to the SECI model are specific to the case study of Philips UK although speculation can be made that these adaptations can be generalised across other organisations. Although no assertions can be made as to the validity of these findings when transferred to any other organisational setting due to the small scale of this study.


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