Knowledge Management (KM) models can basically be split into two types, Epistemological models which focus on the knowledge and how it can be decomposed but ignore relationship or flows of knowledge and Ontological models which concentrate on the relationship and flows of knowledge but treat knowledge as a black-box. McAdam et al (1999) identify a third type of model, Intellectual Capital models which view knowledge as an asset.
Nonaka’s SEDI model (1991) is an example of an epistemological model as it uses tacit and explicit knowledge in four different ways to create knowledge within an organisation. McAdam et al (1999) criticism of this model concludes that the tacit and explicit categorisation is limited as it does not include for example P (programmable) and Q (questioning insight) knowledge identified by McLoughlin (1993) and that this categorisation is too mechanistic.
While it is understandable that McAdam et al view this categorisation as too mechanistic, P and Q knowledge is included in the model as P and Q knowledge are just subcategories of explicit and tacit knowledge respectively.
However, it seems that the SEDI model is too specialised; it seems to concentrate on transferring knowledge between individuals rather than across the whole organisation and also does not consider external influences from outside the organisation.
Demerest (1997) adaption of Clark’s (1989) model is an example of an ontological model. This model concentrates on the flow of knowledge from Construction, through Embodiment, and Dissemination to Use. It also shows other recursive flows which take place. The model does not distinguish between different types of knowledge and therefore is consistent with both explicit and tacit knowledge.
McAdam et al (1999) think that the model implies that the recursive flows of knowledge are less important than the main flows, which in turn implies that the knowledge flows are too mechanistic. They also note that the Use process only includes organisational output and not external output.
McAdam et al (1999) extend this model by adding more recursive knowledge flows and including both social and scientific constructed knowledge as separate inputs and business benefits and employee emancipation as outputs.
McAdam’s modified version of Demerest model is a more balanced model as it tries to combine both knowledge flow with the different knowledge categories. However, it still implies that the main flow of knowledge is sequential from Creation through to Use. The flows of knowledge are much more intertwined than that and the recursive flows identified should be more important.
In conclusion, there are many different KM models, a few of which were discussed above. Each model has its advantages and disadvantages and could be successfully applied in an organisation given the appropriate context. However, KM models should be treated as an aid to introducing KM into an organisation rather than a pre-defined method.
Practical examples
Example 1: Applying the SEDI model to a university research group.
The university research group example used in the previous article lends itself to an epistemological type model as the knowledge transfer is between tacit and explicit knowledge. An example of Nonaka’s SEDI model being applied is given below.
Socialisation
Discussions.
Debates.
Verbal Presentations.
Externalisation
Research Proposals.
Research Papers.
Presentation Slides.
PhD thesis.
Internalisation
Analysing research paper.
Reading journal articles.
Reading previous research proposals.
Combination
Research Data.
Results of Experiments
Survey Results
Assessment Criteria
Example 2: Applying McAdam’s modified version of Demerest to the degree classification process.
The degree classification process lends itself to an ontological type models as the flows of data are more important. An example is given below (for simplicity I have only included the main data flows):
DIAGRAM
Reflections and personal learning
In week 1 of this module we were asked to set up a learning journal as a blog. Each week we had to publish articles relating to that weeks research topic and also to comment on other people’s blog articles. In week 1, I was very sceptical of the use of a blog as an aid to learning and found it very difficult to both publish and comment on a blog. However, over the last four week I have gradually learnt that blogs can be a good way to share knowledge between groups of people with similar interests. By publishing your own articles you formalise your own thoughts and understanding of the topic area, and then by reading and commenting on others blogs your knowledge and understanding of the topic area increases.
This week we were asked to evaluate both our own use and our organisations use of blogs and comments. My group evaluated the group as a whole, whereas other groups took a more individual approach. The outcome of the discussions helped me formalise what constitutes a good blog as well as a good comment.
References:
Clarke, P. N. Staunton, (1989), Innovation in Technology and Organisation, Routledge, London.
Demerest, M. (1997), ``Understanding knowledge management'', Journal of Long Range Planning, Vol. 30, No. 3, pp. 374-84.
McAdam, R. S. McCreedy, (1999) “A critical review of knowledge management models”, The Learning Organisation, Vol. 6, No. 3 pp. 91 – 100
McLoughlin, H. R. Thorpe, (1993), ``Action learning : a paradigm in emergence: the problems facing a challenge to traditional management education and development'', British Journal of Management, Vol. 4, pp. 19-27.
Nonaka, I. K. Takeuchi (1991), “The Knowledge-Creating Company”, Harvard Business Review, Vol. 69, No. 6 pp 96-104
Thursday, February 12, 2009
Wednesday, February 4, 2009
What is Knowledge Management
By going through the literature, it became clear that there is no consensus on a single definition of knowledge management (KM), different authors seem to have very different perspectives on what constitutes KM.
Wilson (2002) defined knowledge as the mental processes that are carried out in the mind which become information once they are expressed. Therefore, in his opinion, knowledge cannot exist outside the brain, and therefore cannot be managed. He reviewed all journal papers from The Web of Science between 1981 and 2002, which contained KM in the title, and several consultancy and business school views of KM and concluded that they were all describing different types of information systems which had been renamed to include knowledge.
This is a very controversial definition by Wilson, knowledge is not just processes carried out subconsciously by the mind, it must also include knowledge that we know we have and can choose to disseminate to others.
Call (2005) state that IBM, Lotus and Microsoft use more technical definitions, for example he cites Gates (1999) as viewing KM as “...nothing more than managing information flow: getting the right information to the people who need it so that they can act on it quickly”.
This is a very narrow definition, as increasingly KM is considered more than just information and although it can be communicated by technology, there are other social method which can also be used to communicate it. While there are some that believe that KM should be technology based, others believe that KM does not have to be based on technology.
Wiig (1997) acknowledged that managers have very differing view about KM and stated that “the overall purpose of KM is to maximise the enterprise’s knowledge related effectiveness”. He put forward 4 areas of KM emphasis.
Rowley (1999) cited Davenport et al. (1998) as having identified 4 types of KM project objectives. These objectives broadly covered the same areas as Wiig’s emphasis. Rowley’s own definition of KM is very broad and aims to cover all the objectives identified by Davenport. Rowley noted that some authors have taken a more practical view and cited Galagan (1997) as proposing a list of KM processes. This list seems to encompass all the emphasis and objectives identified by Wiig and Davenport but gives them a more practical bias.
The above provide a very comprehensive overview for KM and seem to cover most aspects of it. However, it would be important to include two more processes, (identification and extension); identification is required as organisations need to know where their knowledge is and extension of previous knowledge is needed as most if not all knowledge is derived from knowledge that we already have.
There is a wealth of literature on KM frameworks which are used to classify and understand KM phenomena. Holsapple (1999) compared 10 KM frameworks and classified them into two categories descriptive and presecriptive frameworks. They concluded that they all had differing perspectives and methodologies and that none of them subsumed any other.
Life Cycles, for example Binney (2001), Lee and Hong (2006), are another technique used by some authors to model the development of KM. Binney (2001) uses a four stages lifecycle while Lee and Hong (2006) use a six stages lifecycle. These broadly speaking use similar stages as those identified by Wigg and Davenport but tend to consider KM as a linear sequence of event.
In conclusion, there is not a common agreed definition for KM; it can mean different things within different organisations and depends on the organisation’s perceptions, needs and expertise.
Practical Examples
Example 1: An example of KM that uses codified knowledge as well as explicit and tacit knowledge is given below.
The university has a computer system (MISIS) which stores information about students. At the end of the year, staff enter the grades for their own modules and admin staff enter information about students deferrals and extenuating circumstances. MISIS then produces reports which include the grades for each module along with summary statistics (Explicit knowledge). At the first level exam board, the grades for each module are considered and judgements are made about the grades for that module. Now, the explicit knowledge from the reports is combined with tacit knowledge from the staff present to confirm the grades and discuss problems with a particular module. MISIS is then updated and produces a second set of reports for finalists which include the grade profile for each students, any extenuating circumstances and suggestions for degree classification. At the Finalists board, staff use the MISIS reports as well as minutes from previous finalists boards to decide what degree classification a students should be awarded, based on their grade profile, extenuating circumstances and the personal knowledge that staff have about that student. Again, explicit knowledge is combined with tacit knowledge to confirm each student’s degree classification. During the board, any decision to up-grade a student’s degree classification and the grounds on which this decision was taken are recorded in the boards minutes for use in future finalist’s boards.
Example 2: An example of KM that does not use technology is given below.
Most staff at the university belong to a research group related to their research interest. These groups meet regularly to allow members to discuss their research topics. These meetings allow staff to discuss their current research, issues and problems they are having as well as find out what research other members are involved in. At some of these meetings, staff review each others research proposals in which they use a combination of their knowledge about the proposal topic, knowledge about funding criteria and knowledge of their own experiences of previously successful proposals.
Reflections and personal learning
This week my group, The Eagles, met after the morning lab session, to discuss our understanding of KM. Using a flip chart as an aid, we each gave our definition of KM and outlined what it meant to us. It very quickly became clear that we each had different views on KM, which seemed to relate to our different backgrounds. After further discussion, we realised that although our perceptions of KM overlapped in some places, we could not agree on a single definition, everybody’s definitions seem plausible when related to different organisations. I found this form of discussion very revealing and useful, as it allowed me to increase my understanding of KM.
In the workshop session in the afternoon, the module tutor asked the 6 other groups to debate KM in pairs. Again, it was quickly evident that it was very difficult to find a single definition.
I found the debates very interesting and a useful tool for learning as it forced the individual groups to defend their understanding of KM. However, on occasions the debates turned into arguments between a few members of each group and had the potential to digress away from the subject. This was a negative side to the debates as it stopped group members having different opinions. I found this a very useful tool to aid learning among groups of individuals but I realised that it would need to be moderated carefully to allow all group members to gain something positive from the experience.
References:
Binney D, (2001) “The knowledge management spectrum – understanding the KM landscape”, Journal of Knowledge Management, Vol. 5, No. 1, pp. 33-42
Call D. (2005), “Knowledge Management – not rocket science”, Journal of Knowledge Management, Vol. 9, No. 2, pp. 19 - 30
Davenport, T.H and L. Prusak, (1998) , “Working Knowledge: Managing What Your Organisation Knows”, Harvard Business School Press, Boston, MA
Galagan, P. (1997), “Smart companies (knowledge management)”, Training and Development, Vol. 51 , No. 12, pp. 20-24
Gates, B. (1999), Business @ the Speed of Thought: Using a Digital Nervous System, Warner Books, New York, NY, pp. 238 - 239
Hicks, R. C. (2006), “The five tier knowledge management hierarchy”, Journal of Knowledge Management, Vol. 10, No. 1, pp. 19 – 31
Holsapple, C.W.and K.D. Joshi, (1999), “Description and Analysis of Existing Knowledge Management Frameworks” Proceedings of the 32nd Hawaii International Conference on System Science
Lee, S. M., S. Hong (2002) “An enterprise-wide knowledge management system infrastructure”, Industrial Management and Data Systems, Vol. 102, No.1, pp. 17-25
Rowley, J. (1999) “What is knowledge management”, Library Management, vol. 20, No. 8, pp. 416-420
Wiig, K. M. (1997) “Knowledge Management: An Introduction and Perspective”, Journal of Knowledge Management, Vol. 1, No. 1, pp. 6 - 14
Wilson, T.D. (2002) “The nonsense of ‘knowledge management”, Information Research, Vol. 8, No. 1. Pp. 1-54
Wilson (2002) defined knowledge as the mental processes that are carried out in the mind which become information once they are expressed. Therefore, in his opinion, knowledge cannot exist outside the brain, and therefore cannot be managed. He reviewed all journal papers from The Web of Science between 1981 and 2002, which contained KM in the title, and several consultancy and business school views of KM and concluded that they were all describing different types of information systems which had been renamed to include knowledge.
This is a very controversial definition by Wilson, knowledge is not just processes carried out subconsciously by the mind, it must also include knowledge that we know we have and can choose to disseminate to others.
Call (2005) state that IBM, Lotus and Microsoft use more technical definitions, for example he cites Gates (1999) as viewing KM as “...nothing more than managing information flow: getting the right information to the people who need it so that they can act on it quickly”.
This is a very narrow definition, as increasingly KM is considered more than just information and although it can be communicated by technology, there are other social method which can also be used to communicate it. While there are some that believe that KM should be technology based, others believe that KM does not have to be based on technology.
Wiig (1997) acknowledged that managers have very differing view about KM and stated that “the overall purpose of KM is to maximise the enterprise’s knowledge related effectiveness”. He put forward 4 areas of KM emphasis.
Rowley (1999) cited Davenport et al. (1998) as having identified 4 types of KM project objectives. These objectives broadly covered the same areas as Wiig’s emphasis. Rowley’s own definition of KM is very broad and aims to cover all the objectives identified by Davenport. Rowley noted that some authors have taken a more practical view and cited Galagan (1997) as proposing a list of KM processes. This list seems to encompass all the emphasis and objectives identified by Wiig and Davenport but gives them a more practical bias.
The above provide a very comprehensive overview for KM and seem to cover most aspects of it. However, it would be important to include two more processes, (identification and extension); identification is required as organisations need to know where their knowledge is and extension of previous knowledge is needed as most if not all knowledge is derived from knowledge that we already have.
There is a wealth of literature on KM frameworks which are used to classify and understand KM phenomena. Holsapple (1999) compared 10 KM frameworks and classified them into two categories descriptive and presecriptive frameworks. They concluded that they all had differing perspectives and methodologies and that none of them subsumed any other.
Life Cycles, for example Binney (2001), Lee and Hong (2006), are another technique used by some authors to model the development of KM. Binney (2001) uses a four stages lifecycle while Lee and Hong (2006) use a six stages lifecycle. These broadly speaking use similar stages as those identified by Wigg and Davenport but tend to consider KM as a linear sequence of event.
In conclusion, there is not a common agreed definition for KM; it can mean different things within different organisations and depends on the organisation’s perceptions, needs and expertise.
Practical Examples
Example 1: An example of KM that uses codified knowledge as well as explicit and tacit knowledge is given below.
The university has a computer system (MISIS) which stores information about students. At the end of the year, staff enter the grades for their own modules and admin staff enter information about students deferrals and extenuating circumstances. MISIS then produces reports which include the grades for each module along with summary statistics (Explicit knowledge). At the first level exam board, the grades for each module are considered and judgements are made about the grades for that module. Now, the explicit knowledge from the reports is combined with tacit knowledge from the staff present to confirm the grades and discuss problems with a particular module. MISIS is then updated and produces a second set of reports for finalists which include the grade profile for each students, any extenuating circumstances and suggestions for degree classification. At the Finalists board, staff use the MISIS reports as well as minutes from previous finalists boards to decide what degree classification a students should be awarded, based on their grade profile, extenuating circumstances and the personal knowledge that staff have about that student. Again, explicit knowledge is combined with tacit knowledge to confirm each student’s degree classification. During the board, any decision to up-grade a student’s degree classification and the grounds on which this decision was taken are recorded in the boards minutes for use in future finalist’s boards.
Example 2: An example of KM that does not use technology is given below.
Most staff at the university belong to a research group related to their research interest. These groups meet regularly to allow members to discuss their research topics. These meetings allow staff to discuss their current research, issues and problems they are having as well as find out what research other members are involved in. At some of these meetings, staff review each others research proposals in which they use a combination of their knowledge about the proposal topic, knowledge about funding criteria and knowledge of their own experiences of previously successful proposals.
Reflections and personal learning
This week my group, The Eagles, met after the morning lab session, to discuss our understanding of KM. Using a flip chart as an aid, we each gave our definition of KM and outlined what it meant to us. It very quickly became clear that we each had different views on KM, which seemed to relate to our different backgrounds. After further discussion, we realised that although our perceptions of KM overlapped in some places, we could not agree on a single definition, everybody’s definitions seem plausible when related to different organisations. I found this form of discussion very revealing and useful, as it allowed me to increase my understanding of KM.
In the workshop session in the afternoon, the module tutor asked the 6 other groups to debate KM in pairs. Again, it was quickly evident that it was very difficult to find a single definition.
I found the debates very interesting and a useful tool for learning as it forced the individual groups to defend their understanding of KM. However, on occasions the debates turned into arguments between a few members of each group and had the potential to digress away from the subject. This was a negative side to the debates as it stopped group members having different opinions. I found this a very useful tool to aid learning among groups of individuals but I realised that it would need to be moderated carefully to allow all group members to gain something positive from the experience.
References:
Binney D, (2001) “The knowledge management spectrum – understanding the KM landscape”, Journal of Knowledge Management, Vol. 5, No. 1, pp. 33-42
Call D. (2005), “Knowledge Management – not rocket science”, Journal of Knowledge Management, Vol. 9, No. 2, pp. 19 - 30
Davenport, T.H and L. Prusak, (1998) , “Working Knowledge: Managing What Your Organisation Knows”, Harvard Business School Press, Boston, MA
Galagan, P. (1997), “Smart companies (knowledge management)”, Training and Development, Vol. 51 , No. 12, pp. 20-24
Gates, B. (1999), Business @ the Speed of Thought: Using a Digital Nervous System, Warner Books, New York, NY, pp. 238 - 239
Hicks, R. C. (2006), “The five tier knowledge management hierarchy”, Journal of Knowledge Management, Vol. 10, No. 1, pp. 19 – 31
Holsapple, C.W.and K.D. Joshi, (1999), “Description and Analysis of Existing Knowledge Management Frameworks” Proceedings of the 32nd Hawaii International Conference on System Science
Lee, S. M., S. Hong (2002) “An enterprise-wide knowledge management system infrastructure”, Industrial Management and Data Systems, Vol. 102, No.1, pp. 17-25
Rowley, J. (1999) “What is knowledge management”, Library Management, vol. 20, No. 8, pp. 416-420
Wiig, K. M. (1997) “Knowledge Management: An Introduction and Perspective”, Journal of Knowledge Management, Vol. 1, No. 1, pp. 6 - 14
Wilson, T.D. (2002) “The nonsense of ‘knowledge management”, Information Research, Vol. 8, No. 1. Pp. 1-54
Thursday, January 29, 2009
Data, Information, Knowledge
The standard definition of data, information and knowledge (e.g. Bellinger(1997)) is of a liner model where data (raw facts) is processed to become information (processed facts) which in turn is processed to become knowledge (information used in a context to achieve something).
I think that this definition is flawed in two ways:
1. How do you know what data is relevant to a given situation? Don’t you need knowledge to choose relevant data initially
2. How can data be processed into information and information into knowledge without the use of knowledge.
The definition of data being raw or unprocessed fact, figures, symbols get seems to be fairly well accepted as is the definition that information is processed data. However, there seem to be little consensus as to what knowledge is, although several definitions (refs) define knowledge as information used in a context to achieve something.
I think that the KID model is more interrelated, that data, information and knowledge interact with each other. Knowledge is used to define data and process it into information as well as being data or information used in a context to achieve some meaningful goal.
Putting this into the context of a university.
At the end of the year every module in the university has a set of grade which have to be entered MISIS. At this point, the grades are data; they are just a list of figures. At the exam board, all the grades for each module are considered, along with the statistical summaries. At this point, the summaries become information; they can be used to indicate how well a module has run and to compare different modules. Later at the progression board, all the grades for each individual student are considered, these grades are again information, they allow judgements to be made about each student’s progress. After the boards, staff can use the student’s grade and their knowledge of the student to predict their final grade. Now staff are using previous knowledge they have about the student (i.e. the student attitude towards work, personal commitments etc.), their own expertise and the students grades to predict their future grade.
Bellinger, G., Castro, D. and Mills, A. (1997), ‘Data, Information, Knowledge, and Wisdom’,
http://www.outsights.com/systems/dikw/dikw.htm
I think that this definition is flawed in two ways:
1. How do you know what data is relevant to a given situation? Don’t you need knowledge to choose relevant data initially
2. How can data be processed into information and information into knowledge without the use of knowledge.
The definition of data being raw or unprocessed fact, figures, symbols get seems to be fairly well accepted as is the definition that information is processed data. However, there seem to be little consensus as to what knowledge is, although several definitions (refs) define knowledge as information used in a context to achieve something.
I think that the KID model is more interrelated, that data, information and knowledge interact with each other. Knowledge is used to define data and process it into information as well as being data or information used in a context to achieve some meaningful goal.
Putting this into the context of a university.
At the end of the year every module in the university has a set of grade which have to be entered MISIS. At this point, the grades are data; they are just a list of figures. At the exam board, all the grades for each module are considered, along with the statistical summaries. At this point, the summaries become information; they can be used to indicate how well a module has run and to compare different modules. Later at the progression board, all the grades for each individual student are considered, these grades are again information, they allow judgements to be made about each student’s progress. After the boards, staff can use the student’s grade and their knowledge of the student to predict their final grade. Now staff are using previous knowledge they have about the student (i.e. the student attitude towards work, personal commitments etc.), their own expertise and the students grades to predict their future grade.
Bellinger, G., Castro, D. and Mills, A. (1997), ‘Data, Information, Knowledge, and Wisdom’,
http://www.outsights.com/systems/dikw/dikw.htm
What is the difference between Organisations, Social/Business Network and Communities of Practice (CoP)?
There is a plethora of definitions of organisations that can be found in the literature. The common theme among these definitions is the arrangement of people and objects to achieve a common goal. For more definitions about organisations and their behaviour see Robbins and Judge, (2009).
Wikipedia’s definition of organisations as “a social arrangement which pursues collective goals, controls its own performance, and has a boundary separating it from its environment” is as common as many others that can be found in the literature. For comparative definitions see Jones, (2008).
Social/Business Networks
Like many other terms there is no single definition for the term “Network”. Increasingly the qualification of a Network adds a significant contextualisation and descriptive interpretation to the term (e.g. social networks, computer networks, mathematical networks, neural networks and business networks). However, common threads among definitions of these types of networks are the attempt to define their structures and the use of terms such as nodes, cells and links is common.
In the case of a social network the nodes represent individuals or organisations and the links are the “things” which ties them together, such as friendship, vision etc. These common interests allow them to communicate with each other. Social networks go back thousands of years and understanding their roots has been studied in other feilds such as anthropology. The advent of the internet had added a new and some argues more “populous” definitions to the term. It now commonly refers to famous internet sites such as Facebook, YoTube, Friends United etc. Rosenthal, (1997) presents an interesting examination of Social networks and team performance. She examines how the structure of relationships (links) can enhance or impede team performance. She concluded that interactions within social networks can affect team performance.
The term Business Networks could be confusing in this context as it normally refers to organisations that serve the interest of its members such as Business Link, Institute of Directors etc. Whilst some coverage of KM can be found in relation to social network, little is available in relation to Business Networks. An illustrative difference between Business organisations and Business Networks is that in Business Organisations the binding theme is financial survival whilst in Business Networks the binding theme is common interest.
Community of Practice (CoP)
The concept of a community of practice (CoP) refers to the process of social learning that occurs among humans with emphasis on creation and dissemination of knowledge among this “community”. CoP has received significant attention in the KM literature, for example, Ardichvili et al, (2006) and Kimble et al (2005).
Kimble et al (2005) examine the relationship KM and CoP in general and especially virtual CoP. They conclude that for KM to work in a CoP, there needs to be a balance between both hard and soft knowledge, and that the failure of many KM initiatives is due to the lack of facilities for people to communicate and interact socially.
Whilst Ardichvili et al, (2006) explore cultural factors influencing knowledge sharing strategies in virtual CoP where members are in different countries. They conclude that all KM systems must be tailored to the cultural need and preferences of the employee in the host country.
Examples
How do these definitions relate to a university?
As an Organisation
The staff in a university are a group of individuals who work together to educate students and to carry out research and consultancy, these can be viewed as their main goals. A university has a finite structure and therefore has boundaries which separate it from other universities and the world.
Within the university, staff are grouped into subject areas called schools, these can also be viewed as organisations.
As a Social Network
Most staff also have contacts or other individual, with which they interact, both inside and outside the university. These can be viewed as social networks, individuals who are communicating to exchange ideas with common interest and professional relationships (links).
As a CoP
Most staff have research interests and belong to research groups which meet to discuss their research interests, these are communities of practice with emphasis on creating and disseminating knowledge.
References
Ardichvili, A., et al, (2006) “Cultural influences on Knowledge sharing through online communities of practice”, Journal of Knowledge Management, Vol. 10, No. 1, pp 94-107.
Jones, G. R. (2008), “Organizational Theory, Design, and Change”, 5/E Pearson.
Kimble, C., P. Hildreth (2005), “Dualities, distributed communities of practice and knowledge management”, Journal of Knowledge Management, Vol. 9, No. 4, pp 102-113.
Robbins, O. S. P., T A. Judge (2009) “Organizational Behaviour”, 13/E, Pearson.
Rosenthal, E. (1997) “Social networks and team performance”, Team Performance Management, Vol. 3, No. 4, pp288-294.
Reflections and Personal Learning
This week in the workshop session, individual organisations (groups) presented an organisational view point of their findings and understanding of this week’s topic areas to the other organisations with the CoP (class). This was an amalgamation of the group’s individual member’s personal research and a group discussion. Each group also explained how they interacted together and what methods and tools they used.
The groups took very different approaches to transferring their knowledge both within their own groups and also to the whole class. It was very interesting how different people interacted with each other and also their varying view point on the topic area, some of which was very different to my own interpretation.
Wikipedia’s definition of organisations as “a social arrangement which pursues collective goals, controls its own performance, and has a boundary separating it from its environment” is as common as many others that can be found in the literature. For comparative definitions see Jones, (2008).
Social/Business Networks
Like many other terms there is no single definition for the term “Network”. Increasingly the qualification of a Network adds a significant contextualisation and descriptive interpretation to the term (e.g. social networks, computer networks, mathematical networks, neural networks and business networks). However, common threads among definitions of these types of networks are the attempt to define their structures and the use of terms such as nodes, cells and links is common.
In the case of a social network the nodes represent individuals or organisations and the links are the “things” which ties them together, such as friendship, vision etc. These common interests allow them to communicate with each other. Social networks go back thousands of years and understanding their roots has been studied in other feilds such as anthropology. The advent of the internet had added a new and some argues more “populous” definitions to the term. It now commonly refers to famous internet sites such as Facebook, YoTube, Friends United etc. Rosenthal, (1997) presents an interesting examination of Social networks and team performance. She examines how the structure of relationships (links) can enhance or impede team performance. She concluded that interactions within social networks can affect team performance.
The term Business Networks could be confusing in this context as it normally refers to organisations that serve the interest of its members such as Business Link, Institute of Directors etc. Whilst some coverage of KM can be found in relation to social network, little is available in relation to Business Networks. An illustrative difference between Business organisations and Business Networks is that in Business Organisations the binding theme is financial survival whilst in Business Networks the binding theme is common interest.
Community of Practice (CoP)
The concept of a community of practice (CoP) refers to the process of social learning that occurs among humans with emphasis on creation and dissemination of knowledge among this “community”. CoP has received significant attention in the KM literature, for example, Ardichvili et al, (2006) and Kimble et al (2005).
Kimble et al (2005) examine the relationship KM and CoP in general and especially virtual CoP. They conclude that for KM to work in a CoP, there needs to be a balance between both hard and soft knowledge, and that the failure of many KM initiatives is due to the lack of facilities for people to communicate and interact socially.
Whilst Ardichvili et al, (2006) explore cultural factors influencing knowledge sharing strategies in virtual CoP where members are in different countries. They conclude that all KM systems must be tailored to the cultural need and preferences of the employee in the host country.
Examples
How do these definitions relate to a university?
As an Organisation
The staff in a university are a group of individuals who work together to educate students and to carry out research and consultancy, these can be viewed as their main goals. A university has a finite structure and therefore has boundaries which separate it from other universities and the world.
Within the university, staff are grouped into subject areas called schools, these can also be viewed as organisations.
As a Social Network
Most staff also have contacts or other individual, with which they interact, both inside and outside the university. These can be viewed as social networks, individuals who are communicating to exchange ideas with common interest and professional relationships (links).
As a CoP
Most staff have research interests and belong to research groups which meet to discuss their research interests, these are communities of practice with emphasis on creating and disseminating knowledge.
References
Ardichvili, A., et al, (2006) “Cultural influences on Knowledge sharing through online communities of practice”, Journal of Knowledge Management, Vol. 10, No. 1, pp 94-107.
Jones, G. R. (2008), “Organizational Theory, Design, and Change”, 5/E Pearson.
Kimble, C., P. Hildreth (2005), “Dualities, distributed communities of practice and knowledge management”, Journal of Knowledge Management, Vol. 9, No. 4, pp 102-113.
Robbins, O. S. P., T A. Judge (2009) “Organizational Behaviour”, 13/E, Pearson.
Rosenthal, E. (1997) “Social networks and team performance”, Team Performance Management, Vol. 3, No. 4, pp288-294.
Reflections and Personal Learning
This week in the workshop session, individual organisations (groups) presented an organisational view point of their findings and understanding of this week’s topic areas to the other organisations with the CoP (class). This was an amalgamation of the group’s individual member’s personal research and a group discussion. Each group also explained how they interacted together and what methods and tools they used.
The groups took very different approaches to transferring their knowledge both within their own groups and also to the whole class. It was very interesting how different people interacted with each other and also their varying view point on the topic area, some of which was very different to my own interpretation.
My Knowledge Management Blog
I am currently a part-time student on the MSc Knowledge Management programme at Middlesex University.
This blog is being created as part of the Knowledge Management Strategies module.
The purpose of this blog is to record and organise my learning about Knowledge Management. Hopefully it will help me gain an insight into what Knowledge Management is, and how is can be deployed with various organisations.
Please feel free to share your thought and knowledge with me, especially how Knowledge management is used within your own organisations.
This blog is being created as part of the Knowledge Management Strategies module.
The purpose of this blog is to record and organise my learning about Knowledge Management. Hopefully it will help me gain an insight into what Knowledge Management is, and how is can be deployed with various organisations.
Please feel free to share your thought and knowledge with me, especially how Knowledge management is used within your own organisations.
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