Abstract: Examining signals from practitioners and academics that suggest Knowledge Management (KM) to be under performing, this paper undertakes research to determine potential causation. Issues of language, definition, literature and lack of a common framework are identified as potential inhibitors to value creation. This leads to a meta-synthesis of 287 pieces of literature that concludes KM to consist of four functions and 12 enablers. As a result of these findings, a definition of KM is offered. Further research into 71 KM models and frameworks suggests there to be a gap in literature in meeting the findings of the initial meta-synthesis. The paper offers further conclusions that contribute to the general knowledge of the field.

Keywords: Frameworks and Models; Knowledge Management; Meta-synthesis; Organisational knowledge

Introduction

‘Welcome to the most “successful” fuzzy idea in the history of management’ [1, p. 43]

This paper attempts to lay out for the reader issues surrounding the field of Knowledge Management (KM), building an argument that then informs enquiry objectives and methodology for the research.

Knowledge and its coordination appears to remain a priority for organisations regardless of discipline/sector [2]. Yet evidence presented suggests the field to be affected by a lack of common language, definition and framework or model. This paper reports on an ongoing attempt to construct a meta-synthesis of the literature on and existing Frameworks for KM in organisations.

Though the value of knowledge and its coordination is widely accepted, low levels of investment of time and money and a lack of motivation are suggested as contributing to a low satisfaction in KM as an effective management tool [2]. In a 2006 survey of management tools utilised by 1221 global executives [3] KM ranked 22 out of 25 for satisfaction and received the lowest rating of all management tools within respondents from large organisations. Though it ranked as the 8th most popular tool in European business, up from 15th in 2004, it only received a 17% satisfaction rating. This suggests a lack of congruence between perceived value and actual performance.

This appears to be echoed in the academic field where Smith [4] cites Fahey & Prusak, amongst several others, in demonstrating the growing concerns of theorists that KM is failing to deliver value. Mekhilef & Flock [5], in a synthesis

David Griffiths is a PhD candidate and associate lecturer on the MSc in Management of Training and Development at The University of Edinburgh (e-mail: dgkmedin@yahoo.co.uk)

Shona Morse is a lecturer at The University of Edinburgh on the MSc in Management of Training and Development (e-mail: shona.morse@ed.ac.uk)

of 658 KM papers, signpost a potential critical gap in literature when they failed to identify a single common framework or theory across six disciplines of Business & Management; Engineering; Decision Science; Computer Science; Medicine & Health and Social Science. Metaxiotis et al. [6] support this stating, ‘the main and accepted finding is that a codified, universally accepted framework has not been established for KM’ (p. 11).

Defining KM

KM is acknowledged as difficult to define [6], with theorists offering alternatives such as:

‘…a framework that builds on past experiences and creates new mechanisms for exchanging and creating knowledge’ [7, p.78]

and:

‘…the exploitation and development of the knowledge assets of an organisation with a view to furthering the organisation’s objectives’ [6, Davenport & Prusak cited, p. 9]

Some posit that the definition is situated according to discipline and/or sector [8]. Whilst Kulkarne et al. [9] suggest KM to be conditional according to a ‘personalisation strategy’ for tacit knowledge, or a ‘codification strategy’ for explicit knowledge. Sarah & Haslett [10] suggest ‘Knowledge as a product’ and ‘Knowledge as a process’ and other authors [9] discuss the management of knowledge or the management of knowledge workers.

Yet others state that KM cannot be managed. Prusak [cited in 11] could be seen to be contradicting work cited earlier with Davenport in stating that ‘you cannot manage knowledge like you cannot manage love, patriotism or your children’ (p. 45).

Hellstrom & Jacob [12] suggest that KM emerges from a process that mirrors the generation of knowledge. This suggests that KM could be based on what Holsapple [13] refers to as the ‘Primary’ types of knowledge: ‘Know What’, descriptive knowledge; ‘Know How’, procedural knowledge; ‘Know Why’, reasoning knowledge. This approach appears to originate from the work of Ryle [14] who suggests that ‘knowing what’ is the declarative knowledge that provides an understanding of facts and ‘knowing how’ is the procedural knowledge that provides understanding of how to do things. If Hellstrom & Jacob are correct and KM emerges from the knowledge generation process, it would seem possible that a KM definition lies within a situated understanding of ‘What’, ‘How’ and ‘Why’ as suggested by Holsapple.

Garavan et al. [15] suggest KM to be generic, proposing it to be contextual according to the value placed on knowledge by aspects of organisation outcomes. This consideration of

Griffiths, David A; Morse, Shona M

generic KM functions is explored in a limited literature review, encompassing the work of 6 studies, conducted by Qureshi et al. [16]. The authors examined research conducted between 1997 and 1999 to suggest that KM consists of five basic functions: Create; Collect; Organise; Deliver; Use. The literature, whilst limited in breadth, scale and cultural perspective, appears to provide a signpost toward a basic definition of KM.

The work of Qureshi et al. is supported by a similar synthesis of literature carried out by Supyuenyong & Islam [17]. This work, originating in Thailand, encompasses a more diverse, but still limited, review of 12 studies. Their research suggests four ‘sub-processes’ of KM, which would actually seem to consist of 6 individual processes: knowledge creation and acquisition; knowledge organisation and retention; knowledge dissemination; and knowledge utilisation. This appears to extend the work of Qureshi et al. through the addition of ‘retention’, which could also be reasonably defined as ‘storage’. This is a clear omission in the work of Qureshi et al. and provides an indicator of potential weaknesses in current research, where literature reviews lack value due to their insufficient breadth and depth. Gupta [18] continues this theme, providing a singular narrative research to suggest that KM consists of five key steps: Generation, Sharing, Adaption, Application and Modification. This could be seen as interesting due to the absence of ‘retention' and 'acquisition’. The author also defines ‘modification’ as the generation of new knowledge. However, he fails to differentiate between ‘knowledge creation’ and ‘the generation of new knowledge’. This would seem to demonstrate the lack of ‘know what’ in literature, which could detract from the effective transmission and development of ‘know how’. For if ‘know what’ is not clearly signposted, it would not seem possible to manipulate the ‘what’ to develop ‘how’.

Despite the wealth of definitions available it appears difficult to identify a succinct explanation for the KM process. It would therefore seem feasible to suggest that this apparent lack of clarity could contribute to confusion on the part of the practitioner, which could result in poor performance.

This lack of clarity stimulates further enquiry into the language used within the KM field. If KM is generic, as suggested by Garavan et al. [15] and progressed by Qureshi et al. [16], Gupta [18] and Supyuenyong & Islam [17], KM should presumably possess a common language. However, it is also identified as contextual [15; 8, in which case there may be a significant barrier to a common vocabulary.

KM Language

The literature review conducted by Qureshi et al. [16] (Table 1), when compared to the review conducted by Supyuenyong & Islam [17] (Table 2) suggests a potential issue of vocabulary, which could distort the understanding of KM.

Table 1 – Research findings Qureshi et al.

Taken from Qureshi et al. [16]

Table 2 – Research findings Supyuenyong & IslamTaken from Supyuenyong & Islam [17]

The comparison demonstrates the variable of vocabulary utilised to discuss KM. These differences are minor compared with the findings of Mekhilef & Flock [5], who suggested 1317 key words and phrases to be associated with the field as part of their research coding protocol. This could be an issue, for if the initiator and receiver do not share the same language there is a possibility that meaning could be lost in the sense giving and sense reading process [19]. This could therefore be seen as a contributing factor in the findings of Rigby & Bilodeau [3].

As suggested earlier, authors are attempting to establish research to explain the KM process, which appears to be pointing toward generic definitions that can be become contextually embedded. However, current research would seem to lack robustness, which could be what the field requires if solutions are to be offered to overcome practitioner dissatisfaction.

It would also seem that the research of Qureshi et al. [16] and Supyuenyong & Islam [17] demonstrate a potential flaw in current literature: If Hellstrom [12] is correct and KM is a product of the knowledge creation process and Holsapple [13] is correct in his identification of the primary types of knowledge, then there would appear to be a problem. Research appears to clearly transmit the ‘why’, but appears to be falling short in defining the ‘what’ and ‘how’ of KM. This could be seen as a critical gap in the literature, which could in turn contribute to poor performance.

KM Epistemology

In reviewing the literature, it is apparent that an epistemological continuum exists between two extremes of ‘HR-Centric’ and ‘Techno-Centric’ with a mid point being ‘Interactionist’. It would appear that these views could be interpreted as following:

Techno-Centric: Seemingly embedded within the view of knowledge as an object. Focused on the storage and delivery of knowledge, with aspects of algorithmic knowledge generation emanating from the field of Artificial Intelligence.

HR-Centric: Seemingly presented through the view of knowledge as a process. Its main concern is the interaction of people and the generation and storage of knowledge through socialisation.

Interactionist: Observes the interface between people and technology. Acknowledges the role of technology in creating value and expediting the transmission of knowledge, but also recognises the need to coordinate the interaction between process and object.

This suggests a question: Are epistemological views indigenous to a particular discipline/sector within the framework utilised by Mekhilef&Flock [5]? If so, does it affect the enablers required to coordinate knowledge?

Contextual Issues

The research of Mekhilef & Flock [5] suggests that a common framework for KM could be inhibited by contextual factors. However, their research demonstrates that 50 concepts were shared between Social science, Medicine & Health and Business & Management; 34 concepts were shared between Social Science and Engineering; and 2 were shared by all except Computer Science. This suggests that common functions and enablers do exist, but that they could be inhibited by perceptions of context.

It would seem possible to overcome issues of context by examining the basic function of knowledge creation. Tranfield et al. [20] citing Gibbons, state, ‘knowledge is produced in the context of application’ (p. 212). Hori et al. [21] explore this position, positing that the user determines the context, which in turn determines the value of the process. Their work can be represented through the following formula:

Representational Context [artefacts] + Conceptual Context [existing in the mind] +Real World Context [situated application] = Value

This suggests context to be nothing more than an element of the KM process. A position galvanised by authors such as Edwards & Rees [22] who discuss knowledge creation as a transaction between the person and the environment. They observe ‘every action as intrinsically connected with the context or situation in which it occurs’ (p. 155). It would therefore seem possible to state that context influences the

KM process, but it would not appear to be a hindrance to the discussion of common functions or enablers.

KM Enablers

The definition of KM appears to be diffuse and there seems to be a variable in vocabulary that could further inhibit the understanding of KM as a process. Predominantly, literature appears to discuss functions and processes. However it would seem necessary to investigate the enablers that make-up the process in what could be defined to be Critical Success Factors (CSFs).

Jennex & Zakharove [23] conducted a synthesis of 15 CSF studies, encompassing 78 case studies and 100 organisational surveys, mainly from the late 1990s in North America, to produce the following:

Integrated technical infrastructure A knowledge strategy that identifies users

experience A common enterprise wide knowledge structure Motivation and commitment of users including

incentives and training Organisational culture Senior Management support, including resources Measures to assess the impacts of the Knowledge

Management System Clear goals and purpose of the Knowledge

Management System Learning Organisation Search retrieval and visualisation function of

Knowledge Management System, support knowledge use

Work processes including knowledge capture and use

Security an protection of knowledge [24]

Azmi & Zairi [25] provide a synthesis of 15 authors published between 1996 and 2001, predominantly from the Northern Hemisphere, to suggest 9 CSFs: Training; Sharing; Culture; Transferring; Top-Management Support; Technology Infrastructure; Creating; Knowledge Strategy; Knowledge Infrastructure. This work is interesting in that it appears to suggest that Storing & Gathering and Application are not critical to the KM process. It is also interesting that ‘Training’ is identified as a Critical Success Factor and yet there is little identification as to which aspect or to whom the training should be applied. This would seem to warrant further discussion, as to whether training or learning should be considered a CSF, or is it a by product of the KM and knowledge creation process?

Conley [26], a collaboration analyst with Deloitte, conducting PhD research with Northern Illinois University, suggests 46 unspecified factors from literature and a further 83 unspecified factors from research survey responses giving a total of 129 CSFs.

Further issues arise in the comparison of research between Qureshi et al. [16] and Supyuenyong & Islam [17], and the findings of Jennex & Zakharove [23] where it would seem difficult to identify factors of Applying, Sharing, Storing and Gathering, or Creating Knowledge. This would seem to point toward a lack of congruence in literature when discussing KM CSFs, which appears to further illustrate the diffuse views of the process that exist in the field.

Creation, Training and Learning

In discussing CSFs several authors identified Training or Learning as a key issue in the process [27; 23; 25]. It could be argued that this is a substitute for the knowledge creation process, which is not identified by these authors as being critical to the overall KM process.

Learning has been closely identified with the knowledge process:

‘The recognition of one’s own need to learn, the search for new knowledge, the test of that new knowledge in practical action, and the consolidation of the whole exercise within the memory are all essential to complete learning’ [28, p. 9].

Bhatt [29] argues that poor learning could impinge upon the knowledge creation process, which again suggests a link between knowledge and learning. Jennex & Olfman [30] state that for KM to improve business performance it is necessary to influence organisational learning in the pursuit of improved organisational memory. This is supported by [10, Sanchez & Heene cited] ‘Learning is a process which changes the state of knowledge of an individual or organisation’ (p. 5). Edward & Rees [22] succinctly state that ‘It is clear that managing behaviour, learning and knowledge cannot be separated from one another’ (p.167). This would appear to give validity to the parallel discussion of knowledge and learning when attempting to develop a KM solution.

The literature suggests that it is the process that stimulates knowledge or learning, which would therefore seem to identify it as a product of the process as opposed to being an element of the process. This in turn seems to suggest training to be a stimulus to that process. It would also seem that KM informs organisational training needs and contributes to the activities that shape the learning process, but is not necessarily the appropriate tool for managing these processes.

In an attempt to simplify the field, this paper will separate KM from the activities of organisational Learning and Training and Development, to which it appears to contribute in a reciprocal relationship.

Limitations of the literature

Much of the literature in the field appears to be based on narrative review, which at times is blended with qualitative or quantitative studies. There seems to be little in the form of systematic, evidence-based review, which is a cogenerative method designed to develop a depth of evidence based in both theory and practice. The value of the narrative approach is criticised for ‘being singular descriptive accounts of the contributions made by writer’s in the field, often selected for their inclusion on the implicit biases of the researcher’ [20, p. 208].

Greenwood & Levin [31] offer an insight into the potential failings of literature in fulfilling the primary knowledge need of ‘knowing how’, as suggested by Holsapple [13]. They suggest that for knowledge to be enabled, it needs to be understood for it to be applied in context:

‘The actor needs to make sense of the context to enable appropriate actions. “knowing how” thus implies knowing how in a given context in which appropriate actions emerge from contextual knowing’ (p. 51-2).

The authors appear to support the limitations of narrative based theory expressed by Tranfield et al. [20] in that practitioners should be treated as cogenerative partners in the research process, especially where the research is focused on solving a problem embedded in practice.

Tranfield et al. [20] posit that ultimately systematic evidence-based reviews increase the value of research and improve the flow between academics and practitioners:

‘For academics, the reviewing process increases methodological rigour. For practitioner/managers, systematic review helps develop a reliable knowledge base by accumulating knowledge from a range of studies’ (p. 220).

This would seem to direct KM research toward a meta-synthesis, given the issues of language discussed earlier in this paper.

Enquiry objectives

The foregoing evidence points toward an enquiry that cuts through issues of vocabulary in search of a succinct and applicable definition of KM. This would appear to require an investigation into the primary functions of the KM process. Evidence also suggests there to be a gap in the provision of a common framework for the KM process. This informs an enquiry to determine whether common enablers exist across discipline/sectors, which could then facilitate such a framework. This enquiry will attempt to address the following questions through a systematic evidence-based literature review:

Is it possible to offer a clear definition of the functions of the KM process?

Is there a dominant epistemological view of KM? If there is a dominant epistemological view of KM,

does it have an effect on the functions and enablers of the KM process?

Are there enablers common to the 6 discipline/sectors of KM as utilised in the enquiry by Mekhilef & Flock?

If common functions and enablers exist, are they stable through time, or are they evolving with the apparent emergence of modern KM?

Methodology

This paper develops descriptive research through a systematic, evidence-based, review of literature in order to extend the general body of scientific knowledge.

Tranfield et al. [20] posit that a systematic, evidence-based literature review is needed in order to speak to the needs of the academic and practitioner and should include a variety of sources such as: books, articles, blogs, company website statements, academic articles and conference proceedings. The authors state that this form of review differs from the traditional narrative review in the manner in which it rigorously approaches the literature resources. This moves beyond breadth and scale to encompass a transparent scientific approach that ‘aims to minimise bias through exhaustive literature searches of published and unpublished studies’ (p. 209). Tranfield et al. propose that results provided through this method of review increase the reliability and robustness of findings, surpassing that of the singular narrative review process.

A scoping study was conducted as part of the dissertation phase of the University of Edinburgh Master of Science programme in the Management of Training and Development. This study, encompassing over 60 pieces of academic literature, suggested 15 potential success enablers required for the field of KM. Table 3 highlights the findings of the scoping study, which provided the platform for the coding protocol required for the meta-synthesis.

It was decided to utilise the six-discipline/sector framework formulated by Mekhilef & Flock [5] to populate the literature review. It was believed that by using this framework it would be possible to extend existing research and allow for a direct comparison of findings to determine the potential for a common framework through the identification of common functions and enablers. To address potential issues of dissonance, literature used in the scoping study, that could have been used to ‘fit’ the meta-synthesis to the preliminary findings, was excluded from the next research phase.

A minimum of 40 pieces of literature were targeted for each discipline/sector with a time period from 1900 to present day, to investigate whether findings were evolutionary or stable through time. In total 287 pieces of literature were used to

populate the study. Continental culture was also taken into consideration, with a decision taken that the review should not force findings through equal representation, but that effort should be made to ensure that the search criteria included continental indicators. This decision was taken to address cultural differences that can effect KM implementation [32].

The study was populated with literature accessed via academic and public search engines, using 62 search terms, and included: Academic peer reviewed articles; blogs; bulletin boards; presentations; newspaper and magazine articles; books; white papers; reports; informal research; and a job advert. The literature was then hand coded using 286 descriptors.

The research could be open to debate if a process was not put in place to minimise the possibility of dissonance, beyond that of breadth and scale of literature [33]. Therefore, where search engines, such as Google Scholar, Google and The Brint Institute Portal, provided multiple returns, a random number generator was employed [34]. This approach was used to select 50% of the articles from any given search page and was employed for approximately 33% of the study; this as much of the academic literature was researched through academic bibliographic databases, encompassing a broad variety of discipline/sectors, which required the articles to be handpicked.

Results were screened to filter returns of insufficient length or detail; this was a particular issue when accessing blogs and bulletin boards. Literature was also deemed inappropriate if it did not directly address the utilisation of knowledge for organisational value.

Search returns often straddled discipline/sectors. Where this was the case, the primary focus of the article was used for determining the discipline/sector.

A review was conducted after 50 articles had been coded and an anomaly was identified, being a recurring coding that was not acknowledged in the scoping study, the descriptor, ‘Knowledge Structure’. This was addressed in the coding protocol and a 16th factor was established. The coding descriptors for the Functions and Enablers are detailed in Appendix 2.

The lead researcher solely conducted the coding of the meta-synthesis over a period of 92 days. It could be said that research conducted in this manner could expose the findings to manipulation in order to fit a specific hypothesis. Subjecting the analysis to a single reading could also expose the findings to errors, which, if too great, could seriously affect the outcomes of the study. Therefore an independent Research Assistant was employed to sample 10% of the study to determine validity and error rate. This secondary review returned 3 factors that could not be identified by the reader and 1 factor that had not been included in the initial findings. This translated to an error rate of +0.30% and -0.911%.

Finally there was a need to distil the findings of the study to determine whether results of the investigation were enablers or functions of KM. To determine this a simple test was devised: If findings could be distilled to individual enablers, they were considered to be functions. If not they were determined to be enablers. Thus, Knowledge Creation would be seen as a function as it can be distilled down to enablers such as 'What is known', 'Extending what is known', 'Motivation', 'Context' and 'Reflection'; this is expanded upon later in this article.

Table 3 – Scoping StudyTaken from MSc in Management of Training and Development Scoping Study

Initial Findings

The table of findings can be found in Appendix 1.

Common factors do appear to exist across discipline/sectors. The 16 factors and enablers proposed by this study are exhibited in all discipline/sectors, with a situated frequency range of +38% to -49%, as detailed in Appendix 1.1.

KM appears to be predominantly an Interactionist field, with 45% of literature supporting this view. Business & Management is the exception, where a HR-centric view pervades through 69% of the literature.

The view of KM is created in the main by narrative literature, with only 1% of the literature utilising a form of meta-synthesis.

Only 3% of literature refers to all 16 factors identified in the study, with an average of 62% or 10 factors mentioned in any given piece of literature.

In discipline/sectors with a strong Techno-Centric view of the world there appears to be a lack of focus on the ‘human’ aspects that can affect the KM process. This is demonstrated in Medicine & Health, Decision Science and Computer science when compared against the HR-centric view of Business & Management. For example, 71% of Business & Management acknowledges the need for ‘Motivation’, compared with 27% in Medicine & Health, 36% in decision Science and 23% in Computer Science. Similar results can be observed with ‘Culture’ and ‘Organisation Structure’. However the bias is reversed when observing a similar comparison of ‘Knowledge Structure’, which achieves low recognition in Business & Management when compared to Medicine & Health, Decision science and Computer science.

These biases would seem to be magnified in a comparison of base level research data. The Techno-Centric view produces 1 article that refers to all 16 factors, equating to 2% of literature. The dominant Interactionist view produces 13, or 10% of the literature. This apparent disparity between the two views is enhanced upon examining the number of articles that refer to at least 90%, a range of 13-15, factors. The Techno-Centric view returned 0%. The Interactionist view returned 18%. This appears to suggest that the Techno-Centric view of the world could affect the performance of the field through its failure to transmit factors valued in the literature as a whole.

Extended findings

The analysis of the literature appears to demonstrate a gap in the ‘know what’ aspect of argument development. This can be evidenced through areas, such as ‘creating Knowledge’, where authors have stated that the process is taken for granted [1].

Authors such as Amidon & Davies [35] and Nonaka [36], amongst others acknowledge the importance of knowledge generation to the KM process. However, there appear to be key enablers that need to be discussed if the ‘know what’ is to progress to ‘know how’. These would appear to include:

What is known - Roth [37], Kulkarne et al. [9], Armstrong [38] and Antonacopoulou [28] amongst others have identified the need for pre-existing knowledge in the generation process. Existing knowledge or learning resources bring recognition of meaning to the process of enquiry into new learning or knowledge [28]. ‘The role of knowledge in the learning process comprises drawing connections between what is already known and what may be discovered’ [9, p. 19].

Extending what is known - Enquiry is supported by many authors as being important to knowledge

generation [27; 9; 39]. Antonacopoulou [28] suggests that enquiry is needed to evolve existing knowledge to apply in new contexts. Cook & Brown [40] support Antonacopoulou, arguing that to progress knowledge to knowing it is essential to engage in enquiry, which they define as challenging existing knowledge to advance an answer, solution or resolution

Reflection - Authors suggest that reflection exists in the knowledge generation process [41; 42; 10; 39]. Sarah & Haslett [10] observe reflection as a challenge for organisations in legitimising the knowledge and learning process: ‘For any learning or knowledge-creation to occur, there must be a space and time for evaluation and reflection’ (p. 9). Hedlund [43] states that reflection is essential to the knowledge process as it describes the interaction that occurs between articulation and internalisation.

Context – This was discussed earlier in the paper.

Motivation – Hall [44], Smith [4], Bhalla & Lampel [32] amongst others support Motivation as a CSF for knowledge generation. Hall [44] sees motivation as being more important than the capture storage and socialisation of knowledge. Smith [4] suggests that if organisations fail to engage individuals in a manner that stimulates their intrinsic motivation, they will labour to produce relationships contracted by compliance, which will not produce the knowledge needed for inimitable competitive advantage.

The analysis of the literature demonstrates that enablers of know what’ are frequently missed. This is demonstrated where ‘creating knowledge’ is identified in only 55% of the literature with a discipline/sector variable of +5% and -20%. Where ‘Creating knowledge’ is identified, Reflection is only referred to 47% of the time and Motivation 42%. This would seem to suggest a failure in the literature to develop aspects of ‘know what’ thereby hindering the development of ‘know how’. This could be interpreted as a possible causation for the poor performance of KM in the work place.

To challenge this assertion 10% of literature that identified ‘knowledge creation’ was re-analysed, literature being selected by random number generator, analysis demonstrated that 64% of literature addressed all the enablers listed above. However, 0% discussed the ‘know how’ of ‘knowledge creation’, where direction was given on how to assemble and utilise the enablers to create value for the organisation. This appears to validate McElroy’s [1] assertion that the process is taken for granted.

KM definition

In distilling the findings of the research the following are put forward as functions:

Using Knowledge Interacting Storing & Gathering Creating Knowledge

The following are then proposed to be enablers using the same methodology:

Catalysts (People, Technology, Finance, Time) What is known Extending what is known Reflection Context Motivation Culture Organisational Structure Space Transmitting Artefacts Knowledge Structure

This can be demonstrated through the example of ‘Creating Knowledge’ used earlier.

It would therefore seem to give weight to the succinct definitions of KM discussed earlier by authors such as Qureshi et al. [16]. However to bring congruence to the field, it would appear necessary to align definitions with the findings of this research. Therefore the following is suggested:

KM influences the coordination of People, Technology, Finance and Time to develop value-based solutions for the Use, Interaction, Storing & Gathering and Creation of Knowledge as an organisational resource.

Discussion

This research cannot determine importance, or value of the factors. It only reflects the frequency of reference within literature. This transmits a perceived importance or value, but is not conclusive. Many weaker signals could suffer from a lack of understanding on the part of the authors as to the negative effects attributable to their absence from the KM process. However, there would appear to be a differentiation in the perceived importance, which is evidenced within the ‘Range’ findings, where there are variables according to discipline/sector and epistemological view. This suggests that the value of functions and enablers will vary according the situated needs of the discipline/sector.

The analysis of the literature allows us to determine the existence of the identified functions and enablers. However, limited representation of literature from outside the Northern Hemisphere will not allow for an assertion as to their validity outside of this area. This accepted; the literature represented suggests the findings to be constant regardless of continental context.

The research cannot offer a conclusion with regard to the validity of the findings in relation to specific sectors. The investigation was designed to identify common factors as a generalisation of the field and issues relating to specific sectors fell beyond the scope of this study.

The findings could not determine whether factors were stable through time. There would seem to be indicators that this could be true, however, there was too little literature identified pre-1990 for an assertion to be made in this area.

The analysis failed to adequately interrogate the literature for ‘know how’. Whilst the investigation appears to demonstrate the lack of depth in the area of ‘know what’, it is not possible to determine the extent of ‘know how’ in the literature; beyond the evidence of the 10% sampling and the assertion that without sufficient ‘know what’ it would seem difficult to develop valuable ‘know how’.

Reflection and resolution:

Knowledge content value, was identified as a potential element during the coding process, but was dismissed as it is determined by context, as demonstrated earlier by Tranfield et al. [20], Edwards & Rees [22] and Hori et al. [21].

'Process', was noted as a reoccurring theme during coding, which stimulated debate during review meetings. It is the conclusion of this study that process exists as an artefact of organisational activities aligned against strategy and policy. This appears to be satisfied by the literature where authors such as DeLong [45] posit that, ‘knowledge that is explicit is easily codified and can be shared independent of its human source, or it can be embedded in processes or systems’ (p. 83).

Issues of research bias, such as combinations of qualitative and quantitative research were ignored as being non-essential to the study. The objective of the study was to investigate the type of research being conducted. The findings clearly demonstrated the weight and influence of singular narrative research and therefore it was not deemed necessary to differentiate the study further.

Extended research based on findings

By extending the meta-synthesis to encompass KM models and frameworks, it is possible to compare the findings of the research against existing KM solutions. This aspect of the enquiry evaluated 71 Models and Frameworks from 1996 to 2008. Continuing to employ the evidence-based methodology, Models and Frameworks were analysed for the existence of the 16 Functions and Enablers identified in the first part of this research. Models of KM based on computer programming processes were dismissed from this analysis.

Of the 71 models 55% originated from peer reviewed journals with the remaining coming from other sources as

detailed in the methodology. The search parameters are detailed in the methodology, with this search using 2 search terms. The search returned 45 generic models, and 26 situated models with 30% of models originating from Europe, 34% from north America, 7% from Australia, 22% from Asia; 3% from South America; 1% from India and 3% from Africa.

During an initial sampling of 10 Models and Frameworks an issue of clarity and redundancy was discovered; where the models failed to display all of the functions and enablers discussed in the supporting text, or issues of redundancy where, for example, models highlighted issues of communication and conversation [46]. It was therefore decided that criteria needed to be established to identify an effective construct in the event that a Model or Framework did identify all 16 Functions and Enablers.

Bacharach [47] states that an effective theoretical model needs to address issues of Falsifiability, Utility, Variables, Constructs and Relationships. The author also suggests that an effective model must transmit answers to the questions of ‘how’, ‘what’, ‘why’ and ‘when’. Bacharach also criticises theoretical models for being narrative in approach, which he believes translates to a one-sided description that focuses on the question of ‘what’. ‘The primary goal of theory is to answer the question of how, when, and why, unlike the goal of description, which is to answer the question of what’ (p. 498). This would appear to echo the criticisms of current KM literature highlighted earlier in the paper. Bacharach’s criteria is supported by Shanks et al. [48], who state that in order for a model to provide an appropriate representation of the field to which it is providing a lens, it should address ‘accuracy’, ‘completeness’, be ‘conflict-free’ and there should be ‘no redundancy’. Rasli [49] developed a framework, founded on the work of Bacharach, to specifically investigate a KM framework. The framework also encompasses and expands upon the work of Shanks et al. Rasli simplified the language of Bacharach and Shanks et al. to assess Models and Frameworks based on ‘Comprehensiveness’, ‘Correctness’, ‘Usefulness’, ‘Clarity’ and ‘Conciseness’. Based on the initial findings of the sample, it was decided the simplest approach to evaluating frameworks or models that displayed all 16 Functions or Enablers would be to apply Bacharach’s criteria of How, What, Why and When in conjunction with that of Rasli.

Findings

Of the models reviewed 1.16%, being 1 model, discussed all 16 Functions and Enablers. However, it failed the secondary test in that it was found to be mainly descriptive, failing Bacharach’s test, and lacking in Comprehensiveness and Clarity, this as many of the factors were discretely recognised in the text, but not represented in the visual representation of the model, which fails Rasli’s test. Therefore, it would appear, based on these criteria, that 0% of models reflected the findings of the initial research.

The most frequently represented Factors and Enablers were Resources with 86%; Context 85%; Transmit with 82%; and Interaction with 80%. The least frequent were Spaces with 17%; Organisational Structure with 31%; Motivation with 45%; and Culture with 55%.

In total 721 Functions and Enablers were identified with an average of 10.15 factors being visible in any given model or framework. This would seem to support the findings of the initial meta-synthesis research.

The search returned 45 generic models and 26 discipline/sector specific models. The average of 10.15 factors remained the same regardless of whether the model was generic or contextualised according to discipline/sector.

Discussion

It would seem that there are common functions and enablers that unify the field, which appears to suggest support for a common KM framework. However, there would seem to be little evidence to support the existence of an existing framework that meets the criteria for a valid theoretical model. This would seem to suggest the need for a new framework that meets the criteria for the evaluation of such a model and in doing so address the gap in literature identified by Mekhilef & Flock [5].

There could be issues of interpretation or language such as the ‘Use’ or ‘Creating knowledge’ process suggesting the existence of ’What is known’. However, where this is the case 15% of models still fail to acknowledge any of these factors. Similarly, where ‘Interaction’ could be interpreted as ‘Transmission’, there is still a 6% shortfall in the models.

Peer reviewed models provided an average of 10.65 factors per model, or 66.56%, compared to 9.56 factors, or 59.75%, of other models. This suggests that peer reviewed models and frameworks are more complete, but it also appears to confirm the blockage in the flow between academic and practitioner suggested earlier.

‘Creating knowledge’ is represented in 72% of the research. However, 75% of this number failed to highlight all the factors of knowledge creation discussed earlier in this paper. This would seem to reinforce earlier findings that suggest the field to be deficient in the demonstration of ‘know how’. Bacharach [47] questions the validity of any theoretical model or framework that fails to address ‘know how’, suggesting models of this ilk to be narrative and lacking in value.

Of the 28% of models that failed to recognise ‘Creating knowledge’ as a part of the KM process, 45% also failed to acknowledge the existence of ‘What is known’ or ‘Extending what is known’. This would seem to demonstrate a critical gap in the literature when considering ‘Know what’, which is seen by Bacharach [47] as being another essential criteria for a valid theoretical model.

The research represents a Northern Hemisphere bias and therefore cannot determine the validity of the findings for Southern hemisphere organisations. However, the initial findings would suggest the research to be valid regardless of geographical location. The research did not collect enough information to ascertain the validity of the models for individual discipline/sectors discussed earlier in this paper. It could be said that the 63% of generic models provide a fair representation to suggest that these findings would be valid regardless of discipline/sector.

Conclusion

The following conclusions are offered as a result of the research findings:

The field is would benefit from a simplified definition of KM. The findings appear to demonstrate that there are common functions and enablers that unite discipline/sectors and as such there is scope for an overarching definition of KM. This definition can be contextualised for specific sectors, but the message of KM would remain the constant.

KM appears to be an Interactionist field. As such it would seem important for authors to consider the tensions between Human Resources and technology. There would also seem to be a need for the Techno-Centric view to acknowledge factors external to technology in the KM process.

There appears to be a need for evidence-based research in the field. This approach appears to improve the value of research and stimulate the flow between practitioners and academics.

Further research, specifically enquiry within organisations, is needed to establish value on the part of the practitioner. This will extend knowledge of functions and enablers and allow for the identification of potential issues within the findings. It will also allow for a comparison of actual practice in a focused setting against the representation within the literature. It is suggested that the findings are developed into a format that transmits the process in a clear and concise manner in order to improve satisfaction and performance.

It would appear that there is justification for the development of a KM model that addresses the gaps identified in this research.

The findings would seem bias toward Northern Hemisphere research and practice. Therefore ongoing research will be required if the findings of this paper are to be supported for the Southern Hemisphere.

This paper set out to investigate the field of KM and delivered a view that suggested a lack of satisfaction on the part of the practitioner. It also delivered a view that pointed toward a gap in the literature and a blockage in the flow

between academic and practitioner. KM has been demonstrated to be burdened by an uncommon language and as a result suffers from a lack of definition and focus for the process.

A comprehensive evidence-based review of 287 pieces of literature from 6 discipline/sectors and 71 Models and Frameworks has produced findings that support the initial hypothesis relating to the field.

The field has been demonstrated as mainly Interactionist in outlook. It has been shown to be lacking in value-based research and has exposed potential issues in the way in which theorists are used to promote the narrative view of the author.

The findings have suggested a potential definition of the knowledge coordination process that would seem to provide a platform from which to enter the field for further testing. This evidence-based definition is presented in an attempt to clarify the field and improve understanding of the functions that make up the knowledge coordination process. Evidence has also been presented that suggests there to be 16 common functions and enablers that are shared by the 6 discipline/sectors in this investigation. There would also seem to be a gap within current KM Models and Frameworks in identifying these functions and enablers, which would seem to suggest the need for the development of a common KM framework or model.

Appendix 1Research synthesis – Total findings Appendix 1.1

Research synthesis – Range of findings

Appendix 2Coding Descriptors

Changes have been made to the descriptors as part of the evolution of the research from the scoping study phase. Where this has occurred it has been in order to bring clarity to the field. For example: ‘Creating Knowledge’ was given the descriptor ‘cognition’ during the scoping study, which could drive interpretation toward fields such as cognitive psychology. Therefore, it was decided to utilise terminology that emerged from practice; In this case ‘Creating

knowledge’ provided the highest return during the coding practice, this seemed to reflect research findings more clearly.

Creating knowledge: The process of either extending existing knowledge through combination or generating new knowledge. This refers to indicators in the literature that speak to the need for knowledge generation to be part of the KM remit. However, as is discussed later, knowledge generation requires other factors and whilst acknowledged in its own right within the coding, there is cross over in theoretical terms with the factor of ‘Extending what is known’.

What is known: Refers to that which already exists; encompasses explicit and tacit forms internal and external, embedded within people and artefacts. It is differentiated from ‘artefacts’ through its utilisation, such as through knowledge creation, as opposed to the act of designing an artefact; through codification, in physical or virtual media or products, or by being embedded in services or processes.

Extending what is known: The process of problem solving, where ‘what is known’, within the context of the individual, group or organisation, is insufficient to resolve the problem. Through enquiry, utilising internal or external sources, ‘what is known’ is extended through combination with other ‘knowns’. There could be said to bean issue with extending knowledge, this as the process itself must indicate the existence of basic knowledge to allow for extension. However, this factor indicates the existence of specific issues of new knowledge adaption or formulation through problem solving.

Catalysts: This descriptor refers to key issues that appear to stimulate KM functions and enablers: Time – actual time allocated by the organisation for KM objectives and processes; Finance – budgetary resources; People – Human Resources either acquired or developed by the organisation to fulfil knowledge needs; Technology – Technology enablers designed to provide storage of, access to, transmission of and extension of ‘what is known’, and spaces for virtual interaction.

Transmit: The ability to communicate knowledge in an organisation, verbally, visually and textually. This can be communications in both active and passive form. The basis of this factor exists in Polanyi’s [19] definition of ‘Sense Giving’ and ‘Sense Reading’.

Gathering and Storing: The act of acquiring and storing knowledge for later use. This can take the form of organisational as well as human and technological memory, and can be relevant to internal and external sources.

Context: Relates to the context within which the KM activity is situated. Context appears to be the bedrock of the KM process and, as per Tranfield et al. [20] and Hori et al. [21],

will determine the content of knowledge stores, the application of what is known, the gathering of knowledge, the sharing of knowledge and the manner in which knowledge is extended. Context can be determined by discipline/sector or sector, but can also be focused through organisational vision, strategy and policy.

Motivation: The stimulus to act. This could be through organisational leadership, a knowledge champion or reward and incentive structures to induce knowledge transactions between Human Resources and the environment.

Interact: The need for interaction to stimulate the use, extension, sharing and transmission of knowledge. This can be an internal or external process that takes place in physical or virtual spaces and encompasses explicit and tacit enablers of knowledge.

Culture: The values and ideology of the internal and external environment that influences the situated KM process.

Organisational structure: The organisational architecture that represents the division of labour.

Spaces: The situated location of the organisation in relation to external knowledge assets that will assist in enabling the exchange and extension of what is known. It also applies to the virtual and physical spaces within an organisation and the manner in which they facilitate the functions of KM.

Using knowledge: Refers to the application of what is known, including extended knowledge, in the organisation context.

Artefacts: Refers to knowledge assets that exist in representational form within an organisation. For example: physical text; digital text; processes; patents; products; services; blogs; databases; stories. ‘What is known’ is differentiated from an artefact in that an artefact is external to a person or culture and acts as a representation of what is known.

Review: Relates to the act of reflection, which is seen as being part of the process of creating knowledge. However, this could be seen as too narrow a description within the field of KM. It has therefore been extended to encompass review. This can be interpreted as the reflective aspect of a cognitive process, but also as a formal or informal process of evaluation of the KM process in part or as a whole.

Organising knowledge: Refers to the architecture of knowledge structure within an organisation that indicates where knowledge is located; how it is located; how it is indexed; frameworks for its individual codification and its systematisation within a whole.

Acknowledgment: The authors wish to thank Jeff Haywood, Brian Martin, Serge Koukpaki and the staff of the MSc in

Management of Training and Development at The University of Edinburgh for their contribution to this research.

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